A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model

Yeganantham Dhanesh,Chengcheng Fei,Tássia Mattos Brighenti,Raghavan Srinivasan,Essayas Ayana,V M Bindhu,Javier Senent-Aparicio,P S Smitha

doi:10.3390/rs12183088

Abstract

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.

Highlights

Hydrological models have become efficient tools to understand problems related to water resources and obtain information about the water cycle in a given study area
And monthly precipitation estimates from Climate Forecast System Reanalysis (CFSR) and CHIRPS precipitation products were compared against the rain gauge data
CHIRPS exhibited the best overall performance for the period 1988–2013. This is especially true for a monthly scale where the values of CC, root mean squared error (RMSE), Mean error (ME), and BIAS indicated that CHIRPS data is in closest agreement with the observed data

Summary

Introduction

Hydrological models have become efficient tools to understand problems related to water resources and obtain information about the water cycle in a given study area. Among the various components of the water cycle, rainfall plays a significant role and forms an indispensable element that constitutes the input dataset for hydrological models. Rainfall data, which represent spatio-temporal variability, is an appanage to the distributed and semi-distributed hydrological models. Sparsely distributed rain-gauge networks fail to capture the spatio-temporal variability in rainfall. There are situations where reliable datasets are available, but they are not available in the public domain. In this context, satellite-derived estimates of rainfall data have proven to be effective in capturing the spatial heterogeneity in rainfall to a significant extent [1,2,3]

Objectives

Methods

Results