Related Experiment Video
Updated: Nov 8, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Climate-informed hydrologic modeling and policy typology to guide managed aquifer recharge
Xiaogang He1,2, Benjamin P Bryant2, Tara Moran2
1Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore. hexg@nus.edu.sg hexg@stanford.edu.
Abstract:
Harvesting floodwaters to recharge depleted groundwater aquifers can simultaneously reduce flood and drought risks and enhance groundwater sustainability. However, deployment of this multibeneficial adaptation option is fundamentally constrained by how much water is available for recharge (WAFR) at present and under future climate change. Here, we develop a climate-informed and policy-relevant framework to quantify WAFR, its uncertainty, and associated policy actions. Despite robust and widespread increases in future projected WAFR in our case study of California (for 56/80% of subbasins in 2070-2099 under RCP4.5/RCP8.5), strong nonlinear interactions between diversion infrastructure and policy uncertainties constrain how much WAFR can be captured. To tap future elevated recharge potential through infrastructure expansion under deep uncertainties, we outline a novel robustness-based policy typology to identify priority areas of investment needs. Our WAFR analysis can inform effective investment decisions to adapt to future climate-fueled drought and flood risk over depleted aquifers, in California and beyond.
More Related Videos
11:53Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
05:04Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management
Published on: July 14, 2023
Related Concept Videos
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Typical Model Studies
Design Example: Design of an Irrigation Channel
Modeling and Similitude
Adaptations that Reduce Water Loss
Precipitation Processes