Global Earth Observation
for integrated water resource assessment

Report on the improved Water Resources
Reanalysis

Deliverable No: D.5.2 – Report on the improved Water Resources Reanalysis (WRR
tier 2) Ref.: WP5 - Task 5.1
Date: May 2017

This project has received funding from the European Union’s Seventh Programme forresearch, technological development and demonstration under grant agreement No.
603608
DG Research –FP7-ENV-2013-two-stageWP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Deliverable Title

D.5.2 – Report on the improved Water Resources

Reanalysis
E2O_D52.docx

Filename
Authors

Emanuel, Dutra (ECMWF)

Contributors

Gianpaolo, Balsamo (ECMWF)
Jean-Christophe, Calvet (Meteo-France)
Simon Munier (Meteo-France)
Sophia Burke (AmbioTEK)
Gabriel Fink (Uni Kassel)
Albert Van Dijk (Australian National University/CSIRO)
Alberto Martinez-de la Torre (CEH)
Rens van Beek (Uni. Utrecht)
Ad De Roo (JRC)
Jan Polcher (CNRS)

Date

3/05/2017

Prepared under contract from the European Commission
Grant Agreement No. 603608
Directorate-General for Research & Innovation (DG Research), Collaborative project, FP7ENV-2013-two-stage
Start of the project:
Duration:
Project coordinator:

01/01/2014
48 months
Stichting Deltares, NL

Dissemination level
X

PU

PublicPP

Restricted to other programme participants (including the Commission
Services)RE

Restricted to a group specified by the consortium (including the Commission
Services)

CO

Confidential, only for members of the consortium (including the Commission
Services)

Deliverable status version control
VersionDate

0.0

03/05/2017

AuthorEmanuel,(ECMWF)

Reviewer
Dutra -

Singed-off by-

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Table of Contents
Table of Contents ......................................................................................................................i
List of Figures ........................................................................................................................... iii
List of Tables ........................................................................................................................... vii
1 Executive Summary .................................................................................................................. 1
2 Introduction ............................................................................................................................... 3
3 Modelling protocol ..................................................................................................................... 4
3.1 Meteorological forcing..................................................................................................... 4
3.1.1
MSWEP precipitation
5
3.1.2
Topographic adjustment
7
3.2 Simulations setup ......................................................................................................... 11
3.2.1
WRR2 ensemble
11
4 Modelling systems .................................................................................................................. 12
4.1 HTESSEL-CaMa .......................................................................................................... 13
4.1.1
Model description
13
4.1.2
Data assimilation
14
4.1.3
Evaluation
15(a) WRR2 ................................................................................................................................ 15(b)

Data assimilation.......................................................................................................... 21

4.2

Jules ............................................................................................................................. 26
4.2.1
Model description
26
4.2.2
Evaluation
27
4.3 LISFLOOD .................................................................................................................... 31
4.3.1
Model description
31
4.3.2
Evaluation
32(a) Impact of the model improvements ..................................................................... 32

4.4

(b)

Impact of the new forcing ......................................................................................... 33(c)

Impact of the precipitation ensemble forcing................................................... 40

ORCHIDEE................................................................................................................... 41
4.4.1
Model description
41(a) New ancillary data ....................................................................................................... 41(b)

New formulation for surface roughness ............................................................. 42(c)

New snow scheme ....................................................................................................... 42(d)Soil freezing scheme ................................................................................................... 42(e)

New routing scheme ................................................................................................... 43

4.4.2
4.4.3

Data assimilation
Evaluation

43
43
i

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis(a)

The Kura basin .............................................................................................................. 44(b)

The Rhone and Po ........................................................................................................ 45

4.5

PCR-GLOBWB ............................................................................................................. 46
4.5.1
Model description
46
4.5.2
Data assimilation
48
4.5.3
Evaluation
49
4.6 SURFEX-TRIP.............................................................................................................. 50
4.6.1
Model description
50
4.6.2
Data assimilation
51
4.6.3
Evaluation
52(a) Impact of the new forcing ......................................................................................... 52(b)

Impact of the model improvements ..................................................................... 56(c)

Analysis of the SURFEX LDAS-Monde .................................................................. 58

4.7

W3 ................................................................................................................................ 64
4.7.1
Model description
64
4.7.2
Data assimilation
65
4.7.3
Evaluation
66
4.8 WaterGAP3 .................................................................................................................. 66
4.8.1
Model description
66
4.8.2
Model improvements with respect to WRR1
68
4.8.3
Evaluation
68(a) The global average water balance ......................................................................... 69(b)

The global spatial distribution of major water balance components ...... 69(c)Testing of WaterGAP 3.2 reservoir management scheme with EO data 72(d)Effects on the improvement on water availability and discharge ............ 76

4.9 WaterWorld................................................................................................................... 77
5 Data ........................................................................................................................................ 80
5.1 Data access .................................................................................................................. 80
5.1.1
Meteorological forcing
80
5.1.2
Models output convention
80
6 Summary ................................................................................................................................ 87
7 References ............................................................................................................................. 88ii

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

List of Figures
Figure 3-1: Flow chart summarizing the main steps of the MSWEP methodology. The
different colors represent different time scales. Pn refers to normalized precipitation(i.e. P anomalies). ......................................................................................................................................6
Figure 3-2: Example of the different weights for the 25th of April 2006: (a) total
weight, (b) relative gauge component, (c) relative satellite component and (d)relative reanalysis component. ............................................................................................................7
Figure 3-3: Mean topography of (a) ERAI, (b) WRR1, (c) WRR2, (d) ERAI-WRR2 and(e) WRR1-WRR2. .......................................................................................................................................8
Figure 3-4: ELR estimates from ERAI in January (left) and July (right): multi-year
mean (top), inter-annual variability (middle) and uncertainty (bottom). .........................9
Figure 3-5: Stations altitude difference in respect to ERAI (a) and evolution of thenumber of stations (b). The legend in (b) denotes the total number of unique stations
with more than 5 years of data and the standard deviation of the elevation error for
all stations (black), stations below the model orography (blue) ad stations above themodel orography (red). ....................................................................................................................... 10
Figure 3-6: Mean statistics of the root mean square error (RMSE) for the stations
below the WRR2 orography (a,b), above the WRR2 orography (c,d) and all stations(e,f). The top panels (a,c,e) denote the actual RMSE, while the bottom panels (b,d,f)
the normalize difference in respect to WFDEI025. The error bars denote the 95%
confidence interval of the mean over the stations. In all panels the statistics areshown for the full period (ALL), and the 4 seasons in the horizontal axis. WFDEI
denotes the forcing using in WRR1, WFDEI025 is a bilinear interpolation of WFDEI to
0.25 grid, EI05 and EI025 are the ERAI data with a bilinear interpolation to 0.5 and0.25 grids, respectively, and E2O is the WRR2 forcing (based in EI025 with the ELR
corrections). ............................................................................................................................................. 10
Figure 4-1: Mean annual cycle of precipitation (top left), evapotranspiration (topright), runoff (bottom left) and discharge (bottom right) over the Amazon basin in
WRR1, WRR1_M2, WRR2_F2 and WRR2. On the discharge panel, the observations
from GRDC are the red line................................................................................................................. 16
Figure 4-2: Mean annual cycle of evapotranspiration (top left), soil evaporation (topright), vegetation transpiration (bottom left) and canopy interception evaporation(bottom right) over the Amazon basin in WRR1, WRR1_M2, WRR2_F2 and WRR2. .. 17
Figure 4-3: Mean evapotranspiration (top left) and canopy interception evaporation(top right) in WRR1 and differences between WRR2 and WRR1 (bottom panels) ..... 18
Figure 4-4: As Figure 4-1 but for the Ob basin. ......................................................................... 19
Figure 4-5: Mean annual discharge in Yukon (top left), Mackenzie (top right), Danube(bottom left) and Mississippi (bottom right). Simulations are compared against the
GRDC observations (red) and the mean annual cycle computed only for the periodwith available observations. .............................................................................................................. 20
Figure 4-6: (a) temporal correlation of between daily surface soil moisture and EO
CCI soil moisture for the period 1988 to 2012 in WRR1. Correlation differences ofdifferent model/forcing configurations and WRR1: b) WRR1_M2 – WRR1, c)
WRR1_F2 – WRR1 and d) WRR2-WRR1. ...................................................................................... 21
Figure 4-7: As Figure 4-6 but for the anomalies after removing a 35 days moving
window in both the simulations and EO CCI. .............................................................................. 21
Figure 4-8: Mean annual cycle of snow depth verification over Eurasia using GHCN
stations for the 2000-2010 period (grey line with the number of stations: rightiii

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

vertical axis; dotted symbols with the mean observations of snow depth (m)). The
dashed lines represent the simulation bias and the solid lines the standard deviationof the error for: WRR1 (black), WRR1_M2 (grey), WRR1_F2 (dark magenta), WRR2
(magenta) and WRR2_DA (blue) ...................................................................................................... 22
Figure 4-9: As Figure 4-8 but for Canada. ..................................................................................... 22
Figure 4-10: As Figure 4-8 but for United States. ...................................................................... 22
Figure 4-11: Mean annual discharge in Yukon (top left), Mackenzie (top right),
Danube (centre left), Mississippi (centre right), Ob (bottom left), and Yenisei (bottom
right). Simulations are compared against the GRDC observations (red) and the meanannual cycle computed only for the period with available observations between
2000-2012 (available WRR2_DA). ................................................................................................... 24
Figure 4-12: Mean annual snow mass increments in the WRR2_DA data assimilation
simulation in Yukon (top left), Mackenzie (top right), Danube (centre left),
Mississippi (centre right), Ob (bottom left), and Yenisei (bottom right). ........................ 25
Figure 4-13: Saturation fraction calculated by the PDM scheme in JULES as a functionof soil water storage to generate saturation excess runoff. Left: Variability obtainedby the b parameter, for a fixed S0/Smax=0. Right: Variability of the S0 parameter (green
curves) and variability of b for a fixed S0/Smax=0.5. .................................................................. 26
Figure 4-14: River flow at Kingston (UK), outlet of the Thames catchment simulated.
Green lines are flow observation at the gauge station. Blue lines are river flow
simulated by JULES with the best possible parameterization before introducing the S0
linear dependency on terrain slope (left) and with the new parameters adopted for
WRR2 (right). Total Bias and Nash-Sutcliffe efficiency metrics are given on top of theplots. ............................................................................................................................................................ 27
Figure 4-15: Mean Global Runoff (kg m-2 d-1) from the GCRF-OBS product (Fekete etal., 2002). White areas are not represented by the dataset (based on catchments).... 28
Figure 4-16: Global shift (days) between the maximum values in the WRR
simulations (WRR1_F1, right; WRR2_F1, left) and the GCRF-OBS runoff
benchmarking dataset. ......................................................................................................................... 29
Figure 4-17: RMSE Score (0-1) over the Europe region obtained by the ILAMB
evaluation against the GCRF-OBS runoff benchmarking dataset (WRR1_F1, right;
WRR2_F1, left). ........................................................................................................................................ 29
Figure 4-18: Monthly climatology for runoff (kg m-2 d-1) averaged over the Europe
region (Figure 4-18) by the GCRF-OBS product (grey), WRR1_F1 (red) and WRR2_F1(cyan). ......................................................................................................................................................... 30
Figure 4-19: Observed and LISFLOOD simulated monthly discharge (m3/s) for theRhine basin. .............................................................................................................................................. 35
Figure 4-20 Observed and LISFLOOD simulated monthly discharge (m3/s) for the
Amazon river. .......................................................................................................................................... 35
Figure 4-21 Estimated evapotranspiration deficit (LISFLOOD WRR2 with WFDEI
forcing). ...................................................................................................................................................... 37
Figure 4-22 Estimated evapotranspiration deficit (LISFLOOD WRR2 with MSWEP
forcing). ...................................................................................................................................................... 37
Figure 4-23 Estimated average water availability per capita (LISFLOOD WRR2 withWFDEI forcing)........................................................................................................................................ 38
Figure 4-24 Estimated average water availability per capita (LISFLOOD WRR2 withMSWEP forcing). ..................................................................................................................................... 38
Figure 4-25 Estimated average water exploitation index (LISFLOOD WRR2 withWFDEI forcing)........................................................................................................................................ 39iv

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-26 Estimated average water exploitation index (LISFLOOD WRR2 withMSWEP forcing). ..................................................................................................................................... 39
Figure 4-27 Estimated average water dependency (LISFLOOD WRR2 with WFDEI
forcing). ...................................................................................................................................................... 40
Figure 4-28 Estimated average water dependency (LISFLOOD WRR2 with MSWEP
forcing). ...................................................................................................................................................... 40
Figure 4-29: Precipitation and simulated evaporation averaged over the Kura basin.
The dashed lines in the lower panel are potential evaporation estimated by
ORCHIDEE with the forcing. The 3 simulations presented in these figures are : WRF,
E2OFD and WFDEI forcing. ................................................................................................................ 44
Figure 4-30: Discharge simulated by ORCHIDEE and observed for the Kura river at
the Surra .................................................................................................................................................... 45
Figure 4-31: Observed and simulated discharge of the Rhone at the Beaucaire station.
....................................................................................................................................................................... 46
Figure 4-32: Observed and simulated discharge of the Po at the Pontelagoscurostation. ........................................................................................................................................................ 46
Figure 4-33: Scheme of the WRR2 model version, including the representation offloodplains and groundwater reservoir. ....................................................................................... 51
Figure 4-34: Comparison of Nash-Sutcliffe Efficiency for experiments WRR1_F1(model WRR1, forcings WRR1) and WRR1_F2 (model WRR1, forcings WRR2). .......... 53
Figure 4-35: Comparison of the accumulated distribution of NSE over different
regions of the globe, for both experiments WRR1_F1 and WRR1_F2. ............................... 54
Figure 4-36: Seasonal cycle of the runoff for both WRR1_F1 and WRR1_F2
experiments and comparison with in situ observations. ....................................................... 55
Figure 4-37: Comparison of Nash-Sutcliffe Efficiency for experiments WRR1_F2(model WRR1, forcings WRR2) and WRR2_F2 (model WRR2, forcings WRR2). .......... 56
Figure 4-38: Accumulated distribution of NSE over different regions of the globe, forboth experiments WRR1_F2 and WRR2_F2. ................................................................................ 57
Figure 4-39: Seasonal cycle of the runoff for both WRR1_F2 and WRR2_F2
experiments and comparison with in situ observations. ....................................................... 58
Figure 4-40: Correlation and Root Mean Square Difference (RMSD, in m2/m2) of LAI
for experiments without data assimilation (Model) and with data assimilation(Analysis). ................................................................................................................................................. 59
Figure 4-41: Correlation and Root Mean Square Difference (RMSD, in m3/m3) of SSM
for experiments without data assimilation (Model) and with data assimilation(Analysis). ................................................................................................................................................. 60
Figure 4-42: Comparison of LAI from open-loop, analysis and observation over
Europe. ....................................................................................................................................................... 61
Figure 4-43: LAI seasonal scores of the open-loop and analysis over Europe: Standard
Deviation (SDD), Bias, Root Mean Square Difference (RMSD) and correlation. ............ 62
Figure 4-44: Time series of analysis increments averaged over Europe for bothcontrol variables. WG2 is the water content in the second soil layer (1cm-4cm). Units
are m2/m2 for LAI increments and m3/m3 for WG2 increments. ........................................ 62
Figure 4-45: Average over the whole period of Runoff, Drainage andEvapotranspiration for the open-loop and analysis experiments, and impact of theassimilation. Units are kg/m2/day. ................................................................................................. 63
Figure 4-46: Simplified conception structure of W3 v2.0 ...................................................... 64v

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-47: Example of additional evaporation from non-rainfall sources estimatedfor 03 July 2012 over Pakistan and NW India. Values range from 0 (white) to 5 mm/d
(blue)........................................................................................................................................................... 66
Figure 4-48: Global distribution of mean annual precipitation: a) WRR2 1979-2014b) difference to WRR1 (WRR2-WRR1) 1979-2012. ................................................................. 70
Figure 4-49: Global distribution of mean annual evapotranspiration: a) WRR2 19792014 b) difference to WRR1 (WRR2-WRR1) 1979-2012. ..................................................... 71
Figure 4-50: Global distribution of mean annual runoff: a) WRR2 1979-2014 b)
difference to WRR1 (WRR2-WRR1) 1979-2012. ...................................................................... 72
Figure 4-51: Calculated Water volumes and observed water levels of the Chora Bassa
Reservoir, Mozambique, for the period 2002 - 2014. .............................................................. 73
Figure 4-52: Calculated Water volumes and observed water levels of the Kariba
Reservoir for the period 1992-2004. ............................................................................................. 74
Figure 4-53: Calculated Water volumes and observed water levels of Lake Mead forthe period 2002 to 2014. ..................................................................................................................... 75
Figure 4-54: Calculated Water volumes and observed water levels of Lake Victoria forthe period 1992 to 2014. ..................................................................................................................... 76
Figure 4-55: Modelled Nile River Discharges at Al Akhsas gauging station (GRDC ID:
1362100) for the period 2000-2010. ............................................................................................. 77
Figure 4-56: WorldClim (right) and TRMM (left) rainfall data in the Magdalena
catchment. ................................................................................................................................................. 78
Figure 4-57: Slope angle (from Hydrosheds data) and areas of bare ground (fromMODIS data) in the Magdalena catchment. .................................................................................. 79
Figure 4-58: Water stress by global basin modelled at 10Km resolution. ....................... 79vi

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

List of Tables
Table 3-1 Overview of the meteorological forcing and differences between WRR1 andWRR2. ............................................................................................................................................................4
Table 3-2 Precipitation datasets used in the WRR2 ensemble. ........................................... 11
Table 4-1: Modelling systems main changes from WRR1 to WRR2 ................................... 12
Table 4-2: HTESSEL simulations details ....................................................................................... 15
Table 4-3: Summary table obtained applying the ILAMB system to the WRR1_F1 andWRR2-F2 climatology runoff fields. The overall score is a composition of the rest of 01 scores: Bias Score, RMSE Score, Seasonal Cycle Score and Spatial Distribution Score.
....................................................................................................................................................................... 28
Table 4-4: Summary table obtained applying the ILAMB system to the WRR1_F1 andWRR2-F2 surface soil moisture (top 10 cm) monthly fields. The overall score is acomposition of the rest of 0-1 scores: Bias Score, RMSE Score, Seasonal Cycle Score,
Spatial Distribution Score and Interannual Variability Score............................................... 30
Table 4-5: Summary table obtained applying the ILAMB system to the WRR1_F1 andWRR2-F2 latent heat monthly fields. The overall score is a composition of the rest of
0-1 scores: Bias Score, RMSE Score, Seasonal Cycle Score, Spatial Distribution Score
and Interannual Variability Score. ................................................................................................... 31
Table 4-6: Kling-Gupta Efficiency (KGE) for monthly discharge, for the Rhine andAmazon basin, showing the effects of the LISFLOOD model changes. .............................. 33
Table 4-7: Kling-Gupta Efficiency (KGE) for daily discharge for selected gauging
stations, showing the effects of the LISFLOOD model changes, for the period 19802008............................................................................................................................................................. 33
Table 4-8: Kling-Gupta Efficiency (KGE) for daily discharge, for the Rhine andAmazon basin, showing the effects of the TIER2 forcing changes. For the Rhine, thecomparison is also shown with the 5km resolution LISFLOOD setup, forced with the
JRC meteo data. ....................................................................................................................................... 34
Table 4-9: Kling-Gupta Efficiency (KGE) for daily discharge for selected gauging
stations, showing the effects of the TIER2 forcing changes, for the period 1980-2008.
....................................................................................................................................................................... 36
Table 4-10: Kling Gupta Efficiencies (KGE) for various rivers for the various ensemble
runs with various precipitation forcings. ..................................................................................... 41
Table 4-11: Parameter combinations for the brute force calibration. 24 parameter
combinations are used (3 × 4 × 2). The default parameter setting (Run 10) has been
omitted in favour of Run 24. .............................................................................................................. 49
Table 4-12: Model setups evaluated in WRR2. All model runs cover the period 19792014 with the exception of WRR1_Nat30 which covers the period of the WRR1
forcing 1979-2012 ................................................................................................................................. 49
Table 4-13: Global average water balance calculated by WaterGAP3.1 (WRR1) and
WaterGAP3.2 (WRR2) .......................................................................................................................... 69
Table 4-14: The WaterGAP3 modelled Nile discharges statistics (2000-2012) ofdifferent WRR settings. ........................................................................................................................ 77
Table 5-1: Forcing data ........................................................................................................................ 80
Table 5-2: Institution identifiers in the model output ............................................................. 81
Table 5-3: Experiments version names, associated start and end year and domain. . 81
Table 5-4: List of output variables and conventions ................................................................ 82
Table 5-5: Models output location ................................................................................................... 85vii

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

1 Executive Summary
This report describes the tier-2 water resources reanalysis (WRR2) development,
implementation and data structure and storage. The report is organized in 5 sections
and their main highlights are described in the following paragraphs.
The Earth2Observe WRR1 dataset consisted of an ensemble of ten globalhydrological and land surface models for the 1979-2012 period using a reanalysis
based meteorological forcing dataset. This dataset was developed using state-of-theart modelling systems and provides a robust benchmark for further improvements inthe coming years. WRR2 was based on WRR1 by exploring several potential sourcesof improvements including: (i) meteorological forcing data; (ii) modelling systems
processes description; and (iii) assimilation of Earth Observations.
The modelling protocol of WRR2 followed WRR1 closely in terms of the data
structure, storage and simulations setup. The main difference was the increased
resolution from 0.5°x0.5° in WRR1 to 0.25°x0.25° in WRR2 and a new meteorologicalforcing. The meteorological forcing is based on ERA-Interim, extending from 1979 to2014, with a topographic adjustment of temperature and a new precipitation dataset.
The new precipitation dataset, MSWEP, is a combined product of atmosphericreanalysis, satellite estimates and in-situ observations. In addition to the 1979-2014baseline WRR2 simulations, each modelling system also carried out simulations for ashorted period: 2000-2013 using four different satellite precipitation products.
WRR2 consists of eight global land surface and hydrological models with several
modifications in respect to WRR1. The model changes include a variety of process
oriented developments (e.g. snowpack, runoff generation), model parameters
calibration, and representation of water use (e.g. withdrawal, consumption,
management). The changes were not systematic, i.e. each model addresses different
aspects, mainly driven by their group expertise. While this approach does not allow
an exact evaluation of which factor is more important, it allows for a variety of
approaches and methodologies which are expected to partially encompass theintrinsic uncertainty of large-scale water resources estimates.
Following the technical developments of WRR1, WRR2 data structure and storage
was designed to facilitate, as much as possible, users access. The total models output
is about 5 TB, and all efforts have been made to harmonize the data format andaccess via the water cycle integrator (WCI1) data portal.
This report focus on the technical details of the simulations and the description of themodelling system. Each modelling system presented an evaluation of theirdevelopments along with the impact of the meteorological forcing. Due to thedifferent nature of each modelling and data assimilation system changes, a consistentevaluation among models was not pursued in this report. Each modelling system wasevaluated independently, and this reports serves as a detailed description of thedataset and not an integrated quality assessment of WRR2 dataset. Such analysis will
be presented in following project reports. However, among the several evaluation
examples there is a clear signal of the improvements due to the new meteorological
1 https://wci.earth2observe.eu/

1

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

forcing used in WRR2. This is mainly attributed to the new precipitation dataset(MSWEP) which leads to improvements of the water cycle simulations. Eachmodelling system change also shows improvements, but these tend to be restricted
to the main process that were addressed. Overall, WRR2 brings a significant
improvement in terms of the land water cycle representation when compared with
WRR1. This new dataset will have an important impact on the community and users
due to its higher resolution and state-of-the art modelling systems and data
assimilation.

2

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

2 Introduction
Water resources concern local to global economies independently of their level ofdevelopment. Pressures from demographic, economic and climatic changes further
reinforce the importance of an accurate and reliable estimate of the different
components of the land water cycle. On global scale, there is a limited number ofglobal reanalysis datasets that can support water resources (e.g. Rodell et al.,
2004;Dirmeyer et al., 2006;Reichle et al., 2011;Balsamo et al., 2015;Haddeland et al.,2011;Reichle et al., 2017;van Dijk et al., 2014). However, only Haddeland et al. (2011)
in WATCH combined both global land surface and hydrological models into a single
multi-model ensemble. In Earth2Observe, this has been further explored with therelease of the water resources reanalysis tier-1 dataset (WRR1, see report D5.1 andSchellekens et al., 2016).
The Earth2Observe WRR1 dataset consisted of an ensemble of ten globalhydrological and land surface models for the 1979-2012 period using a reanalysis
based meteorological forcing dataset. This dataset was developed using state-of-theart modelling systems and provides a robust benchmark for further improvements inthe coming years. There are several potential sources of improvements including: (i)meteorological forcing data; (ii) modelling systems processes description; and (iii)
assimilation of new Earth Observations.
Following the achievements of the WRR1 datasets, this report presents the tier-2
version (WRR2) which aims at exploring several potential sources of improvementsof the water resources multi-model dataset. The main developments include: (i)increased resolution from 0.5°x0.5° to 0.25°x0.25° and a new meteorological forcing
extending from 1979 to 2014; (ii) numerous modelling systems updates (fromprocess description to water use) and (iii) data assimilation of different Earth
Observations. As in WRR1, all simulations have a common meteorological forcing.
Each modelling system and data assimilation application was developed
independently by each group to combine and represent, as much as possible, theintrinsic uncertainty of these estimates. This last point, is further explored byproviding a special ensemble of model simulations for the 2000-2013 period using
four different satellite estimates of precipitation.
The main goal of this report is to provide a detailed description of this new dataset,including the simulations setup, modelling systems description and output data
structure and storage. While each of the modelling systems description includes anevaluation of the independent model and data assimilation enhancements, anintegrated evaluation of WRR2 vs WRR1 is not presented as this will be further
explored in upcoming project reports.
The report and organized in three main sections. The next section describes themodelling protocol, including the meteorological forcing and simulations setup.
Section 4 presents the detailed description of each participating modelling systemalong with an initial evaluation of the new simulations. Section 5 describes themodels output structure and location on the data server and a final overview ispresented on the last summary section.

3

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

3 Modelling protocol
The modelling protocol for the tier-2 water resources reanalysis (WRR2) followed
closely the tier-1 reanalysis and definition regarding the models setup and output
formats (WRR1, see the D5.1 report2 and Schellekens et al. (2016)). This simplifies
the comparison between the two datasets, the data storage and users access.
Regarding WRR1, the main differences include i) increased horizontal resolution ii) a
new meteorological forcing and iii) multi-precipitation ensemble. To accommodate
the different requirements of WRR2, the comparison with WRR1 and assessment of
the uncertainty in precipitation and role of Earth Observations (EO), WRR2 contains
two data streams: i) baseline from 1979 to 2014 and ii) multi-precipitation ensemble
from 2000 to 2013. In the following sections the details of the meteorological forcing,simulations setup and data format are presented in detail.

3.1 Meteorological forcing
The meteorological forcing used in WRR1, the Watch Forcing Dataset ERA-Interim(hereafter WFDEI, Weedon et al., 2014) was not suitable for WRR2 due to thehorizontal resolution limitation. Furthermore, the methodology used in WFDEI tocorrect the ERA-Interim reanalysis (ERAI, Dee et al., 2011) could not be directly
applied to the higher resolution proposed for WRR2 as some of the observational
datasets used in WFDEI are not available at higher resolutions (e.g. Climate Research
Unit, CRU, number of wet day or diurnal temperature range).
The WRR2 forcing is based on the original ERA-Interim meteorological databilinearly interpolated to 0.25°. It includes a topographic adjustment to temperatureusing a spatially-temporally varying environmental lapse rate (ELR) and a newly
developed precipitation dataset the Multi-Source Weighted-Ensemble Precipitation(MSWEP, Beck et al., 2017) which are both described in the following sub-sections,and the data access in section 5.1.1. Hence both WRR1 and WRR2 are based on ERAI
3-hourly data differing in:Resolution from 0.5° in WRR1 to 0.25° in WRR2;



Temporal extension: 1979-2012 in WRR1 to 1979-2014 in WRR2;

Corrections applied to ERAI detailed in Table 3-1.

Table 3-1 Overview of the meteorological forcing and differences between
WRR1 and WRR2.
VariableWind

Definition
Wind speed at 10
meters

Units
m s-1

Corrections
WRR1
None

in Corrections
WRR2

in

None

2

http://earth2observe.eu/files/Public%20Deliverables/D5.1_Report%20on%20the%20WRR1%20tier
1.pdf
4

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Variable

Definition

Units

Corrections
WRR1

in Corrections
WRR2

in

Tair

Temperature 2
metersK

Elevation using a
constant ELR, CRU
average and CRU
average diurnal
temperature range

Qair

Specific humidity
at 2 meterskg kg-1

Via changes in Tairand Psurf

Via changes in Tairand Psurf

Psurf

Pressure at thesurfacePa

Via changes in Psurf

Via changes in Tairand Psurf

SwdownSurface incident
shortwave
radiation

W m-2

CRU average cloud
cover andinterannual changes
in atmosphericaerosol loading

None

LwdownSurface incident
longwaveradiation

W m-2

Via fixed relative
humidity andchanges in Tair,
Psurf and Qair

None

Rainf

Rainfall rate (onlyliquid phase)

kg m-2 s-1

CRU numbe of wetdays andprecipitation totals

See MSWEP

Snow

Snowfall rate
(only solid phase)

kg m-2 s-1

CRU number of wetdays andprecipitation totals

See MSWEP

Elevation using avariables ELR

3.1.1 MSWEP precipitation
The Multi-Source Weighted-Ensemble Precipitation (MSWEP) is a global
precipitation dataset for the period 1979-2015 with a 3-hourly temporal resolution
and 0.25° spatial resolution. The datasets were specifically designed for hydrologicalmodelling and described in details by Beck et al. (2017). MSWEP was designed to
optimally merge the highest quality precipitation sources available as a function oftime scale and location (see Figure 3-1).
The long-term mean of MSWEP is based on the CHPclim dataset (Funk et al., 2015)
but replaced with more accurate regional datasets where available (over USA and
New Zealand). Additionally, a correction for gauge under-catch and orographic
effects was also applied by inferring catchment-average precipitation fromstreamflow observations at 13762 stations across the globe.

5

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 3-1: Flow chart summarizing the main steps of the MSWEP methodology.
The different colors represent different time scales. Pn refers to normalized
precipitation (i.e. P anomalies).
The temporal variability of MSWEP was determined by a weighted averaging of
precipitation anomalies from seven datasets: two based solely on interpolation ofgauge observations, three on satellite remote sensing, and two on atmosphericreanalysis. For each grid cell, the weight assigned to the gauge-based estimates wascalculated from the gauge network density, while the weights assigned to thesatellite- and reanalysis- based estimates were calculated from their comparative
performance at the surrounding gauges. Figure 3-2 shows the sum of the weights
used to merge the gauge, satellite and reanalysis component of MSWEP during thestage 3 (see Figure 3-1) at the daily time scale on an example day (25th April 2016),
along with their relative contributions.

6

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Beck et al. (2017) compared MSWEP against other state-of-the-art gauge adjusted
precipitation datasets (including the data used in WRR1) using independent
precipitation data from FLUXNET tower stations attaining the highest daily
correlation coefficient among all the tested datasets. The performance of the HBVhydrological model was also improved when using MSWEP.
Considering the novelty of this datasets, the encouraging results, and use of earth
observations it was it was decided to use MSWEP as baseline precipitation in WRR2.

Figure 3-2: Example of the different weights for the 25th of April 2006: (a) total
weight, (b) relative gauge component, (c) relative satellite component and (d)relative reanalysis component.
3.1.2 Topographic adjustment
The ERAI horizontal resolution of about 0.7° together with the required smoothing of
the topography used by the model due to numerical constrains results is a verysmooth topography with large differences when compared with an average 0.25°
topography. Therefore, just a simple bilinear interpolation of ERAI resolution to the
0.25° would not provide the spatial detailed expected, in particular over complex
terrain regions (see Figure 3-3).

7

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 3-3: Mean topography of (a) ERAI, (b) WRR1, (c) WRR2, (d) ERAI-WRR2
and (e) WRR1-WRR2.
Spatial downscaling of temperature is a complex problem that can be addressed via
dynamical downscaling, but that requires high resolution numerical simulations thatare not computationally feasible on the global scale and for such a long period. The
simplest approach is to apply a topographic adjustment to correct the elevationdifferences between the two topographies. This adjustment requires the definition of
the environmental lapse rate (ELR). It is a common practice to use a spatially andtemporally constant ELR of -6.5 K km-1 (as was in the case in WFDEI for WRR1).
However, the ELR depends on the predominant large-scale circulation and otherlocal features.
Gao et al. (2012) proposed that the ELR could be estimated from the vertical profilesof ERAI and tested the approach over the Swiss Alps. For WRR2, a similar
methodology was used where the ELR was computed from the ERAI model levels
mean monthly vertical profiles between 300 and 1300 meters above the surface. The
first 300 meters near the surface are not considered to avoid local inversions. The
ELR is computed as a temperature differences between several pairs of model levels
in ERAI resulting in a mean ELR along with its standard deviation which provides ameasure of the calculation uncertainty. Figure 3-4 displays the mean ELR derived
from ERAI using this methodology for January and July along with the inter-annual
variability and uncertainty. This methodology leads to a clear annual cycle of the ELR
with large values during the summer in the norther hemisphere and lower values
8

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

during winter in the northern hemisphere. Areas of large inter-annual variability anduncertainty are mainly associated with large-scale subsidence areas occurring mainly
over the oceans and over the Northern continents during winter.

Figure 3-4: ELR estimates from ERAI in January (left) and July (right): multiyear mean (top), inter-annual variability (middle) and uncertainty (bottom).
This methodology was evaluated by comparing the temperature forcing withsynoptic observations available on the Global Telecommunication System (GTS).
Figure 3-5 displays the stations locations and their altitude differences in respect to
ERAI. Although GTS data is available globally, a European domain was selected due to
its reasonably high stations density. The root mean square errors (RMSE) aredisplayed in Figure 3-6 comparing WRR1 and WRR2 forcing along with otherintermediate approaches. The RMSE differences in respect to WRR1 with a bilinear
interpolation to the 0.25 grid is also presented as this would have been the default
option to prepare the WRR1 forcing. The results are aggregated by stations below
the model orography (left panel), above the model orography (centre panels) and all
stations (right panels). The ELR approach to correct for the topographic differences
has a clear positive impact for the stations above the model orography with anaverage reductions of 15% of the RMSE. For the stations below the model orography
the mean impact is small, and mainly visible during summer. This can be explainedby local effects in valleys that are not possible to correct with this simple approach.
Overall considering all stations the selected methodology corrections improved the
air temperature RMSE in about 6% comparing with a simple bilinear interpolation of
WRR1.

9

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 3-5: Stations altitude difference in respect to ERAI (a) and evolution of
the number of stations (b). The legend in (b) denotes the total number of
unique stations with more than 5 years of data and the standard deviation of
the elevation error for all stations (black), stations below the model orography(blue) ad stations above the model orography (red).

Figure 3-6: Mean statistics of the root mean square error (RMSE) for thestations below the WRR2 orography (a,b), above the WRR2 orography (c,d) andall stations (e,f). The top panels (a,c,e) denote the actual RMSE, while thebottom panels (b,d,f) the normalize difference in respect to WFDEI025. The
error bars denote the 95% confidence interval of the mean over the stations. In
all panels the statistics are shown for the full period (ALL), and the 4 seasons in
the horizontal axis. WFDEI denotes the forcing using in WRR1, WFDEI025 is abilinear interpolation of WFDEI to 0.25 grid, EI05 and EI025 are the ERAI data
10

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

with a bilinear interpolation to 0.5 and 0.25 grids, respectively, and E2O is the
WRR2 forcing (based in EI025 with the ELR corrections).

3.2 Simulations setup
The simulations protocol followed WRR1 closely. For the baseline experiments, thesimulations were performed from the 1sth of January 1979 to the 31st of December
2014 in a continuous simulation without any kind of data assimilation. The
initialization method of the model on the 1sth of January 1979 is left to eachmodelling group as different models require different spin-up periods. To achieve a
common spin-up period, the 1st year of simulation (1979) is considered as spin-up
and it should not be used in any evaluation. Regarding static fields (e.g. land cover)each group used their own datasets as this is an integral component of eachmodelling system.
3.2.1 WRR2 ensemble
The WRR2 ensemble component follows the same protocol as the WRR2 baseline
simulation, including the meteorological forcing except for the Rainfall data that is
replace by four different satellite derived products detailed in Table 3-2. The satellite
products were processed to the same horizontal and temporal resolutions as WRR2
and replace the Rainfall data when there is no Snowfall. When WRR2 has Snowfall,the Rainfall was not replaced due to limitations of the satellite data to detect andquantify snowfall events. WRR2 ensemble is restricted to the 2000-2013 period, andthe initial conditions on the 1st of January 2000 are taken from the WRR2 baseline
simulation.
Table 3-2 Precipitation datasets used in the WRR2 ensemble.
Name

Product

TRMM

TRMMRT

GSMAP

CMORPH

Spatialresol.

Temporalresol.

SpatialdomainTemporalcoverage

Reference

TRMM Multi-Satellite
precipitation Analysis(TMPA) 3B42 V7

0.25°

3-hourly

50°S50°N

1998-2014(Huffman etal., 2007)

TRMM Multi-Satellite
precipitation Analysis(TMPA) 3B42 RT

0.25°

3-hourly

50°S50°N

03/200012/2013(Huffman etal., 2007)

Global satellite
Mapping ofPrecipitation Gauge
calibrated reanalysis
product

0.1°

hourly60°S60°N

03/200012/2013(Ushio et al.,2009)

CPC MORPHing
Technique V1 Biascorrected

0.25°

3-hourly

60°S60°N

2000-2013(Joyce et al.,
2004)

11

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

4 Modelling systems
This section presents the detailed description of the modelling systems participatingin WRR2 along with an evaluation of their individual performance. Table 4-1 present
an overview of the main changes of the modelling systems between WRR1 andWRR2, which are detailed in following sub-sections.
Table 4-1: Modelling systems main changes from WRR1 to WRR2
Modeling
systems
HTESSELCaMa

JULES
LISFLOOD

Model changes

Data assimilation

Improved representation of thesnowpack with a multi-layer snow
scheme; increased number of soil
layers.
Rainfal-runoff processes

Optimalinterpolation
snow
analysis using in-situ snow depth
observations

Global scale at 0.1, increased
numberofsoillayers,groundwater abstraction
ORCHIDEE Revision of the ancillary data,
surface roughness, snow scheme,
soil freezing and routing.
PCRImprovements to river routing
GLOBWB
reservoir schemes and water
withdrawal and consumption
SURFEXImprovements in ground water,
TRIP
flood plains, land used, plant
growth, surface energy and snow.
W3
Modified soil and groundwater(versionhydrology equations, resolution
2.0)
increased to 0.05°, improved
parameter estimates, dynamic data
assimilation, evaporation of water
not derived from rainfall (e.g.
irrigation)
WaterGAP3 5 arc minute, assimilation of soil
water estimates and improved
reservoir management

N.A
N.A

Derivation of background albedo
andassimilationofriver
discharge
Assimilation of discharge and soil
moisture
Global LDAS

Some calibration with global
streamflow, in situ soil moistureand FLUXNET data sets. Dynamic
assimilation of MODIS-derived
vegetation properties, albedo,
snow cover, water dynamics, land
surface temperature.
N.A

12

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

4.1 HTESSEL-CaMa
4.1.1 Model description
The land surface model (LSM) HTESSEL (Hydrology Tiled ECMWF Scheme for
Surface Exchanges over Land) computes the land surface response to atmosphericconditions, and estimates the surface water and energy fluxes and the temporal
evolution of soil temperature, soil moisture content, vegetation interception andsnowpack conditions. At the interface to the atmosphere each grid box is divided into
fractions (tiles), with up to six fractions over land (bare ground, low and highvegetation, intercepted water and shaded and exposed snow). Vegetation types andcover fractions are derived from an external climate database, based on the Global
Land Cover Characteristic (Loveland et al., 2000). The grid box surface fluxes arecalculated separately for each tile, leading to a separate solution of the surface energybalance equation and skin temperature.
The interception reservoir is a thin layer on top of soil/vegetation, collecting liquidwater by the interception of rain and the collection of dew, and evaporating at thepotential rate. The maximum capacity of this reservoir is a function of the grid-box
leaf area index. A resistance parameterization is used to calculate the turbulent
fluxes, and in particular for evaporation the aerodynamic resistance is added to the
canopy resistance. The canopy resistance is a function of downward short-wave
radiation, leaf area index, soil moisture, water vapour deficit and a minimum
stomatal resistance. For the soil moisture dependence, the fraction of roots in eachsoil layer is also considered.
In respect to WRR1 model version, two main changes have been introduced:

Replacement of the bulk snow scheme by a multi-layer model;Increased the number of vertical soil layers from 4 to 9.

In WRR1, the snow was represented as an additional layer on top of the upper soil
layer, with independent prognostic, thermal and mass contents (Dutra et al. 2010). It
was represented by a single layer with an evolution of snow temperature, snow
mass, snow density, snow albedo, and a diagnostic formulation for the snow liquidwater content. For WRR2, a multi-layer snow-scheme was developed andimplemented following Dutra et al. (2012). The multi-layer scheme carries the
prognostics of snow mass, density and liquid water content for each layer and asingle snow albedo. The number of snow layers is flexible, dependent on the totalsnow depth with a maximum number of layers that is user defined. In the current
simulations 5 layers were used as the maximum number of snow layers. In addition
to the new snow scheme, several changes were also done to the snow albedo
parameterization and surface energy balance solver.
Below the surface, the soil was discretized in four layers (0.07, 0.21, 0.72 and 1.89 m)
in WRR1 and this was changed to 9 layer in WRR2 (0.01, 0,02, 0.04, 0.08, 0.1, 0.25,
0.5, 1 and 1 m). This change kept the total soil depth similar from 2.89 m in WRR1 to
3 m in WRR2, with higher resolution closer to the surface to enhance fast
water/thermal processes. Soil heat transfer follows a Fourier law of diffusion,
modified to take into account soil water freezing/melting (Viterbo et al. 1999). The
vertical movement of water in the unsaturated zone of the soil matrix follows
13

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Richards’s equation and Darcy’s law. Hydraulic conductivity and diffusivity arederived using van Genuchten formulation and 6 soil textures globally distributed areused. Water movement is limited in the case of partially frozen soil, by reducing thehydraulic conductivity and diffusivity. The top boundary condition is precipitation
minus evaporation minus runoff, and the bottom boundary condition assumes freedrainage.
The increased vertical resolution in both the soil and snow required some changes inthe surface energy balance. In WRR1 the coupling between the skin layer and theunderlying snow or soil was fixed with a constant (temporal/spatial) parameter andin WRR2 this was changes to be a diagnostic based on the underlying layer thickness
and thermal characteristics. In the snow case, an iterative method was also
introduced to limit the skin temperature over exposed snow to the freezing point.
Water leaves the soil column in the bottom layer as free drainage, and this is denoted
as sub-surface runoff. At the surface, a variable infiltration rate that accounts for the
sub-grid variability related to orography is used to compute the surface runoff(Balsamo et al. 2009).
The surface and sub-surface runoff generated by HTESSEL are feed to the Catchmentbased Macro-scale Floodplain model (CaMa-Flood, Yamazaki et al. (2011). CaMaFlood simulates the hydrodynamics in continental-scale rivers. The entire river
network of the world are discretized to hydrological units named unit-catchments forachieving efficient flow computation at the global scale (Yamazaki et al. 2009). The
water level and flooded area are diagnosed from the water storage at each unitcatchment using the sub-grid topographic parameters of the river channel andfloodplains. The river discharge and flow velocity are calculated with the local
inertial equation along the river network map which prescribes the upstreamdownstream relationship of unit-catchments. The time evolution of the water
storage, the only prognostic variable, is solved by the water balance equation which
considers inflow from the upstream cells, outflow to the downstream cell and input
from runoff forcing at each unit-catchment.
The HTESSEL-CaMA simulations were carried out at the default 0.25x0.25 resolution.
The land use maps (e.g. type of vegetation and cover), mean climatologies of leaf areaindex and snow-free surface albedo were prescribed from the data used currently inthe operational weather forecasts at ECMWF. The simulations were performed only
over land points and a 10 year spin-up was carried out: initial simulation from 1st Jan
1979 to 1st Jan 1989, and the land surface state in Jan 1989 was used to initialize themain simulation starting on the 1st January 1979. In both HTESSEL and CaMa-Floodthe default set of parameters were used, with no particular calibration performed forthese simulations.
4.1.2 Data assimilation
For the data assimilations experiments (wrr2da) a 2D optimal interpolation (OI)
methodology (de Rosnay et al., 2014;Brasnett, 1999) was implemented for snow
depth using in-situ snow depth observations from the Global Historical Climatology
Network (GHCN). The 2D OI scheme expresses the observations weighting functions
from vertical and horizontal structure functions. It accounts for the covariancematrices of background and observations errors which enable to optimally combine
model background and observations. Since most of the GHCN station information
14

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

does not contain the exact time of the day of the observations, the data assimilation
was performed with a 24h window using as model background the model state at
00UTC.
The model background and the observations of a particular day are integrated into
the 2D OI that produces an analysis of snow depth. The following step is to propagate
the snow depth analysis increments into the model prognostics. Snow depth
increments could be applied to either snow mass or snow density or both. In thecurrent implementation, it is assumed that snow density does not change, and all thesnow depth increments are applied to snow mass. In addition, the snow mass
increments are distributed to the different snow layers weighted by the thickness, i.e.
thicker snow layers will have higher increments then shallower layers. Finally, oncethe snow mass increments are computed, these are applied to the model prognostic
vector at 00UTC and the next day in simulated. This sequential data assimilation of
the snow depth allows a smooth temporal correction of the model state andconsistent evolution of all the remaining surface conditions. However, the snow mass
increments become a new water source/sink leading to a water budget imbalance if
only precipitation, evaporation and runoff are considered.
4.1.3 Evaluation
To evaluate the impact of the different components in WRR2 (meteorological forcing
and model changes) a set of simulations, described in Table 4-2, were performed
with WRR1/WRR2 forcing and the model version used in WRR1 and WRR2. The
comparison between WRR1_M2 vs WRR1 and WRR2 vs WRR1_F2 will show the
impact of the model changes while WRR1_F2 vs WRR1 and WRR vs WRR1_M2 willaddress the impact of the meteorological forcing.
The evaluation focus mainly on the mean annual cycle of key components of thewater cycle, including the comparison with observed river discharge from the Global
Runoff Data Center (GRDC). The surface soil moisture is also evaluated against the
ESA-CCI soil moisture (Dorigo et al., 2015), and point snow depth with the GHCN insitu data which was also used in the data assimilation simulation.
Table 4-2: HTESSEL simulations details
Simulations

Details

WRR1

WRR1 simulations (black)

WRR1_M2

WRR1 forcing with WRR2 model (grey)

WRR1_F2

WRR1 model with WRR2 forcing (dark magenta)

WRR2

WRR2 simulation (magenta)

WRR2_DA

WRR2 with snow data assimilation (blue, wrr2da)

(a)

WRR2

The mean annual cycle of precipitation, evaporation, runoff and discharge for the
Amazon basin (Figure 4-1) shows a very small impact of the model changes from15

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

WRR1 to WRR2 when compared with the forcing changes. While precipitation isreasonable similar between WRR1 and WRR2, there is a reduction of
evapotranspiration in WRR2 which is compensated with an increased runoff. Since
these changes cannot be attributed to model changes (WRR1_F2 is almost identical toWRR2), it must be due to changes in the meteorological forcing. The mean annual
cycle of the different components of evapotranspiration (Figure 4-2) shows that themain differences between WRR1 and WRR2 occur in the canopy evaporation. This isfurther illustrated on the global maps of differences between WRR2-WRR1 forevapotranspiration and canopy evaporation (see Figure 4-3) indicating that most of
the decrease of evaporation occurring in the tropics results from a reduction of
canopy evaporation.

Figure 4-1: Mean annual cycle of precipitation (top left), evapotranspiration(top right), runoff (bottom left) and discharge (bottom right) over the Amazon
basin in WRR1, WRR1_M2, WRR2_F2 and WRR2. On the discharge panel, theobservations from GRDC are the red line.

16

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-2: Mean annual cycle of evapotranspiration (top left), soil evaporation(top right), vegetation transpiration (bottom left) and canopy interception
evaporation (bottom right) over the Amazon basin in WRR1, WRR1_M2,
WRR2_F2 and WRR2.
Canopy evaporation is a very fast process that occurs after rainfall events
evaporating the intercepted water over the canopy. This is an important process inthe tropics due to the large interception reservoir size (large leaf area index) andenergy availability. There was no change in the interception reservoir capacity from
WRR1 to WRR2, and the changes in the available energy were small (not shown). Oneof the differences in WRR2 was the precipitation data that came from MSWEP thattook advantage of satellite data. Besides the changes on total precipitation, the subdaily distribution was also corrected with a much clear diurnal cycle of precipitation
over tropics compared with WRR1 (not shown). This resulted in less water being
intercepted by the canopy in WRR2 and partially explaining the reduction of
evaporation, and increase in runoff. These results highlight, that in the case of
HTESSEL, the daily precipitation distribution impacts the water balance components.
Furthermore, these results bring some light on two contradictory results, one
suggesting that HTESSEL had too much evaporation (Haddeland et al., 2011) andanother too little (Getirana et al., 2014). In fact the differences seen in those studies
are consistent with WRR1vsWRR2 results that had a different precipitation daily
distribution.

17

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-3: Mean evapotranspiration (top left) and canopy interception
evaporation (top right) in WRR1 and differences between WRR2 and WRR1(bottom panels)
Moving from the tropics to the snow dominated Ob basin (see Figure 4-4), he impactof the model changes from WRR1 to WRR2 are still reduced, when compared withthe meteorological changes. However, there is a slight increase in runoff as well as adelay of the peak discharge, due to the model changes, which goes in a right
direction. This can be primarily attributed to the enhanced snow thermal insulation
with the multi-layer snow scheme that reduced soil freezing allowing for more water
to infiltrate during the melting season.
The mean annual cycle of discharge in Yukon, Mackenzie, Danube and Mississippi(see Figure 4-5) shows a mixed impact of WRR2 v WRR1, with clear improvements inthe northern basins (Yukon and Mackenzie), similar to the Ob basin. On the other
hand, the two mid-latitude basins (Danube and Mississippi) show a deterioration
with a reduction of discharge from WRR1 to WRR2, further away from theobservations. This is primarily attributed to changes in the precipitation from WRR1
to WRR2. However, considering that runoff is much smaller than evaporation inthese basins, errors in evapotranspiration cannot be discarded and a deterioration indischarge cannot be uniquely associated with the changes in the meteorologicalforcing.

18

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-4: As Figure 4-1 but for the Ob basin.

19

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-5: Mean annual discharge in Yukon (top left), Mackenzie (top right),
Danube (bottom left) and Mississippi (bottom right). Simulations arecompared against the GRDC observations (red) and the mean annual cycle
computed only for the period with available observations.
Finally, we compared the surface soil moisture (top 7 cm) with the ESA CCI product.
For each grid-point the time series of surface soil moisture from the simulations
were correlated with the ESA CCI product for the 1988 to 2012 period. The
correlations of the daily time series are showed in Figure 4-6 while the correlation of
the anomalies, after removing a 35 days moving average, are shown in Figure 4-7.
The full correlations show a high values in semi-arid regions, with low values indeserts and northern latitudes. The anomaly correlations are generally lower, as
expected, but the higher values also tend to cluster in semi-arid regions. The higher
agreement over semi-arid regions is expected as in these regions the surface soil
moisture is expected to have a clear response to precipitation variability, while inother regions other factors dominate (e.g. northern areas: snow, soil freezing; tropic:
missing ESA CCI data, and effects of vegetation). The impact of the model changes
from WRR1 to WRR2 is small but tends to be positive, while the impact of the newforcing in WRR2 is clearly positive and dominated the marked improvement from
WRR1 to WRR2 in terms of the agreement with the satellite data.
Overall, there is a clear difference between the model and forcing changes from
WRR1 to WRR2, with the forcing changes bringing a much larger impact than themodel, which is also apparent in other modelling system results (see for example
SURFEX-TRIP results in section 4.6).

20

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-6: (a) temporal correlation of between daily surface soil moisture and
EO CCI soil moisture for the period 1988 to 2012 in WRR1. Correlationdifferences of different model/forcing configurations and WRR1: b) WRR1_M2– WRR1, c) WRR1_F2 – WRR1 and d) WRR2-WRR1.

Figure 4-7: As Figure 4-6 but for the anomalies after removing a 35 days moving
window in both the simulations and EO CCI.
(b)

Data assimilation

The data assimilation simulations (wrr2da) were evaluated along with wrr1 andwrr2 by comparing the grid-point evolution of snow depth with the GHCN in-situ
observations. The statistics are presented in terms of the mean annual cycle for theperiod 2000-2010 in three domains: Eurasia (Figure 4-8), Canada (Figure 4-9) andUnited States (Figure 4-10 ), including the mean bias and standard deviation of theerrors. In all domains the model changes were positive during the accumulation topeak winter time snow depth, while there is some deterioration during the spring
melt season in the Canada and United States domains. In these latter two regions,
there is also a clear benefit of the new meteorological forcing, while over Eurasia theimpact of the meteorological forcing is less evident, but still positive. The simulations
with the data assimilation (wrr2da, blue lines in Figure 4-8 to Figure 4-10) show asignificant improvement with almost zero mean bias and a clear reduction of thestandard deviation of the errors. This comparison has a caveat since most of the data
used for the evaluation was also used during the data assimilation. A clear evaluation
should exclude some of the station data from the data assimilation to allow an
independent verification. However, since the main goal of wrr2da was to produce the21

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

best estimate possible, all available stations were used by the data assimilation
system.
Figure 4-8: Mean annual cycle ofsnow depth verification over Eurasia
using GHCN stations for the 20002010 period (grey line with thenumber of stations: right verticalaxis; dotted symbols with the meanobservations of snow depth (m)). The
dashedlines
representthesimulation bias and the solid lines thestandard deviation of the error for:
WRR1 (black), WRR1_M2 (grey),
WRR1_F2 (dark magenta), WRR2
(magenta) and WRR2_DA (blue)

Figure 4-9: As Figure 4-8 but for
Canada.

Figure 4-10: As Figure 4-8 but for
United States.

22

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Since wrr2da was sequential data assimilation, it is expected that the snow mass
increments will impact other model components. The mean annual cycle of dischargein 6 selected large-scale basins, 4 snow dominated (Yukon, Mackenzie, Ob andYenisei) and 2 mid-latitude basins (Danube and Mississippi) illustrate the impact ofthe data assimilation on the simulated river discharge (see Figure 4-11). On the snow
dominated basins, the data assimilation had a negative effected while on the midlatitude basins there was a slightly positive effect. The deterioration over the snow
dominated regions is contra-intuitive sine wrr2da had a better snow depth evolution
as shown in the previous results. To further understand these results, the snow mass
increments from the data assimilation are shown in Figure 4-12. Over Yukon,
Mackenzie, Ob and Yenisei, there is a clear negative snow mass increment during themelting seasons. This suggests that the model has a systematic melting bias, melting
too late which is corrected by the data assimilation. However, the negative snow
mass increments are removed from the surface, and are not available for infiltrationand latter runoff. This explains the deterioration of wrr2da simulation in terms ofriver discharge. These results indicate the 2D OI scheme used is very effective arecorrecting the snow depth evolution but it is not capable to trigger the model snow
melt and the negative increments are removed from the surface. Further
developments are required in this area to better use the observations to constrain
the model evolution so that the data assimilation can improve not only the snow
depth evolution but also the melting.

23

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-11: Mean annual discharge in Yukon (top left), Mackenzie (top right),
Danube (centre left), Mississippi (centre right), Ob (bottom left), and Yenisei(bottom right). Simulations are compared against the GRDC observations (red)and the mean annual cycle computed only for the period with available
observations between 2000-2012 (available WRR2_DA).

24

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-12: Mean annual snow mass increments in the WRR2_DA data
assimilation simulation in Yukon (top left), Mackenzie (top right), Danube(centre left), Mississippi (centre right), Ob (bottom left), and Yenisei (bottom
right).

25

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

4.2 Jules
4.2.1 Model description
General JULES model description is available on the D5.1 report WRR1. The mainmodel development adapted for WRR2 was the inclusion of a terrain slope
dependency in the saturation excess runoff scheme (PDM, Moore, 1985). The WRR1
version used PDM, that calculates a saturation fraction for each model grid cell as:𝑏

𝑆 − 𝑆0 𝑏+1
𝐹𝑠𝑎𝑡 = 1 − (1 −)𝑆𝑚𝑎𝑥 − 𝑆0
where S is the grid cell soil water storage, S0 the minimum storage below which there
is no surface saturation, Smax is the maximum grid cell storage and b is a shapeparameter. Any saturation excess over the saturated area then generates surface
runoff (Clark and Gedney, 2008). Plots in Figure 4-13 show how the b parameter
shape up the curve for saturation fraction with soil water content in the x-axis, andhow the S0 parameter constrains the saturation fraction and the b variability. The subsurface runoff is obtained as free drainage at the bottom of the soil column (at 3 m
depth).

Figure 4-13: Saturation fraction calculated by the PDM scheme in JULES as afunction of soil water storage to generate saturation excess runoff. Left:
Variability obtained by the b parameter, for a fixed S0/Smax=0. Right: Variabilityof the S0 parameter (green curves) and variability of b for a fixed S0/Smax=0.5.
JULES in the WRR1 version had the S0 parameter hardwired to be zero. This together
with a fixed value of b (b=0.35 in WRR1) give no room for sub-grid variability in therunoff generation of the model. After a deep study of the b and S0 variabilities for aset of catchments in Great Britain using the CHESS 1 km meteorological driving
dataset (Robinson et al., 2017), we adopted a new approach for WRR2, using a higher
value of b=2 and a variable value for S0/Ssat, that depends on the terrain slope as
𝑆0 ⁄𝑆𝑚𝑎𝑥 = 1 − 𝑠𝑙𝑜𝑝𝑒 ∗ (1/𝑠𝑙𝑜𝑝𝑒𝑚𝑎𝑥
26

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

where slope is the mean terrain slope for the given grid-cell and slopemax is the
maximum slope to apply the equation, taking S0/Smax=0 for higher slopes. This way
flatter regions will obtain higher values of S0 and therefore constraint the saturation
excess runoff production as compared to WRR1, while regions of high slope willproduce higher runoff that WRR1 due to the high value of b. Figure 4-14 shows the
improvement in Nush-Sutcliffe efficiency due to a better representation of the baseflow using the new WRR2 approach for the flat Thames catchment in the UK (using
the aforementioned 1km driving data).

Figure 4-14: River flow at Kingston (UK), outlet of the Thames catchment
simulated. Green lines are flow observation at the gauge station. Blue lines areriver flow simulated by JULES with the best possible parameterization before
introducing the S0 linear dependency on terrain slope (left) and with the newparameters adopted for WRR2 (right). Total Bias and Nash-Sutcliffe efficiency
metrics are given on top of the plots.
Finally, WRR2 also has a climatology for LAI, whereas WRR1 used a fixed LAI for each
PFT (plant functional type).
4.2.2 Evaluation
To evaluate the impact of model development we have carried out an analysis
comparing WRR1_F1 and WRR2_F2 to a series of hydrological benchmark datasets
using the ILAMB system (https://bitbucket.org/ncollier/ilamb, Mu et al, 2016). The
system has already been uses in the project to evaluate the WRR1 runs for all models(Schellekens et al., 2016).
To focus on the runoff production, which is the more direct impact of WRR2
developments, we use the GCRF (Global Composite Runoff Fields) product (Fekete etal., 2002), particularly the OBS field that uses a 30 min river network to extrapolate
gauging stations river flows from the GRDC (Global Runoff Data Center) database to
produce a gridded 0.5x0.5 resolution global climatology for runoff. To use thisbenchmark dataset, we have created the runoff climatologies from WRR1_F1 and
WRR2_F1 runs.

27

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-15: Mean Global Runoff (kg m-2 d-1) from the GCRF-OBS product(Fekete et al., 2002). White areas are not represented by the dataset (based oncatchments).
The resultant scores applying the ILAMB benchmarking system to compare with
GCRF-OBS are summarized inTable 4-3. The total global mean runoff has been
reduced with the new WRR2 developments, and the overall score has improved
slightly, from 0.64 to 0.656. The more significant changes in score from WRR1 to
WRR2 are the improvements in seasonal score and RMSE score. A better represented
seasonal cycle is therefore the first conclusion for the model development detailed on
the previous section. Figure 4-16 shows the shift in days between the maximum
values in the WRR simulations and the benchmark dataset, and it is clear that theshift has been significantly improved in west Europe and Asia, where the maximum
values come too early in WRR1.
Table 4-3: Summary table obtained applying the ILAMB system to the WRR1_F1
and WRR2-F2 climatology runoff fields. The overall score is a composition of
the rest of 0-1 scores: Bias Score, RMSE Score, Seasonal Cycle Score and Spatial
Distribution Score.
RunoffPeriodMean [kgm-2 d-1]

Bias [kgm-2 d-1]

RMSE[kg m-2
d-1]

Phase Shift
[d]

Bias
Score [1]RMSE
Score [1]Seasonal
Cycle
Score [1]SpatialDistribution
Score [-]Overall
Score [1]GCRF-OBS

0.865

JULESWRR1_F1

0.949

0.083

0.935

-23.394

0.8

0.387

0.738

0.89

0.64

JULESWRR2-F1

0.921

0.056

0.913

-14.654

0.802

0.413

0.766

0.887

0.656

28

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-16: Global shift (days) between the maximum values in the WRR
simulations (WRR1_F1, right; WRR2_F1, left) and the GCRF-OBS runoff
benchmarking dataset.
The improvement in skill by WRR2 can be closely assessed using regional analysis
with the ILAMB system. A series of region where included in this study and in Figure
4-17 we show the RMSE score maps for Europe (WRR1 and WRR2). The red regions
in west Europe have been significantly improved, and again this improvement can beseen in the seasonal cycle represented by both simulations as a space integration
over the Europe region (together with the GCRF-OBS product) in Figure 4-18. It isfrom metrics in flatter regions like west Europe that WRR2 has reached an overall
global skill improvement.
WRR1_F1

WRR2_F1

Figure 4-17: RMSE Score (0-1) over the Europe region obtained by the ILAMB
evaluation against the GCRF-OBS runoff benchmarking dataset (WRR1_F1,
right; WRR2_F1, left).

29

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-18: Monthly climatology for runoff (kg m-2 d-1) averaged over theEurope region (Figure 4-18) by the GCRF-OBS product (grey), WRR1_F1 (red)and WRR2_F1 (cyan).

Other benchmarking datasets for water resources has been included in our ILAMB
evaluation, particularly the ESA-CCI surface soil moisture product (Dorigo et al.,2015) and the FLUXNET-MTE latent heat product derived from empirical up-scaling
eddy covariance measurements (Jung et al., 2009). The improvement in skill of runoff
production does not affect negatively other components of the water cycle as we cansee from Table 4-4 and Table 4-5 summarizing the global ILAMB evaluation of
surface soil moisture and latent heat. Particularly for soil moisture, WRR2 improve
the skill from a better characterization of the spatial distribution, as now high andlow altitude regions do behave differently in the runoff production and therefore thewater availability for the top soil layers.
From previous analysis (Deliverable D4.2) we knew that the JULES evaporation
simulation was high as compared to other sources (GLEAM satellite based
evapotranspiration product). Hence it is not surprising that and increase in the global
latent heat by WRR2 (1% from 44.0 W m-2 with WRR1 to 44.44 W m-2) provokes
lower skill metrics in Table 4-5. The overall decrease, however, is not as high as theincreases in soil moisture and runoff.
Table 4-4: Summary table obtained applying the ILAMB system to the WRR1_F1
and WRR2-F2 surface soil moisture (top 10 cm) monthly fields. The overall
score is a composition of the rest of 0-1 scores: Bias Score, RMSE Score,
Seasonal Cycle Score, Spatial Distribution Score and Interannual Variability
Score.
Surface
Soil
Moisture

ESA-CCIJULESWRR1_F1

PeriodMean[m3 m-3]

Bias [m3m-3]RMSE[m3 m-3]

Phase
Shift [d]BiasScore [1]RMSE
Score [1]Seasonal
Cycle
Score [1]SpatialDistribu
tion
Score [-]

Interannual
Variability Score[1]Overall
Score
[1]

-0.03

0.09

-9.11

0.714

0.622

0.723

0.765

0.62

0.678

0.196
0.166

30

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis
JULESWRR2-F1

0.164

-0.03

0.088

-11.0

0.724

0.63

0.727

0.826

0.634

0.695

Table 4-5: Summary table obtained applying the ILAMB system to the WRR1_F1
and WRR2-F2 latent heat monthly fields. The overall score is a composition of
the rest of 0-1 scores: Bias Score, RMSE Score, Seasonal Cycle Score, Spatial
Distribution Score and Interannual Variability Score.
Bias [Wm-2]

RMSE[W m-2]

Phase
Shift [d]BiasScore [1]RMSE
Score [1]Seasonal
Cycle
Score [1]SpatialDistribu
tion
Score [-]

Interannual
Variability Score[1]Overall
Score
[1]

44.00

4.283

16.01

0.599

0.748

0.638

0.894

0.976

0.603

0.749

44.44

4.717

16.70

0.082

0.728

0.628

0.882

0.97

0.609

0.741

LatentHeat

PeriodMean [Wm-2]

FLUXNET-MTE
JULESWRR1_F1

39.23

JULESWRR2-F1

We have focused in this chapter in the evaluation of model improvements, rather
than driving data improvements, as we will be evaluating that on future reports,
namely D4.3 and D5.3.

4.3 LISFLOOD
Contributor: JRC
4.3.1 Model description
Between WRR1 and WRR2, the LISFLOOD model has been modified quite
substantially. While still running at 0.1 degree, the following changes were
implemented

LISFLOOD has been fully recoded into Python-PCRaster, and is using netcdf as
input and output file formats, next to the PCRaster map format;

The number of computational soil layers has been increased from 2 to 3, withlayer 1 representing the top 5cm of the soil, allowing for data assimilation ofsatellite soil moisture;Paddy rice and crop-irrigation water demand calculations are now embeddedin LISFLOOD, including abstraction, conveyance losses, efficiencies, and net
consumption and return flow. This has the advantage that irrigation demand
will now change with a changed forcing, or a change in irrigated area. Or achange in efficiency;The simulation of groundwater has been improved by implementing
groundwater abstraction and including a threshold value, underneath which
no base-flow occurs to the river system. Thus, the aquifer might temporarily
lose its connection to the river due to climate forcing or over abstraction;

31

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

water allocation scheme is introduced whereby first water is made available
for the public sector, followed by energy sector, manufacturing industry,
livestock, crop irrigation. Also, an optional ‘environmental flow’ threshold ofriver discharge is implemented. If the actual local daily river discharge falls
below the environmental flow threshold, any water abstraction is temporarily
stopped until the minimum is exceeded again; this may lead to a ‘water
shortage’ per sector, which is recorded and available as model output;

LISFLOOD produces several additional water resources indicators – when set
in the settings file -, such as the Water Exploitation Index (WEI) for demandand consumption, the Falkenmark indicator showing the amount offreshwater available per capita per year (both including and excluding
upstream inflow), and the new Water Dependency Index, showing thedependency of a region on upstream freshwater inflow to fulfil its local water
demand;The 0.1. global LISFLOOD setups now includes an increased number of lakes
and reservoirs and their estimated parameterisation;Assimilation of discharge, soil moisture and snow cover is possible, but not
executed yet.

For WRR2, LISFLOOD was not calibrated.
4.3.2 Evaluation(a)Impact of the model improvements

To evaluate the impact of the model changes between WRR1 and WRR2 setup, asimulation has been carried using the model setups of WRR1 and WRR2 but the same
forcing of WRR1 for both runs. Results of these simulations are compared toobserved monthly and daily discharges at selected gauges (see Table 4-6 and Table
4-7).
It can be concluded that the updated LISFLOOD version at 0.1 degree for WRR2
simulates river discharge less good than the previous version. This maybe partiallyrelated to the increased number of reservoirs and their uncertain parameterisation.
But most likely, it is due to the fact that for WRR2 the same parameterisation wasused as for the slightly calibrated WRR1 version. Since LISFLOOD has beensignificantly changed with automated irrigation, enhanced abstraction andgroundwater models, an updated calibration is likely needed to improve the resultscompared to WRR1.
Some basin show improvements from WRR1 to WRR2, such as the Oder river, theAmazon. Some are unchanged, such as Danube, Thames. But for several rivers suchas Ebro, Elbe, Rhine, Zambezi and others, the WRR1 calibrated LISFLOOD version
outperfoms the uncalibrated WRR2 LISFLOOD version.

32

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Table 4-6: Kling-Gupta Efficiency (KGE) for monthly discharge, for the Rhine
and Amazon basin, showing the effects of the LISFLOOD model changes.

Table 4-7: Kling-Gupta Efficiency (KGE) for daily discharge for selected gauging
stations, showing the effects of the LISFLOOD model changes, for the period
1980-2008.

(b)

Impact of the new forcing

To evaluate the impact of the new forcing used in the WRR2 setup, simulations havebeen carried out using the WRR2 model setup with the various forcing from WRR2 aswell as the WRR1 WFDEI forcing. Results of these simulations are compared toobserved monthly and daily discharges at selected gauges.
The impacts of different forcing datasets on LISFLOOD model output is quite
significant. Differences vary from basin to basin, and some forcing data produce
better results in one basin, but not necessarily in other basins.

33

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

In general, it can be concluded that the MSWEP precipitation forcing outperforms the
WFDEI forcing. Some individual basins show the opposite results, the majority ofinvestigated basins performs significantly better with MSWEP. Especially for the
Amazon basin, extremely good results are obtained using MSWEP (see Table 4-8 and
Figure 4-19, Figure 4-20). In the Rhine (Figure 4-19), the results of the 0.1 degree
LISFLOOD setup have been compared with the calibrated 5km setup forced with the
JRC gridded observed meteorological dataset. The latter outperforms the 0.1 degree
run with MSWEP, but this may well be related to resolution and calibration as well,and not only to forcing.
In addition, several simulation experiments were done with various
evapotranspiration settings. As in Tier1 it was found that LISFLOOD potential
evapotranspiration estimates were somewhat below other models, we experimented
with including a ‘gain’ factor of 1.118, so an 11.8 % bias correction. This increase
does bring the overall simulated discharges closer to the observed ones. The summed
up KGE for the stations in Table 4-9 is higher (5.3) than before (4.7). But there areregional differences, and in various river basins this correction shows opposite
effects. Using the evapotranspitration dataset provided by the VU did yield in aslighty less performing model with respect to daily river discharge, with a summed
up KGE of 3.6 compared to 4.7.
Table 4-8: Kling-Gupta Efficiency (KGE) for daily discharge, for the Rhine andAmazon basin, showing the effects of the TIER2 forcing changes. For the Rhine,
the comparison is also shown with the 5km resolution LISFLOOD setup, forced
with the JRC meteo data.

34

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-19: Observed and LISFLOOD simulated monthly discharge (m3/s) for
the Rhine basin.

Figure 4-20 Observed and LISFLOOD simulated monthly discharge (m3/s) for
the Amazon river.

35

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Table 4-9: Kling-Gupta Efficiency (KGE) for daily discharge for selected gauging
stations, showing the effects of the TIER2 forcing changes, for the period 19802008.

Summary water indicators outputs of the LISFLOOD model, such as the evaporative
deficit, the amount of freshwater available per capita do change as well based on
various forcing data, but the overall patterns and key messages stay the same (seeFigure 4-21 to Figure 4-28).
The multiannual average evapotranspiration deficit (Figure 4-21 and Figure 4-22)
seems not to be different from WFDEI to MSWEP forcings, but there will bedifferences for individual months.
The freshwater availability per capita maps (Figure 4-23, Figure 4-24) are different
in several water scarce areas such as the the Middle East, northern Africa, but themain areas stay the same. 1700 m3 per capita per year is typically used as a criticalthreshold to identify water scarce regions.
The Water Exploitation Index ‘plus’ (WEI+) is defined as net water consumption as afraction of available freshwater, including upstream inflow. Net consumption is
including all sectors: irrigared agriculture, livestock, manufacturing industry, coolingfor energy production, public/domestic water consumption. Return flows areaccounted for. This is a core indicator used in European water policy. Values above
0.2 are critical, and above 0.4 indicate severe water stressed areas. At the global
scale, and for the average WEI+, only marginal differences occur when using WFDEIor MSWEP forcing, so they key message stays the same (Figure 4-25, Figure 4-26).
The Water Dependency Index is defined as the fraction of water demand that cannot
be met by local freshwater supply and is not met by desalination. This is typically thedemand part for which freshwater from upstream inflowing rivers is used. The Nilein Egypt is a typical example here, where Egypt depends for a large part on this36

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

upstream, cross border water. Changes in WDI may be an indicator of risk of water
conflicts and the need for basin wide integrated water policies. Also for the WDI,
forcing changes from WFDEI to MSWEP lead to changes, but the overall critical areas
remain the same (see Figure 4-27, Figure 4-28).

Figure 4-21 Estimated evapotranspiration deficit (LISFLOOD WRR2 withWFDEI forcing).

Figure 4-22 Estimated evapotranspiration deficit (LISFLOOD WRR2 withMSWEP forcing).

37

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-23 Estimated average water availability per capita (LISFLOOD WRR2
with WFDEI forcing).

Figure 4-24 Estimated average water availability per capita (LISFLOOD WRR2
with MSWEP forcing).

38

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-25 Estimated average water exploitation index (LISFLOOD WRR2 withWFDEI forcing).

Figure 4-26 Estimated average water exploitation index (LISFLOOD WRR2 withMSWEP forcing).

39

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-27 Estimated average water dependency (LISFLOOD WRR2 withWFDEI forcing).

Figure 4-28 Estimated average water dependency (LISFLOOD WRR2 withMSWEP forcing).
(c)Impact of the precipitation ensemble forcing

The LISFLOOD ensemble runs with the various precipitation forcings while keeping
all other input identical - analysed for the period 2000-2012 – shows that the
LSIFLOOD run with MSWEP is closed to the observed discharge. The summed KGE
for the stations in Table 4-10 is 3.5. WFDEI is the second-best performer, while thereal-time and uncorrected TRMMRT shows the worst performance. For some basins,

40

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

though – Ob, Ebro, Thames, Amazon, Congo -, TRMMRT is producing reasonable
results.
Table 4-10: Kling Gupta Efficiencies (KGE) for various rivers for the various
ensemble runs with various precipitation forcings.

4.4 ORCHIDEE
4.4.1 Model description
The model used is version #4030 of the ORCHIDEE-ROUTING branch. This is to verysmall details the same release as the one to be used by IPSL for CMIP6. A number ofinnovations have been introduced in this version compared to the one used for
WRR1 of Earth2Observe.
(a)

New ancillary data

The ORCHIDEE group has taken the opportunity of CMIP6 and the new regional
applications to update most of the ancillary data used to set surface parameters. The
objective is to take advantage of new data sets, often remote sensing based, as well as
increasing the resolution of the maps used.
The latest version of the ESA CCI land cover is used. In contrast to the previously
used IGBP map, this one is directly reporting fractions of plant functional types (PFT)
as defined by ORCHIDEE. Thus, it is not any more the model which transforms the
Olson classification into the PFTs and thus this operation could be done with morecare. This in particular decreases the fraction of bare soil for many ecosystems. Basedon the ESA-CCI land cover, a probability distribution of PFTs is computed for each41

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

grid box. The model then only interpolates this distribution onto the grid used. This
allows to preserve more of the original information.
For the soil texture classes the model has moved from the Zobler maps (Post andZobler, 2000) to the USDA maps. The original maps were at 0.5° resolution and thenew ones are at 0.08°.This allowed to increase the resolution at which soil types aredescribed and thus represent contrasts in the vertical water transports up to higher
resolutions of the model.
In WRR1, ORCHIDEE used to define the background albedo of soils, i.e. when thesurface is not covered by vegetation, through a soil colour map (Dickinson et al.,1993). This has now been replaced by a map derived from MODIS albedo using thevegetation states predicted by ORCHIDEE. By assimilating the MODIS albedo into themodel the background albedo was determined as the residual of the vegetation. It
thus ensures that given the correct vegetation albedo the soil will have the rightreflective characteristics (Peylin et al. Personal communication). This procedure also
produced a new table of optimal albedo for the 13 PFTs simulated by ORCHIDEE.
(b)

New formulation for surface roughness

In WRR1 ORCHIDEE had the same surface roughness for momentum and sensible
and latent heat fluxes. This is a simplification which needed to be removed from themodel and it was chosen to use the parametrisation proposed by Su et al. (2001).
Based on the “localized near-field” Lagrangian theory the model predicts theroughness values to compute latent and sensible heat fluxes based on the state of the
canopy. The implementation of this approach in ORCHIDEE was tested against a large
number of FLUXNET stations and proved to reduce the systematic biases whichcharacterized our model. In particular the overestimation of bare soil evaporation
was largely reduced. It also produced very similar effective roughness length thanthe 3D energy balance version of ORCHIDEE (Ryder et al., 2016). This innovation in
our model reduces the overestimation of evaporation which was noted in the WRR1
simulation.
(c)

New snow scheme

A major innovation in ORCHIDEE for CMIP6 and WRR2 is the introduction of amultilayer snow scheme. This scheme is documented in Wang et al. (2013).
Compared to the version described in the original paper, some modification had to be
applied so that the snow temperature profile can be solved implictly and thus
coupled with the energy balance of ORCHIDEE. Furthermore the scheme has been
extended to treat properly fractional snow covers in the grid boxes to make it moremeaningful at the coarse resolution used in climate models. This scheme has beenfound to improve significantly the predicted albedo of snow covered regions andmore generally is beneficial for the simulated water cycle of cold regions of theworld.
(d)Soil freezing scheme

A soil freezing scheme has been introduced by (Gouttevin et al., 2012) in themultilayer soil moisture model. After a validation period this formulation is now fully
operational in ORCHIDEE. Some improvement had to be made to ensure that during
42

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

brief freezing events, which only affect the top soil layers, not all the rainfall or snow
melt is directed toward surface runoff. So the impact of soil freezing on infiltration
has been revised so as to take into account the sub-grid processes which allow
incoming water to infiltrate and be stored in the soil. This avoid early soil drying inthe summer in boreal regions.
New routing scheme(e)

The model has been equipped with a new version of the routing scheme which isbased on the HydroSHED database (Lehner and Grill, 2013) which provides a
description of the worlds rivers at 1km resolution. This allows to route the runoff
and drainage produced by ORCHIDEE to the oceans and endorheic lakes and thus
simulate the discharge of all major rivers at their gauging stations. Thesedevelopment are document in a publication under preparation (Nguyen Quang et al.
2017). This routing scheme is an ideal tool to validate the continental water cycle
simulated by the model and also offers an opportunity of data assimilation.
As the HydroSHED database is limited to the continents south of 60° North, it cannot
yet be applied at the global scale. But it has been used here to validate the model andthe Earth2Observe forcing data set over the Mediterranean drainage basin as shown
below.
4.4.2 Data assimilation
Data assimilation has been used at CNRS for two applications with the
Earth2Observe project :to determine the optimal background albedo and the albedo of the 13 plant
functional types by merging the model output with MODIS observations, andto evaluate the evaporation error over the Mediterranean basin in WRR2 by
assimilating into the routing the GRDC (Fekete et al., 2000) data base ofobserved river discharge.

Results of these studies will be reported elsewhere.
4.4.3 Evaluation
In an effort to validate the Earth2Observe forcing data in for WRR2 (hereafter,
E2OFD) we compare the WRR2 simulation with the WRR1 simulation, which used
the WFDEI atmospheric conditions, and an independent simulation of
WRF/ORCHIDEE at a resolution of 20km². The region chosen is the Mediterraneanbasin as it is the focus of the HyMex project and includes a number of test basinschosen for Earth2Observed. The period for the validation was chosen to be 19901999 as this is a golden period of the GRDC database.
Within the Mediterranean basin two regions are presented here as they were foundto show the largest precipitation difference between MSWEP (the precipitation
product used in E2OFD) and WFDEI. These region are the Caucasus and the Alps. To
simplify the discussion only the mean annual cycle over the 1990-1999 period arepresented here.

43

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis(a)

The Kura basin

The Kura basin takes its source in the Caucasus and thus an ideal basin toevaluate which forcing data provides the most realistic discharge and help us
decide which precipitation estimation is more realistic. Figure 4-29 shows that thedifference between the precipitations of E2OFD, WFDEI and WRF are quite large
with the first one clearly showing the highest values. On the other hand the phaseof the annual cycle is similar between E2OFD and WFDEI. On the other hand WRF
proposes a winter maximum precipitation which is too early and a very dry summer.

Figure 4-29: Precipitation and simulated evaporation averaged over the Kurabasin. The dashed lines in the lower panel are potential evaporation estimatedby ORCHIDEE with the forcing. The 3 simulations presented in these figures are: WRF, E2OFD and WFDEI forcing.
The other variable which is largely driven by the forcing is the Penman-Monteithpotential evaporation, shown as dashed lines in Figure 4-29. Here we clearly see that
WRF has the largest values and E2OFD are the lowest. This does not necessarily leadto corresponding differences in the evaporation as can be seen in the same figure.
The atmospheric demand can be better satisfied in E2OFD with it large precipitation
values and thus soil moisture stresses. This leads to a higher total evaporation in
E2OFD when compared to the two other estimates.
The resulting discharge of the Kura can be compared to the climatology (i.e. a long
period before the years 1990-1999) at the station Surra (see Figure 4-30). It showsthat with the E2OFD and WRF atmospheric conditions the model carries too muchwater in the river. On the other hand for WFDEI the annual mean discharge is very
close to the observations and the model only has an error in the phasing of the peak
44

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

flows. From this analysis we can conclude that MSWEP has in all likelihood too large
precipitation values over the Caucasus. The errors in WRF and WFDEI are difficult toattribute. These can be the result of model or forcing deficiencies.

Figure 4-30: Discharge simulated by ORCHIDEE and observed for the Kura riverat the Surra(b)

The Rhone and Po

The two large basins fed by Alpine precipitation, the Rhone and the Po, are alsointeresting. The WFDEI forcing shows over the Alps the largest mean annual rainfallwith the largest differences mostly during the autumn. While WRF, over the Rhone
basin, displays larger precipitation values in winter when compared to WFDEI.
Overall it is E2OFD which has the smallest precipitation values over this mountainsystem.
In both the Rhone and Pol (Figure 4-31 and Figure 4-32) the simulation forced by
E2OFD shows too low flows during the summer. The annual mean discharge is alsostrongly underestimated. On the other hand the WFDEI forced run displays discharge
values very close to observations. It is most obvious in the Rhone that WRF has toomuch snowfall in winter and thus exaggerates the flow of the river during the melt
period.
This short discussion focuses on two problem areas of the E2OFD forcing. Thisshould not hide the fact that in general the behaviour of ORCHIDEE is much better
when forced with E2OFD as could be evaluated with over 300 gauging stations on therivers of the Mediterranean region. The higher resolution brings spatial details to themodel’s simulations which are in good agreement with WRF but absent from WFDEI.
For a complete analysis of the simulated hydrological cycle of the mountainous areas
within the Mediterranean the separation of precipitation in rainfall and snow fallshould be analysed in detail. ORCHIDEE is quite sensitive to this distinction. A more
complete analysis of these simulation will be proposed in a separate report after anin depth study of the three simulated continental water cycles.

45

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-31: Observed and simulated discharge of the Rhone at the Beaucaire
station.

Figure 4-32: Observed and simulated discharge of the Po at the Pontelagoscurostation.

4.5 PCR-GLOBWB
4.5.1 Model description
The global hydrological model PCR-GLOBWB (PCRaster GLOBal Water Balance, van
Beek et al., 2011) is a leaky bucket type of model providing a regular grid based
representation of terrestrial hydrology. PCR-GLOBWB is forced by temperature,
precipitation and reference potential evapotranspiration. PCR-GLOBWB responds tothe prescribed atmospheric conditions through simulating the resulting fluxes andchanges in the water content of its different storages (i.e. canopy interception, snow,
soil, groundwater, channels and water bodies) for the land and water surface withineach cell.
Over land, precipitation can fall as rainfall and snow. Snow fall, accumulation andmelt are temperature dependent and modelled using a temperature-based snow
module similar to the HBV model (Bergström, 1995) using 0°C as melt temperature.
Any precipitation can be intercepted and evaporate, the remainder reaching the soil
surface where it is partitioned into direct runoff and infiltration that enters the upperof two stacked soil layers. Through the soil layers water moves vertically and cancome to delayed runoff as interflow or base flow. Base flow originates from the46

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

groundwater store in that is situated below the second soil layer and that is
replenished by net recharge (percolation minus capillary rise). Water stored in the
groundwater reservoir is exempt from any direct influence of vegetation.
All drainage from the soil and groundwater layers accumulates as discharge and isrouted along a river network. The routing can be described by the kinematic wave or,alternatively, by the computationally more efficient travel time approach. Channels
can be intersected by lakes and reservoirs with one of the three water surface types
prevailing in a cell. From all water surfaces water can be lost to evaporation.
In addition to the subdivision between land and open water, PCR-GLOBWB includes
sub-grid variability with regards to land cover, soil conditions and lithology. Land
cover is subdivided on the basis of the GLCC dataset (USGS EROS Data Center, 2002)
for natural vegetation and this is can be complemented with agricultural land covertypes (e.g., paddy and non-paddy irrigated areas and rain-fed agriculture). For eachcover type, vegetation phenology and crop calendars are prescribed by repeating a
specific annual course. Sub-grid variations in soil and hydro-lithological propertiesare derived from global data sources (FAO DSW, Gleeson et al., 2014) and assigned
as effective value per land cover type. A noticeable exception is the distribution of thewater holding capacity that is parameterized by its mean and minimum value and ashape factor, representing the combined effect of soil and vegetation properties
which is used to generate the direct runoff using the Improved Arno Scheme (Todini,
1996 ;Hagemann and Gates, 2003). The distribution of the surface elevation withineach cell, as used to compute inundation and the capillary rise, is described using acumulative
function
of
the
1×1
km
Hydro1k
data
set(https://lta.cr.usgs.gov/HYDRO1K).
Further anthropogenic interference with the global water cycle can be included
optionally by considering dynamic withdrawal, allocation and consumptive use ofground- and surface water resources –including irrigation- and return flows of
unconsumed water to surface water and groundwater resources (Wada et al.,2014;Sutanudjaja et al., 2014). Allocation of the gross demand and subsequent return
flows is dynamic and responds over time the changing availability of surface waterand groundwater (de Graaf et al., 2014).
Reservoirs can be included, and more than 6000 reservoirs can be included in theparameterization on the basis of the GRanD (Lehner et al., 2011) database, and alter
the river flow regime as a function of storage, inflow and downstream demand.
Since WRR1, several improvements in the underlying model code have been carriedout. For Tier 1 of Earth2Observe, PCR-GLOBWB simulations were carried out toprovide a baseline reference run and only “natural” (non-humanly modified)components of the water cycle were considered. For Tier2, these human influences
are included in full:

Paddy and non-paddy irrigated areas have been included in the land coverparameterization in addition to the natural short and natural tall vegetation
types considered in WRR1 and the prescribed areas represent thedevelopment over time;

Irrigation water demand is computed for these areas using the FAO guidelines(Allen et al., 1998);

47

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis



For three other sectors -livestock, municipalities and industry- water demandis prescribed at a monthly resolution and reflecting the historicaldevelopment over the period 1961-2014. Gross demand prescribes thepotential withdrawal and net demand the eventual return flows;Desalination is included as an external supply (Wada et al., 2011);

Reservoirs, available in the GRaND dataset, have been included.

In WRR2, the temporal resolution of all simulations was daily. The spatial resolutionof the simulations was either 0.5° or r0.08333° (30 and 5 arc minutes respectively)
globally. The degraded spatial resolution of 0.5° was preferred to increase thecomputational speed for the more demanding simulations and to facilitatecomparison with the outcomes of WRR1. For computational expedience, also therouting was performed with the less demanding travel time option. Meteorologicalinput for WRR2 was available at 0.25° and was used without downscaling in thehigher-resolution simulations in order to assess the effects of model resolution more
directly. Precipitation and air temperature provided at the daily resolution and usedaccordingly. Potential reference evapotranspiration was computed from theprovided non-precipitation data according to the FAO guidelines (Allen et al., 1998).
For all simulations, a substantially long spin-up period was used, typically using themeteorological forcing twice back-to-back before the eventual simulation was carriedout.
4.5.2 Data assimilation
Pending the completion and analysis of this data assimilation, a brute force
simulation has been carried out including 24 members, including the global
pre-factors for the parameters of PCR-GLOBWB shown in
Table 4-11. This dataset is evaluated against GRDC discharge over the available
period. Eventually, as part of the data assimilation within WRR2, the globalhydrological model PCR-GLOBWB is embedded in an Ensemble Kalman Filter
framework (EnKF). Hydrological states and fluxes are constrained on the basis of theavailable in-situ river discharge measurements of the Global Runoff Data Centre(GRDC) and soil moisture measurements (ESA-CCI, Dorigo et al., 2015). The
assimilated information will lead to a calibrated version of PCR-GLOBWB in whichparameters are related to the snow module, rainfall-runoff generation, unsaturated
zone process and base flow generation. The calibration period covers 1979-2014 –the later years affected by the absence of discharge measurements, the earlier onesby the absence of soil moisture- and the performance is evaluated using the longterm GRDC dataset and evaporation from satellites and FLUXNET observations(Miralles et al., 2011;Jung et al., 2009). Calibration is performed at the 0.5° spatial
resolution, including all available human influences and starting from 1961 as this is
the earliest year with demand information, replicating the climate forcing of WRR2
up to the start of the actual simulation period in 1979. A similar setup has also been
adopted for the intermediate brute force simulation.

48

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Table 4-11: Parameter combinations for the brute force calibration. 24
parameter combinations are used (3 × 4 × 2). The default parameter setting(Run 10) has been omitted in favour of Run 24.

Ru
n
0
1
2
3
4
5
6
7
8
9
11
12

Saturatedhydraulicconductivity(log-scale)

Recessioncoefficient

Storage
capacity(log-scale)

-0.25
-0.25
-0.25
-0.25
-0.25
-0.25
-0.25
-0.25
0.00
0.00
0.00
0.00

-0.50
-0.50
0.00
0.00
0.50
0.50
1.00
1.00
-0.50
-0.50
0.00
0.50

(linearscale)
1.00
1.25
1.00
1.25
1.00
1.25
1.00
1.25
1.00
1.25
1.25
1.00

Recessioncoefficient

Storage
capacity

Run

Saturatedhydraulicconductivity(log-scale)(log-scale)(linear scale)

13
14
15
16
17
18
19
20
21
22
23
24

0.00
0.00
0.00
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.00

0.50
1.00
1.00
-0.50
-0.50
0.00
0.00
0.50
0.50
1.00
1.00
0.00

1.25
1.00
1.25
1.00
1.25
1.00
1.25
1.00
1.25
1.00
1.25
0.80

4.5.3 Evaluation
The influence of the differences in meteorological forcing and model changes
between WRR1 and WRR2 were evaluated progressively by the model setups listed
in Table 4-12.
Table 4-12: Model setups evaluated in WRR2. All model runs cover the period
1979-2014 with the exception of WRR1_Nat30 which covers the period of the
WRR1 forcing 1979-2012
Forcing

Spatialresolution

WRR1_Nat30

WRR1

30”

WRR2_Nat30

WRR2

30”

WRR2_Hum30

WRR2

30”

WRR2_Hum05

WRR2

5”

WRR2_Hum30_BF

WRR2

30”

WRR2_Hum30_DA

WRR2

30”

Model name

DetailsNatural only: two land cover types; no water use or reservoirs,
WRR1 forcing
As above but WRR2 forcing
Including full human interaction using 4 land cover types (incl.
irrigation), water use and reservoir operations using WRR2
forcing
As above but with increased spatial resolution from 30 to 5”
As above at 30” with consideration of the parameter
combination of Table 1 (brute force calibration) pending theresults of the full data assimilation
As above but with EnKF, to replace the information of
WRR2_Hum30_BF

For the evaluation of the model runs, the following aspects are considered:

The components of the global water balance and their average seasonalvariation over the common period (1979-2012);



The agreement between monthly discharge as simulated and observed atselected stations of the Global Runoff Data Centre (GRDC);

49

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Total water demand and the provenance of water withdrawals from surface
water and groundwater;

The range in parameter values and performance of the calibrated model (bybrute force or data assimilation) in terms of observed discharge and thestorage of soil moisture and rate of actual evapotranspiration inferred fromindirect measurements.

4.6 SURFEX-TRIP
4.6.1 Model description
The SURFEX-TRIP modelling system of the CNRM is based on the coupling of the
ISBA Land Surface Model and the CNRM version of the TRIP river routing model. In
the WRR1 version, ISBA computes the soil/snow/vegetation energy and water
budgets and provides TRIP with runoff and drainage which are then routed within
the river network.
Two main improvements have been implemented in the WRR2 version. First, asimple groundwater scheme has been added in the TRIP model to simulate the lowfrequency variations of groundwater and the groundwater-river interactions(Vergnes and Decharme, 2012). The two-dimensional groundwater flow equation issolved for the uppermost unconfined aquifer and groundwater-river interactions arecomputed from the water level in both pools. The new parameters of thegroundwater scheme are based on lithological and hydrogeological information
available at the global scale. Namely, the World-wide Hydrogeological Mapping andAssessment Programme (WHYMAP, http://www.whymap.org) was used as primary
information to delineate major regional groundwater basins concerned by diffusive
groundwater movements. Moreover, the new groundwater scheme is coupled with
ISBA to simulate upward capillary fluxes from shallow groundwater into theunsaturated soil column (Vergnes et al., 2014). The simulated water table depth isused as the lower boundary condition for the soil moisture diffusive equation.
Compared to a free drain experiment, upward capillary fluxes at the bottom of thesoil tend to decrease the simulated recharge from ISBA to the aquifers. Globally the
impact on the river discharge remains low while the mean annual evapotranspirationslightly increases over the aquifer domains.
The second main improvement is the implementation of a flood model which
describes the flood dynamics through the daily coupling between ISBA and TRIP
including a prognostic flood reservoir (Decharme et al., 2012). This reservoir fills
when the river height exceeds the critical river bank full height and vice versa. The
flood interacts with the soil hydrology through infiltration and with the overlying
atmosphere through precipitation interception and free water surface evaporation.
Generally, the flood model significantly improves the discharge estimation first byincreasing the evapotranspiration that limits the annual discharge overestimation,
second by smoothing river peak flows when the floodplain storage is significant.

50

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-33: Scheme of the WRR2 model version, including the representationof floodplains and groundwater reservoir.
In WRR2, the ISBA simulations were carried out at the 0.25°x0.25° resolution. On theother hand, since no river routing network were available at this resolution, theresolution for the TRIP model was 0.5°x0.5° as in WRR1. Hence, output variablesfrom TRIP (RivOut, SurfStor, GroundMoist) are given at this coarser resolution.
Besides, a 50 years spin-up was carried out in order to get the groundwater storageat its equilibrium point. As in WRR1, in both ISBA and TRIP the default set ofparameters was used, with no particular calibration performed for the simulations.
The model state of ISBA and TRIP on 01-01-2000 from the baseline simulation wasused as a restart for the ensemble simulations.
4.6.2 Data assimilation
In addition to the four experiments of WRR2 ensemble simulations based on
different precipitation forcing fields, a data assimilation experiment has been
conducted with the SURFEX-TRIP model. This experiment is an extension of theexisting SURFEX Land Data Assimilation System to the global scale (LDAS-Monde)that was recently implemented over France (Barbu et al., 2014).
Here, two global operational datasets derived from satellite observations are
assimilated simultaneously using a multivariate Extended Kalman Filter (EKF): (i)Surface Soil Moisture (SSM) from the ESA Climate Change Initiative and (ii) Leaf Area
Index (LAI) from the Copernicus Global Land Service project. The SSM observations
consist in daily maps at the resolution of 0,25° covering the period 1978-2015, while
the LAI observations are given every 10 days at the resolution of 1 km over theperiod 1999-2015. The SSM data were bias corrected with respect to the modelclimatology by using a seasonal-based CDF (Cumulative Distribution Function)
matching technique. Each model grid cell is divided into a number of land covers,
each having its own set of prognostic variables. The filter algorithm is designed toprovide a distinct analysis for each land cover while using one observation per gridcell. The updated values are aggregated by computing a weighted average.
The SURFEX-TRIP model configuration and meteorological forcings are the same as
for the WRR2 baseline simulation. Nevertheless, in order to limit the computation
51

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

time, the simulations were conducted at the global scale at a 1 degree spatial
resolution over the period 2000-2013. In the next section, the results of the LDASMonde simulation are compared to a reference run in which no assimilation is done.
The reference run is equivalent to the WRR2 baseline simulation, but with a 1 degree
resolution.
4.6.3 Evaluation
In this section, the performances of the WRR2 experiment are analysed. The analysis
criterion is based on the Nash-Sutcliffe Efficiency computed for a large number ofworld-wide in situ gage stations where discharge observations were available.
Discharge measurements were collected from different sources, including the Global
Runoff Data Centre (GRDC), the United States Geological Survey (USGS), the HYBAM
network (HYdrologie et Biogéochimie du Bassin Amazonien) and Frenchhydrological network (Banque HYDRO).
(a)

Impact of the new forcing

To evaluate the impact of the forcing used in the WRR2 setup, an extra
simulation has been carried out using the model setup of WRR1 and forcingsfrom WRR2 (WRR1_F2). Results of this simulation are compared to resultsfrom WRR1 (WRR1_F1), so that only the forcing fields change from one
experiment to the other. Figure 4-34 compares the Nash-Sutcliffe Efficiency
over the available gage stations. It is shown that the WRR2 forcing has neutral
to positive impact on simulation of the discharge almost all over the globe,
even though a large number of stations still show quite poor performances,such as in North America. As shown in, where accumulated distribution of NSE is plotted for several regions of the globe,
the improvement is quite significant, except over the Eastern part of USA and over
the Mediterranean region. Finally, Figure 4-36 shows the seasonal cycle of the runoff
for 14 major basins for both experiments and from in situ observations. The newforcing has a clear impact of discharge simulations. In some basins, such as the
Amazon or the Mekong basins, it clearly improves the discharge, while the impact isnegative for the Danube basin.

52

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-34: Comparison of Nash-Sutcliffe Efficiency for experiments WRR1_F1(model WRR1, forcings WRR1) and WRR1_F2 (model WRR1, forcings WRR2).

53

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-35: Comparison of the accumulated distribution of NSE over different
regions of the globe, for both experiments WRR1_F1 and WRR1_F2.

54

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-36: Seasonal cycle of the runoff for both WRR1_F1 and WRR1_F2
experiments and comparison with in situ observations.

55

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis(b)

Impact of the model improvements

The same analysis is performed to analyze the impact of the model changes. The
previous WRR1_F2 experiment is compared to the WRR2_F2 experiment which has
been run with the WRR2 version of the model and the new forcings. Hence, in thefollowing, only the impact of the model improvements are analyzed. From Figure
4-37, Figure 4-38, and Figure 4-39, it is clear that the model changes have much less
impacts than the forcing. Figure 4-37 and Figure 4-38 show that the impact is neutralor positive for every stations. Most of the improvements are located in Siberia, Africa
and in the Amazon basin. Figure 4-39 shows that most of the major basins areslighlty impacted by the model changes. Nevertheless, the representation of thefloodplain in the Niger River Basin significantly decreases the peak discharge, namely
because of the direct evaporation from the Niger Inner Delta which is better
represented. The effect is quite similar for the Ob river, even though the peakdischarge occurs one month earlier than expected, which is probably due to a too fast
melting of the snow layer.

Figure 4-37: Comparison of Nash-Sutcliffe Efficiency for experiments WRR1_F2(model WRR1, forcings WRR2) and WRR2_F2 (model WRR2, forcings WRR2).
56

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-38: Accumulated distribution of NSE over different regions of the
globe, for both experiments WRR1_F2 and WRR2_F2.

57

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-39: Seasonal cycle of the runoff for both WRR1_F2 and WRR2_F2
experiments and comparison with in situ observations.
(c)Analysis of the SURFEX LDAS-MondeThis section presents the main results of the SURFEX LDAS-Monde. Performances of
the analysis are evaluated against an open-loop simulation in which no satellite data
are assimilated. The open-loop simulation is referred as ‘Model’ in the results.
Figure 4-40 and Figure 4-41 show the improvement due to the assimilation, in termsof correlation and Root Mean Square Difference (RMSD) with respect to theobservations, on both assimilated variables LAI and SSM. The column on the left andthe one in the middle correspond to the open-loop and to the analysis, respectively,
while the column on the right represents their difference. The assimilation has aclear impact on the LAI all over the globe, with resulting correlations close to 1 andvery low RMSD compared to the open-loop. Namely, in the tropical zone and at highlatitudes, the model is not able to correctly represent the vegetation dynamics, which

58

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

is efficiently corrected by the assimilation. The impact on the SSM is much lessimportant but still positive.

Figure 4-40: Correlation and Root Mean Square Difference (RMSD, in m2/m2) of
LAI for experiments without data assimilation (Model) and with data
assimilation (Analysis).

59

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-41: Correlation and Root Mean Square Difference (RMSD, in m3/m3) of
SSM for experiments without data assimilation (Model) and with data
assimilation (Analysis).
In Figure 4-42, the modelled LAI averaged over Europe is compared to observations
for both the open-loop and the analysis. This figure shows that most of thecorrections provided by the assimilation is done during the second part of the year.
Indeed, the growing phase of plants is quite well simulated by the ISBA model. The
simulated vegetation peak is a little delayed, as well as all the senescence phase,
which is responsible for the limited performances of the open-loop. The assimilation
is able to partly correct this delay. Similar conclusions can be drawn from Figure
4-43 which shows four metrics describing the seasonal performances of both the
open-loop and the analysis: the standard deviation (SDD), the bias, the RMSD and the60

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

correlation. All the metrics are greatly improved by the assimilation. Finally, theanalysis increments averaged over Europe are plotted in Figure 4-44. The increments
represent the values added or subtracted to the control variables (LAI and WG2which represent the surface soil moisture) during the assimilation. It is shown that,for both variables, the corrections are quite seasonal and occur mostly during
summer and automn, which is consistent with previous remarks. Besides, theassimilation tend to remove some vegetation (negative LAI increments) and to add
water in the soil (positive WG2 increments). It has to be noticed that the averaged of
WG2 increments over Europe increased during the 14 years of simulation. The
discontinuity of the satellite missions integrated into the ESA-CCI SSM product ispotentially responsible for this increase. Namely, the number of available
observations drastically increased in 2003 with the integration of AMSR-E
observations, and since 2010 with the integration of SMOS (Dorigo et al., 2017, underreview).

Figure 4-42: Comparison of LAI from open-loop, analysis and observation over
Europe.

61

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-43: LAI seasonal scores of the open-loop and analysis over Europe:
Standard Deviation (SDD), Bias, Root Mean Square Difference (RMSD) andcorrelation.

Figure 4-44: Time series of analysis increments averaged over Europe for bothcontrol variables. WG2 is the water content in the second soil layer (1cm-4cm).
Units are m2/m2 for LAI increments and m3/m3 for WG2 increments.
Lastly, Figure 4-45 shows the impact of the assimilation on variables of the water
budget: runoff, drainage and evapotranspiration. Contrarily to LAI and SSM, this is anindirect impact since these variables are not assimilated. Even though the impact of
the assimilation is much lower than for LAI and SSM, it is not negligible and can reach
10 % or more in some regions. Globally, the assimilation increases the runoff anddrainage and decreases the evapotranspiration, especially in the tropical regions.

62

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-45: Average over the whole period of Runoff, Drainage andEvapotranspiration for the open-loop and analysis experiments, and impact of
the assimilation. Units are kg/m2/day.

63

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

4.7 W34.7.1 Model description
The World-Wide Water (W3) model (formerly W3RA) is a grid-based globalhydrological model based on the AWRA-L series of models used operationally by theAustralian Bureau of Meteorology for national-scale water assessments, wateraccounts and the daily-updated Australian Landscape Water Balance web site(http://www.bom.gov.au/water/landscape/). W3 version 2.0 builds on a global
implementation of AWRA-L v5.0. The original AWRA-L model (version 0.5) iscomprehensively described in a technical report (Van Dijk, 2010), while the changes
in version 5.0 are described in (Viney et al., 2015). The conceptual model structure isillustrated in Figure 4-46.

Figure 4-46: Simplified conception structure of W3 v2.0
For implementation at global scale, a number of modifications were made.


A snow model was coupled to AWRA-L v5.0 based on HBV. This was also donein the W3RA version contributed to ensemble WRR1. However, for WRR2
parameters for the HBV model were obtained for HBV-SIMREG for WRR1
(Beck et al., 2016).



The model was run at 0.05° resolution. As the data to be supplied to WRR2
was requested at not more than 0.25°, a distinction between subgrid
vegetation response units (i.e., deep- or shallow-rooted vegetation) was
considered unnecessary. Instead, each grid cell was assigned to either classbased on available vegetation mapping.



The WRR2 forcing data were downscaled to 0.05° for precipitation andtemperature. This was done using a monthly climatology of average
precipitation gain (a multiplier) and temperature offset (in °C), based on theWorldClim (Hijmans et al., 2005) data set.

Compared to the WRR1 contribution (which was based on AWRA-L v0.5), W3
version 2.0 has considerable changes in the description of soil andgroundwater hydraulics (Viney et al., 2015).
64

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis



A global high-resolution map of Height Above Nearest Drainage (HAND) wasused for sub-grid topography parameterisation, used in the simulation ofsaturated area and vegetation root access to groundwater. The map wasderived primarily from the SRTM DEM completed with GTOPO30 at higher
latitudes (G. Donchyts, Deltares, pers. comm).



The radiation and energy balance description were slightly modified, which
improved simulations particularly in high latitudes.

Land surface temperature was simulated as a diagnostic, which allowed theassimilation of MODIS land surface temperature to estimate water use fromsources other than rainfall (e.g. irrigation, wetlands and water bodies).

Other changes related mainly to the input fields, which included:•

ocean mask (0.01° NOAA);•

slope (SRTM/GTOPO30);•

aquifer hydraulic properties (GLHYMPS, Gleeson et al., 2014);•

groundwater recession coefficient (GSCD, Beck et al., 2016);•

soil hydraulic properties (GSDE , Shangguan et al., 2014);•

permanent water, permanent ice, impervious areas (GlobCover);•broad vegetation type (MOD12C1);•

vegetation height (ICESat-GLAS, Simard);

4.7.2 Data assimilation
For the data assimilation (online) run, the following MODIS-derived 0.05° resolution
data products were assimilated:•

white- and black-sky albedo (MCD43C3.005);•

BRDF reflectance (MCD43C4.005);•

inundation fraction (Van Dijk et al., 2016);•

leaf area index (GLASS);•

snow cover (MCD43C3.005/ MCD43C4.005);•

land surface temperature (MOD11C1.006);

The data assimilation approach was an efficient as well as somewhat crude updating
using either nudging or direct insertion. Dynamic updating was done for 9
parameters and 3 (non-mass) states. Because water store were not directly updated,water balance is maintained even in the data assimilation run.
The evaporation of water not derived from rainfall is estimated through theassimilation of LST and inundation observations (Figure 4-47). These estimates are
not reconcile within the model water budget and should be seen as separate and in
addition to rainfall driven ET. They are however considered in estimating net riverdischarge, where they are routed as a loss that goes to the detriment of runoff gains.

65

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-47: Example of additional evaporation from non-rainfall sources
estimated for 03 July 2012 over Pakistan and NW India. Values range from 0(white) to 5 mm/d (blue).
4.7.3 Evaluation
Model simulations were evaluated against streamflow from around 9,000 stations
worldwide (cf. Van Dijk et al., 2016), as well as observations the in situ global soil
moisture data base and daytime-average estimates of radiation budget components
as well as latent and sensible heat flux from the FLUXNET network, made available
through the La Thuile data set. These showed satisfactory agreement between modeland observations. It is noted that the additional evaporation estimates derived from
LST assimilation have not been evaluated against independent data sources andtherefore should be considered experimental. A more comprehensive evaluation will
be documented in forthcoming publications.

4.8 WaterGAP3
4.8.1 Model description
The global water model WaterGAP3 (Water – Global Assessment and Prognosis) is a
grid-based, integrative assessment tool to examine the state of global freshwater
resources. The model framework consists of a spatially-distributed rainfall-runoff
model, five sectorial water use models, and a large-scale water quality model.
The global hydrological model simulates the terrestrial part of the globalhydrological cycle by a sequence of storage equations for the most relevant
continental storage compartments: canopy, snowpack, soil, renewable groundwater,and surface water bodies. The model requires daily fields of precipitation, nearsurface air temperature, downwelling shortwave and longwave radiation as external
meteorological forcing. Potential evapotranspiration is estimated according to the66

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Priestley-Taylor approach with surface net radiation calculated on the basis of landcover dependent albedo and emissivity values.
The canopy storage is conceptualized as a single layer that intercepts precipitation
until the maximum storage capacity is exceeded, and intercepted water evaporates atpotential rate. Maximum canopy storage depends on daily LAI which is modelled as afunction of land use dependent maximum LAI, fraction of deciduous plants andclimate.
Snow accumulation and melt are simulated on a 1 arc minute sub-grid following a
degree-day approach based on land-cover specific melting rates. Surface airtemperature is disaggregated from 5 to 1 arc minute according to the elevationdifference between each 5 arc minute cell and its corresponding sub-grid cells.
The soil column is represented as a single layer whose storage capacity is determined
as a function of the soil texture dependent available water capacity (derived from soilmaps) and the land-cover specific rooting depth. Total runoff from land is a functionof effective precipitation, soil saturation, a non-linearity parameter and the fractionof urban area. The remaining part (effective precipitation minus runoff from land) is
passed as infiltration to the soil storage. Groundwater recharge is calculated as afunction of slope, soil texture, aquifer type, and the occurrence of
permafrost/glaciers. Renewable groundwater resources are represented as a linear
storage whose outflow is passed as subsurface runoff to the routing scheme.
Surface and subsurface runoff generated in each grid cell and inflow from upstream
cells is transported through a series of linear and nonlinear retention storages
representing lakes, reservoirs, and wetlands before contributing to streamflow. Flowvelocity in the river segment is calculated as a function of river bed roughness, river
bed slope and hydraulic radius of the channel according to the Manning-Strickler
equation (Verzano et al., 2012). Lateral flow, i.e. between grid cells, is assumed tooccur as streamflow only.
WaterGAP3 is calibrated in a basin-specific manner against long-term meandischarge by adjusting a runoff-nonlinearity parameter in each basin. In basins
where adjusting this parameter does not provide an acceptable runoff estimate, i.e.
the deviation from observed discharge remains larger than ±1%, an additional runoff
correction factor is assigned to each cell within the basin (same value for each cell).
This correction factor effects all water balance components; if simulated discharge is
too low, runoff will be increased and evaporation will be reduced and vice versa. This
procedure is aimed to ensure a closed water balance, hence mass conservation,
within the individual basin.
WaterGAP3 is explicitly designed to account for human interference on the natural
streamflow regime through water abstraction and flow regulation by large dams andreservoirs. Spatially explicit time series of water withdrawal and water consumption
are provided by the WaterGAP3 water use models (aus der Beek et al., 2010;Flörke etal., 2013) for five sectors: Domestic use (households and small businesses),
manufacturing industries, thermal power plant cooling, irrigated agriculture andlivestock farming. Sectorial water demands can be abstracted from surface water(rivers, reservoirs and lakes) and groundwater resources. Water demands forthermoelectric power plant cooling and livestock farming are assumed to be
exclusively abstracted from surface water. For the remaining sectors, water
withdrawals are allocated to groundwater and surface water abstractions according67

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

to sector- and cell-specific temporally constant groundwater use fractions derived
from national and sub-national statistics (Döll et al., 2012).
The WaterGAP3 simulations were carried out at 5 by 5 arc minute horizontal spacing.
Storage compartments were initialized by re-running the first year of available
meteorological forcing (1979) ten times. In a final step, simulated daily states andfluxes were scaled from the native 5 arc minute model resolution to 0.25° WRR-tier 2
resolution through taking the maximum (river discharge) or thorough area-weighted
arithmetic averaging and summation, respectively.
4.8.2 Model improvements with respect to WRR1
Discharges at the major rivers are regulated to use the available river waters forhydropower generation, irrigation, water supply, flood control, and other aims. The
extensive surface water usage affects discharges of the global major rivers.
Hydrological models fail to reproduce the hydrographs of large rivers if they don’t
consider reservoir management. Hence, in E2O WP5 it was the University Kassel’s
primary goal to improve WaterGAP3’s reservoir management representation. By
upgrading WaterGAP3.1 (used in WRR1) to WaterGAP3.2 (used in WRR2) the humanimpact on river discharges are now much better resolved: an improved prospective
management scheme defines the target storage in a monthly interval based on an
objective function for one of the following reservoir purposes (for simplicity eachreservoir is assumed to have only a single purpose):

Flood control: flood damages shall be minimised.

Hydropower: hydropower generation shall be maximised.

Navigation: deviations from constant flow shall be minimised.

There are constraints for the management scheme: the reservoir capacity cannot be
exceeded. Reservoir content at the end of each time interval is below the sum of thestorage at the beginning of each time step plus reservoir inflow. Sufficient storage
should be kept to maintain a minimum flow for 30 days. Sufficient storage capacity
should be reserved to absorb larger floods. Finally, for flood protection, the releaseshould be smaller than bankfull flow.
The daily release is calculated based on the difference between target and actual
storage, the predicted inflow, and the number of remaining days. The new
management scheme was tested and evaluated with different EO data (see next
section).
4.8.3 Evaluation
The evaluation of the WaterGAP’s improved WRR calculations is performed in four
aspects: (i) The global average water balance components of WRR1 and WRR2 arecompared and improvements are evaluated; (ii) the change in spatial distribution ofdifferent water balance components is discussed; (iii) the new implemented
reservoir management representation in WaterGAP3.2 is evaluated with respect todifferent EO data products at four large reservoirs; (iv) WaterGAP’s improved world
water resources recalculation (especially river discharge) is discussed with respect
to changed model representation and the new meteorological forcing.

68

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis(a)

The global average water balance

The global distribution of major water balance components is primarily affected by
the new spatial resolution and the new WRR2 forcing. The model improvements
focused on the reservoir management scheme. Thus the model improvements affectprimarily the water stored in the reservoirs (see next section) and not the surface
runoff, evapotranspiration, and other surface fluxes. Hence, for the evaluation of the
improvement of the water cycle representation between WRR1 and WRR2 Table
4-13 provides some basic data: the main difference between the calculated waterbalances in WRR1 and WRR2 are caused by the higher global average precipitation in
the new MSWEP global gridded precipitation of about 896 mm/yr in WRR2 (863mm/yr in WRR1). This increase of +33 mm is primarily transferred into a higher
Runoff. In a global view this indicates that the average global evaporation, which
increases by only 7 mm, is more sensitive to atmospheric conditions (airtemperature and radiation) than to local water stress and precipitation.
The total increase of Runoff + Evaporation (+36 mm) corresponds to the increase ofPrecipitation and the water balance is closed. The change in soil and other storages
decreased from +8 to +5mm per year between WRR1 and WRR2 and during theperiod 1979-2014.
Table 4-13: Global average water balance calculated by WaterGAP3.1 (WRR1)
and WaterGAP3.2 (WRR2)
P [mm/yr]
WaterGAP3.1 WRR1 863
WaterGAP3.2 WRR2 896(b)

R [mm/yr]306
335

ET [mm/yr]549
556

ΔS [mm/yr]+8+5

The global spatial distribution of major water balance components

Precipitation: The major change between the WRR1 and WRR2 forcing is the new
precipitation data (see section 3.1.1, the mean annual MSWEP precipitation on the
WRR2 grid is shown in Figure 4-48a). Beside several changes, the most distinct
difference to the WRR1 precipitation is in areas of high precipitation such as the
Amazonas basin (Figure 4-48b). E.g. a WRR1 precipitation hotspot in the Kongo area
is now leveled to its surrounding area.
Evapotranspiration: The WRR2 WaterGAP3.2 calculations show that the mostwater is evaporated in the tropic zone (Figure 4-49a). The results confirm features
such as a relative high evapotranspiration in the eastern part of the U.S. relative toother areas at the same latitudes. The differences/improvements with respect to the
WRR1, besides the better spatial resolution, go along with the changes inprecipitation (Figure 4-49b). E.g., higher precipitation in some areas of the amazon
cause altered evapotranspiration in those areas.
Runoff: As it was shown for Evapotranspiration, the changes in Runoff between
WRR1 and WRR2 are highly correlated to the changes in precipitation (Figure 4-50).
The pattern of changes is very similar.

69

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-48: Global distribution of mean annual precipitation: a) WRR2 19792014 b) difference to WRR1 (WRR2-WRR1) 1979-2012.

70

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-49: Global distribution of mean annual evapotranspiration: a) WRR2
1979-2014 b) difference to WRR1 (WRR2-WRR1) 1979-2012.

71

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-50: Global distribution of mean annual runoff: a) WRR2 1979-2014 b)
difference to WRR1 (WRR2-WRR1) 1979-2012.
(c)

Testing of WaterGAP 3.2 reservoir management scheme with EO data

The major improvement beside the better meteorological forcing and the higher
spatial resolution is the new implementation of a reservoir management scheme (seesection 4.8.2). EO data of reservoir surface water level were used to evaluate thebetter representation of the impact of reservoir management on the global water
cycle. However, on the one hand the satellites provide water surface levels and noreservoir volumes, on the other hand WaterGAP3 provides reservoir water volume
information but no surface water level. Both variables are physically linked by thereservoir’s bathymetry. Hence, the evaluation of the models performance in terms ofthe temporal behavior can be performed by (i) visually compare the time series and,more informative, by (ii) calculating the correlation coefficient.
72

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

We used 4 different EO products (reservoir water level time series) to test andvalidate the modeled reservoir management:


DAHITI data (Schwatke et al., 2015): multi-mission satellite altimetry

TP satellite data (prepared and provided by project partner I-MAGE)

Envisat EO data (prepared and provided by project partner I-MAGE)Jason2 EO data (prepared and provided by project partner I-MAGE)

These data were compared with WaterGAP3.2 calculations at four large reservoirs:
Cahora Bassa
The African Cahora Bassa Reservoir in Mozambique is a reservoir in the Zambezi
river system (15°35′09″S 32°42′17″E). The surface area is about 2739 km² with amaximum water depth of 157m, and a total capacity of 55.8 km³.
Figure 4-51 compares the satellite measurements of the Cahora Basse reservoir
surface water level and the WaterGAP3.2 estimated reservoir volumes (monthly
means). A correlation coefficient of about 0.71 indicates a high linear dependency
between both data. The modeled annual cycle corresponds to the measured one.
Especially the low water levels/volumes in 2005 are reproduced by the model very
well.

Figure 4-51: Calculated Water volumes and observed water levels of the Chora
Bassa Reservoir, Mozambique, for the period 2002 - 2014.
Kariba Reservoir
The Kariba Reservoir in Zimbabwe and Zambia is another big reservoir in theZambezi river system (16°55′S 28°00′E). The surface area is about 5580 km², themaximum water depth is 97 m, and the total maximum capacity is 180 km³. The main
purpose is hydropower generation.
Figure 4-52 compares the satellite measurements of the Kariba reservoir surface
water level and the WaterGAP3.2 estimated reservoir volumes (monthly means). A
correlation coefficient of about 0.85 indicates a high linear dependency between both73

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

data. The modeled annual cycle corresponds to the measured one. Especially the low
water levels/volumes in the period 1992-1997 and the following increase until 2000
are reproduced by the model very well.

Figure 4-52: Calculated Water volumes and observed water levels of the Kariba
Reservoir for the period 1992-2004.
Lake Mead (U.S.)
Lake Mead, formed by the Hoover Dam, is a large reservoir in the Colorado River
catchment (36.25°N 114.39°W). The surface area is about 640 km², the maximum
water depth is 162 m.
Figure 4-53 compares the satellite measurements of the Lake Mead surface waterlevel and the WaterGAP3.2 estimated reservoir volumes (monthly means). A
correlation coefficient of about -0.54 indicates an inverse linear dependency between
both data sources. The modeled annual cycle corresponds to the measured one.
However, the long term trends are not calculated by the model. Only the decreasingwater levels/volumes since 2013 are captured.

74

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-53: Calculated Water volumes and observed water levels of Lake Meadfor the period 2002 to 2014.
Lake Victoria
This is Africa’s largest lake by area (68800 km²). It is a formally natural lake situatedin the Nile basin. The water level is regulated since decades, thus it is an additionallake that can be used to test the reservoir management scheme.
Figure 4-54 compares the satellite measurements (three different products) of theLake Victoria surface water level and the WaterGAP3.2 estimated reservoir volumes(monthly means). A correlation coefficient of about 0.59 between the TP satellite
measurements and the model calculations indicates that the model captures the realreservoir management. The Envisat data and Jason data are shifted with respect to
the TP satellite data and are not reliable due to the difference to DAHITI data (notshown in Figure 4-54). The fluctuations of the lake are different to the smaller
reservoirs, e.g. there is no distinct annual cycle. The modeled temporal fluctuations
correspond to the measured one.

75

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-54: Calculated Water volumes and observed water levels of Lake
Victoria for the period 1992 to 2014.
(d)

Effects on the improvement on water availability and discharge

Because the model improvements affect primarily the reservoir algorithm, the Nile
catchment was selected to highlight these effects on the modelled river discharges.
There are numerous reservoirs in this catchment (e.g. Lake Victoria) which makes
this basin an appropriate choice for such an evaluation. Figure 4-55 compares thecalculated Nile River hydrographs at Al Akshas (GRDC station 1362100), situatedapproximately 30km upstream of Kairo. Some basic statistics are listed in Table 4-14
for WRR1, WRR2 and the WRR1_F2 setup. Here, WRR1_F2 is the old model forced
with the meteorology of WWR2.
There is a clear difference between WRR1 and WRR2: on average, the Nile discharge
is higher than in the WRR1 calculations and it is less variable. The latter indicates ahigher impact of the management schemes on the discharge (“flattening” of thehydrograph) in the WRR2 calculations. However, this is not caused by the better
representation of the reservoir management. But it is an effect of higher water
availability: the WRR1 model, forced with the new meteorology, deviates only
slightly from the WRR2 setup (compare green and red line in Figure 4-55). To
conclude, there is less fluctuation on a higher discharge level in WRR2. This is
primarily because of the new forcing. The effects of the new precipitation data
overtop the effects of the better representation of reservoir management.

76

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-55: Modelled Nile River Discharges at Al Akhsas gauging station(GRDC ID: 1362100) for the period 2000-2010.
Table 4-14: The WaterGAP3 modelled Nile discharges statistics (2000-2012) ofdifferent WRR settings.

Average monthly discharge[km³/month]
Variance of the monthly discharge[(km³/month)²]

WRR1

WRR2

WRR1_F2

9.2

9.5

10.0

10.7

5.8

5.0

4.9 WaterWorld
The WWR2 data used by the WaterWorld model and the model changes being
reported here include (a) the use of downscaled WFDEI (WATCH Forcing Datamethodology applied to ERA-Interim data) data in the Estonia Case Study supplied at
1Km spatial resolution by our Partners at Deltares, (b) the use of downscaled TRMM
data in the Magdalena, Colombia Case Study, (c) the improvements made to theWaterWorld (WW) model to allow better Policy decision making by users of the EO
products uploaded to WW and (d) adapting of WW from 1Km scale to 10Km scale for
global application.
The WaterWorld model (WW) is parameterised with 140 inputs from an extensive
global gridded database of more than 600 variables, developed from many sources,
since 1998, the so-called simTerra database. All of these data are available globallyat 1km resolution and some variables (terrain, land cover, urban areas, water bodies)
are available globally at 1ha resolution. WaterWorld requires some 140 maps to run
including monthly climate data, land cover and use, terrain, population, water bodies
and more.
Whilst publically-available terrain and land cover data are now well developed for
local scale application, climate and land use data remain a challenge. The global
climate data products made available by WRR2 remain at 0.25 degree resolution orworse, which is rather coarse for local application. Our options for new andimproved WRR2 datasets to integrate into WW are limited to the 1Km spatial datarequired by the WW model. We have used WFDEI downscaled to 1km resolution to77

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

provide an alternative input to WaterWorld's preloaded climate data. Downscaling
was performed by Deltares.
We have then examined the impacts of the new data on key hydrological outputs ofwater balance and water quality for the entire catchment of Estonia at 1Km
resolution. Estonia is a Case Study area in Earth2Observe. In D5.3 we discuss theremaining challenges of using datasets like these for local scale application anddecision making.
In addition to WFDEI data, the WW Model has been parameterised with other EO
datasets. As reported in D6.2, WorldClim rainfall data was compared to an alternative
high resolution global rainfall database: TRMM 2b31 (Tropical Rainfall Measurement
Mission, mean of all rainfall observations to March 2006). The data was originally
5km resolution, but has been downscaled to 1km for upload into WW.
Approximately 50,000 satellite swaths (0.5 TB of data) were processed and theresults are shown below.
Rainfall is broadly similar between the two products, though the TRMM data has ahigher catchment total rainfall. Upon comparing WaterWorld results , TRMM data
produced a higher water balance, but a lower erosion rate. The latter is due tospatial pattern of TRMM rainfall in relation to the distribution of bare ground andslope in the catchment. The TRMM data has lower rainfall than WorldClim in areas
dominated bare areas and steep slopes on mountain peaks (red) which may be
erosion hotspots (see Figure 4-56 and Figure 4-57).

Figure 4-56: WorldClim (right) and TRMM (left) rainfall data in the Magdalena
catchment.

78

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Figure 4-57: Slope angle (from Hydrosheds data) and areas of bare ground(from MODIS data) in the Magdalena catchment.
The WW policy support system itself has been extended in a number of ways. Users
can now upload their own land use and climate data so that the impact of uploaded
new land use climate scenarios can be analysed. WW users therefore have more
options for testing the impact of their decisions. The new 2016 land use scenario
options include:●

Business as Usual (eg same deforestation rate)●

Accelerated deforestation●

Percentage change of individual land classes●

Option to allocate new land use by agricultural suitability●

Include planned infrastructure / transport routes●

Vary Management Effectiveness Index - for Protected Areas for example

These can then be used in the new ‘Stacked Scenario’ model function where users can
combine a number of plausible scenarios for land use, climate, population, and assess
the impacts of these multiple changes as well as each individual scenario on water
resources. For example, users can assess the impact of a combination of climatechange, deforestation, population growth and rural-urban migration, mining and oil
and gas exploitation.
Finally, in order to incorporate coarse scale global datasets, work is in progress toadapt WW to 10Km resolution simulations globally. This is still in testing phase(v2.92). Figure 4-58 shows WaterWorld's index of water stress. Water Stress is the% of water demand that is unavailable or contaminated. The data are averaged bywatershed.

Figure 4-58: Water stress by global basin modelled at 10Km resolution.

79

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

5 Data5.1 Data access5.1.1 Meteorological forcing
The meteorological forcing used in WRR2 baseline is available on the WCI3 dataserver and the direct links listed in Table 5-1. The Rainfall products used in WRR2
ensemble are available on the ftp server and their links also listed in table Table 5-1
Table 5-1: Forcing data
Forcing

Location

WRR1

http://wci.earth2observe.eu/thredds/catalog/ecmwf/m
et_forcing_v0/catalog.html

WRR2 except
Rainfall & Snowfall

http://wci.earth2observe.eu/thredds/catalog/ecmwf/m
et_forcing_v1/catalog.html

WRR2 MSWEP
Rainfall & Snowfall

http://wci.earth2observe.eu/thredds/catalog/jrc/MSW
EP/3hourly_e2o_netcdf_convention/catalog.html
http://wci.earth2observe.eu/thredds/catalog/jrc/MSW
EP/daily_e2o_netcdf_convention/catalog.html

WRR2 TRMM
RainfallWRR2 TRMMRT
RainfallWRR2 GSMAP
RainfallWRR2 CMORPH

ftp://wci.earth2observe.eu/exchange/e2o_ecmwf/forcin
g_wrr2ens/rainf_trmm/
ftp://wci.earth2observe.eu/exchange/e2o_ecmwf/forcin
g_wrr2ens/rainf_trmmrt/
ftp://wci.earth2observe.eu/exchange/e2o_ecmwf/forcin
g_wrr2ens/rainf_gsmap/
ftp://wci.earth2observe.eu/exchange/e2o_ecmwf/forcin
g_wrr2ens/rainf_cmorph/5.1.2 Models output convention
All models used the same grid for the post-processing (equal to the meteorologicalforcing), for both the baseline and ensemble simulations with the exception of thedata assimilations experiments and river discharge of SURFEX-TRIP. The simulations
are available on the server with a daily frequency, and their location is shown in
Table 5-5. For the models with a sub-daily time-step, the daily means are computedfrom all the mode time steps. The monthly means are computed from the dailymeans.

3 https://wci.earth2observe.eu/

80

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

As in WRR1, the file format is netcdf4 4 containing a single variable following theconvention:
e2o_[ID]_[VER]_[DOMAIN]_[FREQ]_[VAR]_[YEAR_START-YEAR_END].nc
1. ID: Institution identifier (see Table 5-2 );2. VER: experiment names (see Table 5-3);
3. DOMAIN: domain identification (see Table 5-3);
4. FREQ: output frequency: day (daily means) mon (monthly means);
5. VAR: variable name contained in the file (see Table 5-4);
6. YEART_START-YEAR_END: start and end year denoting the temporalextension (see Table 5-3).
Table 5-2: Institution identifiers in the model output
Institution ID Institution (mode)
ecmwf

ECMWF (HTESSEL, see 4.1)

univu

University of Utrech (PCR-GLOBWB, see 4.5)

metfr

Meteo-France (SURFEX-TRIP, see 4.6)

nerc

NERC, CEH (JULES, see 4.2)

jrc

JRC (LISFLOOD, 4.3)

cnrs

CNRS (ORCHIDEE, see 4.4)

univk

University of Kassel (WaterGAP3, see 4.8

Anu

Australian National University (W3, see 4.7) (csiro in wrr1)

ambio

Ambiotek (WaterWorld, see 4.9)

Table 5-3: Experiments version names, associated start and end year anddomain.
Experimentname

YEAR_START – YEAR_END

DomainDescription

wrr1

1979-2012glob30

WRR1 simulations

wrr2

1980-2014 (monthly files)

glob15a

WRR2 simulations

1980-1989 (daily 1st decade)1990-1999 (daily 2nd decade)2000-2014(daily 3rd decade)wrr2gsmap

2000-2013glob15a

WRR2 ensemble forced
with GSMAP rainfall

wrr2trmm

2000-2013glob15a

WRR2 ensemble forced

4 https://www.unidata.ucar.edu/software/netcdf/

81

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis
Experimentname

YEAR_START – YEAR_END

DomainDescription
with TRMM rainfall

wrr2cmorph

2000-2013glob15a

WRR2 ensemble forced
with CMORPH rainfall

wrr2trmmrt

2000-2013glob15a

WRR2 ensemble forced
with TRMMRT rainfall

wrr2da2000-2013glob15a,b

WRR2
with
assimilationdataa – glob30 for the river routing related variables for metfr simulationsb – glob60 for the metfr simulations

Table 5-4: List of output variables and conventions
Name

long_nameattribute
total
precipitation

standard_nameattribute
precipitation_flux

units
attributekg m-2 s-1

Evapa

total
evapotranspir
ationwater_evaporation_flu
xkg m-2 s-1

Runoffa

Total runoff

runoff_flux

kg m-2 s-1

Rainf

rainfallrainfall_flux

kg m-2 s-1

Qsa

surface runoff

surface_runoff_flux

kg m-2 s-1

Qsba

Subsurface
runoffsubsurface_runoff_flux

kg m-2 s-1

Qrec

Recharge

N.A

kg m-2 s-1

Qsm

snowmeltsurface_snow_melt_flu
xkg m-2 s-1

Precipa

Definition
Average of total
precipitation(Rainf+Snowf)
Sum of all
evaporation
sources,averaged over a
grid cell
Average totalliquid waterdraining fromland
Average of thetotal rainfall(liquid phase)
Runoff from theland surface
and/orsubsurface
stormflow
Gravity drainage
and/or slow
response lateral
flow. Groundwater recharge
will have theopposite sign.
Recharge fromriver to the flood
plain and fromgroundwater tosoil column
Average liquidwater generated
from solid toliquid phasechange in thesnow

Positive
direction
downwards
downwards

into gridcell
downwards
into gridcell

into gridcell

into ofgridcellsolid toliquid82

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis
Namelong_nameattribute
potential
evapotranspir
ation

standard_nameattributewater_potential_evapo
ration_flux

units
attributekg m-2 s-1

ECanopinterception
evaporationwater_evaporation_flu
x_from_canopy

kg m-2 s-1

TVeg

vegetationtranspirationtranspiration_flux

kg m-2 s-1

ESoilbare soil
evaporation
Open water
evaporationwater_evaporation_flu
x_from_soil
N.A

kg m-2 s-1

riverdischarge
point riverdischarge
Net shortwave
radiation

N.A

m3 s-1N.A

m3 s-1surface_net_downward_shortwave_flux

W m-2

LWnet

Net longwave
radiation

surface_net_downward_longwave_flux

W m-2

Qle

Latent heatflux

surface_downward_lat
ent_heat_flux

W m-2

Qh

Sensible heatflux

surface_downward_se
nsible_heat_flux

W m-2

AvgSurfT

Averagesurfacetemperaturesurface_temperature

K

PotEvap

EWaterRivOutDis
SWnet

kg m-2 s-1

Definition
The flux ascomputed forevapotranspirati
on but will all
resistances set
to zero, except
the aerodynamic
resistance.
Evaporationfrom canopy
interception,
averaged overall vegetation
types within agrid cell.
Vegetationtranspiration,
averaged overall vegetation
types within agrid cell.
Evaporationfrom bare soil.
Evaporationfrom surface
water storage(lakes, river
Chanel,floodplains, etc.)
Water volume
leaving the cell
Water volume
leaving the cell
Incoming solar
radiation lessthe simulated
outgoing
shortwave
radiation,
averaged over a
grid cell
Incident
longwaveradiation lessthe simulated
outgoing
longwaveradiation,
averaged over a
grid cell
Energy ofevaporation,
averaged over a
grid cell
Sensible energy,
averaged over a
grid cell
Average of allvegetation, baresoil and snow
skintemperatures

Positive
direction
downwards

downwards

downwards

downwards
downwards

downstreamdownstreamdownward

downward

downward

downward-83

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis
Namelong_nameattribute
Surface
Albedostandard_nameattributesurface_albedo

units
attribute-

LAI

Surface
Albedoleaf_area_index

-

SWEa

Snow waterequivalentliquid_water_content_o
f_surface_snow

Kg m-2

CanopInt

Total canopy
water storage

N.A

Kg m-2

SWEVegSWEinterceptedby thevegetation

N.A

Kg m-2

SurfStor

Surface Water
Storage

N.A

Kg m-2

WaterTabl
eD

Water table
Depth

N.A

M

SnowFrac

Snow covered
fraction
Depth of snow
layer
Surface soil
moisturesurface_snow_area_fra
ction
surface_snow_thicknes
s
N.A

-

Kg m-2

RootMoista

Root zone soil
moisture

N.A

Kg m-2

TotMoist

Total soil
moisture

N.A

Kg m-2

AlbedoSnowDepth
SurfMoista

M

Definition
Grid cell averagealbedo for all
wavelengths.
Gridl cellaverage lead
area index
Total watermass of thesnowpack(liquid or
frozen),averaged over a
grid cell(including
SWEVeg)Total canopy
interception,
averaged overall vegetation
types within agrid cell(included bothsolid and liquid)
Total watermass of thesnowpack(liquid or
frozen),averaged over a
grid cell andinterecepted by
the canopy
Total liquidwater storage,
other than soil,
snow orinterception
storage (i.e.
lakes, river
channel ordepression
storage).
Depth of thewater table(distance fromsurface)
Grid cell snow
covered fraction
total snow depth
best of 5 cm
depth or firstmodel layer
Total soil
moisture
available forevapotranspirati
on (or up to 1
meter depth if
not defined)
Verticallyintegrated totalsoil moisture

Positive
direction
-

-

-

-

-

-

-

84

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis
Namelong_nameattributeground water

standard_nameattribute
N.A

units
attribute
Kg m-2Land sea
mask
Surface soil
saturation

land_area_fraction-

N.A

Kg m-2

RootSoilSat

Root soil
saturation

N.A.

Kg m-2

TotSoilSat

Total soil
saturation

N.A

Kg m-2GroundMoi
stlsm
SurfSoilSal

Definition
ground water
not directly
available forevapotranspirati
onfraction of landin each cellsaturation soil
moisture for thelayer reported
in SurfMoist
saturation soil
moisture for thelayer reported
in RootMoist
saturation soil
moisture for thelayer reported
in TotMoist

Positive
direction-a Default variables available daily on the WRR2 ensemble.

Table 5-5: Models output location
Model
HTESSEL

Direct link
WRR1 / WRR2

WRR2-ENS

wrr1

wrr2trmm

wrr2

wrr2trmmrt

Comments

wrr2gsmap
wrr2cmorph
wrr2daJULES

wrr1

wrr2trmm

wrr2

wrr2trmmrt
wrr2gsmap

WRR2-ENS data available only on the
publicftp(user:
e2o_guest,

password: oowee3WeifuY1aeb)

wrr2cmorph
LISFLOOD

wrr1

wrr2trmm

wrr2

wrr2trmmrt
wrr2gsmap

Currently the wrr2-ens only contains
the River discharge data. Remaining
data will be made available.

wrr2cmorph
ORCHIDEE

wrr1

wrr2trmm

wrr2

wrr2trmmrt
wrr2gsmap

wrr2-ens simulation are not available
yet due to technical problems, andwill be made available as soon as
possible

wrr2cmorph
wrr2da85

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Model
PCR-GLOBWB

Direct link
wrr1

wrr2trmm

wrr2

wrr2trmmrt
wrr2gsmap

Comments
wrr2 data to be made available soon.
Wrr2-ens data not available due totechnical problems

wrr2cmorph
wrr2daSURFEX

wrr1

wrr2trmm

wrr2

wrr2trmmrt
wrr2gsmap

wrr2da is available at 1°x1°
resolution (glob60) for all point
variables and at 0.5°x0.5° for the river
routing variables (glob30)

wrr2cmorph
wrr2daWaterGAP3

wrr1

wrr2trmm

wrr2

wrr2trmmrt
wrr2gsmap
wrr2cmorph

W3

wrr1

SWBM

wrr1

wrr2daIn wrr1 the model institution id was“csiro” and in wrr2da is “anu”. wrr2da
is available from 2000 to 2014.
Did not participated in WRR2

86

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

6 SummaryThis report describes the tier-2 water resources reanalysis (WRR2) development,
implementation and data structure and storage. This work was built on the initial
WRR1 dataset by exploring several key sources of improvement. The mainachievements include:A new high resolution 0.25x0.25 global meteorological forcing with a 3-hourly
frequency covering 1979 to 2014. It is based on ERA-Interim with a
topographic temperature adjustment and a new precipitation dataset that
integrates atmospheric reanalysis, satellite products and in-situ observations;

A variety of modelling systems enhancements including process description(e.g. snowpack, runoff generation, groundwater representation, etc..),calibration and water use (withdrawal, consumption, management).



Different data assimilation methodologies and use of earth observations (e.g.
snow depth, soil moisture, river discharge, vegetation state, etc...)

Ensemble of simulation using four different satellite precipitation products.

The report focused on the technical details of the simulations and the description of
the modelling system. Each modelling system presented an evaluation of theirdevelopments along with the impact of the meteorological forcing. Due to thedifferent nature of each modelling and data assimilation system changes, a consistentevaluation among models was not pursued in this report. On the contrary, eachmodelling system was evaluated independently, and this reports serves as a detailed
description of the dataset and not an integrated quality assessment of its quality.
Such analysis will be presented in following project reports. However, among theseveral evaluation examples there is a clear signal of the improvements due to thenew meteorological forcing used in WRR2. This is mainly attributed to the new
precipitation datasets (MSWEP) which leads to clear improvements of the models
simulations. Each modelling system change also shows improvements, but these tend
to be restricted to the main process that was addressed. Overall, it is expected that
WRR2 will bring a significant improvement in terms of the land water cycle
representation over land when compared with WRR1. This new dataset will have an
important impact on the community and users due to its higher resolution and stateof-the art modelling systems and data assimilation.

87

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

7 References
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration, FAO Irrig.
Drain. Pap. 56, Food and Agric. Organ., Rome, 1998.
aus der Beek, T., Flörke, M., Lapola, D. M., Schaldach, R., Voß, F., and Teichert, E.:
Modelling historical and current irrigation water demand on the continental scale:
Europe, Adv. Geosci., 27, 79-85, 10.5194/adgeo-27-79-2010, 2010.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra,E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.:
ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci.,
19, 389-407, 10.5194/hess-19-389-2015, 2015.
Barbu, A. L., Calvet, J. C., Mahfouf, J. F., and Lafont, S.: Integrating ASCAT surface soil
moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data
assimilation application over France, Hydrol. Earth Syst. Sci., 18, 173-192,
10.5194/hess-18-173-2014, 2014.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J.,
and Bruijnzeel, L. A.: Global-scale regionalization of hydrologic model parameters,
Water Resources Research, 52, 3599-3622, 10.1002/2015WR018247, 2016.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B.,
and de Roo, A.: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) bymerging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589-615,
10.5194/hess-21-589-2017, 2017.
Bergström, S.: The HBV model in: Computer models of watershed hydrology, edited
by: Singh, V. P., Water Resources Publications, Highlands Ranch, Colorado, U.S.A,
1995.
Brasnett, B.: A Global Analysis of Snow Depth for Numerical Weather Prediction,
Journal of Applied Meteorology, 38, 726-740, 10.1175/15200450(1999)038<0726:agaosd>2.0.co;2, 1999.
Clark, D. B., and Gedney, N.: Representing the effects of subgrid variability of soil
moisture on runoff generation in a land surface model, J. Geophys. Res. Atmos., 113,n/a-n/a, 10.1029/2007JD008940, 2008.
de Graaf, I. E. M., van Beek, L. P. H., Wada, Y., and Bierkens, M. F. P.: Dynamic
attribution of global water demand to surface water and groundwater resources:
Effects of abstractions and return flows on river discharges, Advances in Water
Resources, 64, 21-33, http://doi.org/10.1016/j.advwatres.2013.12.002, 2014.

88

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., and Isaksen, L.:
Initialisation of Land Surface Variables for Numerical Weather Prediction, Surv
Geophys, 35, 607-621, 10.1007/s10712-012-9207-x, 2014.
Decharme, B., Alkama, R., Papa, F., Faroux, S., Douville, H., and Prigent, C.: Global offline evaluation of the ISBA-TRIP flood model, Clim. Dyn., 38, 1389-1412,
10.1007/s00382-011-1054-9, 2012.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae,U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg,
L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L.,
Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi,M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay,P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Quarterly Journal of
the Royal Meteorological Society, 137, 553-597, 10.1002/qj.828, 2011.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P.: Biosphere-atmospheretransfer scheme (BATS) version 1e as coupled to the NCAR community climate
model, NCAR Boulder Colorado, 1993.
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2:
Multimodel Analysis and Implications for Our Perception of the Land Surface,
Bulletin of the American Meteorological Society, 87, 1381-1397, 10.1175/bams-8710-1381, 2006.
Döll, P., Hoffmann-Dobrev, H., Portmann, F. T., Siebert, S., Eicker, A., Rodell, M.,
Strassberg, G., and Scanlon, B. R.: Impact of water withdrawals from groundwaterand surface water on continental water storage variations, Journal of Geodynamics,
59–60, 143-156, http://dx.doi.org/10.1016/j.jog.2011.05.001, 2012.
Dorigo, W. A., Gruber, A., De Jeu, R. A. M., Wagner, W., Stacke, T., Loew, A., Albergel, C.,
Brocca, L., Chung, D., Parinussa, R. M., and Kidd, R.: Evaluation of the ESA CCI soil
moisture product using ground-based observations, Remote Sens. Environ., 162, 380395, http://doi.org/10.1016/j.rse.2014.07.023, 2015.
Dutra, E., Viterbo, P., Miranda, P. M. A., and Balsamo, G.: Complexity of Snow Schemes
in a Climate Model and Its Impact on Surface Energy and Hydrology, J.
Hydrometeorol., 13, 521-538, 10.1175/jhm-d-11-072.1, 2012.
Fekete, B., Vorosmarty, C., and Wolfgang, G.: Global, Composite Runoff Fields Basedon Observed River Discharge and Simulated Water Balances, Global Runoff Data
Centre, Koblenz, Germany: UNH/GRDC, 2000.
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.: High-resolution fields of global runoff
combining observed river discharge and simulated water balances, Global
Biogeochemical Cycles, 16, 15-11-15-10, 10.1029/1999GB001254, 2002.
89

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., and Alcamo, J.: Domestic andindustrial water uses of the past 60 years as a mirror of socio-economic
development: A global simulation study, Global Environ. Change, 23, 144-156,
http://dx.doi.org/10.1016/j.gloenvcha.2012.10.018, 2013.
Funk, C., Verdin, A., Michaelsen, J., Peterson, P., Pedreros, D., and Husak, G.: A global
satellite-assisted precipitation climatology, Earth Syst. Sci. Data, 7, 275-287,
10.5194/essd-7-275-2015, 2015.
Gao, L., Bernhardt, M., and Schulz, K.: Elevation correction of ERA-Interim
temperature data in complex terrain, Hydrol. Earth Syst. Sci., 16, 4661-4673,
10.5194/hess-16-4661-2012, 2012.
Getirana, A. C. V., Dutra, E., Guimberteau, M., Kam, J., Li, H.-Y., Decharme, B., Zhang, Z.,
Ducharne, A., Boone, A., Balsamo, G., Rodell, M., Toure, A. M., Xue, Y., Peters-Lidard, C.
D., Kumar, S. V., Arsenault, K., Drapeau, G., Ruby Leung, L., Ronchail, J., and Sheffield,
J.: Water Balance in the Amazon Basin from a Land Surface Model Ensemble, J.
Hydrometeorol., 15, 2586-2614, 10.1175/JHM-D-14-0068.1, 2014.
Gleeson, T., Moosdorf, N., Hartmann, J., and van Beek, L. P. H.: A glimpse beneathearth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability andporosity, Geophys. Res. Lett., 41, 3891-3898, 10.1002/2014GL059856, 2014.
Gouttevin, I., Krinner, G., Ciais, P., Polcher, J., and Legout, C.: Multi-scale validation of a
new soil freezing scheme for a land-surface model with physically-based hydrology,
The Cryosphere, 6, 407-430, 10.5194/tc-6-407-2012, 2012.
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F., Arnell, N. W., Bertrand, N.,Best, M., Folwell, S., Gerten, D., Gomes, S., Gosling, S. N., Hagemann, S., Hanasaki, N.,
Harding, R., Heinke, J., Kabat, P., Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P.,
Weedon, G. P., and Yeh, P.: Multimodel Estimate of the Global Terrestrial Water
Balance: Setup and First Results, J. Hydrometeorol., 12, 869-884,
10.1175/2011jhm1324.1, 2011.
Hagemann, S., and Gates, L. D.: Improving a subgrid runoff parameterization scheme
for climate models by the use of high resolution data derived from satellite
observations, Clim. Dyn., 21, 349-359, 10.1007/s00382-003-0349-x, 2003.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.: Very highresolution interpolated climate surfaces for global land areas, International Journal of
Climatology, 25, 1965-1978, 10.1002/joc.1276, 2005.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y.,
Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis(TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine
Scales, J. Hydrometeorol., 8, 38-55, 10.1175/jhm560.1, 2007.

90

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A Method that Produces
Global Precipitation Estimates from Passive Microwave and Infrared Data at HighSpatial and Temporal Resolution, J. Hydrometeorol., 5, 487-503, 10.1175/15257541(2004)005<0487:camtpg>2.0.co;2, 2004.
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of
FLUXNET eddy covariance observations: validation of a model tree ensemble
approach using a biosphere model, Biogeosciences, 6, 2001-2013, 10.5194/bg-62001-2009, 2009.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P.,
Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N.,
and Wisser, D.: High-resolution mapping of the world's reservoirs and dams forsustainable river-flow management, Front. Ecol. Environ., 9, 494-502,
10.1890/100125, 2011.
Lehner, B., and Grill, G.: Global river hydrography and network routing: baseline dataand new approaches to study the world's large river systems, Hydrological
Processes, 27, 2171-2186, 10.1002/hyp.9740, 2013.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J.
W.: Development of a global land cover characteristics database and IGBP DISCoverfrom 1 km AVHRR data, Int. J. Remote Sens., 21, 1303-1330, 2000.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., andDolman, A. J.: Global land-surface evaporation estimated from satellite-based
observations, Hydrol. Earth Syst. Sci., 15, 453-469, 10.5194/hess-15-453-2011, 2011.
Moore, R. J.: The probability-distributed principle and runoff production at point andbasin scales Hydrological Sciences Journal, 30, 273-297, 1985.
Post, W. M., and Zobler, L.: Global Soil Types, 0.5-Degree Grid (Modified Zobler),
Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory
Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A., 2000.
Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q., Mahanama, S. P.
P., and Touré, A.: Assessment and Enhancement of MERRA Land Surface Hydrology
Estimates, J. Clim., 24, 6322-6338, 10.1175/JCLI-D-10-05033.1, 2011.
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P. P., Koster, R. D., and De
Lannoy, G. J. M.: Assessment of MERRA-2 Land Surface Hydrology Estimates, J. Clim.,
30, 2937-2960, 10.1175/JCLI-D-16-0720.1, 2017.
Robinson, E. L., Blyth, E., Clark, D. B., Comyn-Platt, E., Finch, J., and Rudd, A. C.: Climatehydrology and ecology research support system meteorology dataset for Great91

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Britain (1961-2015) [CHESS-met] v1.2, in, NERC Environmental Information Data
Centre, 2017.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault,
K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin*, J. K., Walker, J. P., Lohmann, D.,
and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American
Meteorological Society, 85, 381-394, 10.1175/bams-85-3-381, 2004.
Ryder, J., Polcher, J., Peylin, P., Ottlé, C., Chen, Y., van Gorsel, E., Haverd, V., McGrath, M.
J., Naudts, K., Otto, J., Valade, A., and Luyssaert, S.: A multi-layer land surface energy
budget model for implicit coupling with global atmospheric simulations, Geosci.
Model Dev., 9, 223-245, 10.5194/gmd-9-223-2016, 2016.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna
Weiland, F., Minvielle, M., Calvet, J. C., Decharme, B., Eisner, S., Fink, G., Flörke, M.,
Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo,W., and Weedon, G. P.: A global water resources ensemble of hydrological models: theeartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., 2016, 1-35,
10.5194/essd-2016-55, 2016.
Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative
approach for estimating water level time series over inland waters using multimission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345-4364, 10.5194/hess-194345-2015, 2015.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data set for earthsystem modeling, Journal of Advances in Modeling Earth Systems, 6, 249-263,
10.1002/2013MS000293, 2014.
Su, Z., Schmugge, T., Kustas, W. P., and Massman, W. J.: An Evaluation of Two Models
for Estimation of the Roughness Height for Heat Transfer between the Land Surface
and the Atmosphere, Journal of Applied Meteorology, 40, 1933-1951, 10.1175/15200450(2001)040<1933:aeotmf>2.0.co;2, 2001.
Sutanudjaja, E. H., Van Beek, L. P. H., Wada, Y., Wisser, D., Graaf, I. E. M., Straatsma, M.
W., and Bierkens, M. F. P.: Development and validation of PCR-GLOBWB 2.): a 5 arc
min resolution global hydrology and water resources model., EGU, Vienna, Austria,
2014,
Todini, E.: The ARNO rainfall—runoff model, Journal of Hydrology, 175, 339-382,
http://dx.doi.org/10.1016/S0022-1694(96)80016-3, 1996.
USGS EROS Data Center: Global land cover characteristics data base version 2.0,
http://edc2.usgs.gov/glcc/globe_int.php, 2002.

92

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Ushio, T., Sasashige, K., Kubota, T., Shige, S., Okamoto, K. i., Aonashi, K., Inoue, T.,
Takahashi, N., Iguchi, T., Kachi, M., Oki, R., Morimoto, T., and Kawasaki, Z.-I.: A Kalman
Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from
Combined Passive Microwave and Infrared Radiometric Data, Journal of the
Meteorological Society of Japan. Ser. II, 87A, 137-151, 10.2151/jmsj.87A.137, 2009.
van Beek, L. P. H., Wada, Y., and Bierkens, M. F. P.: Global monthly water stress: 1.
Water balance and water availability, Water Resources Research, 47, W07517,
10.1029/2010wr009791, 2011.
Van Dijk, A. I. J. M.: AWRA Technical Report 3, Landscape Model (Version 0.5)
Technical Description, WIRADA/CSIRO Water for a Healthy Country Flagship,
Canberra, [Available at
http://www.clw.csiro.au/publications/waterforahealthycountry/2010/wfhc-auswater-resources-assessment-system.pdf.] 2010.
van Dijk, A. I. J. M., Renzullo, L. J., Wada, Y., and Tregoning, P.: A global water cycle
reanalysis (2003&ndash;2012) merging satellite gravimetry and altimetry
observations with a hydrological multi-model ensemble, Hydrol. Earth Syst. Sci., 18,2955-2973, 10.5194/hess-18-2955-2014, 2014.
Van Dijk, A. I. J. M., Brakenridge, G. R., Kettner, A. J., Beck, H. E., De Groeve, T., andSchellekens, J.: River gauging at global scale using optical and passive microwave
remote sensing, Water Resources Research, 52, 6404-6418,
10.1002/2015WR018545, 2016.
Vergnes, J. P., and Decharme, B.: A simple groundwater scheme in the TRIP river
routing model: global off-line evaluation against GRACE terrestrial water storage
estimates and observed river discharges, Hydrol. Earth Syst. Sci., 16, 3889-3908,
10.5194/hess-16-3889-2012, 2012.
Vergnes, J. P., Decharme, B., and Habets, F.: Introduction of groundwater capillaryrises using subgrid spatial variability of topography into the ISBA land surface model,
J. Geophys. Res. Atmos., 119, 2014JD021573, 10.1002/2014JD021573, 2014.
Verzano, K., Bärlund, I., Flörke, M., Lehner, B., Kynast, E., Voß, F., and Alcamo, J.:
Modeling variable river flow velocity on continental scale: Current situation andclimate change impacts in Europe, Journal of Hydrology, 424–425, 238-251,
http://dx.doi.org/10.1016/j.jhydrol.2012.01.005, 2012.
Viney, N., Vaze, J., Crosbie, R. S., Wang, B., Dawes, W., and Frost, A.: AWRA-L v5.0:
technical description of model algorithms and inputs., CSIRO Land and Water,
Canberra, Australia., 2015.
Wada, Y., van Beek, L. P. H., Viviroli, D., Dürr, H. H., Weingartner, R., and Bierkens, M.
F. P.: Global monthly water stress: 2. Water demand and severity of water stress,
Water Resources Research, 47, n/a-n/a, 10.1029/2010WR009792, 2011.
93

WP5 - Task 5.1 – D.5.2
Report on the improved water resources reanalysis

Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal, allocationand consumptive use of surface water and groundwater resources, Earth Syst.
Dynam., 5, 15-40, 10.5194/esd-5-15-2014, 2014.
Wang, T., Ottlé, C., Boone, A., Ciais, P., Brun, E., Morin, S., Krinner, G., Piao, S., and Peng,
S.: Evaluation of an improved intermediate complexity snow scheme in the
ORCHIDEE land surface model, J. Geophys. Res. Atmos., 118, 6064-6079,
10.1002/jgrd.50395, 2013.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: TheWFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to
ERA-Interim reanalysis data, Water Resources Research, 50, 7505-7514,
10.1002/2014wr015638, 2014.

94