A watershed rainfall data recovery approach with application to distributed hydrological models

2011 ◽  
Vol 26 (13) ◽  
pp. 1937-1948 ◽  
Author(s):  
Jinggang Chu ◽  
Chi Zhang ◽  
Yilun Wang ◽  
Huicheng Zhou ◽  
Christine A. Shoemaker
Keyword(s):  
Rainfall Data ◽  
Data Recovery ◽  
Hydrological Models ◽  
Distributed Hydrological Models ◽  
Recovery Approach

Split-parameter structure for the automatic calibration of distributed hydrological models

2007 ◽  
Vol 332 (1-2) ◽  
pp. 226-240 ◽  
Author(s):  
Félix Francés ◽  
Jaime Ignacio Vélez ◽  
Jorge Julián Vélez
Keyword(s):  
Automatic Calibration ◽  
Hydrological Models ◽  
Split Parameter ◽  
Distributed Hydrological Models

scholarly journals Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 594 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch ◽  
Luca Brocca
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

scholarly journals Model Uncertainty Analysis Methods for Semi-Arid Watersheds with Different Characteristics: A Comparative SWAT Case Study

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1177 ◽  
Author(s):  
Lufang Zhang ◽  
Baolin Xue ◽  
Yuhui Yan ◽  
Guoqiang Wang ◽  
Wenchao Sun ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
River Basin ◽  
Model Uncertainty ◽  
Assessment Tool ◽  
Rapid Development ◽  
Swat Model ◽  
Observation Data ◽  
Hydrological Models ◽  
Distributed Hydrological Models ◽  
Different Characteristics

Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.

Integration of earth observation data in distributed hydrological models: the Senegal River basin

2003 ◽  
Vol 29 (6) ◽  
pp. 701-710 ◽  
Author(s):  
Inge Sandholt ◽  
Jens Andersen ◽  
Gorm Dybkjær ◽  
Lotte Nyborg ◽  
Medou Lô ◽  
...  
Keyword(s):  
River Basin ◽  
Earth Observation ◽  
Observation Data ◽  
Hydrological Models ◽  
Senegal River ◽  
Earth Observation Data ◽  
Distributed Hydrological Models ◽  
Senegal River Basin

scholarly journals Exploring the Use of the Three Rainfall Remote Sensing Products for Flood Prediction in the Brahmaputra Basin

10.29007/h6z1 ◽  
2018 ◽  
Author(s):  
Maurizio Mazzoleni ◽  
Biswa Bhattacharya ◽  
Miguel Angel Laverde Barajas ◽  
Dimitri Solomatine
Keyword(s):  
The Other ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Satisfactory Model ◽  
Fusion Methods ◽  
Brahmaputra Basin ◽  
Distributed Hydrological Models ◽  
Gauged Basins ◽  
Rainfall Estimates ◽  
Accurate Quantification

An important aspect in hydrological modelling is the accurate quantification and prediction of rainfall. In ungauged or poorly gauged basins ground data is sparse and often is complemented by rainfall satellite products, which brings additional uncertainties. The main objective of this research is to assess performance of distributed hydrological models using the remotely sensed rainfall estimates as forcings for the model. The model, based is based on the conceptual HBV-96 model and the PCRaster framework, is implemented for the Brahmaputra basin. Three different remote sensed datasets of precipitation (MSWEP, TMPA and PERSIANN-CDR) are used. Simple fusion methods are used to combine models results generate by the dataset of precipitation. The preliminary results of this study show that better model results are achieved merging the output results. Using MSWEP and TMPA as the forcing data provides satisfactory model results. On the other hand, use of PERSIANN-CDR leads to better prediction of flow peaks but overestimations of the hydrographs’ falling limbs.

scholarly journals Uncertainties in land use data

2006 ◽  
Vol 3 (6) ◽  
pp. 3439-3472 ◽  
Author(s):  
G. Castilla ◽  
G. J. Hay
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Distribution ◽  
Conventional Method ◽  
Confusion Matrix ◽  
Alternative Methods ◽  
Hydrological Models ◽  
Basic Requirement ◽  
Economic Role ◽  
Distributed Hydrological Models

Abstract. This paper deals with the description and assessment of uncertainties in gridded land use data derived from Remote Sensing observations, in the context of hydrological studies. Land use is a categorical regionalised variable returning the main socio-economic role each location has, where the role is inferred from the pattern of occupation of land. There are two main uncertainties surrounding land use data, positional and categorical. This paper focuses on the second one, as the first one has in general less serious implications and is easier to tackle. The conventional method used to asess categorical uncertainty, the confusion matrix, is criticised in depth, the main critique being its inability to inform on a basic requirement to propagate uncertainty through distributed hydrological models, namely the spatial distribution of errors. Some existing alternative methods are reported, and finally the need for metadata is stressed as a more reliable means to assess the quality, and hence the uncertainty, of these data.

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