Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy)

Abstract

Precipitation is often the most important input data in hydrological models when simulating streamflow. The Soil and Water Assessment Tool (SWAT), a widely used hydrological model, only makes use of data from one precipitation gauge station that is nearest to the centroid of each subbasin, which is eventually corrected using the elevation band method. This leads in general to inaccurate representation of subbasin precipitation input data, particularly in catchments with complex topography. To investigate the impact of different precipitation inputs on the SWAT model simulations in Alpine catchments, 13years (1998-2010) of daily precipitation data from four datasets including OP (Observed precipitation), IDW (Inverse Distance Weighting data), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and TRMM (Tropical Rainfall Measuring Mission) has been considered. Both model performances (comparing simulated and measured streamflow data at the catchment outlet) as well as parameter and prediction uncertainties have been quantified. For all three subbasins, the use of elevation bands is fundamental to match the water budget. Streamflow predictions obtained using IDW inputs are better than those obtained using the other datasets in terms of both model performance and prediction uncertainty. Models using the CHIRPS product as input provide satisfactory streamflow estimation, suggesting that this satellite product can be applied to this data-scarce Alpine region. Comparing the performance of SWAT models using different precipitation datasets is therefore important in data-scarce regions. This study has shown that, precipitation is the main source of uncertainty, and different precipitation datasets in SWAT models lead to different best estimate ranges for the calibrated parameters. This has important implications for the interpretation of the simulated hydrological processes.

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

“…The CHIRPS data resulted in 'satisfactory' simulations of the stream flow [26]. However, the TRMM data set was associated with 'unsatisfactory' model performance by Tuo et al [26]. In the current work, TRMM and CHIRPS data were ranked at 16th and 17th, respectively, indicating poor overall model performance relative to the other MAP methods tested.…”

“…When calibrated SWAT model performance was ranked, the IDW_2 method performed best overall (i.e., all time steps tested were considered) ( Table 8). The IDW_2 method has also been shown to work well in other locations with considerably different climate and physiographic characteristics compared to the study catchment [4,17,26]. For example, Tuo et al [26] showed that the IDW method was the most accurate method tested for SWAT model precipitation input on SWAT simulated stream flow in an alpine Aldige river basin of north-eastern Italy.…”

“…The IDW_2 method has also been shown to work well in other locations with considerably different climate and physiographic characteristics compared to the study catchment [4,17,26]. For example, Tuo et al [26] showed that the IDW method was the most accurate method tested for SWAT model precipitation input on SWAT simulated stream flow in an alpine Aldige river basin of north-eastern Italy. Szczesniak and Pinieski (2015) showed that IDW was more accurate than the centroid method in the Sulejów reservoir catchment in central Poland.…”

“…Tuo et al [26] tested the influence of satellite-based data sets (TRMM and CHIRPS) on SWAT simulated stream flow. The CHIRPS data resulted in 'satisfactory' simulations of the stream flow [26]. However, the TRMM data set was associated with 'unsatisfactory' model performance by Tuo et al [26].…”

Wet Spells and Associated Moisture Sources Anomalies across Danube River Basin

“…For instance, CHIRPS has been Water 2017, 9, 615 4 of 17 used to quantify the impact of decreasing precipitation trends and increasing temperatures trends in the Greater Horn in Africa [22], and it validated using the data from 21 ground stations in Northeast Brazil [23]. CHIRPS dataset has also been used it to obtain a comprehensive evaluation of eight high spatial resolution precipitation products in an Alpine catchment, the Adige Basin in Italy [24], and to validate a hydrological model to simulate stream flow in a complex topography [25]. Additionally, the same database was successfully used for the analysis of precipitation extremes over Cyprus [26] and Bhutan [27].…”