Evaluation of gridded meteorological datasets and their potential hydrological application to a humid area with scarce data for Pirapama River basin, northeastern Brazil

Jussara Freire De Souza Viana,Bernardo Barbosa Da Silva,Celso Augusto Guimarães Santos,Richarde Marques Da Silva,Diego Cezar Dos Santos Araujo,Clara Gadelha Tavares,Raghavan Srinivasan,Suzana Maria Gico Lima Montenegro

doi:10.1007/s00704-021-03628-7

Abstract

This work evaluated the simulation of streamflow using observed and estimated gridded meteorological datasets and the Soil and Water Assessment Tool (SWAT) model for a humid area with scarce data in northeastern Brazil. The coefficient of determination (R2), Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), normalized root mean square error (NRMSE), and percent bias (PBIAS) were used to assess the SWAT results yielded by estimated and observed rainfall data. The hydrological modeling data from three streamflow stations were used (2000 to 2006 for calibration and 2007 to 2010 for validation). The results show that at daily scale, the estimated rainfall data show a poor agreement (R2 ranging from 0.22 to 0.04) with the observed rainfall but good agreement at monthly (R2 = 0.85) and annual scales (R2 = 0.80). The results showed that estimated accumulated precipitation overestimated the observed data. The results showed that R2 ranged from 0.51 to 0.55 at monthly scale and 0.44-0.52 at annual scale. However, the global data can represent well the variability of rainfall within the region. The results indicated a good correlation in the seasonal variability (R2 ranged from 0.72 to 0.60). The modeling results using monthly TRMM data and observed rainfall data showed good values of NS and R2 during calibration and validation, but PBIAS was unsatisfactory for the three streamflow gauges. The streamflow estimates from the SWAT model using data from the TRMM satellite showed that such data are capable of generating satisfactory results after calibration, although measured rainfall data presented better results; the data could support areas with scarce rainfall data and be applied to other river basins, for example, to analyze the hydrological potential of other basins in the coastal region of northeastern Brazil. Over the past three decades, considerable advances have been made in remote sensing with environmental satellites, increasing the amount of information available, including rainfall estimates. In this context, the use of TRMM data to estimate rainfall has ultimately been shown to be an interesting alternative for areas with scarce rainfall data.

Highlights

Understanding the spatial variability of rainfall in densely populated regions dependent on the supply of water for agriculture and human consumption is essential and indispensable for many sectors of the economy (Zhu et al, 2018; Cunha et al, 2021). Acquiring this knowledge is important in regions both with scarce data and with historic problems involving access to adequate water in the Recife Metropolitan Region (RMR), located in the coastal zone of northeastern Brazil (Braga et al, 2013)
The results presented in this research, in relation to hydrological modeling, are similar to the results obtained by Santos et al (2014), who performed simulations with the Soil and Water Assessment Tool (SWAT) model for the Tapacurá River Basin in northeastern Brazil; the authors obtained good results in both calibration and validation, with Nash-Sutcliffe efficiency (NS) and R2 values of 0.78 and 0.79, respectively, for the calibration period and 0.85 and 0.86, respectively, for the validation period
The precipitation product derived from Tropical Rainfall Measuring Mission (TRMM) 3B42 showed poor correlation with the gaugemeasured precipitation

Summary

Introduction

Understanding the spatial variability of rainfall in densely populated regions dependent on the supply of water for agriculture and human consumption is essential and indispensable for many sectors of the economy (Zhu et al, 2018; Cunha et al, 2021). Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are datasets at global or quasi-global scales. These datasets have been available in the last few years (Tapiador et al, 2012; Ferreira da Silva et al, 2020; Santos et al, 2021), and several studies have been carried out using open access meteorological data for the flow simulation. The majority of such studies focused only on rainfall data

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Conclusion