Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning

Area of Science:

• Environmental science
• Hydrology
• Ecosystem management

Background:

• Semi-arid regions face increasing water conservation (WC) stress due to climate change.
• WC dynamics involve complex interactions between vegetation, soil, and topography.
• Existing research methods struggle with the nonlinear nature of WC.

Purpose of the Study:

• To develop a novel, integrated approach for analyzing regional water conservation patterns and driving mechanisms.
• To evaluate water conservation characteristics and changes in Xiong'an New Area from 2000-2020.
• To identify key factors influencing water conservation and propose targeted management strategies.

Main Methods:

• Utilized InVEST modeling to assess water conservation depth and spatial distribution.
• Employed spatiotemporal transfer analysis to track Water Conservation Reserves (WCR) and land use changes.
• Applied deep learning techniques to quantify data complexity and identify critical influencing factors.

Main Results:

• Water conservation depth in Xiong'an New Area increased by an average of 74% with an inverted "V" spatial pattern.
• Water Conservation Reserves (WCR) showed a primary shift from lowest to lower WCR areas.
• Potential areas for WCR enhancement were identified, concentrated in the northern region.
• Deep learning highlighted land use, precipitation, and drought as critical drivers of WC.

Conclusions:

• The integrated approach effectively reveals complex spatial and temporal WC patterns and their drivers.
• Personalized WC zones and management strategies can be developed based on these findings.
• This research offers valuable insights for managing water resources in semi-arid regions under climate change.