Precipitation interpolation, autocorrelation, and predicting spatiotemporal variation in runoff in data sparse regions: Application to Panama

Abstract

Study region: Panama faces seasonal floods and droughts, and rising freshwater demand for domestic consumption, hydropower, and the operation of the Panama Canal. A process-based hydrological model of the country would complement the existing national water security plan as a scenario planning tool. Study focus: In Panama, as in much of the Global South, sufficient observed data do not exist for all watersheds to calibrate complex hydrological models. Understanding and improving the performance of uncalibrated hydrological models could greatly expand their utility in such regions. In this study, we build and validate an uncalibrated Soil and Water Assessment Tool (SWAT) model for Panama. We extend the default precipitation submodel and demonstrate the importance of accounting for spatial autocorrelation patterns in precipitation inputs: we found large improvements over the default model, not only for monthly means (NSE = 0.88, from 0.69 for default SWAT), but especially for standard deviations (NSE = 0.59, from 0.27) and maxima (NSE = 0.51, from 0.21) of discharge across locations and months. New hydrological insights for region: We found a strong seasonal trend and regional differences in the spatial autocorrelation of rainfall, suggesting that this phenomenon should not be modeled statically. The resulting precipitation and hydrology models provide important baseline information for Panama, especially on variability and extremes, and could serve as a template for other regions with limited data.

Keywords

• Hydrological modeling
• Soil and water assessment tool
• Neotropical hydrology
• Spatial autocorrelation