Evaluating the performance of streamflow simulated by an eco-hydrological model calibrated and validated with global land surface actual evapotranspiration from remote sensing at a catchment scale in West Africa

Abstract. Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r &approx; 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.

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Uncertainty reduction and parameters estimation of a~distributed hydrological model with ground and remote sensing data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-6215-2014 ◽

2014 ◽

Vol 11 (6) ◽

pp. 6215-6271

Author(s):

F. Silvestro ◽

S. Gabellani ◽

R. Rudari ◽

F. Delogu ◽

P. Laiolo ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Hydrological Model ◽

Hydrological Cycle ◽

Remote Sensing Data ◽

Hydrologic Model ◽

Parameters Estimation ◽

State Variables ◽

Test Bed ◽

Sensing Data

Abstract. During the last decade the opportunity and usefulness of using remote sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite based products often provide the advantage of observing hydrologic variables in a distributed way while offering a different view that can help to understand and model the hydrological cycle. Moreover, remote sensing data are fundamental in scarce data environments. The use of satellite derived DTM, which are globally available (e.g. from SRTM as used in this work), have become standard practice in hydrologic model implementation, but other types of satellite derived data are still underutilized. In this work, Meteosat Second Generation Land Surface Temperature (LST) estimates and Surface Soil Moisture (SSM) available from EUMETSAT H-SAF are used to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. This work aims at proving that satellite observations dramatically reduce uncertainties in parameters calibration by reducing their equifinality. Two parameter estimation strategies are implemented and tested: a multi-objective approach that includes ground observations and one solely based on remotely sensed data. Two Italian catchments are used as the test bed to verify the model capability in reproducing long-term (multi-year) simulations.

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The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products

Remote Sensing ◽

10.3390/rs5052436 ◽

2013 ◽

Vol 5 (5) ◽

pp. 2436-2450 ◽

Cited By ~ 42

Author(s):

Xiang Zhao ◽

Shunlin Liang ◽

Suhong Liu ◽

Wenping Yuan ◽

Zhiqiang Xiao ◽

...

Keyword(s):

Remote Sensing ◽

Data Processing ◽

Land Surface ◽

Processing System ◽

Remote Sensing Data ◽

Data Processing System ◽

Sensing Data ◽

Global Land

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Consistency between hydrological model, large aperture scintillometer and remote sensing based evapotranspiration estimates for a heterogeneous catchment

Hydrology and Earth System Sciences ◽

10.5194/hess-16-2095-2012 ◽

2012 ◽

Vol 16 (7) ◽

pp. 2095-2107 ◽

Cited By ~ 20

Author(s):

B. Samain ◽

G. W. H. Simons ◽

M. P. Voogt ◽

W. Defloor ◽

N.-J. Bink ◽

...

Keyword(s):

Remote Sensing ◽

Energy Balance ◽

Latent Heat ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Sensible Heat ◽

Catchment Scale ◽

Large Aperture

Abstract. The catchment averaged actual evapotranspiration rate is a hydrologic model variable that is difficult to quantify. Evapotranspiration rates – up till present – cannot be continuously observed at the catchment scale. The objective of this paper is to estimate the evapotranspiration rates (or its energy equivalent, the latent heat fluxes LE) for a heterogeneous catchment of 102.3 km2 in Belgium using three fundamentally different algorithms. One possible manner to observe this variable could be the continuous measurement of sensible heat fluxes (H) across large distances (in the order of kilometers) using a large aperture scintillometer (LAS), and converting these observations into evapotranspiration rates. Latent heat fluxes are obtained through the energy balance equation using a series of sensible heat fluxes measured with a LAS over a distance of 9.5 km in the catchment, and point measurements of net radiation (Rn) and ground heat flux (G) upscaled to catchment average through the use of TOPLATS, a physically based land surface model. The resulting LE-values are then compared to results from the remote sensing based surface energy balance algorithm ETLook and the land surface model. Firstly, the performance of ETLook for the energy balance terms has been assessed at the point scale and at the catchment scale. Secondly, consistency between daily evapotranspiration rates from ETLook, TOPLATS and LAS is shown.

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Observed Land–Atmosphere Coupling from Satellite Remote Sensing and Reanalysis

Journal of Hydrometeorology ◽

10.1175/2011jhm1380.1 ◽

2011 ◽

Vol 12 (6) ◽

pp. 1221-1254 ◽

Cited By ~ 58

Author(s):

Craig R. Ferguson ◽

Eric F. Wood

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Satellite Remote Sensing ◽

Deep Convection ◽

Weather Prediction ◽

Remote Sensing Data ◽

Feedback Signal ◽

Atmospheric Conditions ◽

Global Land

Abstract The lack of observational data for use in evaluating the realism of model-based land–atmosphere feedback signal and strength has been deemed a major obstacle to future improvements to seasonal weather prediction by the Global Land–Atmosphere Coupling Experiment (GLACE). To address this need, a 7-yr (2002–09) satellite remote sensing data record is exploited to produce for the first time global maps of predominant coupling signals. Specifically, a previously implemented convective triggering potential (CTP)–humidity index (HI) framework for describing atmospheric controls on soil moisture–rainfall feedbacks is revisited and generalized for global application using CTP and HI from the Atmospheric Infrared Sounder (AIRS), soil moisture from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E), and the U.S. Climate Prediction Center (CPC) merged satellite rainfall product (CMORPH). Based on observations taken during an AMSR-E-derived convective rainfall season, the global land area is categorized into four convective regimes: 1) those with atmospheric conditions favoring deep convection over wet soils, 2) those with atmospheric conditions favoring deep convection over dry soils, 3) those with atmospheric conditions that suppress convection over any land surface, and 4) those with atmospheric conditions that support convection over any land surface. Classification maps are produced using both the original and modified frameworks, and later contrasted with similarly derived maps using inputs from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications (MERRA). Both AIRS and MERRA datasets of CTP and HI are validated using radiosonde observations. The combinations of methods and data sources employed in this study enable evaluation of not only the sensitivity of the classification schemes themselves to their inputs, but also the uncertainty in the resultant classification maps. The findings are summarized for 20 climatic regions and three GLACE coupling hot spots, as well as zonally and globally. Globally, of the four-class scheme, regions for which convection is favored over wet and dry soils accounted for the greatest and least extent, respectively. Despite vast differences among the maps, many geographically large regions of concurrence exist. Through its ability to compensate for the latitudinally varying CTP–HI–rainfall tendency characteristics observed in this study, the revised classification framework overcomes limitations of the original framework. By identifying regions where coupling persists using satellite remote sensing this study provides the first observationally based guidance for future spatially and temporally focused studies of land–atmosphere interactions. Joint distributions of CTP and HI and soil moisture, rainfall occurrence, and depth demonstrate the relevance of CTP and HI in coupling studies and their potential value in future model evaluation, rainfall forecast, and/or hydrologic consistency applications.

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The Use of Remote Sensing-Based ET Estimates to Improve Global Hydrological Simulations in the Community Land Model Version 5.0

Remote Sensing ◽

10.3390/rs13214460 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4460

Author(s):

Dayang Wang ◽

Dagang Wang ◽

Chongxun Mo

Keyword(s):

Remote Sensing ◽

Water Supply ◽

Bias Correction ◽

Land Surface ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Correction Effect ◽

Surface Models ◽

Global Land

Terrestrial evapotranspiration (ET) is a critical component of water and energy cycles, and improving global land evapotranspiration is one of the challenging works in the development of land surface models (LSMs). In this study, we apply a bias correction approach into the Community Land Model version 5.0 (CLM5) globally by utilizing the remote sensing-based ET dataset. Results reveal that the correction approach can alleviate both overestimation and underestimation of ET by CLM5 over the globe. The adjustment to overestimation is generally effective, whereas the effectiveness for underestimation is determined by the ET regime, namely water-limited or energy-limited. In the areas with abundant precipitation, the underestimation is effectively corrected by increasing ET without the water supply limit. In areas with rare precipitation, however, increasing ET is limited by water supply, which leads to an undesirable correction effect. Compared with the ET simulated by CLM5, the bias correction approach can reduce the global-averaged relative bias (RB) and the root mean square error (RMSE) by 51.8% and 65.9% against Global Land Evaporation Amsterdam Model (GLEAM) ET data, respectively. Meanwhile, the correlation coefficient (CC) can also be improved from 0.93 to 0.98. Continentally, the most substantial ET improvement occurs in Asia, with the RB and RMSE decreased by 69.7% (from 7.04% to 2.14%) and 70.2% (from 0.312 mm day−1 to 0.093 mm day−1, equivalent to from 114 mm year−1 to 34 mm year−1), and the CC increased from 0.92 to 0.99, respectively. Consequently, benefiting from the improvement of ET, the simulations of runoff and soil moisture are also improved over the globe and each of the six continents, and the improvement varies with region. This study demonstrates that the use of satellite-based ET products is beneficial to hydrological simulations in land surface models over the globe.

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Consistency between hydrological model, large aperture scintillometer and remote sensing based evapotranspiration estimates for a heterogeneous catchment

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-10863-2011 ◽

2011 ◽

Vol 8 (6) ◽

pp. 10863-10894 ◽

Cited By ~ 3

Author(s):

B. Samain ◽

G. W. H. Simons ◽

M. P. Voogt ◽

W. Defloor ◽

N.-J. Bink ◽

...

Keyword(s):

Remote Sensing ◽

Energy Balance ◽

Latent Heat ◽

Land Surface ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Model ◽

Sensible Heat ◽

Catchment Scale ◽

Daily Time

Abstract. The catchment averaged actual evapotranspiration rate is a hydrologic model variable that is difficult to quantify. Evapotranspiration rates can – up till present – not be continuously observed at the catchment scale. The objective of this paper is to estimate the evapotranspiration rates (or its energy equivalent, the latent heat fluxes LE) for a heterogeneous catchment of 102.3 km2 in Belgium using three fundamentally different algorithms. One possible manner to observe this variable could be the continuous measurement of sensible heat fluxes (H) across large distances (in the order of kilometers) using a Large Aperture Scintillometer (LAS), and inverting these observations into evapotranspiration rates. Latent heat fluxes are obtained through the energy balance equation using a series of sensible heat fluxes (H) measured with a LAS over a distance of 9.5 km in the catchment, and point measurements of net radiation (Rn) and ground heat flux (G) upscaled to catchment average through the use of TOPLATS, a physically based land surface model. The resulting LE-values are then validated by comparing them to results from the remote sensing based surface energy balance algorithm ETLook and the land surface model. Firstly, it is demonstrated that ETLook is able to estimate the energy balance terms for daily time steps at the point scale and at the catchment scale. Secondly, consistency between daily evapotranspiration rates from ETLook, TOPLATS and LAS is shown. As such, ETLook provides the opportunity to estimate continuous series of the energy balance terms of a large area for daily time steps and can thus e.g. be used as a validation tool for LAS-measurements, whereas LAS is able to estimate the latent heat fluxes (evapotranspiration rates) for a large and heterogeneous catchment at an hourly time step which can be used for the forcing or validation of hydrologic models.

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The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Water ◽

10.3390/w11081615 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1615 ◽

Cited By ~ 18

Author(s):

Dejuan Jiang ◽

Kun Wang

Keyword(s):

Remote Sensing ◽

Human Activities ◽

Land Surface ◽

Hydrological Modeling ◽

Hydrological Model ◽

Remotely Sensed ◽

Machine Learning Techniques ◽

Remotely Sensed Data ◽

Coupled Models

A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling.

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Using remote sensing evapotranspiration solely calibrating hydrology model for predicting runoff time series in ungauged regions

10.5194/egusphere-egu2020-12227 ◽

2020 ◽

Author(s):

Yongqiang Zhang

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Hydrological Model ◽

International Association ◽

Remote Sensing Data ◽

Water Source ◽

Annual Runoff ◽

Runoff Prediction ◽

Predictions In Ungauged Basins ◽

Parameter Values

<p>It is important yet challenging to predict runoff in data sparse regions or ungauged regions, majority of which belong to headwater catchments that are normally the major water source for middle and lower river reaches. There are numerous studies carried out since the launch of the Predictions in Ungauged Basins (PUB) initiative by the International Association of Hydrological Sciences (IAHS) in 2003. Most runoff prediction studies rely on modelling approaches via two steps. The first step is to calibrate the hydrological model against observed streamflow at the gauged catchments. The second step is regionalization in which the set of calibrated parameter values from a suitable donor catchment is used for predicting runoff in a targeted ungauged catchment. The major challenge of this approach is that when the gauged catchments are sparsely distributed or little available, it is hard to get sensible regionalization results. This study develops a new approach to calibrate a hydrological model purely against remote sensed actual evapotranspiration data obtained from 8-day and 500 m resolution PML-V2 products and the calibrated parameters can be directly used for runoff prediction across global land surface. This approach has been successfully used for predicting daily, monthly and annual runoff in Australia and southeastern Tibetan Plateau. This is an exciting research domain for hydrologists to pursue since remote sensing data is accumulated in a fast-increasing rate, and will provide researchers an unprecedent opportunity.</p>

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