Modeling seasonal water yield for landscape management: Applications in Peru and Myanmar. (15th September 2020) - Hamel, Perrine; Valencia, Jefferson; Schmitt, Rafael; Shrestha, Manish; Piman, Thanapon; Sharp, Richard P.; Francesconi, Wendy; Guswa, Andrew J.
Abstract: A common objective of watershed management programs is to secure water supply, especially during the dry season. To develop such programs in contexts of low data and resource availability, program managers need tools to understand the effect of landscape management on the seasonal water balance. However, the performance of simple, parsimonious models is poorly understood. Here, we examine the behavior of a geospatial tool, developed to map monthly water budgets and baseflow contributions and forming part of the InVEST (integrated valuation of ecosystem services and trade-offs) software suite. The model uses monthly climate, topography, and land-use data to compute spatial indices of groundwater recharge, baseflow, and quickflow. We illustrate the model application in two large basins in Peru and Myanmar, where we compare results with observed data and alternative hydrologic models. We show that the spatial distribution of baseflow contributions correlated well with an established model in the Peruvian basin (r 2  = 0.81 at the parcel scale). In Myanmar, the model shows an overall satisfactory performance for representing month to month variation (Nash-Sutcliffe-Efficiency 0.6–0.8); however, errors are scale dependent highlighting limitations in representing processes in large basins. Our study highlights modeling challenges, in particular trade-offs between model complexity and accuracy, and illustrates the role that parsimonious models can play to support watershed management programs. Highlights: We examine the InVEST-SWY model, a geospatial tool quantifying the seasonal water balance. We apply the tool in the context of watershed management in Peru and Myanmar. The model compares with a more complex model and matches observations. We highlight the strengths, weaknesses and implications for further research.