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Xevi, Emmanuel; Christiaens, Kim; Espino, A.; Sewnandan, W.; Mallants, Dirk; Sørensen, Henrik Refstrup; Feyen, Jan

doi:10.1023/a:1007977521604

Cited by 72 publications

(11 citation statements)

References 20 publications

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“…This was the case for the rainfall and snow cover in the upper mountainous Tarim basin regions. This study confirmed the conclusions made by several authors (Kite and Droogers, 2000;Biftu and Gan, 2001;Droogers and Kite, 2002;Stisen et al, 2008;Immerzeel et al, 2009; among others) that RS data are useful as input data source and also as additional reference data to evaluate the model performance (Refsgaard, 1997;Xevi et al, 1997). Despite a limited decrease in the model efficiency, model results confirm that the RS data are qualified to replace the conventional SB data.…”

Section: Conclusion and Discussionsupporting

confidence: 88%

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

Liu

Willems

Feng

et al. 2011

Hydrological Processes

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Abstract:The ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station-based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS-based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data.

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Section: Conclusion and Discussionsupporting

confidence: 88%

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

Liu

Willems

Feng

et al. 2011

Hydrological Processes

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“…It is plausible to analyze and compare the usefulness of PTFs by using them as input information in Earth system models and evaluating the Earth system model performance rather than just PTF performance. Such "functional evaluation" can clearly quantify the utility and value of the PTFs (Vereecken et al, 1992;Xevi et al, 1997). Finally, PTFs form no goal in themselves; their function is in estimating functional soil properties that users are interested in such as water supply capacity and leaching of chemicals.…”

Section: Random Forestmentioning

confidence: 99%

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Looy

Bouma

Herbst

et al. 2017

Reviews of Geophysics

397

297

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Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.

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“…Some physics-based hydrological models, such as SWAT [7][8][9], TOPMODEL/BTOP-MC [10,11], and MIKE SHE [12,13], have already been widely used to simulate the rainfall-runoff relationship for runoff prediction because of the definite and explainable mechanisms and the theoretically high accuracy of the modelling results, in addition to the user-friendly interfaces of the modelling software. However, the above-mentioned physics-based models usually require the observed rainfall as the input and the observed runoff data for calibration/validation and some additional data, like the digital elevation model (DEM), soil types, and land use information.…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Untitled

Cited by 72 publications

References 20 publications

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

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