Estimation of Global Hydrologic Components in the 20th Century.
*Yukiko Hirabayashi1, Shinjiro Kanae2, 3, Taikan Oki3 , Kuniyoshi Takeuchi 1(1: University of Yamanashi, 2: Research Institute of Humanity and Nature, 3: IIS, University of Tokyo)* University of Yamanashi, Japan, 400-8511, Takeda,
Kofu-shi, Yamanashi, Japan. e-mail: hyukiko@yamanashi.ac.jp
Abstract
A 100-year (1901-2000) off-line simulation by a land surface model (LSM) has been completed by obtaining 100-year
atmospheric forcing data sets from available global observations. It was found that the long-term terrestrial water fluxes canbe estimated well from a LSM driven by long-term atmospheric forcing data that was stochastically estimated from monthly
mean time series of precipitation and temperature. High correlations of annual runoff variations are obtained at many basins
globally, however, the correlations of annual runoff are low in dry areas. Low correlations are also obtained incool-temperate zones. The model successfully replicated annual snow covered area in North America and annual summer
soil moisture in Mongolia. Several trends indicated by previous studies, the descending trend of snow covered area innorthern America, and the increasing trend of summer soil moisture in Mongolia were also appeared in the simulation. With
the benefit of 100-year simulation results for these variables, however, these trends appear to be within a range of natural
long-term variability rather than systematic changes in hydrological condition. The 100-year estimation of hydrologicalcomponent, such as annual and inter-annual variation of runoff, snow and soil moisture, would be useful for examining
behaviors of a LSM with a perspective of long-term variations. They are also useful for water resources assessment rather
than short-term estimation, because of the inclusion of several extremes and longer natural variability. It is also useful toobtain long-term hydrologic component especially for less-observed variables or in poorly-gauged basins; they can be used
as a reference data to check GCMs and LSMs and water resource assessment in un-gauged basins.
Keyword: LSM, global water cycle

1. Introduction
Long-term variations in water fluxes at the land
surface have been getting more attention in recent decades.
What is the natural temporal variability of runoff, snow andsoil moisture, and do these variables exhibit long-termtrends? How will they be impacted by future global
warming? To answer such questions, description,
understanding, and prediction of the global water cycle
have been promoted by numerical land surface models(LSMs). LSMs range in complexity from simple budget
models, such as the traditional bucket model, tosophisticated physically based models, which incorporateradiation transfer, evapotranspiration, runoff and snow
processes. These models solve energy and water balance
equations, driven by atmospheric forcing input such as
precipitation, radiation, and wind velocity, to estimate thetime series of runoff, snow and soil moisture.
In an "off-line" LSM simulation, atmospheric forcing
data is applied in one direction to drive the LSM model,
without any feedback. Where observed atmosphericforcing data is used, hydrological variables predicted by an
off-line LSM simulation can be interpreted as a mixture ofmodel and observation. There are a lot of global data setsobtained by off-line LSM simulations. In this study weestimate a set of hydrological components for the
precipitation, wind, vapour pressure and downward
longwave and shortwave radiation – although sub-daily
data is preferable, where available. Since only monthly
data for precipitation, precipitation days and maximum andminimum temperature were available for the objectiveperiod (1901-2000), this study used three procedures toderive higher resolution atmospheric forcing datasets fromthe available monthly data; these procedures aresummarized in Figure 1. A stochastic distribution model

was used to disaggregate monthly precipitation into a daily
time series, and a stochastic weather generator was used tocreate daily downward shortwave radiation andtemperature time series data. Empirical equations werethen used to obtain downward longwave radiation andspecific humidity. The parameters for these statisticalmodels and equations are estimated from available daily
global data sets.

2. Modeling
MATSIRO is a land surface process model, which is acomponent of the general circulation model of the Centerfor Climate System Research and National Institute forEnvironmental Studies (CCSR/NIES), which balances bothenergy and water, at spatial resolutions from a fraction of adegree to several degrees latitude by longitude. A detailed
description of the MATSIRO model is given in Takata et al.
(2003). In this paper, the grid size of MATSIRO wasMonthly
PCRU

Daily

3HoulyWindPISLSCP-II 1995

Marcov, Gamma
Tmax,CRU

P

Tmin,CRU

Tmax

Sin-curve
TTmin

Weather generator

Srad

ratio of pot. Srad

Srad
hourlyPTmax

qTmin

PSSradEa

Empirical Eqn.

Fig. 1:TEa ・ σT4

Global data set for 100-year forcing.

Lraddefine annual runoff rather than usinghydrological year may be a cause of low
correlation of annual runoff. Since
MATRIP do not employ calibration ofmodel parameter values for specific river
basins, Figure 2 provides an overview ofthe model's ability to simulate the annual
variation in basin hydrology for a rangeof soil type, vegetation type, basin sizes
and topographies, and contrastingclimatic conditions around the globe.
Only the global long-term simulation
which is firstly conducted in this paper
enable to provide this kind of global
Fig.2: Correlation coefficient of annual runoff between model and observation.
analysis of LSMs. Deficiencies of LSMs
indicatedfromlong-term
LSMdefined as 1 degree by 1 degree globally.
simulation would be helpful to improve future LSMs, withThe MATSIRO model computes vertical energy anda help of detailed analysis in basin-scale validation such as
water fluxes in a grid cell based upon specifications of soil
a Project for Intercomparison of Land-Surface
properties and vegetation coverage for each grid. In theParameterization Schemes (PILPS).
MATSIRO model, vegetation processes are mainly based
LSM validation, especially for runoff simulation, hason the SiB2 model, which uses the multi-layer canopy
been conducted by comparing predicted and observed
model of Watanabe (1994). In order to obtain river runoff,seasonal patterns in runoff hydrographs for locations withthe grid runoff estimation was then integrated over theshort historical runoff records, and using longer termbasin unit using a 1-degree version of the TRIP model (Okicomparisons for specific basins with longer historicaland Sud, 1998); the combination is known as the MATRIPrecords. The results shown in Figure 2 stress the(MATSIRO + TRIP) model.
importance of long-term validation of LSMs, particularly ifthey are to be used for long-term climate simulations.

3. Result
3.1.
RunoffFigure 2 shows a map of the correlation coefficients
between simulated and observed time series of annual
runoff. Correlation coefficients were estimated at locations
where discharge records were available for more than 10years during the 100-year period. High correlations areobtained at basins along the west coast and eastern regionsof the North America, as well as South America, WesternEurope, Sahel, the Indochina peninsula and the central andsouthern regions of China. Correlations are low in dryareas, such as the central regions of the USA, themid-latitude regions of the Eurasian continent and mostparts of Australia. Low correlations are also obtained incool-temperate zones, such as the basins of Canada,Eastern Europe and Russia. One possible reason of low
correlations in dry areas is that MATSIRO model may notadequately estimate very low flow since runoff generation
processes in the model are based on TOPMODEL. BecauseTOPMODEL was originally developed to simulate
catchment under humid conditions (such as in the U.K, inthe eastern USA and Scotland), the model has not likely
provided good simulation of discharge under dryconditions. Another possible reason of low correlations in
dry areas is human effect, because the percentage of total
river water usage may be higher in dry regions. Inadequate
detection of snow phase of precipitation and inadequate
snow melting process, may lead inadequate runoff by snow
melting in cool-temperate zones. River freezing and its
break, that are not included in the TRIP model may playsome role of poor estimations. Using calendar year to3.2.

Snow

Figure 3 plots predicted and observed annual variationsin the spring (April and March average) snow covered area
for the North America and Canada. Bars along the top
indicate the fraction of the overall objective area which
was not covered by CRU precipitation gauging in eachyear of the objective period. For North America, gauge
coverage exceeds 95 % of the objective are since 1950.
Groisman et al. (1999) analyzed the snow covered area
data obtained by satellite from 1973 to 1995 and showedthat averaged snow area of April and March is clearlydecreasing in the Northern hemisphere in spring; thisreduction is also predicted by the 100-year simulation
results shown in Figure 3. However, if the historical trends
obtained by the model are correct, the decrease in springsnow covered area from the 1970's actually reflects

Fig.3: Spring (AM: average of April and March)snow area in North America.

Fig.4: Annual trend of averaged JJA soil moisture inMongollia. Area average of soil moisture from 5cm to50cm depth of the 100-year simulation (thin solid) andstation averages of observation (dot).
decadal-scale natural variability rather than a specifictrend.

3.3.

Soil Moisture

Temporal variations in soil moisture were examined for
Mongolia (40-59N), where long-term observations ofroot-zone soil moisture are available from the Global Soil
Moisture Data Bank (GSMDB) (Robock et al, 2000).
Figure 4 compares observations of summer (JJA) soil
moisture with model predictions, with both datasets
representing the areally averaged root zone soil moisturefrom 5cm to 50cm depth over the region. Long-term trends
in the predicted and observed values agree quite well,although predicted short-term inter-annual variability inaverage JJA soil moisture is larger than observed. The
correlation coefficient between the model predictions andobservations is 0.755.
By analysing GSMDB data, Robock et al. (2000) showd
that JJA average soil moisture exhibits an increasing trend
at the same region for the past several decades (1979 to1994). This study also shows good agreement withpredicted annual trends for Mongolia during the period1975 to 1995 obtained in this investigation. However, the100-year soil moisture data also pointed out that theincreasing trend suggested by Robock et al. (2000)
appeared to reflect natural long-term variability rather thansystematic changes.

4. Summary
This paper describes the derivation of 100-year daily
estimations of terrestrial land surface water fluxes, whichhave a wide applicability for various studies such as
analyzing the inter-annual variability of land surfacehydrology and detecting long-term trends of water fluxes.
Most distinguished characteristic of this data set comparedwith prior data sets is its long coverage of 100 year. Global
retrospective estimations of terrestrial water cycle by
LSMs have been limited for few decades because of thelimitation of available atmospheric forcing data. This study
tried to estimate water fluxes for longer period thanprevious studies, driving a LSM under the long-term
atmospheric forcing that was stochastically estimated frommonthly mean time series of precipitation and temperature.
Globally, most place showed high correlation
coefficients of annual runoff with observations. In dryregions and cold regions, however, predicted annual runoff
time series was poorly correlated to observations.
Correlation coefficients of annual snow covered area
between satellite observation and MATRIP were 0.731 in
North America and 0.577 in northern part of Eurasia
continent. Summer soil moisture in Mongolia showed goodcorrelation with observation with correlation coefficients of0.755.
The 100-year estimation of hydrological component, suchas annual and inter-annual variation of runoff, snow andsoil moisture, would be useful for examining
characteristics and behaviors of LSMs and GCM
experiments, especially for less-observed variables such as
soil moisture. Several errors of hydrological componentsthat were found from the long-term estimation should be
carefully analyzed, and investigation of the cause of model
deficiency is highly expected. Because the 100-year LSM
simulation suggested in this paper requires only monthly
mean time series of precipitation and temperature, themethod implemented in this study has wider applicability
for simulating global-scale water fluxes even inpoorly-gauged regions. Blind un-calibrated numericalsimulation applied for global area in this paper clearly
indicated regions where simulation result is bad. This issuggestive of systematic errors in the model scheme orerrors in the atmospheric forcing for such regions,emphasizing the importance of long-term validation of landmodel simulations, to allow these deficiencies to beidentified and resolved.

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