SC.HAWQS: A User-Friendly Web-Based Decision Support System for Regional Water Resources Management Under a Changing Climate

Large-scale watershed modeling tools are essential for efficient water resources management. However, the setup and calibration of these models typically require substantial data resources and time commitments, posing challenges for non-experts. To address this issue, the South Carolina Hydrologic and Water Quality System (SC.HAWQS) was developed as a user-friendly, web-based decision support system, utilizing the Soil and Water Assessment Tool (SWAT) as its core engine. SC.HAWQS integrates the latest weather, soils, topography, and land use data to support hydrologic and water quality researchers as well as water resources policymakers in South Carolina, United States. Here, we conducted calibration and validation for six key watersheds in SC.HAWQS and employed this platform to investigate the long-term (1992–2020) regional water cycle dynamics and their responses to a warming climate. Seasonal variations of actual evapotranspiration (AET), precipitation (P), and discharge (Q) revealed significant contributions (54–90%) of baseflow to streamflow across these watersheds. We did not find statistically significant trends in AET, P, and Q, indicating overall stability in the water cycle. We demonstrated that SC.HAWQS effectively captured the dynamics of potential evapotranspiration (PET), AET, P, and Q using the Budyko curve. Furthermore, our analysis showed that groundwater flux was more sensitive to temperature changes than did surface water flux, with baseflow showing the highest sensitivity (3.7% per $$^\circ{\rm C}$$ ∘ C ), followed by lateral flow (1.3% per $$^\circ{\rm C}$$ ∘ C ) and surface runoff (1.1% per $$^\circ{\rm C}$$ ∘ C ). Our study highlights the sensitivity of baseflow to temperature increase, especially in regions where groundwater dynamics significantly influence the water balance. The SC.HAWQS platform could serve as a valuable tool for regional water resources management, offering accessible modeling capabilities for sustainable decision-making under a changing climate.