Abstract
Abstract. Runoffs from hydrologic models are often used in flood models, among other applications. These runoffs are converted from rainfall, signifying the importance of weather data accuracy. A common challenge for modelers is local weather data sparsity in most watersheds. Global weather datasets are often used as alternative. This study investigates the statistical significance and accuracy between using local weather data for hydrologic models and using the Climate Forecast System Reanalysis (CFSR), a global weather dataset. The Soil and Water Assessment Tool (SWAT) was used to compare the two weather data inputs in terms of generated discharges. Both long-term and event-based results were investigated to compare the models against absolute discharge values. The basin’s average total annual rainfall from the CFSR-based model (4062 mm) was around 1.5 times the local weather-based model (2683 mm). These basin precipitations yielded annual average flows of 53.4 cms and 26.7 cms for CFSR-based and local weather-based models, respectively. For the event-based scenario, the dates Typhoon Ketsana passed through the Philippine Area of Responsibility were checked. CFSR only read a spatially averaged maximum daily rainfall of 18.8 mm while the local gauges recorded 157.2 mm. Calibration and validation of the models were done using the observed discharges in Sto. Niño Station. The calibration of local weather-based model yielded satisfactory results for the Nash-Sutcliffe Efficiency (NSE), percent of bias (PBIAS), and ratio of the RMSE to the standard deviation of measured data (RSR). Meanwhile, the calibration of CFSR model yielded unsatisfactory values for all three parameters.
Highlights
Hydrologic models, in the form of numerical modelling, are useful in a number of applications. Results of such models can subsequently be used as input to hydrodynamic, hydraulic, and even water quality models
This sparsity and the huge number of missing data in the available local weather datasets render hydrologic models using them as data input to be less accurate
This study focuses on the global weather dataset of Climate Forecast System Reanalysis (CFSR), which has recently been an alternative input to hydrologic models for data-sparse regions (Bui et al, 2021)
Summary
Hydrologic models, in the form of numerical modelling, are useful in a number of applications. Km, there are only 14 weather stations that contain data of more than 10 years (i.e. 13 – 44 years of weather data, inclusive of gaps or missing data). Almost half of these stations are located in Marikina River Basin, leaving some of the 24 subbasins to have no weather station at all. This sparsity and the huge number of missing data in the available local weather datasets render hydrologic models using them as data input to be less accurate. There is a reactive instead of a proactive approach to monitoring these weather stations
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