Soil moisture estimation using ERS 2 SAR data: a case study in the Solani River catchment/Estimation de l’humidité du sol grâce à des données ERS-2 SAR: étude de cas dans le bassin de la rivière Solani

Abstract

Abstract The information regarding spatial and temporal variation of soil moisture in a catchment is of utmost importance in hydrological, as well as many other studies. Point measurements from gravimetric and other methods for soil moisture determination are insufficient to understand the spatial behaviour of soil moisture in a region. Microwave remote sensing data from active sensors on board various satellites are increasingly being used to map spatial distribution of soil moisture within the 0–10 cm top surface. The northern part of India has a network of large rivers and canals and, therefore, spatial and temporal distribution of soil moisture in this region has a significant bearing on the hydrology of the region. In this paper, results on estimation of soil moisture from an ERS-2 SAR image in the catchment of the Solani River (a tributary to the River Ganga) in and around the town of Roorkee, India, have been presented. The radar backscatter coefficient for each pixel of the image has been modelled from the digital numbers of the SAR image. Gravimetric measurements have been made simultaneously during the satellite pass to determine the concurrent value of volumetric soil moisture at a large number of sample points within the satellite sweep area. The backscatter coefficient is found to vary from –30 dB to –42 dB for a variation in soil moisture from 30 to 75%. Regression analyses between volumetric soil moisture and both the digital numbers and backscatter coefficients were performed. Strong correlations between volumetric soil moisture and digital number were observed with R ² values of 0.84, 0.75 and 0.83 for bare soil, vegetative and combined surfaces, respectively. A similar trend was observed with the relationship between backscatter and volumetric soil moisture with R ² values of 0.60, 0.89 and 0.67 for bare soil, vegetative and combined surfaces, respectively. These results demonstrate the utilization of SAR data for estimation of spatial distribution of soil moisture in the region of the present study.     

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.     

Determining hydrological regimes in an agriculturally used tropical inland valley wetland in Central Uganda using soil moisture,groundwater, and digital elevation data

Inadequate knowledge exists on the distribution of soil moisture and shallow groundwater in intensively cultivated inland valley wetlands in tropical environments, which are required for determining the hydrological regime. This study investigated the spatial and temporal variability of soil moisture along 4 hydrological positions segmented as riparian zone, valley bottom, fringe, and valley slope in an agriculturally used inland valley wetland in Central Uganda. The determined hydrological regimes of the defined hydrological positions are based on soil moisture deficit calculated from the depth to the groundwater table. For that, the accuracy and reliability of satellite‐derived surface models, SRTM‐30m and TanDEM‐X‐12m, for mapping microscale topography and hydrological regimes are evaluated against a 5‐m digital elevation model (DEM) derived from field measurements. Soil moisture and depth to groundwater table were measured using frequency domain reflectometry sensors and piezometers installed along the hydrological positions, respectively. Results showed that spatial and temporal variability in soil moisture increased significantly (p < .05) towards the riparian zone; however, no significant difference was observed between the valley bottom and riparian zone. The distribution of soil hydrological regimes, saturated, near‐saturated, and nonsaturated regimes does not correlate with the hydrological positions. This is due to high spatial and temporal variability in depth to groundwater and soil moisture content across the valley. Precipitation strongly controlled the temporal variability, whereas microscale topography, soil properties, distance from the stream, anthropogenic factors, and land use controlled the spatial variability in the inland valley. TanDEM‐X DEM reasonably mapped the microscale topography and thus soil hydrological regimes relative to the Shuttle Radar Topography Mission DEM. The findings of the study contribute to improved understanding of the distribution of hydrological regimes in an inland valley wetland, which is required for a better agricultural water management planning.     

Integration of remote sensing data into hydrological models for reservoir management

Abstract

The purpose of this paper is to present the methodology set up to derive catchment soil moisture from Earth Observation (EO) data using microwave spaceborne Synthetic Aperture Radar (SAR) images from ERS satellites and to study the improvements brought about by an assimilation of this information into hydrological models. The methodology used to derive EO data is based on the appropriate selection of land cover types for which the radar signal is mainly sensitive to soil moisture variations. Then a hydrological model is chosen, which can take advantage of the new information brought by remote sensing. The assimilation of soil moisture deduced from EO data into hydrological models is based principally on model parameter updating. The main assumption of this method is that the better the model simulates the current hydrological system, the better the following forecast will be. Another methodology used is a sequential one based on Kalman filtering. These methods have been put forward for use in the European AIMWATER project on the Seine catchment upstream of Paris (France) where dams are operated to alleviate floods in the Paris area.     

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.     

Climate sensitivity of water use by riparian woodlands at landscape scales

Semi-arid riparian woodlands face threats from increasing extractive water demand and climate change in dryland landscapes worldwide. Improved landscape-scale understanding of riparian woodland water use (evapotranspiration, ET) and its sensitivity to climate variables is needed to strategically manage water resources, as well as to create successful ecosystem conservation and restoration plans for potential climate futures. In this work, we assess the spatial and temporal variability of Cottonwood (Populus fremontii)-Willow (Salix gooddingii) riparian gallery woodland ET and its relationships to vegetation structure and climate variables for 80 km of the San Pedro River corridor in southeastern Arizona, USA, between 2014 and 2019. We use a novel combination of publicly available remote sensing, climate and hydrological datasets: cloud-based Landsat thermal remote sensing data products for ET (Google Earth Engine EEFlux), Landsat multispectral imagery and field data-based calibrations to vegetation structure (leaf-area index, LAI), and open-source climate and hydrological data. We show that at landscape scales, daily ET rates (6–10 mm day⁻¹) and growing season ET totals (400–1,400 mm) matched rates of published field data, and modelled reach-scale average LAI (0.80–1.70) matched lower ranges of published field data. Over 6 years, the spatial variability of total growing season ET (CV = 0.18) exceeded that of temporal variability (CV = 0.10), indicating the importance of reach-scale vegetation and hydrological conditions for controlling ET dynamics. Responses of ET to climate differed between perennial and intermittent-flow stream reaches. At perennial-flow reaches, ET correlated significantly with temperature, whilst at intermittent-flow sites ET correlated significantly with rainfall and stream discharge. Amongst reaches studied in detail, we found positive but differing logarithmic relationships between LAI and ET. By documenting patterns of high spatial variability of ET at basin scales, these results underscore the importance of accurately accounting for differences in woodland vegetation structure and hydrological conditions for assessing water-use requirements. Results also suggest that the climate sensitivity of ET may be used as a remote indicator of subsurface water resources relative to vegetation demand, and an indicator for informing conservation management priorities.     

Remote sensing of snow and ice / La télédétection de neige et de glace

Abstract

Monitoring of snow and ice on the Earth's surface will require increasing use of satellite remote sensing techniques. These techniques are evolving rapidly. Active and passive sensors operating in the visible, near infrared, thermal infrared, and microwave wavelengths are described in regard to general applications and in regard to specific USA or USSR satellites. Meteorological satellites (frequent images of relatively crude resolution) and Earth resources satellites such as Landsat (less frequent images of higher resolution) have been used to monitor the areal extent of seasonal snow, but problems exist with cloud cover or dense forest canopies. Snow mass (water equivalent) can be measured from a low-flying aircraft using natural radioactivity, but cannot yet be measured from satellite altitudes. A combination of active and passive microwave sensors may permit this kind of measurement, but not until more is known about radiation scattering in snow. Satellite observations are very useful in glacier inventories, correcting maps of glacier extent, estimating certain mass balance parameters, and monitoring calving or surging glaciers. Ground ice is virtually impossible to monitor from satellites; ice on rivers and lakes can be monitored only with very high-resolution sensors. Microwave sensors, due to their all-weather capability (the ability to see through clouds) provide exciting data on sea ice distribution. Analysis of digital tapes of satellite data requires the archiving and scanning of huge amounts of data. Simple methods for extracting quantitative data from satellite images are described.     

Parameterizing a physically based shallow landslide model in a data poor region

Shallow landslides and consequent debris flows are an increasing concern in the Western Ghats of Kerala, India. Their increased frequency has been associated with deforestation and unfavourable land‐use practices in cultivated areas. In order to evaluate the influence of vegetation on shallow slope failures a physically based, dynamic and distributed hydrological model (STARWARS) coupled with a probabilistic slope stability model (PROBSTAB) was applied to the upper Tikovil River basin (55·6 km²). It was tuned with the limited evidence of groundwater conditions during the monsoon season of 2005 and validated against observed landslide activity in the hydrological year 2001–2002. Given the data poor conditions in the region some modifications to the original model were in order, including the estimation of parameters on the basis of generalized information from secondary sources, pedo‐transfer functions, empirical equations and satellite remote sensing data. Despite the poor input, the model captured the general temporal and spatial pattern of instability in the area. Sensitivity analysis proved root cohesion, soil depth and angle of internal friction as the most dominant parameters influencing slope stability. The results indicate the importance of root cohesion in maintaining stability and the critical role of the management of rubber plantations in this. Interception and evapotranspiration showed little influence on the development of failure conditions. The study also highlights the importance of high resolution digital terrain models for the accurate mechanistic prediction of shallow landslide initiation. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Temporal and spatial variability in the Guadalquivir estuary: a challenge for real-time telemetry

Meteorological, hydrological, and hydrodynamic data for 3 years (2008–2010) have been used to document and explain the temporal and spatial variability of the physical–biogeochemical interactions in the Guadalquivir River Estuary. A real-time, remote monitoring network has been deployed along the course of the river between its mouth and Seville to study a broad range of temporal scales (semidiurnal, diurnal, fortnightly, and seasonal). This network consists of eight hydrological monitoring stations capable of measuring temperature, conductivity, dissolved oxygen, turbidity, and chlorophyll fluorescence at four depths. In addition, six stations have been deployed to study hydrodynamics, obtaining 20-cell water column current profiles, and there is a meteorological station at the river mouth providing data for understanding atmospheric interactions. Completing this data-gathering network, there are several moorings (tide gauges, current/wave sensors, and a thermistor chain) deployed in the estuary and river mouth. Various sources of physical forcing, such as wind, tide-associated currents, and river discharge, are responsible for the particular temporal and spatial patterns of turbidity and salinity found in the estuary. These variables force the distribution of biogeochemical variables, such as dissolved oxygen and chlorophyll fluorescence. In particular, episodes of elevated turbidity (when suspended particle matter concentration >3,000 mg/l) have been detected by the network, together with episodes of declining values of salinity and dissolved oxygen. All these patterns are related to river discharge and tidal dynamics (spring/neap and high/low tide).     

Evaluation of remote‐sensing‐based rainfall products through predictive capability in hydrological runoff modelling

The emergence of regional and global satellite‐based rainfall products with high spatial and temporal resolution has opened up new large‐scale hydrological applications in data‐sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite‐based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub‐catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC‐FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC‐FEWS, produced good results with values of R²_NS between 0·84 and 0·87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products. Copyright © 2009 John Wiley & Sons, Ltd.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

Land cover mapping using time series HJ-1/CCD data

It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively.     

Something old,something new,something red,something blue*

Abstract

Hydrologists responsible for flood management need real-time data in order to manage imminent or ongoing floods. In this paper, innovative methods for accessing hydrological data and their spatial visualization are introduced. A multitude of relevant real-time, forecast and historical information is provided in a single, self-updating hydrological map information system. The system consists of a central database and a cartographic user interface and provides harmonized and filtered data in the form of interactive, customizable maps. Maps may also be cross-referenced with historical maps or may be animated for improved comprehension and decision making. Emphasis is placed on the development of the hydrological real-time database that manages large amounts of spatial, temporal and attributive data. The paper focuses on the cartographic user interface, its functionality and the resulting interactive hydrological maps.

Citation Lienert, C., Weingartner, R. &; Hurni, L. (2011) An interactive, web-based, real-time hydrological map information system. Hydrol. Sci. J. 56(1), 1–16     

Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, China

The integrated application of remote sensing, geographic information system and quantitative analytical modeling can provide scientific and effective methods for monitoring and studying urban heat island, based on land surface temperature (LST) retrieved from thermal infrared channel data of sensors. In this paper, LST is retrieved from Landsat TM6 and ETM + 6 data of Shanghai central city in 1989, 1997, 2000 and 2002, by using the mono-window algorithm. Based on the data, global and local spatial autocorrelation analysis, and geostatistical methods are adopted to quantitatively describe the characteristics of spatial heterogeneity and temporal evolution of land surface thermal landscape at different scales and periods in Shanghai central city, by utilizing exploratory spatial data analysis. Results show that LST field in Shanghai central city tends to fragmentize and complicate with the development of Shanghai, and its global spatial difference becomes greater gradually. The spatial variance pattern of the change of LST field from 1997 to 2002 indicates that the dynamic change of LST presents a tendency of increase in circularity. LST declines distinctly in the districts of Puxi and Pudong near and inside the inner ring road, while it rises obviously outside the central city and near the out ring road. The extrema of temporal change in LST field have a characteristic of spatial clustering. Besides, as the city of Shanghai expands in a circular pattern as a whole, the directional difference of dynamic change of urban surface thermal landscape exists but is not very obvious.     

我国湖泊水文学研究进展与展望

湖泊是地球表层水体的重要组成部分,在区域社会经济发展和生物多样性保护等方面发挥着不可替代的作用.气候变化和高强度的水资源开发利用等,导致湖泊物理、化学特性在时空格局上发生显著的变化,引起一系列的社会、环境、气候等响应.湖泊水文学研究湖泊水文要素及其时空变化特征、平衡关系与变化规律,在水文过程演变与归因解析、湖泊洪旱发生机理与调控、湖泊资源评估与可持续利用等方面,解决了众多理论和实践问题,为区域发展提供了强大支撑.本文评述了近50年来我国湖泊水文学的发展与研究进展,重点阐述湖泊水量平衡与水量变化、湖泊水动力与水文过程调蓄、湖泊极端水文事件成因、湖泊水文遥感反演等方面的研究进展,展望了湖泊水文学的未来发展趋势.     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

Sub-monthly gravity field recovery from simulated multi-GRACE mission type

Monthly solutions of the current GRACE mission are affected by the aliasing problem. In fact, sub-monthly temporal sampling may reduce the temporal aliasing errors but this will be done at the cost of reduced spatial sampling. Reducing the effects of temporal aliasing can be achieved by setting two pairs of satellites in different orbital planes. In this paper, we investigate the so-called Multi-GRACE constellation to improve temporal and spatial resolution for the GRACE-type mission without deteriorating accuracy. We investigate two scenarios: the Multi-GRACE ΔM that improves the temporal sampling only and the Multi-GRACE ΔΩ that improves the spatial sampling besides the temporal one in time span of only 12 days for the hydrological signal as a time-varying gravity field component. Our findings indicate that the hydrological signal can be submonthly recovered and the aliasing errors can be reduced as well by increasing temporal resolution (sub-month) via the Multi-GRACE ΔΩ constellations.     

Modelling the impacts of spatial heterogeneity in soil hydraulic properties on hydrological process in the upper reach of the Heihe River in the Qilian Mountains,Northwest China

Spatial heterogeneity of soil has great impacts on dynamic processes of the hydrological systems. However, it is challenging and expensive to obtain spatial distribution of soil hydraulic properties, which often requires extensive soil sampling and observations and intensive laboratory analyses, especially in high elevation, hard to access mountainous areas. This study evaluates the impacts of soil heterogeneity on hydrological process in a high elevation, topographically complex watershed in Northwest China. Two approaches were used to derive the spatial heterogeneity of soil properties in the study watershed: (1) the spatial clustering method, Full‐Order‐CLK was used to determine five soil heterogeneous clusters (configurations 97, 80, 60, 40 and 20) through large number of soil sampling and in situ observations, and (2) the average values of soil hydraulic properties for each soil type were derived from the coarse provincial soil data sets (Gansu Soil Handbook at 1 : 1 000 000 scale). Subsequently, Soil and Water Assessment Tool model was used to quantify the impact of the spatial heterogeneity of soil hydraulic properties on hydrological process in the study watershed. Results show the simulations by Soil and Water Assessment Tool with the spatially clustered soil hydraulic information from the field sampling data had much better representation of the soil heterogeneity and had more accurate performance than the model using the average soil property values for each soil type derived from the coarse soil data sets. Thus, incorporating detailed field sampling, soil heterogeneity data greatly improve performance in hydrological modelling. Copyright © 2015 John Wiley & Sons, Ltd.     

Intercomparison across scales between remotely-sensed land surface temperature and representative equilibrium temperature from a distributed energy water balance model

Abstract

Quantifying the reliability of distributed hydrological models is an important task in hydrology to understand their ability to estimate energy and water fluxes at the agricultural district scale as well the basin scale for water resources management in drought monitoring and flood forecasting. In this context, the paper presents an intercomparison of simulated representative equilibrium temperature (RET) derived from a distributed energy water balance model and remotely-sensed land surface temperature (LST) at spatial scales from the agricultural field to the river basin. The main objective of the study is to evaluate the use of LST retrieved from operational remote sensing data at different spatial and temporal resolutions for the internal validation of a distributed hydrological model to control its mass balance accuracy as a complementary method to traditional calibration with discharge measurements at control river cross-sections. Modelled and observed LST from different radiometric sensors located on the ground surface, on an aeroplane and a satellite are compared for a maize field in Landriano (Italy), the agricultural district of Barrax (Spain) and the Upper Po River basin (Italy). A good ability of the model in reproducing the observed LST values in terms of mean bias error, root mean square error, relative error and Nash-Sutcliffe index is shown.

Editor Z.W. Kundzewicz; Associate editor D. Gerten