Hydrology 2020 - Technologies
Technology - n. Scientific methods used in a particular field
Collins, Bank of English
Hydrology is a fundamental and diverse subject area; consequently the technologies
employed within the hydrological sciences are many and varied. In this overview,hydrological technologies are summarised and the future prospects for new and more
powerful technologies are reviewed. Technologies are classified as measurement (ie.
providing observations of hydrological variables) and analysis technologies (ie. providing
theoretical frameworks within which observations are processed to derive deeper
understanding of hydrological processes).
1 MEASUREMENT TECHNOLOGIES
1.1 Introduction
The natural environment displays great variability across the entire range of spatial scales,both in terms of land surface characteristics and climatic regimes. Data to define thisvariability must therefore play an integral role in hydrological analyses. Traditionally,
field measures of hydrological properties and fluxes have tended to be point measures.
Recent advances in remote sensing and data availability are also providing new insights
into a number of hydrological problems; hence, spatial data are increasingly being used inconjunction with hydrological models. The incorporation of spatial data is not however
without complications.
1.2 Traditional hydrologic field measurement
In very general terms, quantitative hydrology is concerned with the estimation of one ormore of the terms within the hydrological water balance, at a range of spatial andtemporal scales;

Q = R − E − ∆S(1)where Q is surface discharge, R is rainfall, E is evapotranspiration and ∆S is the change in
sub-surface storage. Whilst this equation is an inherently simple mass balance, many
practical difficulties arise in the measurement, modelling and prediction of each term. In
common, most hydrological measurements are point measures, or at best integrate over avariable but typically unknown area or volume.
1.2.1 Discharge
The prediction of surface discharge is critical for a whole range of applications.
Catchment discharge provides surface water for abstraction, maintains many surface
ecosystems, and periodically brings devastating floods. Catchment discharge is usuallyestimated through the installation of a gauging device such as a weir or flume. Theserelate the water level upstream of the device to the discharge through it. As the derivationof the basic form of this relationship is based on idealised fluid mechanics, thecoefficients of the relationship for each device must be calibrated either in the laboratory
or in-situ. Catchment surface discharge represents a catchment-integrated hydrologicalflux in that the response of the catchment area should drain through one point (or outlet).
In practice, hydrological catchments are typically defined according to surface
topography, which may or may not correspond to areas where sub-surface controls may
dominate fluxes.
1.2.2 Rainfall
Rainfall might be seen as the starting point of land surface hydrology as all consequenthydrological fluxes are derived from rainfall. Measurement of rainfall has traditionallybeen achieved by direct sampling of precipitation volumes through the use of raingauges.
Whilst a variety of designs of gauge exists, they all represent essentially point measures.
1.2.3 Evapotranspiration
The understanding of evapotranspiration is required for estimating catchment losses,
vegetative growth, crop yields, the partition of rainfall into surface runoff etc.
Evapotranspiration is perhaps the most difficult hydrological flux to measure.
Evaporation pans have been devised to provide estimates of potential evaporation.
However, they cannot represent vegetative controls on moisture loss. Additionally, theuse of pans is complicated by feedbacks with the moisture content of the overlying nearsurface atmosphere. If the actual evaporation rate is low, then the atmospheric demand ofmoisture is high thus elevating pan losses, and vice versa. To account for these feedbacks,
empirical ‘pan coefficients’ are employed as corrections.
More recently, Bowen ratio and eddy correlation devices have been devised to measure
actual evapotranspiration losses. Whilst these represent a significant improvement on theuse of pans, they measure fluxes from an area (or fetch) of between 100 – 1000 m3 thatvaries according to wind direction strength and convective stability of the atmosphericboundary layer. While they provide areally-integrated measures, it is clear that significant
variability of evapotranspiration may exist within the area of the fetch. This variability
may be especially important in that it will determine the available storage, which given
rainfall, will determine the occurrence of saturation that is responsible for fast-flowing
surface runoff.
Scintillometry…
1.2.4 Storage (sub-surface flows)
Changes in sub-surface storage and groundwater flows are typically measured throughdirect observation of water levels or contents. In shallow porous media this is usuallyachieved through installing piezometers at different locations. In deeper groundwater
systems, boreholes are sunk from which the water table can be measured. Networks ofboreholes provide hydraulic gradients from which groundwater velocities can be inferred.
Hydraulic conductivity is estimated through slug (pulse) tests where water is removed(added) and the rate of recovery to the prior level is observed. This provides a measure of
conductivity of the media surrounding the borehole.

1.3 Remote sensing
Remote sensing in hydrology is used for the identification and monitoring of relevant
features such as snow-covered areas. Depending on the item of interest, processing to
extract that feature requires varying levels of interpretation, classification and validation.
Traditionally, hydrological science has not been the primary focus in the development of
remote sensors and platforms. Consequently, our current ability to exploit remotesensing as a tool for hydrological science is sensor and platform dependent. We are
restricted by the sensor’s spectral resolution, and monitoring wavelengths as well as theplatform’s sampling frequency and spatial resolution. In general, the parameters inhydrological models for which RS are currently most commonly used are land-cover andsnow-cover extent.
Typical sensors and platforms that have been successfully used to extract vital hydrologic
information include, Landsat TM, MSS, NOAA, SPOT, and ERS 2 (land cover/use andvegetation); ERS-1,2, Radarsat, JERS-1, and SSM/I (soil moisture); SPOT ERS-1,2,
Radarsat, Landsat TM and MSS (surface water); Nimbus 5 (spring runoff); ERS-1 andJERS-1 (temporal changes in snowmelt and soil moisture); Landsat and Topex-Poseidon,(water depth); NOAA, SPOT, Landsat, and MODIS (snow covered area); GOES,
Nimbus7 (snow depth); DMSP, SSMI/I MOS-1 and MSR (snow water equivalent); ERS1,2, and Radarsat (wet snow); NOAA and Landsat TM (surface temp); Meteor (solar
radiation); AVHRR, ERS2, and GOES (surface albedo); NOAA (evapotranspiration);
Meteosat, GOES, and SSMI/I (precipitation).
Nearly all the development and testing of algorithms relies on ground based
measurements that are generally made at scales of less than a kilometer. Length scales
are surface and time dependent. In addition, many hydrological models are not designed
to use the type of spatial data provided by remote sensing and the accuracy ofvariables/parameters derived from remotely-sensed data may be insufficient to warrant
the development of new models.
Data fusion which involves combinations of remotely sensed data and other informationsuch as digital terrain models, etc., seem to offer the most promising approaches for the
purpose of hydrological modelling.
1.3.1 Remote sensing of rainfall
Remote sensing has been used for decades to provide estimates of precipitation. The
GOES precipitation index for example is one of the simplest and most widely used IR
indices of precipitation in the tropics and subtropics. Most algorithms dealing in thevisible, near infrared and thermal infrared wavelengths utilize information on cloudheight and coldness which are both related to the amount of precipitation that is expectedfrom the cloud. Visible and infrared methods for estimating precipitation tend to producesmoothed representations of instantaneous rainfall fields and thus, tend only to be usefulover larger spatial or temporal scales. They need to be carefully calibrated for theintended region and season of use.
Alternatively, passive microwave algorithms can lead to the instantaneous estimates ofprecipitation. There are numerous algorithms that rely on empirically derived functions of
the lower frequency passive microwave channels of such sensors as SSM/I. Microwave
methods do not represent surface precipitation rates but are actually vertical integrationsof rain and/or ice water content in the atmosphere. Poor temporal sampling due todeployment on polar satellites reduces the precision with which short term precipitation
can be estimated.
Upcoming developments involve multi-sensor algorithms that attempt to combine thebenefits of frequent infrared imagery with the more physically direct detection of
precipitation by microwave methods. However, the calibration and validation of satellite
based rainfall estimation algorithms are considered unresolved problems. The successful
evaluation of these algorithms can only be made if there is an appropriate reference
standard and the accuracy of this reference is known. As stated earlier, satellite data areintegrated observations of the atmospheric column while rain gauges are point
measurements with often, questionable accuracy. Sparse rain gauge networks do notprovide sufficient information for realistic representations of rainfall fields and are thusof limited use for calibrating and validating satellite based algorithms for precipitation.
Improvement in the use of remotely sensed estimates of precipitation requires quantifying
the sources of errors, which depending on the band region used, may be due to suchthings as uncertainties in absolute brightness temperature, and the deviation of rain drops
from spheres that are not taken into account by the algorithms. Studies have shown thatvisible and infrared methods are suitable for monthly instead of daily rainfall totals.
Passive microwave algorithms with SSM/I observations may or may not perform better
than visible/infrared algorithms for instantaneous rain rates but performance tends to belocation dependent. Visible/infrared rainfall estimation algorithms have been
recommended for estimating climatic scale distributions of convective rainfall often
found in tropics and subtropics during warm seasons.
Remotely sensed estimates of precipitation have been successfully used in hydrologicalmodelling when conducting monthly water balance. They have been used operationallyas input in hydrological models in the Sudan, Egypt and other places but most of theoperational satellite applications for precipitation use visible or infrared wavelengths.
Precipitation products are currently available through or sponsored by a variety ofagencies including the NOAA, NASA, WMO and NCDC.

1.3.2 Remote sensing of discharge
Discharge is one area where direct measurement using remote sensing is difficult andprimarily indirect methods are used. Delineating surface waters can be achieved by using
the fact that water absorbs near and middle infrared wavelengths. Microwave sensors arealso sensitive to water content and provide all weather viewing. The visible red band canbe used to discern stream channel networks, while river levels can be determined using
radar altimeters. The accuracy of these river elevations has been estimated at ±10 cm to±20 cm. Satellites with onboard radar altimeters include Geosat, Seasat, ERS-1 and
TOPEX/Poseidon.
Few surface-based measurements are currently available forcomparison but some studies have shown that for lakes directly crossed by the altimeter
track, lake level errors of ±15 cm can be obtained (with ±10 cm being attributed to orbit
error). Routine lake level monitoring using radar altimetry is costly and complex andthus, the accuracy achieved may not be worth the expense. Low altitude laser
measurements can be used to produce topography, stream channel and gully crosssections and vegetation height, cover and distribution.

1.3.3 Remote sensing of evapotranspiration
Many tasks in hydrology require an accurate representation of how incoming radiantenergy is partitioned at the land surface into sensible and latent heat. However, as noted
earlier evaporative fluxes are inherently difficult to measure, especially at regional scales
and beyond. Evapotranspiration remains the variable with the greatest uncertainty inhydrological water balances because of its significant spatial and temporal variability.
Remote determinations of surface temperature, vegetation cover, and upper layer soil
moisture are generally the components used to estimate evapotranspiration using remotesensing.
Various land surface models have been developed which describe the physical state of
the land surface, the atmospheric boundary layer and the subsurface. The atmosphericforcing variables in these models include radiation, air temperature, humidity and windspeed. Realistic evapotranspiration prediction with such models requires the specificationof a range of land surface parameters. However, there is considerable uncertainty
associated with such predictions, particularly across regions with spatially varying
vegetation, soil and terrain characteristics.
Land surface processes are controlled by a hierarchy of parameters and variables. For
example, the estimation of evaporation requires information on the amount of available
radiative energy, the type/nature of surface and the presence of water that can beevaporated. Remote sensing can provide insight in the spatial and temporal variability ofseveral key parameters and variables. Remote sensing data can be used to obtain radiation,
mapping land use (changes) and vegetation parameters. However, information on the
aerodynamic properties of the surface and on near-surface airflow is perhaps moredifficult to estimate from remote sensing.

Remotely sensed thermal imagery can be used with surface energy budget models of land
surface fluxes over a range of scales. Thermal imagery from a variety of satellites sensors
may be used to obtain land surface temperature observations (eg. AVHRR; LANDSAT;
SPOT). It is well known that regions of high moisture content or dense vegetation withaccess to moisture exhibit cooler surface temperatures than dry soil or stressed vegetation.
At drier sites, the surface energy balance is dominated by sensible heat exchange,
whereas wetter sites are characterized by surface cooling due to evaporation. Moreprecisely, it is the rate of change in surface temperature, which is related to soil moistureavailability (surface wetness). This provides an indication of the surface resistance toevapotranspiration from the land surface.
Increased attention has been given to the use of thermal imagery obtained withgeostationary meteorological satellite sensors. Such satellites include the Geostationary
Operational Environmental Satellite (GOES), the European METEOSAT satellite and theJapanese Geostationary Meteorological Satellite (GMS). These sensors generate denser
data streams of thermal and visible imagery than the polar-orbiting NOAA-AVHRR, butthey have a coarser spatial resolution.
Significant relationships are known to exist between passive microwave observations andsurface temperature. Spatial resolution of passive microwave sensor data are one to twoorders of magnitude larger than operationally available infrared sensors; hence,
application of passive microwave data for estimating ET may be restricted to regionaland global scales. Satellite estimates of net longwave flux at the surface have beendeveloped using sounding data and can be used with the Tiros Operational Vertical
Sounder (TOVS) of the NOAA satellites, which contains infrared and microwave sensors.
Statistical approaches using slowly varying surface properties such as surface albedo andsoil moisture with remotely sensed data can be used to estimate net radiation.
Other techniques use narrow band reflectance data and brightness temperature fromaircraft and satellite-based platforms for estimating the upwelling radiation components.
In addition, numerous studies have found a significant negative correlation between
NDVI and radiant temperature over different surfaces. However, this assumes complete
canopy cover which may not be naturally occurring. Humidity profiles can be retrievedby measurements in the 183 GHz range. This has been exploited by the SSM/T2 sensor
on the DMSP satellites.
1.3.4 Remote Sensing of Soil Moisture
Research in the microwave remote sensing domain has offered some promise ofdelivering distributed data sets of moisture data useful in model calibration and testing.
However, several problems exist. The accuracy of estimated absolute soil moistures iscomplicated by factors such as differences in soil characteristics, surface roughness(especially vegetation type and density), and topography. A further problem is thatmicrowave remote sensing, even in ideal conditions, can detect soil moisture fields onlyin the uppermost few centimetres of the soil, whereas hydrological models typicallysimulate variable moisture contents over deeper layers. Although continuing attempts arebeing made to derive relationships between surface soil moisture and the profile in deeper
layers, both runoff generation and evapotranspiration are predominantly controlled by the
moisture content at deeper layers. Further limitations result from the reduced sensitivityof the radar signal to soil moisture changes close to saturation and the specular nature of
the signal in areas where water becomes ponded leading to decreases in thebackscattering coefficient. Studies have also shown that freezing and thawing can cause
problems in the estimation of soil moisture. Research using C-band from SAR images ortruck-mounted scatterometers working in L and X-band is still on going.
The problems of deriving surface roughness for active microwave remote sensing are
significant. It is also apparent that even if robust measurements could be achieved, theworth of surface measures, as opposed to profile (depth-integrated) measures, remains
questionable.
Also, use of passive microwave for assimilation of soil moisture into landsufaceatmospheric models…
The passive microwave region, particularly around 21 cm wavelength has been reported
as the most exploited band thus far for the estimation of soil moisture content. Moisture
content can be measured to a depth of about 50 mm. The potential of microwave passive
sensors in soil moisture measurement has been demonstrated and operational systems
have been tested in the U.S.S.R. where maps with four levels of soil moisture have beenobtained using a two frequency airborne sensor at 1.0 and 1.7 GHz.
Again, data combination and fusion provides the best approach for improving the use ofboth active and passive microwave sensors for soil moisture estimation. For example: a
combination of airborne active microwave (C-band) and passive thermal infrared remotesensing has been shown to improve estimates of soil moisture to root zone depths.

1.3.5 Remote sensing of floodplain inundation
Hydrological research has sought to develop numerical models of water flow to predict
the extent of flood inundation area associated with a given magnitude of flow. A key
control on flow and inundation depth is the local roughness coefficient (Manning’s
coefficient). However this empirical factor is subject to considerable uncertainty. To
apply flood inundation models therefore requires calibration against available data. Thisis usually achieved using discharge data measured at occasional points along the reach ofinterest (where available).
In application, after calibration against point flow data, the models perform accurately in
reproducing the flow volume characteristics. However, the spatial extent of floodinundation is often poorly reproduced. Recent work has demonstrated the worth ofremotely sensed spatial data, particularly in correcting the model simulations ofinundation extent. Using RADARSAT synthetic aperture radar data coinciding with flood

events, flood inundation model may be calibrated to the measured areal extent to theflood.
Spatial data tend to contain more information than point data because of their higher
dimensionality (2-D against 0-D), but this is often partially offset by the temporal natureof the point data. Discharge measurements are generally recorded as functions of time,
whereas many spatial data sets (e.g. remotely sensed ones) are synoptic views, offering
an observation at only one instant. In this sense we would expect conditioning against
spatial and point data to provide information on two different aspects of model
behaviour: spatial and temporal. Some degree of correlation between these behaviours isoften assumed, for example, when predictions of inundated area from a model
conditioned against point data are used. In this respect, there is a reliance on the model
conditioned on one aspect of its behaviour to effectively reproduce another.
Floodplain models conditioned on discharge data alone exhibit a higher degree ofprediction uncertainty when the inundated area is considered, when compared to models
conditioned on the satellite sensor data itself. This emphasises an important aspect of the
behaviour of these models: their complex response to their parameterisation means thatmodels calibrated to one data set are not necessarily the best predictors of otherobservable features of the flood (i.e. inundation area). The incorporation of the spatial
inundation data can be seen to increase the accuracy of the predictions of the model, thus
providing greater confidence in its application.
Flood mapping needs real-time coverage and unfortunately, clouds are frequently present
when floods occur. As a result, current research is focusing on the use of satellite-based
radar to provide high resolution, all-weather coverage at the time of flooding. Active andpassive microwave data have been used to monitor flooding: Merged SAR data from
ERS-1 with multispectral data from Landsat and SPOT were successfully used to monitor
the 14 July 1993 floods in St Louis, USA.

1.3.6 Future Sensing Platforms
New missions for covering a wide range of hydrological parameters include TRMM,
AMSU, GOMS VISSR (precipitation), HYDROSTAR (soil moisture) MODIS, MIROS
on the SMOS mission.

1.4 Subsurface tomography
ERT
XRT
???

2 ANALYSIS TECHNOLOGIES
2.1 Introduction
flood frequency analysis
flow duration
empirical regionalisation relationships
idf

2.2 Computers and Hydrology

Computer- gis- rs- simulation modelling

2.3 Geographic Information Systems
Geographic Information Systems provide powerful platforms on which typically pointhydrological measures can be collated and viewed within a spatial and multivariate
framework. This enables rapid generation and analysis of derived spatial fields. For
instance, point raingauges can be plotted in relation to the topographic features of a study
catchment. Through the use of interpolation techniques such as thin-plate splines or othergeostatistical methods, a continuous field of rainfall characteristics can be derived
utilising the observed relationship between rainfall and topography.
GIS is used in hydrology in several ways including: (1) data visualization; (2) parameter
estimation; and (3) modelling. Terms used to describe the types of hydrological data
determined through GIS include topographic data, which are used to describe thetopography of a region, topographically derived attributes known as topological datasuch as watershed area, flow lengths, land slope, and stream network, that are somehow
derived from the topographic source data; and thematic data such as soil type, land cover,geological formations, and precipitation. Attributes derived from thematic data may
include SCS curve numbers and other indices of runoff.
Possibly the most important spatial data within GIS frameworks have been Digital
Terrain Models (DTMs). DTMs were originally built through the collation of point
measures of elevation obtained through traditional manual surveying techniques. The
advent of remote sensing devices, such as airborne laser altimetry (LiDAR), has meantthat high resolution DTMs can be obtained for almost any study area. The readyavailability of digital terrain models enables simple, routine delineation of surface

catchment boundaries, drainage networks and flow paths. DTMs have been put to manyother uses within hydrology including the estimation of relative moisture content based
on topographic controls. Many GIS packages now included automated algorithms fortypical hydrological applications.

2.4 Hydrologic modelling
The historical development of hydrological models has been primarily influenced by data
availability and computer constraints. Nevertheless, there is a plethora of hydrologicalmodels due in part to the application specific nature of many of these models (example,
urban hydrology versus forest hydrology). This application specific quality is primarily
due to the inherent complexity of hydrological systems. In most classification systems ofhydrological models, there are two branches at the most basic level: deterministic models
and stochastic models. Once considered distinct branches, hydrological modelers are now
realizing that successful hydrological modelling will involve aspects of both branches.
2.4.1 Deterministic Hydrological Models: Empirical, Conceptual and PhysicallyBased Models
The empirical model uses non-physical parameters to modify a given input to some
output. Also referred to as black-box models, they tend to have few parameters making
them convenient to use and easy to calibrate. However, empirical models need longmeteorological and hydrological records for calibration that are not always available; andbecause of the non-physical nature of the parameters used in these models, it is difficult
to assess whether a calibrated parameter is appropriate since it cannot be measured in thefield. Conventional models in engineering hydrology for predicting runoff generation are
primarily composed of black-box concepts such as baseflow separation and unithydrograph techniques.
A quasi-physically based model, also known as a conceptual model, represents middleground between empirical models and physically-based models. They have theconvenience of only having to calibrate a few parameters, yet the parameters retain somephysical meaning because the model has reduced the real system to a few basic physicalprincipals that are representative of the system.
At the other end of the spectrum is the physically-based model. A physically-basedmodel is a type of model that simulates processes using equations derived from thephysics governing the system and fundamental physical laws. This tends to makephysically-based models naturally more attractive when compared to empirical models.
The parameters have physical meaning and can theoretically be measured in the field.
The models are generally very complex and involve numerous parameters. However, theuse of these models and their appropriateness has recently come into question as thetheoretical advantages of physically-based models remain unproven in practice and that
there is a tendency for users to give rise to “uncritical believe” in their predictions.

Physically-based models are, like empirical models, simplified representations of reality
and may imply a false sense of accuracy and confidence in the user.

2.4.2 Lumped vs Distributed Modelling
The most conventional and basic modelling unit in hydrology is the subbasin, which may
be considered a hydrologic response unit over which all attributes are averaged. A
lumped application assumes a homogeneous distribution of input values or basincharacteristics. Fully-distributed models however, account for most or all the important
heterogeneity that exists in the system by discretizing the system into individual units.
Within these units, basin characteristics and inputs are treated in a lumped way. The
distinction between lumped parameter and distributed models is not as clear as might bedesired, because the subbasin may be taken to be arbitrarily large or small.
Due to their nature, black-box or empirical models often involve lumped applications.
Predictions resulting from lumped applications may compromise model accuracy because
the averaging process may ignore important heterogeneity in the system. An example ofa lumped parameter model is the Unit Hydrograph method in which the ‘lumped
parameter’ is the time of concentration, which is held constant for all storms.
Many physically-based models employ a fully-distributed approach which means that agreat deal of data is required to run the model. Fully-distributed, physically-basedmodels have benefited from the increase in computer technology and the invention of
Geographic Information Systems (GIS). The full benefits of fully-distributed, physicallybased modelling are rarely realized due to a lack of data availability. In addition, all data
will have some degree of error and as the amount of data required by a model increases,
the greater the effect of compounded errors.

2.5 analysis techniques
2.5.1 time series analysis
2.5.2 Artificial Neural Nets
2.5.3 Bayesian analyses
2.5.4 Uncertainty and Fuzzy concepts
others…