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This study demonstrates the critical role of precipitation intensity in groundwater recharge generation and terrestrial water storage (TWS) change. We conducted two experiments driven by precipitation products with close annual totals but distinct intensity in Arizona, using the Noah‐MP model with advanced soil hydrology. The experiment with higher precipitation intensity (EXPHI) produces an annual groundwater recharge of 6.91 mm/year in Arizona during 2001–2020, ∼15 times that of the experiment with lower precipitation intensity (EXPLI). Correspondingly, EXPLI produces a declining groundwater storage (GWS) trend of −${-}$ 0.51 mm/month, nearly triple that of EXPHI. GWS change dominates the TWS trend. EXPLI shows a declining TWS trend of −${-}$ 0.57 mm/month, nearly twice that of EXPHI. Higher precipitation intensity reduces evapotranspiration and enhances infiltration and percolation, allowing more precipitation to recharge groundwater. This study underscores the need to ensure the accuracy of precipitation intensity in hydrological modeling for reliable water resources assessment and projection.

Groundwater sustainability in Central and Southern Arizona is threatened by prolonged droughts, rising temperatures, reduced surface water supplies, and groundwater overdraft. Recent studies indicate accelerating declines in regional groundwater storage. While the anthropogenic drivers of these declines are relatively well understood, the role of natural hydroclimatic variability in groundwater storage changes has received less attention. In this study, we utilize NASA’s Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On (GRACE-FO), together with NASA’s Western Land Data Assimilation System (WLDAS) to evaluate the hydroclimatic controls on Groundwater Storage Anomalies (GWSA) variability in Central and Southern Arizona. We quantify long-term standardized trends, subbasin-scale correlations, and dominant modes of natural variability using Principal Component Analysis (PCA) from 2004 to 2021. Principal components are then used to group groundwater subbasins using k-means clustering, an unsupervised machine learning algorithm, and results are compared with in situ measurements and local management strategies. Our results show substantial spatial heterogeneity across the region. This heterogeneity is characterized by recharge-responsive northern and central subbasins, which are influenced primarily by precipitation and subsurface runoff, and loss-dominated southern subbasins, associated with weaker natural recharge and stronger atmospheric demand. PCA shows that natural hydroclimatic variability is statistically aligned with approximately 16% of the inter-subbasin spatial variance in GRACE/FO groundwater storage trends. Within this natural component, total evapotranspiration (~ 29%), precipitation (~ 23%), and subsurface runoff (~ 20%) represent the largest contributors to the explained variance. The remaining spatial variance may reflect anthropogenic influences, geologic heterogeneity, and residual observational or modeling uncertainties. Our diagnostic framework identifies groundwater subbasin clusters driven by shared hydroclimatic modes. It has the potential to serve as a transferable tool for recharge feasibility analysis, groundwater sustainability assessments, and future local groundwater planning in the Lower Colorado River Basin.

This dataset provides high-resolution (60 m) global irrigation maps to support water resource and agricultural management. It identifies the likely irrigation status (rainfed or irrigated) and water source (groundwater or surface water) of croplands for 2000, 2005, 2010, and 2015. We downscaled a 10-km irrigation dataset derived from national and subnational statistics (GMIA) using (i) spatial patterns between high-resolution (30 m) cropland and nearby surface water, and (ii) irrigation water requirements from a global crop model. Validation used household agriculture surveys in India (N = 8,355) and a U.S. well database (N = 1,505,371). In the U.S., our method achieved 85% accuracy in distinguishing groundwater use within 2 km of wells – substantially higher than GMIA (25%). In India’s groundwater-dominated regions, our estimates performed comparably to GMIA (73% vs. 72%). These results suggest our dataset offers a more accurate and spatially detailed representation of irrigation water sources, enabling improved analysis of agricultural water use.

Stormwater has been a significant source of pollution in water bodies adjacent to urban area. In cities with combined sewer systems, stormwater also causes combined sewer overflows (CSOs), resulting in disturbance of water uses and threats to human and ecosystem health. Green infrastructure (GI), which utilizes natural hydrological processes to treat stormwater, is argued to be a more sustainable solution for stormwater pollution comparing to traditional engineering solutions. However, literature has indicated that decision makers are facing the uncertainty concerning GI efficacy in controlling stormwater pollution and costs. To facilitate GI planning to manage the uncertainty, this dissertation develops three innovative tools for adaptive investment planning, integrated evaluation and planning, and CSO control for GI, respectively. Chapter 2 introduces a new method for adaptive investment planning based on the idea of Bayesian inference where the near-term investment may result in learning about GI’s cost-effectiveness, while constraining the risk of the undesired outcomes at a user specified level. Although this method is developed for GI planning, it is generalized for adaptive management problems. A hypothetical example is presented to demonstrate its ability to identify tradeoffs between alternative Pareto optimal investment strategies. Chapter 3 introduces the integrated evaluation and planning framework for GI, which combines hydrological simulation for GI performance assessment and optimization of GI investment. The GI investment planning extends the adaptive investment planning method with the consideration of performance deterioration and the knowledge transfer between locations in a case study in Philadelphia, PA. Chapter 4 presents a theoretical framework for the evaluation of GI performance in CSO control. The analysis focuses on the characterization of the interactions between GI, the watershed and the climate. Furthermore, boundary lines are derived from the theoretical framework and the critical flow for CSO that separate CSO generating storms and indicate which storms may be treated by GI. Simulations are performed with several decades of precipitation records from Philadelphia and Seattle. In conclusion, this dissertation develops a new method for adaptive investment planning, an integrated framework for GI evaluation and planning, and a theoretical framework for CSO control.