Abstract
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.
Highlights
Reliable prediction of runoff in large catchments has been a subject of interest in hydrologic sciences for some time and is significant for sustainable management of water resources, design of water infrastructure, and flood risk management [1,2,3]
The application of Soil Water Assessment Tool (SWAT) model for estimating runoff in other river basins of Iran demonstrates the importance of river discharge monitoring in Iran, which is mostly located in arid and semi-arid regions of the middle east
The estimation of river discharge in poorly gauged basin is fundamental for flood risk mitigation and water resources management
Summary
Reliable prediction of runoff in large catchments has been a subject of interest in hydrologic sciences for some time and is significant for sustainable management of water resources, design of water infrastructure, and flood risk management [1,2,3]. Understanding the complex relationships between rainfall and runoff processes is essential to accurately predicting surface runoff [4] This can be achieved by hydrological modeling which, besides simulating surface runoff, aids in understanding, predicting, and managing water resources and modeling impacts of climate and land use changes on the surface water balance [5]. The Semi-distributed Soil Water Assessment Tool (SWAT) hydrological model [6] is one of the most useful tools for simulating runoff, sediment and water quality of agricultural watersheds over the last decades [7]. The performance of hydrological models in predicting streamflow relies heavily on the quality and spatial distribution of the input rainfall observations [10,11,12]. Rain gauges as reference instruments provide accurate measurements of rainfall, because of the variability of rainfall in time and space, they do not often provide adequate spatial representation of rainfall, especially in poorly gauged basins [13]
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