ABSTRACT The Chesapeake Bay has been at the forefront of large‐scale efforts to reverse human‐induced eutrophication in coastal waters. While significant progress has been made, 15 years of intensive implementation efforts reveal that outcomes in many areas have fallen short of water quality goals. Future program goals and implementation plans will increasingly be challenged by uncertainties and limits in the effectiveness of nonpoint source programs and in the estuaries' response to changing pollutant inputs and environmental conditions. This paper argues that policy changes are needed to adapt to these management realities. We outline three policy alternatives—tiered implementation of an existing Total Maximum Daily Load (TMDL), changes in the accounting and modeling of nonpoint sources, and policy sandboxing—that together offer practical pathways to accelerate learning and improve outcomes of implementation programs.
Colorado Senate Bill 24-037 (SB24-037) directs the University of Colorado and Colorado State University, with the Colorado Department of Public Health and Environment (CDPHE), to evaluate the feasibility of alternative compliance programs using green infrastructure (GI) and to establish up to three pilot projects. This technical note examines how water quality trading (WQT)-a regulatory framework allowing NPDES point sources to meet permit obligations through offset credits-implemented in other states can inform SB24-037 pilot design. We define GI and nature-based solutions in Colorado's context and develop three pilot archetypes spanning the state's compliance pressures, relevant out-of-state analogues, and the financing structures used in US WQT practice: riparian restoration for temperature compliance, nutrient offset trading in urbanizing watersheds, and stormwater retention credit banking. Drawing on Oregon temperature trading, Wisconsin and Chesapeake Bay nutrient trading, and the DC stormwater retention credit program, we find WQT can provide a regulatory pathway for GI-based compliance, but realistic expectations are essential: most US trading programs have generated fewer trades than initially projected. SB24-037 pilots should prioritize clear permit integration, conservative credit quantification, intermediate monitoring indicators, multi-year maintenance funding, and explicit accounting for interactions with existing conservation subsidy programs.
Over the last half-century, land use changes, including deforestation, urban sprawl, and open-pit surface mining, have accelerated across the Susurluk Basin in northwestern T & uuml;rkiye. This study analysed how land use changes, damming and mining activities affected basin hydrology using empirical and analytical methods and the process-based Water Supply Stress Index Model (WaSSI). The monthly WaSSI water balance model was validated using streamflow data from gaging stations between 1980 and 2005. Two of the eight subbasins exhibited streamflow reductions of about 32%-42%, with mean annual discharge decreasing between 1980-1989 and 1990-2005, primarily due to land use change rather than climate variability. The runoff coefficient (Runoff/Precipitation) dropped from 22% during 1980-1989 to 12% during 1990-2005 in one rural subbasin containing several surface-mine ponds. Overall, empirical and process-based modelling indicated that land use dynamics, rather than climate, were responsible for the hydrological change. The monthly WaSSI showed satisfactory performance in subbasins with low human impacts (NSE > 0.50) but considerably lower performance (NSE < 0.20) in highly human-modified areas. This integrated study concludes that land use activities, especially pond creation for pit mining, were the dominant drivers of hydrological changes in the study area.
Taking the water body of the Hun River in Shenyang as the research object, the pollution status of six heavy metals including arsenic (As), cadmium (Cd), lead (Pb), zinc (Zn), copper (Cu), and chromium (Cr) was analyzed in detail. The concentrations of each heavy metal element were determined by inductively coupled plasma mass spectrometry, and the pollution degree was evaluated using the Enmaro comprehensive index method. The pollution sources were analyzed through the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model. The results showed that the arsenic concentration in the water body of the study area was 0.65-8.82 mu g/L, cadmium was 0.03-0.33 mu g/L, lead was 0.42-2.12 mu g/L, zinc was 4.55-32.90 mu g/L, copper was 1.24-12.11 mu g/L, and chromium was 2.36-9.90 mu g/L. All of them did not exceed the limit values of Class II of the "Surface Water Environmental Quality Standards" (GB3838-2002). The heavy metal concentrations in the river water showed a characteristic that they were higher in the rainy season than in the dry season. The high concentration areas were mainly concentrated at the junction of Shenyang and Fushun and the Xi River Basin. Due to the influence of industrial, agricultural production activities, and emissions from mining enterprises, there might be slight pollution pressure in some local areas.
Two key components of large-scale ecosystem restoration planning are: (1) prioritizing individual projects based on how living resources respond, and (2) selecting and analyzing indicators of living resources to assess restoration progress and performance. We present an approach for performing statistical and ecological modeling analyzes to examine the in situ responses of living resources to restoration actions. The results can be directly used for the prioritization of project areas and the analysis of performance using ecological indicators. The approach uses a flowchart showing the logic of analyzes. With 12 ecological concepts and principles as the foundation, the approach enables the development of an analysis plan that details the statistical and modeling analyzes. We illustrate the approach with the Chesapeake Bay Program. The approach is generally applicable and can be tailored to many large-scale estuarine and coastal restoration programs.
ABSTRACT Water systems in the American West are under mounting stress as climate change accelerates aridification and tensions grow over competing demands for scarce water resources. As socio‐ecological pressures compound, siloed thinking and governance undermine the development of innovative water management approaches. A new generation of water practitioners is rising to meet this challenge. But working within this complexity requires systems understanding, transdisciplinary expertise, cultural competence, and collaborative leadership. As a multidisciplinary group of graduate students and field program leaders working on water in the West, we argue that experiential learning offers a powerful pathway to prepare future water practitioners with the skills they need to address wicked water problems. In this Commentary we present a set of best practices, grounded in our shared experiential learning experience, to guide the design of inclusive and transformative field opportunities for students engaging with water challenges across diverse geographic contexts: (1) dedicated time to orient as a team; (2) circular mentorship that generates radial impacts; (3) place‐based site visits; and (4) secure funding. We offer a case study in the Colorado River Basin to demonstrate how the application of these design principles can support future experiential learning initiatives that cultivate water practitioners equipped to work across institutional, cultural, and hydrologic boundaries.
Long-term drought and aridification challenge water managers in the southwestern United States to ensure water security for growing urban populations. As reliable supply dwindles, managers must increasingly rely on demand management strategies whose success is predicated on community water literacy, an aggregate of water knowledge, attitudes, and behaviors. High community water literacy can build trust in water managers, fuel timely responses to drought, and expose water inequities. Following a 2002 drought that caused near-failure to their water system, Aurora Water (Colorado, US) has become a state leader in demand management and water literacy programs. To understand the intersections between experiences, conservation, and water literacy, we surveyed residents and conducted focus groups in 2021-2022. Our results reveal structures that both help and hinder community water literacy. Lived experiences of drought and engagement with Aurora Water's outreach programs positively correlate with measures of water literacy. However, social dynamics, contradictory perceptions, and certain institutions introduce barriers to residential water conservation. These findings highlight the need for coordinated cross-institutional approaches to strengthen water literacy as a foundation for demand management. Interventions that align municipal practices and community governance structures can help reduce these barriers, reinforce pro-conservation norms, and advance urban water sustainability.
Stream temperature is a key performance driver for aquatic species and a direct metric of climate impacts. We adapted a stream temperature model to predict daily temperatures across the Pacific Northwest USA through 2100. Our results suggested that stream temperatures may rise by similar to 1 degrees C by the 2050's and 2 degrees C by the 2080's, with seasonal and geographic nuances. Snowmelt-fed streams were especially likely to experience seasonal shifts in temperature patterns. We built local models for two watersheds to show how predictions can guide Pacific salmon conservation planning. In the North Santiam River, a Willamette River tributary in Oregon, we found that Chinook salmon and Steelhead spawners and eggs may face increasingly stressful conditions below large dams. Whereas above-dam thermal habitat will remain or become suitable, suggesting the importance of maintaining dam passage. In the Wenatchee River, an upper Columbia River tributary in Washington, future temperatures may contribute to higher pre-spawn mortality of Chinook salmon. Cooler thermal refuges may persist in glacial and snow-influenced parts of the landscape, but reaching high-elevation refuges will require migrating through warming rivers. It is essential to consider temperature patterns alongside other cumulative impacts throughout the salmon life cycle. Future stream temperature estimates can help anticipate climate-driven habitat changes and support proactive conservation strategies for lotic species.
Many resource management plans use ensembles of global climate models (GCMs) to represent a range of potential future climates. Hydrologic models are used to translate these climates into projections of water resources to evaluate their long-term vulnerability. GCM ensembles are typically selected for water resource vulnerability assessments based on their collective ability to represent the full range of projected regional changes in precipitation and temperature. However, this approach may miss potential hydrologic shifts due to the heterogenous and nonlinear impacts of climate on hydrology in snow-dominated mountain headwaters. We demonstrate this challenge in the Walker River Basin (WRB) of California and Nevada. After comparing 32 climate projections and their associated hydrologic model (VIC) outputs we run subsets of projections through a water allocation model (MODSIM) to predict impacts on agricultural water use. Projected end-of-century changes in WRB precipitation vary (-20% to +40%) leading to an even greater range in projected streamflow changes (-50% to +75%). Model outputs suggest that maintaining historical levels of water supply reliability through 2100 would require > 50% greater mean annual streamflow. Our study shows the value of selecting GCM ensembles based on locally-derived hydrologic (rather than purely climatic) metrics. Other GCM selections may not span all potential impacts of changing climate on agricultural water resources.
ABSTRACT This review integrates hydrogeological and geochemical processes to assess the impacts of climate change on groundwater quality in the Northern Atlantic Coastal Plain (NACP). Projected changes in air temperature, precipitation, and sea‐level rise are expected to influence groundwater recharge, discharge, storage, and seawater intrusion in shallow unconfined aquifers, thereby modifying aquifer dynamics. Such changes affect biogeochemical reactions, contaminant transport, and chemical stability, leading to both short‐ and long‐term impacts on groundwater quality. Climate‐related stressors such as flooding and drought, combined with known and emerging contaminants including per‐ and polyfluoroalkyl substances (PFAS) and microplastics (MPs), pose significant risks to drinking water and ecosystem health. The review identifies processes most vulnerable to climate‐driven changes, providing insights to support the design of adaptive groundwater monitoring networks and modeling strategies. Long‐term monitoring is essential to track contaminant trends, inform regulatory decision‐making actions, and reduce human exposure risks in the NACP and comparable coastal regions. However, quantifying these climate‐related impacts on groundwater quality remains challenging due to uncertainties in climate projections and hydrogeological complexity, emphasizing the need for integrated modeling frameworks and adaptive management approaches.
Wetland classification has always been a difficult task for researchers. Its land-cover classes share similar spectral signatures and have complex spatial patterns. In this paper, we developed Graph-Mamba with U-Net and GRU (GMUG) for joint HSI and LiDAR classification. The model uses a U-Net model to capture spatial structure, Mamba and GRU modules to refine features, and a graph-based classification stage to contain spatial relationships. GMUG was evaluated on three benchmark HSI/LiDAR datasets, MUUFL, Houston, and Trento, and compared with DAHGMN, MHST, HLMamba, and CMFAEN. Its clearest advantage appeared on MUUFL, where stronger class imbalance and class confusion have challenged all other models. The model also performed well on the Houston and Trento datasets. The ablation experiments showed that both U-Net and GRU improved the full model. When applied to the Louisiana wetland HSI/LiDAR dataset, GMUG classified most of the classes very well, although confusion persisted between classes with very similar signatures, such as Riverine and Lake. In conclusion, GMUG is a useful tool for complex wetland scenes where similar classes are difficult to separate.
Noise is an inherent component of observed time series and may be treated either as an undesirable disturbance or as an intrinsic system characteristic containing information about underlying dynamics. In the latter perspective, noise behavior must be analyzed and quantified, and the model used for time-series synthesis should be compatible with the identified noise characteristics. A recent study used power spectrum analysis to quantify colored noise components (Brown, Pink, and Black noise) and identify the dominant noise type in monthly streamflow across Ontario. Two hydrometric stations, Basswood River and Black Sturgeon, were found to be dominated by Brown noise. Accordingly, a stochastic model compatible with Brown-noise-dominated monthly streamflow is developed. Brownian-based models are suitable for such series; however, standard Geometric Brownian Motion tends to produce unbounded exponential growth over long horizons. To address this limitation, an Extended Geometric Brownian Motion (EGBM) model is developed. The streamflow series is divided into two seasons based on the Lyapunov time horizon, and a monthly seasonal factor is incorporated with parameters optimized using a Genetic Algorithm. The model is applied to synthesize monthly streamflow at the Basswood and Black Sturgeon stations. Compared with a multiplicative ARIMA model, EGBM better reproduces the statistical properties of historical streamflow and yields residuals with statistically insignificant lag-1 autocorrelation.
This study reveals the critical interplay between streamflow hysteresis and local hydrogeomorphic conditions. Hysteresis is known to be influenced by characteristics such as bed slope, roughness, and wave intensity, but there has been no comprehensive study of the conditions facilitating hysteresis. Our 1D HEC-RAS study was conducted to highlight the hysteresis response to local hydro-morphological changes acting in isolation or combination. Wave intensity, backwater condition, bed slopes (0.0001 < S-0 < 0.001), and roughness (0.02 < n < 0.2) in simple and compound channels were gradually varied from a base scenario of the Illinois River at Henry, IL. A Random Forest analysis revealed that hysteresis is controlled 28% by wave intensity, 23% by bed slope, 16% by roughness, and 15% by backwater. We found that bed slope and backwater are the most persistent controls on the expression of hysteresis, with mild bed slopes and high backwater consistently generating strong hysteresis signals. We also explored the impact of various discharge estimation techniques on modeled streamflow, demonstrating how flow boundary conditions that account for hysteresis produce a more accurate response in the modeled reach compared to those provided by conventional methods. In understanding the sensitivity of streamflow hysteresis and its drivers, we narrow the gap between our evolving knowledge of flow dynamics and strategies for monitoring, modeling, and forecasting rivers under natural unsteady conditions.
This study applies a statistically sound methodology for contrasting real-valued datasets against simulated results generated from the US Army Engineer Research and Development Center's Coastal and Hydraulics Laboratory's first-order flood inundation hydrologic modeling suite, AutoRoute, to determine the impact of spatial resolution on flood inundation extent, velocity, depth, and top-width estimates. Nine AutoRoute simulations were conducted for Buncombe County, NC, for flooding events resulting from Hurricane Helene in September 2024. Each AutoRoute simulation was run with a unique combination of spatial resolutions for two of its primary input products: digital elevation model (DEM) and land cover (LC). The velocity, depth, and top-width (VDT) outputs were tested for distributional nonoverlap by calculating Cliff's delta estimate, the Spatial Bhattacharyya Coefficient (SBC), and polygonal overlap with NASA-JPL's Observational Products for End-Users from Remote Sensing Analysis observed flood extents. The results confirm DEM products more heavily influence VDT output than LC, and lower input product resolution causes lower output velocity values and higher output top-width values, potentially resulting in velocity underestimation and top-width overestimation. This methodology offers an efficient and systematic evaluation of real-valued outputs and can be applied to additional areas of interest, flood extents, flow scenarios, and input products of varying spatial resolutions.
Floods are one of the leading causes of death from natural hazards in the United States (US). Better prediction of bankfull flow is needed in order to communicate potential flood risks and initiate response within forecasting frameworks. Traditional bankfull flow estimations are based on empirical equations whose accuracy is shown to be insufficient across a large range of streams and large geographic domains. We investigate the variability in bankfull flow estimates using well-established indices across the contiguous United States, including the National Weather Service (NWS) Action Flow. Its association with bankfull flow and utility as a bankfull flow proxy is explored. At 812 US Geological Survey (USGS) gauges where an NWS Action Flow is defined, we computed bankfull flow estimates using the minimum width-to-depth ratio, the Bench Index, and the USGS 17C flood frequency analysis. We provide a geospatial dataset of these bankfull flow estimates, the NWS Action flows, and observational bankfull flow when available (similar to 10% of the locations). At 87% of the locations, we find a high degree of variability between the estimates produced by these methods, indicating that the method selected has a significant impact. We find the NWS Action Flow a reasonable bankfull flow proxy, performing as well as or better than the established methods considered.
Areal reduction factors (ARFs), also referred to as depth-area ratios, are applied to point-scale observations of precipitation to estimate the average accumulation across an area. They are commonly used by engineers to design storm drainage, transportation infrastructure, and conservation practices. In the Desert Southwest, determination of ARFs is challenged by the summer North American Monsoon, which spawns air-mass thunderstorms that feature short, intense, localized rainfall events. The Walnut Gulch Experimental Watershed (WGEW) in SE Arizona has an extensive, high-resolution rain gauge network operating since 1953. In 1980, ARFs were computed using observations from WGEW for the period 1957-1976 and were compared to curves presented in NOAA Atlas 2. This study updated the WGEW ARFs for a longer period of record (1957-2018) and compared them to the 1980 ARFs. Additionally, spatial variations in ARFs and extreme rainfall across WGEW were examined. Results indicate the updated ARFs are larger (therefore resulting in less of a reduction) for design storm durations of 60, 120, and 360 min with return periods of 10 and 100 years. For these cases, it is implied that infrastructure designs using the 1980 ARFs are now under-designed. Spatial variations between ARFs computed across the WGEW were relatively small, in the range of 1%-6%.
ABSTRACT This work presents the development and validation of the River Network Streamflow Temperature Model (RNSTM), which solves the energy balance equations at the air‐water interface within the channels to estimate their temperature. RNSTM considers solar radiation, net longwave radiation, evaporative heat flux, and convective heat transfer. Additionally, it includes sub‐surface heat transfer and the rainfall effects on water temperature. First, we present the formulation and testing of a lumped‐energy balance model. For this test, we used atmospheric forcings from ground‐based observations and the High‐Resolution Rapid Refresh (HRRR) weather forecasting system. Next, we formulated RNSTM for a general river network using the ordinary differential equations (ODE) solver that is part of the Hillslope Link Model (HLM). We tested RNSTM using HRRR meteorological data and discharge simulations from HLM and we validated it using United States Geological Survey (USGS) water temperature observations at the Cedar River at Waverly, Iowa, for 2021. Our model results show potential for large‐scale deployment and water quality‐related applications.
ABSTRACT California's efforts to advance the Human Right to Water are constrained by limited household‐level data on water insecurity. Using the 2021 CalSpeaks statewide probability survey ( n = 628) this study offers the first representative look at water insecurity across California households. Results reveal significant water‐related struggles: approximately 23% of respondents worried about having enough water to meet their household needs, 15% worried about perceived health impacts from water quality, and 7% struggled with affordability. Negative binomial regression models show that water insecurity is not randomly distributed: households with non‐white residents, households which rely on private or small water companies for water service or receive public assistance experience disproportionately higher water insecurity. Sensitivity analyses indicate that some predictors—such as household size and employment—are more strongly associated with frequent water insecurity experiences, underscoring the need for multidimensional monitoring. These findings highlight that system‐level compliance metrics substantially underestimate household‐level challenges and reinforce the need for policies that address affordability, quality, accessibility, worry, and trust to achieve California's Human Right to Water.
Forested watersheds regulate flood response through canopy interception, infiltration, and soil-channel interactions, but combined effects of forest conversion, legacy disturbance, and channel modification on headwater hydrology are poorly understood. This study examines impacts of historical forest disturbance and channel modification on flood behavior in Big Barren Creek (48 km(2)), a headwater watershed in southeastern Ozark Highlands, Missouri. 1880-1920 intensive logging removed > 90% native shortleaf pine, shifting to hardwood-dominated forests with soil degradation and channel modifications. Using HEC-HMS, early-spring storm flood response (minimal interception, high risk) was simulated under present-day, pre-settlement, and post-disturbance conditions. Pine-to-hardwood conversion reduced interception similar to 50%, increasing peak discharge 24%, runoff 32%, and shortening lag time 7% versus pre-settlement. Channel modifications increased flashiness, raising peaks +9% and advancing time-to-peak 45 min. Post-disturbance forests produced similar to 20% less runoff than present-day, but degraded soils increased peak discharge 17% and prolonged duration 8%. Restoring 100% shortleaf pine nearly reproduced pre-settlement hydrology; complete hardwood conversion had little additional impact on current conditions. Vegetation composition, soil condition, and channel morphology are primary controls on headwater flood response; integrated forest and channel management can enhance watershed resilience.
Stream-aquifer connections in incised coastal plain floodplains with shallow aquifers are controlled by sediment type, seasonal climate forcing, and channel geometry, but remain poorly quantified. This study characterizes these interactions along Cub Creek, a coastal plain stream, using an integrated approach that combines borehole stratigraphy, natural gamma logs, electrical resistivity (ER) surveys, and 2 years (2022-2023) of hydrologic monitoring. A shallow unconfined aquifer was identified along the studied stream reach within Quaternary alluvium and the Wilcox Formation and is underlain by the Porters Creek Clay confining unit. ER imaging indicated lateral discontinuity of sand bodies within the Wilcox Formation and an incised channel geometry in the downstream alluvium. Grain-size analysis also showed a downstream coarsening trend (upstream: 67%-71% fines; downstream: 59%-66% fines). These textural differences are reflected in soil-moisture behavior at the two monitoring locations. During cooler seasons, VWC at 150 cm stabilized near equilibrium values of similar to 45% upstream and similar to 38% downstream. Together, these conditions contributed to differences in water-table fluctuations between the upstream and downstream reaches. Mean annual groundwater recharge was 0.40 m year(-1) at the upstream monitoring well and 0.43 m year(-1) at the downstream well. Recharge variability was greater downstream due to coarser sediments and a thicker vadose zone. Recharge events occurred more frequently during cooler seasons and less frequently during warmer months. Consistent with these observations, the upstream reach of Cub Creek likely behaved as a losing stream during warmer periods, while the downstream reach acted as a gaining stream. During construction, a channel blockage in October 2023 produced an abrupt upstream water-table rise, indicating the sensitivity of shallow coastal plain aquifers to channel modification. These results establish a foundational hydrogeological framework for assessing restoration effectiveness in reestablishing floodplain-aquifer connectivity.
