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

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.

Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

“…(meters above sea level) in the northern parts. The climate of the basin is mainly characterized by Mediterranean climate conditions with annual rainfall varying from 150 to 250 mm in the flat south and from 550 to 750 mm in the mountainous north [42,43].…”

“…In the middle and upper reaches, the coldest month of the year, January, experiences a mean temperature of −2 °C, and the warmest month, August, of 29 °C. In the lower regions of the KRB, the The climate of the basin is mainly characterized by Mediterranean climate conditions with annual rainfall varying from 150 to 250 mm in the flat south and from 550 to 750 mm in the mountainous north [42,43].…”

“…In this paper we describe a new application of the NARX neural network to better predict continuous rainfall series across the Karkheh river basin (KRB), Iran, which has been the focus of several studies of the authors over the last years (e.g., [42]). To this end, changes of relative AMSR-E satellite soil moisture and measured temperature data are considered as input data in NARX to estimate the rainfall.…”

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

“…A more straightforward type of RZSM modelling approach is the use of an exponential filter to predict RZSM from satellite data [31,32]. In this regard, one of the recent models is the Soil Moisture Analytical Relationship (SMAR), developed by Manfreda et al, 2014 and tested with satellite data [32][33][34][35].Despite the importance of water in arid and semi-arid regions, there are only a few studies regarding the evaluation and monitoring of environmental parameters in Iran [36][37][38][39][40]. In particular, a multiyear SSM dataset was extracted from SMOSL3, ECV-SM ERA-Interim and ERA-Interim/Land SSM products in 2015 over six sub regions of Iran with meteorological data characterized by different climate conditions [41].…”

“…Despite the importance of water in arid and semi-arid regions, there are only a few studies regarding the evaluation and monitoring of environmental parameters in Iran [36][37][38][39][40]. In particular, a multiyear SSM dataset was extracted from SMOSL3, ECV-SM ERA-Interim and ERA-Interim/Land SSM products in 2015 over six sub regions of Iran with meteorological data characterized by different climate conditions [41].…”