A multi-objective optimization approach for harnessing rainwater in changing climate
Yu Meng (Shanghai Institute of Technology)
Zhan Tian (Southern University of Science and Technology , Pengcheng Laboratory)
Dong Li Fan (Shanghai Institute of Technology)
F. van de Ven (Deltares, TU Delft - Water Resources)
Laixiang Sun (University of Maryland)
Qing Hua Ye (Deltares)
San Xiang Sun (Lanzhou Jiaotong University)
Jun Guo Liu (Southern University of Science and Technology )
Laura Nougues (Deltares)
Daan Rooze (Deltares)
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Abstract
As the world grapples with the profound impacts of climate change, water scarcity has become a pressing issue. However, there is a shortage of in-depth research on the trade-offs between water resource dependence and the economic, ecological, and social needs of arid and semi-arid regions like Lanzhou, China. Flower cultivation in Lanzhou relies heavily on the Yellow River, often overlooking the potential of natural rainfall. Here we first calibrated a water balance model through artificial precipitation experiments in a Soil and Water Conservation Demonstration Park in Lanzhou. We then developed a multi-objective optimization model to balance the cost-benefit considerations of various plausible measures across economic, ecological, and social dimensions in the searching for solutions that are more adaptable to climate change and local development needs. Model simulations show that the solutions we designed can effectively manage water-shortage days, significantly reduce Yellow River water extraction, and improve cost-effectiveness, meeting 66%–80% of water needs for flower cultivation in the studied park. The findings highlight the potential of rainwater collection and utilization solutions to mitigate water scarcity in arid and semi-arid cities, thereby enriching water resource management.
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