Modelling the water balance of a mesoscale catchment basin using remotely sensed land cover data

Hydrological modelling of mesoscale catchments is often adversely affected by a lack of adequate information about specific site conditions. In particular, digital land cover data are available from data sets which were acquired on a European or a national scale. These data sets do not only exhibit a restricted spatial resolution but also a differentiation of crops and impervious areas which is not appropriate to the needs of mesoscale hydrological models. In this paper, the impact of remote sensing data on the reliability of a water balance model is investigated and compared to model results determined on the basis of CORINE (Coordination of Information on the Environment) Land Cover as a reference. The aim is to quantify the improved model performance achieved by an enhanced land cover representation and corresponding model modifications. Making use of medium resolution satellite imagery from SPOT, LANDSAT ETM+ and ASTER, detailed information on land cover, especially agricultural crops and impervious surfaces, was extracted over a 5-year period (2000–2004). Crop-specific evapotranspiration coefficients were derived by using remote sensing data to replace grass reference evapotranspiration necessitated by the use of CORINE land cover for rural areas. For regions classified as settlement or industrial areas, degrees of imperviousness were derived. The data were incorporated into the hydrological model GROWA (large-scale water balance model), which uses an empirical approach combining distributed meteorological data with distributed site parameters to calculate the annual runoff components. Using satellite imagery in combination with runoff data from gauging stations for the years 2000–2004, the actual evapotranspiration calculation in GROWA was methodologically extended by including empirical crop coefficients for actual evapotranspiration calculations. While GROWA originally treated agricultural areas as homogeneous, now a consideration and differentiation of the main crops is possible. The accuracy was determined by runoff measurements from gauging stations. Differences in water balances resulting from the use of remote sensing data as opposed to CORINE were analysed in this study using a representative subcatchment. Resulting Nash–Sutcliff model efficiencies improved from 0.372 to 0.775 and indicate that the enhanced model can produce thematically more accurate and spatially more detailed local water balances. However, the proposed model enhancements by satellite imagery have not exhausted the full potential of water balance modelling, for which a higher temporal resolution is required.     

Hydrological modelling of temporally-varying catchments: facets of change and the value of information

Abstract

River basins are by definition temporally-varying systems: changes are apparent at every temporal scale, in terms of changing meteorological inputs and catchment characteristics due to inherently uncertain natural processes and anthropogenic interventions. In an operational context, the ultimate goal of hydrological modelling is predicting responses of the basin under conditions that are similar or different to those observed in the past. Since water management studies require that anthropogenic effects are considered known and a long hypothetical period is simulated, the combined use of stochastic models, for generating the inputs, and deterministic models that also represent the human interventions in modified basins, is found to be a powerful approach for providing realistic and statistically consistent simulations (in terms of product moments and correlations, at multiple time scales, and long-term persistence). The proposed framework is investigated on the Ferson Creek basin (USA) that exhibits significantly growing urbanization during the last 30 years. Alternative deterministic modelling options include a lumped water balance model with one time-varying parameter and a semi-distributed scheme based on the concept of hydrological response units. Model inputs and errors are respectively represented through linear and nonlinear stochastic models. The resulting nonlinear stochastic framework maximizes the exploitation of the existing information by taking advantage of the calibration protocol used in this issue.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Simulated impacts of climate change and land‐clearing on runoff from a small Sahelian catchment

In the Sahel, there are few long‐term data series available to estimate the climatic and anthropogenic impacts on runoff in small catchments. Since 1950, land clearing has enhanced runoff. The question is whether and by how much this anthropogenic effect offsets the current drought. To answer this question, a physically based distributed hydrological model was used to simulate runoff in a small Sahelian catchment in Niger, from the 1950–1998 rain‐series. The simulation was carried out for three soil surface states of the catchment (1950, 1975 and 1992). The catchment is characterized by an increase in cultivated land, with associated fallow, from 6% in 1950 to 56% in 1992, together with an increase in the extent of eroded land (from 7 to 16%), at the expense of the savanna. Effects of climate and land use are first analysed separately: irrespective of the land cover state, the simulated mean annual runoff decreases by about 40% from the wet period (1950–1969) to the dry period (1970–1998); calculated on the 1950–1998 rainfall‐series, the changes that occurred in land cover between 1950 and 1992 multiplies the mean annual runoff by a factor close to three. The analysis of a joint climatic and anthropogenic change shows that the transition from a wet period under a ‘natural’ land cover (1950) to a dry period under a cultivated land cover (1992) results in an increase in runoff of the order of 30 to 70%. At the scale of a small Sahelian catchment, the anthropogenic impact on runoff is probably more important than that of drought. This figure for relative increase in runoff contributions to ponds, preferential sites of seepage to groundwater, is less than that currently estimated for aquifer recharge, which has been causing a significant continuous water table rise over the same period. Copyright © 2004 John Wiley & Sons, Ltd.     

Effects of spatial aggregation of soil spatial information on watershed hydrological modelling

Many researchers have examined the impact of detailed soil spatial information on hydrological modelling due to the fact that such information serves as important input to hydrological modelling, yet is difficult and expensive to obtain. Most research has focused on the effects at single scales; however, the effects in the context of spatial aggregation across different scales are largely missing. This paper examines such effects by comparing the simulated runoffs across scales from watershed models based on two different levels of soil spatial information: the 10‐m‐resolution soil data derived from the Soil‐Land Inference Model (SoLIM) and the 1:24000 scale Soil Survey Geographic (SSURGO) database in the United States. The study was conducted at three different spatial scales: two at different watershed size levels (referred to as full watershed and sub‐basin, respectively) and one at the model minimum simulation unit level. A fully distributed hydrologic model (WetSpa) and a semi‐distributed model (SWAT) were used to assess the effects. The results show that at the minimum simulation unit level the differences in simulated runoff are large, but the differences gradually decrease as the spatial scale of the simulation units increases. For sub‐basins larger than 10 km² in the study area, stream flows simulated by spatially detailed SoLIM soil data do not significantly vary from those by SSURGO. The effects of spatial scale are shown to correlate with aggregation effect of the watershed routing process. The unique findings of this paper provide an important and unified perspective on the different views reported in the literature concerning how spatial detail of soil data affects watershed modelling. Different views result from different scales at which those studies were conducted. In addition, the findings offer a potentially useful basis for selecting details of soil spatial information appropriate for watershed modelling at a given scale. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin

Abstract

Remote sensing is considered the most effective tool for estimating evapotranspiration (ET) over large spatial scales. Global terrestrial ET estimates over vegetated land surfaces are now operationally produced at 1-km spatial resolution using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MOD16 algorithm. To evaluate the accuracy of this product, ground-based measurements of energy fluxes obtained from eddy covariance sites installed in tropical biomes and from a hydrological model (MGB-IPH) were used to validate MOD16 products at local and regional scales. We examined the accuracy of the MOD16 algorithm at two sites in the Rio Grande basin, Brazil, one characterized by a sugar-cane plantation (USE), the other covered by natural savannah vegetation (PDG) for the year 2001. Inter-comparison between 8-day average MOD16 ET estimates and flux tower measurements yielded correlations of 0.78 to 0.81, with root mean square errors (RMSE) of 0.78 and 0.46 mm d^-1, at PDG and USE, respectively. At the PDG site, the annual ET estimate derived by the MOD16 algorithm was 19% higher than the measured amount. For the average annual ET at the basin-wide scale (over an area of 145 000 km²), MOD16 estimates were 21% lower than those from the hydrological model MGB-IPH. Misclassification of land use and land cover was identified as the largest contributor to the error from the MOD16 algorithm. These estimates improve significantly when results are integrated into monthly or annual time intervals, suggesting that the algorithm has a potential for spatial and temporal monitoring of the ET process, continuously and systematically, through the use of remote sensing data.

Editor D. Koutsoyiannis; Associate editor T. Wagener

Citation Ruhoff, A.L., Paz, A.R., Aragao, L.E.O.C., Mu, Q., Malhi, Y., Collischonn, W., Rocha, H.R., and Running, S.W., 2013. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin. Hydrological Sciences Journal, 58 (8), 1658–1676.     

Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins

In climate‐change studies, a macroscale hydrologic model (MHM) operating over large scales can be an important tool in developing consistent hydrological variability estimates over large basins. MHMs, which can operate at coarse grid resolutions of about 1° latitude by longitude, have been used previously to study climate change impacts on the hydrology of continental scale or global river basins. They can provide a connection between global atmospheric models and water resource systems on large spatial scales and long timescales. In this study, the variable infiltration capacity (VIC) MHM is used to study large scale hydrologic impacts of climate change for Indian river basins. Large‐scale changes in runoff, evapotranspiration and soil moisture for India, as well as station‐scale changes in discharges for three major river basins with distinct climatic and geographic characteristics are examined in this study. Climate model projections for meteorological variables (precipitation, temperature and wind speed) from three general circulation models (GCMs) and three emissions scenarios are used to drive the VIC MHM. GCM projections are first interpolated to a 1° by 1° hydrologic model grid and then bias‐corrected using a quantile–quantile mapping. The VIC model is able to reproduce observed statistics for discharges in the Ganga, Narmada and Krishna basins reasonably well, even at the coarse grid resolution employed using a calibration period for years 1965–1970 and testing period from 1971–1973/1974. An increasing trend is projected for summer monsoon surface runoff, evapotranspiration and soil moisture in most central Indian river basins, whereas a decrease in runoff and soil moisture is projected for some regions in southern India, with important differences arising from GCM and scenario variability. Discharge statistics show increases in mid‐flow and low flow at Farakka station on Ganga River, increased high flows at Jamtara station upstream of Narmada, and increased high, mid‐flow and low flow for Vijayawada station on Krishna River in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Regional‐scale mapping of groundwater discharge zones using thermal satellite imagery

Mapping groundwater discharge zones at broad spatial scales remains a challenge, particularly in data sparse regions. We applied a regional scale mapping approach based on thermal remote sensing to map discharge zones in a complex watershed with a broad diversity of geological materials, land cover and topographic variation situated within the Prairie Parkland of northern Alberta, Canada. We acquired winter thermal imagery from the USGS Landsat archive to demonstrate the utility of this data source for applications that can complement both scientific and management programs. We showed that the thermally determined potential discharge areas were corroborated with hydrological (spring locations) and chemical (conservative tracers of groundwater) data. This study demonstrates how thermal remote sensing can form part of a comprehensive mapping framework to investigate groundwater resources over broad spatial scales. Copyright © 2013 John Wiley & Sons, Ltd.     

A review of low‐cost space‐borne data for flood modelling: topography,flood extent and water level

During the last two decades, remote sensing data have led to tremendous progress in advancing flood inundation modelling. In particular, low‐cost space‐borne data can be invaluable for large‐scale flood studies in data‐scarce areas. Various satellite products yield valuable information such as land surface elevation, flood extent and water level, which could potentially contribute to various flood studies. An increasing number of research studies have been dedicated to exploring those low‐cost data towards building, calibration and evaluation, and remote‐sensed information assimilation into hydraulic models. This paper aims at reviewing these recent scientific efforts on the integration of low‐cost space‐borne remote sensing data with flood modelling. Potentials and limitations of those data in flood modelling are discussed. This paper also introduces the future satellite missions and anticipates their likely impacts in flood modelling. Copyright © 2015 John Wiley & Sons, Ltd.     

Radar remote sensing of the source areas from the Coët-Dan catchment

As interest shifts to the development of models for predicting runoff quality, identification of the source areas for runoff becomes increasingly important. Active microwave remote sensing has a unique potential for surveying source areas at the catchment scale. Thresholding of the back-scattering coefficient was initially proposed but proved unsatisfactory when applied to the ERS-1 SAR multitemporal images acquired during winter 1992 over the Coët-Dan catchment, concomitantly with ground observations. Difference images may, instead, allow the wettest part of the catchment to be identified provided that the two images encompass a marked hydrological event. A saturation plot could not however be obtained for each date; the use of a pair of images may be further limited by the residual speckle (although carefully filtered using the multitemporal information) and a slight inaccuracy in the SAR image calibration. It is therefore argued that considering the whole temporal back-scatter profile would be, at present, a safer approach to the remote sensing of saturated areas. The back-scatter temporal standard deviation appears, in this light, as a possible good indicator of the local saturation likelihood during the period of study: it is based on the fact that saturation develops on parts of the catchment that are wetter than the others through lateral recharge. Possible applications within the TOPMODEL framework are discussed. © 1998 John Wiley & Sons, Ltd.     

Glacier changes and their impacts on the discharge in the past half-century in Tekes watershed,Central Asia

The glacier is an important and stable water supply in Central Asia. Monitoring the change of glacier and understanding the impacts of glacier change on river discharge are critical to predict the downstream water availability change in future. Glacier changes were discussed and their impacts on river discharge were evaluated by hydrological modeling with a distributed hydrological model SWAT under two land use and land cover scenarios (1970 and 2007) in Tekes watershed, the most important source of water discharge to the Ili River. Compared to the glacier area of 1511 km² in 1970s it decreased by 332 km² in 2007, which resulted in the contribution the discharge from precipitation in the glacier area to the average annual discharge of the watershed changing from 9.8% in the period 1966–1975 to 7.8% in the period 2000–2008. In the month scale, with the decrease of glacier area, the distribution of the contribution of monthly discharge from precipitation in the glacier area to the total of the watershed changed from bimodal pattern to unimodal pattern. By linking a hydrological model to remote sensing image analysis and Chinese glacier inventories to determine glacier area change our approach in quantifying the impacts of glacier changes on hydrology at different scales, will provide quantitative information for stakeholders in making decisions for water resource management.     

Complementary relationships for near-instantaneous evaporation

The complementary relationship between actual and potential evaporation provides evaporation (i.e. evapotranspiration) estimates from minimal data. Some versions that require a land surface temperature instead of a humidity measurement could potentially be used with routine remotely sensed surface temperature data. A comparison of alternative complementary approaches, including those that require land surface temperatures, was made at small (10–30 min) time scales with point measurements spatially, using data from the FIFE, CASES, SGP, and Sahel field experiments. The advection-aridity version and a related version based on the Penman and the Priestley–Taylor equations performed the best overall. One of the four versions that incorporated land surface temperature performed fairly well. The complementary approach appears to remain viable, especially in remote sensing applications with sparse data.     

Remotely sensed rivers in the Anthropocene: state of the art and prospects

The rivers of the world are undergoing accelerated change in the Anthropocene, and need to be managed at much broader spatial and temporal scales than before. Fluvial remote sensing now offers a technical and methodological framework that can be deployed to monitor the processes at work and to assess the trajectories of rivers in the Anthropocene. In this paper, we review research investigating past, present and future fluvial corridor conditions and processes using remote sensing and we consider emerging challenges facing fluvial and riparian research. We introduce a suite of remote sensing methods designed to diagnose river changes at reach to regional scales. We then focus on identification of channel patterns and acting processes from satellite, airborne or ground acquisitions. These techniques range from grain scales to landform scales, and from real time scales to inter-annual scales. We discuss how remote sensing data can now be coupled to catchment scale models that simulate sediment transfer within connected river networks. We also consider future opportunities in terms of datasets and other resources which are likely to impact river management and monitoring at the global scale. We conclude with a summary of challenges and prospects for remotely sensed rivers in the Anthropocene. © 2019 John Wiley & Sons, Ltd.     

IV. Water resource modelling using remote sensing and object-oriented simulation

Remote sensing technology has matured significantly over the past decade. Operational satellites provide reliable, periodic coverage for all areas of the Earth. Data from these satellites are in a digital format that provides enhanced flexibility in hydrological modelling. Considerable advances in acquiring hydrological data from airborne and in situ sensors have also been achieved. Additionally, data from non-traditional remote sources such as weather radar from which spatial and temporal rainfall rates may be estimated are widely available. These new data acquisition capabilities have been paralleled by equal advancements in digital array processing and geographic information systems, which allow the effective extraction of both temporal and spatial information. This paper examines the use of object-oriented programming techniques to create dynamic hydrological models, and explores their potential to receive real and near real-time data from remote sensors as input to improve hydrological forecasting. In particular, the COE SSARR model is used to illustrate how an established hydrological model may be adapted to create a dynamic object model.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.     

Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales

Abstract

The aim of this paper is to quantify meteorological droughts and assign return periods to these droughts. Moreover, the relation between meteorological and hydrological droughts is explored. This has been done for the River Meuse basin in Western Europe at different spatial and temporal scales to enable comparison between different data sources (e.g. stations and climate models). Meteorological drought is assessed in two ways: using annual minimum precipitation amounts as a function of return period, and using troughs under threshold as a function of return period. The Weibull extreme value type 3 distribution has been fitted to both sources of information. Results show that the trough-under-threshold precipitation is larger than the annual minimum precipitation for a specific return period. Annual minimum precipitation values increase with spatial scale, being most pronounced for small temporal scales. The uncertainty in annual minimum point precipitation varies between 68% for the 30-day precipitation with a return period of 100 years, and 8% for the 120-day precipitation with a return period of 10 years. For spatially-averaged values, these numbers are slightly lower. The annual discharge deficit is significantly related to the annual minimum precipitation.

Citation Booij, M. J. & de Wit, M. J. M. (2010) Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales. Hydrol. Sci. J. 55(8), 1289–1301.     

Application of hydrological models with increasing complexity to subarctic catchments

Many available complex models tend to demand far more input information than is afforded by subarctic remote regions, such as vast areas of North America and Eurasia. A suitable level of model complexity must be sought so that the model matches both the availability of data, but also the spatial and temporal scale at which the major hydrological processes occur. The present paper describes a method to seek a level of model complexity suitable for simulation of runoff for a particular environment at a particular scale, commensurate with the limited data availability in remote areas. Processes in a simple model are stepwise replaced by representations taken from a more complex model, to achieve a balance between data requirement and model complexity at different spatial and temporal scales. The results suggest that it is not always necessary to switch directly from a simple hydrological model to complex one, because at particular spatial and temporal scales, runoff may be sensitive to only a number of processes.     

Mapping Potential Groundwater‐Dependent Ecosystems for Sustainable Management

Ecosystems which rely on either the surface expression or subsurface presence of groundwater are known as groundwater‐dependent ecosystems (GDEs). A comprehensive inventory of GDE locations at an appropriate management scale is a necessary first‐step for sustainable management of supporting aquifers; however, this information is unavailable for most areas of concern. To address this gap, this study created a two‐step algorithm which analyzed existing geospatial and remote sensing data to identify potential GDEs at both state/province and aquifer/basin scales. At the state/province scale, a geospatial information system (GIS) database was constructed for Texas, including climate, topography, hydrology, and ecology data. From these data, a GDE index was calculated, which combined vegetative and hydrological indicators. The results indicated that central Texas, particularly the Edwards Aquifer region, had highest potential to host GDEs. Next, an aquifer/basin scale remote sensing‐based algorithm was created to provide more detailed maps of GDEs in the Edwards Aquifer region. This algorithm used Landsat ETM+ and MODIS images to track the changes of NDVI for each vegetation pixel. The NDVI dynamics were used to identify the vegetation with high potential to use groundwater—such plants remain high NDVI during extended dry periods and also exhibit low seasonal and inter‐annual NDVI changes between dry and wet seasons/years. The results indicated that 8% of natural vegetation was very likely using groundwater. Of the potential GDEs identified, 75% were located on shallow soil averaging 45 cm in depth. The dominant GDE species were live oak, ashe juniper, and mesquite.     

Publications received by the Editor

Abstract

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m.

Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.