Abstract Depth–duration–frequency (DDF) curves are essential tools in hydrologic and hydraulic engineering, used to estimate the magnitude and frequency of extreme precipitation events across varying durations. They play a critical role in flood risk assessment, infrastructure design, and urban drainage planning. Traditionally, DDF curves are derived from long-term rain gauge records; however, this method can be limited in regions with sparse observational networks or complex terrain, such as Puerto Rico (PR) and the US Virgin Islands (USVI). To address these limitations, this study presents updated DDF curves for PR and USVI using high-resolution ( 1 km × 1 km ) radar data from the Next Generation Weather Radar Level III archive spanning 2002–2023. Four radar-based precipitation products [digital storm total precipitation (DSP), one-hour precipitation (N1P), storm total precipitation (NTP), and dual-polarization one-hour precipitation (OHA)] were evaluated against gauge observations to identify the most suitable dataset; among them, the N1P product demonstrated the strongest agreement with ground-based measurements and was selected for further analysis. Using N1P, radar-derived DDF curves were developed at 1-km spatial resolution and compared with curves from the National Oceanic and Atmospheric Administration (NOAA), which are based on gauge records ending in 2004 for both PR and USVI. The analysis revealed notable differences, particularly for shorter durations (1–3 h), where radar estimates frequently surpassed the NOAA values. For longer durations, discrepancies exhibited greater variability. These differences may reflect a combination of factors, including differences between radar and gauge measurements, spatial averaging, record length, and radar retrieval uncertainty. The DDF curves developed in this study provide spatially detailed inputs that can support flood modeling, infrastructure resilience assessment, and climate adaptation planning.
Abstract Urban water management faces increasing challenges from rapid urbanization, climate change, and rising water demand, yet quantitative accounting of engineered evaporation sources remains incomplete. Rooftop cooling towers (CTs) represent a significant but often overlooked component of urban water fluxes, with limited spatial data available to support planning and policy. This study presents an integrated workflow combining YOLOv11 object detection, pretrained SAM2 segmentation, and a targeted human-in-the-loop (HITL) verification stage to identify and delineate CTs from high-resolution aerial imagery. The pipeline was trained on annotated imagery from Boston and tested at two locations near Phoenix, Arizona, achieving a high detection rate (recall of 96% and 82%), robust segmentation quality [intersection over union (IoU) of 76% and 67%], and requiring review of only a small fraction (3.5%) of total imagery to remove all false positives. The approach demonstrated label-free geographic generalization, maintaining performance across regions with markedly different climatic and architectural characteristics without fine-tuning. The resulting spatially explicit geometric measurements of fan radius should enable estimation of evaporative surface area for incorporation into urban water and hydrothermal models. This framework reduces manual annotation effort, lowers computational cost, and scales efficiently from local to regional applications, as demonstrated by its extension to Maricopa County. The method provides a transferable, operationally viable tool for integrating engineered evaporation sources into urban water budgets and supporting climate-resilient resource management.
Abstract Climate change is profoundly altering hydrological processes in alpine regions, which are acutely sensitive to warming; however, accurate projections of hydrological fluxes, notably runoff, remain challenging. In this study, six hydrological conceptual models and their combinations were first evaluated through comparative experiments for daily runoff simulation. Downscaled CMIP6 climatic variables were then used to drive the selected model/combination for future runoff variations using the Datong River Basin on the northeast Tibetan Plateau, China as a case study. The results indicate that: (1) among the single and combined models, the IHACRES-CMD and GR4JSG combination has shown satisfactory performance for runoff simulation in alpine basins; (2) future projections of climate and hydrological variables based on the selected model driven by CMIP6 outputs reveal a consistent warming-wetting tendency, with mean annual temperature, precipitation and runoff increasing by 1.6°C–3.8°C, 25.5–55.8 mm, and 0.8 – 1.4 billion m 3 , respectively, under various Shared Socioeconomic Pathways (SSP) scenarios between 2015 and 2100 relative to the historical period; and (3) runoff changes also demonstrate significant future hydrological shifts, showing increased interannual runoff but a more uniform intra-annual distribution, manifested through reduced runoff in July but increase in June. In addition, results also show larger runoff variability with increased occurrence of above-normal runoff in the future. These changes imply an elevated flood-risk potential during early summer, which poses a significant challenge to existing reservoir operation rules, necessitating adaptive strategies and climate-resilient allocation frameworks for water resource systems in alpine basins.
Abstract Groundwater seepage is influenced by various anthropogenic factors during urban construction, among which subsurface structures and surface hardening are two key factors. This study employed numerical simulations to investigate the impacts of rainfall infiltration on groundwater seepage under the effect of urban construction. A numerical model was established to predict groundwater level (GWL) in the phreatic aquifer (Aq0), incorporating the actual distribution of impervious surfaces and subsurface structures for a representative urban area in Shanghai. The model accounted for land surface conditions through rainfall infiltration coefficients, while subsurface structures were parameterized using equivalent hydraulic conductivity ( K eq ) derived from effective medium theory. Validation results show that the model achieved a maximum error of only 1.7%, demonstrating reliable performance in predicting groundwater seepage. There is 23.23% area with GWL declining more than 0.4 m in the actual scenario with 93% impervious surface hardening comparing without surface hardening. Prediction scenarios with varying reduction rates of impervious surface compared with the actual scenario ( R a ) were conducted to investigate the impact of surface hardening on GWL. The results indicate average GWL of the study area presents exponential decay with the impervious surface ratio. Further, the variations in GWL of Aq0 were investigated by reducing hardened surfaces in different locations with the same R a . As the proportion of underground structures in the reduced impervious surface increases, the area with the same increment of GWL decreases. Prioritizing low impact development in areas with lower subsurface structure density can significantly enhance the efficiency of groundwater recharge by rainfall infiltration.
Infiltration excess is the dominant runoff mechanism in many drylands. Arid and semiarid landscapes often feature mosaic patterns of bare soils and stone cover, interspersed with vegetated patches. In response to raindrop impact, bare surfaces are prone to the development of physical crusts with reduced infiltration rates. Surfaces shielded by plant canopy, litter, and nonembedded stones prevent crusting and maintain higher infiltration capacity. The source area for runoff in dryland watersheds is expected to vary depending on rainfall characteristics and surface conditions because bare soils exceed infiltration capacity sooner and at lower rainfall intensities compared with shielded patches. In this study, we therefore hypothesize that the variable source area (VSA) concept developed for saturation excess runoff in humid climates can be extended to drylands. We test this hypothesis at the hillslope scale for two basins in the Walnut Gulch Experimental Watershed in Arizona. By building split models, infiltration and runoff from shielded and bare surfaces within each basin were simulated separately. Infiltration parameters were estimated from 148 plot-scale rainfall simulator tests. Performance of the split modeling strategy was compared to distributed and lumped approaches. Results show that split and gridded models performed significantly better than the lumped alternative across various performance metrics. This indicates that the VSA concept can be extended to patchy arid and semiarid landscapes, whereby the source area contributing runoff to the basin outlet is at least in part modulated by soil surface condition. By providing a parsimonious alternative to conventional lumped models, the novel split approach has clear potential for improving runoff predictions in dryland settings with complex mosaic landscapes.
