Exploring Multi-Sector Impacts of Uncertain Future Water Availability
Access to water is vital for sustaining life and producing food and energy. Predicting future water availability is challenging due to uncertain climate and social changes that will impact both the supply and demand of water, and the inherent randomness of the hydrologic cycle. This study introduces a method that generates many realizations of the water supply that can be used to explore how uncertainty in future water availability impacts different sectors of the economy. This modeling approach allows the identification of regions and sectors that are most strongly affected by potential future variability in water availability.
This research addresses the important challenge of understanding how uncertainty in future water supply may impact water and land use around the world. We present a novel method to develop many simulations of renewable water supply that properly capture extremes (drought and abundance), that are important for water management. This study is the first to examine the multi-sector impacts of future global hydrologic uncertainty by simulating many future water scenarios using a human-environmental systems model. This work enhances our understanding of how water supply changes may affect various regions and sectors of the economy.
Our research explores the multi-sector impacts of uncertainty in future global water supply. We develop a computationally efficient stochastic watershed model that can be used to generate many plausible time series of water supply. We simulate an ensemble of future water supply scenarios using the Global Change Analysis Model (GCAM), allowing us to explore the multisectoral impacts of hydrologic variability on water and agricultural resources.
Our findings reveal that in regions such as the Indus River basin, Iran, and Northwest Mexico Coast, variability in renewable water supply significantly affects water withdrawals and irrigated cropland allocation, particularly as groundwater resources are depleted. We also demonstrate the potential for using our framework to explore the global consequences of local hydrologic extremes, which can offer valuable insights for infrastructure planning and resource management in water-stressed regions. This work highlights the importance of integrating stochastic modeling with multisector dynamics to navigate the uncertainties of future water availability.