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In the current study, we explicitly address the impacts of urban irrigation on the local hydrological cycle by integrating a previously developed irrigation scheme within the coupled framework of the Weather Research and Forecasting-Urban Canopy Models (WRF-UCM) over the semiarid Los Angeles metropolitan area. We focus on the impacts of irrigation on the urban water cycle and atmospheric feedback. Our results demonstrate a significant sensitivity of WRF-UCM simulated surface turbulent fluxes to the incorporation of urban irrigation. Introducing anthropogenic moisture, vegetated pixels show a shift in the energy partitioning toward elevated latent heat fluxes. The cooling effects of irrigation on daily peak air temperatures are evident over all three urban types, with the largest influence over low-intensity residential areas (average cooling of 1.64°C). The evaluation of model performance via comparison against CIMIS (California Irrigation Management Information System) evapotranspiration (ET) estimates indicates that WRF-UCM, after adding irrigation, performs reasonably during the course of the month of July, tracking day-to-day variability of ET with notable consistency. In the nonirrigated case, CIMIS-based ET fluctuations are significantly underestimated by the model. Our analysis shows the importance of accurate representation of urban irrigation in modeling studies, especially over water-scarce regions such as the Los Angeles metropolitan area. We also illustrate that the impacts of irrigation on simulated energy and water cycles are more critical for longer-term simulations due to the interactions between irrigation and soil moisture fluctuations.

Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow-Atmosphere-Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service's (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2-4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3-4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating. © 2008 American Meteorological Society.

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction. © 2008 Elsevier B.V. All rights reserved.

A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/ pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single-criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar "physical meaning" across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil-related parameter. Copyright 2006 by the American Geophysical Union.

This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/ gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance. © 2005 American Meteorological Society.

This paper presents an overview of water resources issues in the context of world population growth, climate change, and variability, and provides examples of how these issues affect local and regional water policy concerns. Also discussed is the associated research of the international scientific community in regard to physically-based modeling of the hydrological cycle, with special focus on the Global Energy and Water cycle EXperiment (GEWEX) Programme. The critical role of precipitation measurements for climate model accuracy is emphasized, with a review of several satellite methods and strategies for improving precipitation measurements. Finally, the impact of semiarid regions on global hydrologic issues is underscored with a review of research conducted by SAHRA, the National Science Foundation Science and Technology Center dedicated to Sustainability of semi-Add Hydrology and Riparian Areas.

While wildfires are a natural occurrence and ecosystems have evolved with fire, a changing climate is extending the wildfire season increasing the number, size and severity of fires in the western United States. In 2018, wildfire consumed 30% more area in the Western United States than the average ten years prior, and 76% more area than the average twenty years ago. These recent wildfires have impacted communities in the southwestern Rocky Mountains. In 2018, the 416 Fire, burned over 21,000 ha of public and private lands in southwestern Colorado. The 416 Fire is uniquely located in a legacy mine region in Colorado. The fire occurred in the Animas River watershed, which was already recovering from impacts of the Gold King Mine release of 2015. Three years of water quality monitoring after the 416 Fire have demonstrated elevated total and dissolved metal concentrations downstream of the burn area in Hermosa Creek and the Animas River. Following high-intensity rainstorm events, concentrations of metals such as aluminum, cadmium, iron, lead, manganese and zinc were significantly higher compared to pre-fire conditions in the burned watershed, and several metals often exceeded water quality standards for aquatic life. Macroinvertebrate monitoring in the Animas River and the main fire-impacted tributary indicate substantially altered insect communities. Macroinvertebrate tissue samples, with high concentrations of aluminum, iron, lead and nickel provide evidence that metals observed in the water column of fire-impacted streams were transferred to the benthic communities. In contrast, algae tissue from below the fire did not have elevated metals. High sediment volumes with absorbed metals from mineral rich and mined hillsides were transported to the streams and their aquatic ecosystems after the fire. Results from this study highlight the post-fire mobilization of naturally occurring metals to streams that already experience elevated metals from legacy mines, and will help in development of mitigation efforts in downstream communities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. In this work, we updated and utilized a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of two ecological restoration scenarios: (1) an alternative streambed material limecrete and (2) shading effects of tree planting in riparian areas. The i-Tree Cool River model was modified to account for diurnal fluctuations of streambed temperature, which is relevant in shallow urban streams where lack of natural shading combined with low heat capacity of the water column can make diurnal fluctuations relatively extreme. The model was calibrated and validated on a 4.2 km reach of Compton Creek in the Los Angeles River watershed, California. Two native fish, arroyo chub (Gila orcuttii) and unarmored threespine stickleback (Gasterosteus aculeatus williamsoni), were considered the target species for assessing thermal habitat suitability. Key findings include: (1) model performance was improved when accounting for diurnal fluctuations in bed temperature (R 2 increased from 0.43 to 0.68); and (2) substrate rehabilitation and tree planting can potentially reduce summertime temperatures to within the documented spawning temperature thresholds for the focal fish species. Using limecrete as an alternative material for the concrete bottom decreased the median river temperature metrics: maximum weekly maximum, maximum weekly average, and minimum weekly minimum temperatures by an average of 3 °C (13%) to 20.4 °C, 19.7 °C, and 17.8 °C, respectively. Tree planting in the riparian corridor decreased the average river temperature metrics by an average of 0.9 °C (4%) to 22.7 °C, 22 °C, and 19 °C, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 °C (18%) to 18.2 °C. Therefore, water temperature would not be a limiting factor in potential reintroduction of the focal fish species to Compton Creek if restoration were implemented. Implications of this work could be used by urban forest and water managers for restoring thermally polluted rivers in other urban areas., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

A new Green Infrastructure Placement Tool coupled with Storm Water Management Model (GIP-SWMM) is developed for selection and strategic placement of Green Infrastructure (GI) practices. The tool supports GI placement at multiple scales - from a few city blocks to large watersheds. GI is a multi-benefit option for stormwater management and can revitalize communities while reducing sewage overflows and improving runoff quality. However, cost-effective planning and placement of GI to achieve management goals remains a challenge and requires an integrated watershed approach. An optimization approach was developed coupled with the SWMM to find optimal combination and placement of GI to meet target flow and pollutant load reduction while minimizing cost at a watershed scale. The tool includes 13 GI types and selection and placement of GI within a watershed is based on their capital cost and effectiveness. The tool generates cost-effectiveness curves (cost vs size of GIs) for discharge and pollutants. GIP-SWMM provides a means for objective analysis of managing alternatives among multiple interacting and competing options. The desired outcome from the system application is a thorough, practical, and informative assessment considering economic, and engineering factors. The performance of the tool was evaluated in the Meade-Hawthorne drainage basin in Rapid City, South Dakota. GI placement options were assessed for multiple target levels for discharge, TSS, E coli, and Fecal coliform. Cost-effectiveness curves were developed for discharge, TSS, E coli, and Fecal coliform. Total GI cost increased as the target discharge, TSS, E coli, and Fecal coliform concentration at the outlet of the watershed was reduced. The tool placed a larger percentage of the GIs at the locations where most of the discharge and pollutant loads originated. This case study demonstrates that GIP-SWMM is a planning level decision support framework that allows for optimization of GIs and is adaptable for use in addressing regulatory compliance and practices across the U.S., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Urbanization can dramatically alter stormwater, both the quantity and quality, by engendering larger peak flows and through the introduction of contaminants into runoff. The current study builds on previous research that developed relationships between a suite of nonpoint source contaminants, known as trace organic contaminants (TOrCs), and hydrologic measurements for a series of storms (one site had 15 storms and the other had 19 storms) in Madison, WI, by creating statistical and deterministic models. Correlations and regressions were calculated between TOrC loads and hydrologic measurements for a series of storms for both a commercial site and a high-density residential site. From the regressions, it became evident that loading responses to precipitation were not the same between the two land covers for some TOrCs, indicating varying load responses for TOrCs depending on land cover. The regressions were utilized in the Source Loading and Management Model for Windows (WinSLAMM), an event-based hydrologic and water-quality model, to demonstrate that it can be used to model novel contaminants. The regressions were also used to estimate mean annual loads of TOrCs from all commercial and high-density residential areas in Madison, WI, for the watersheds to which Madison discharges its stormwater. The mean annual loads varied between grams per year to tens of thousands of grams per year depending on the TOrC and watershed. This work will ultimately allow managers to simulate the presence of, establish total maximum daily loads for, and mitigate the loads of TOrCs through stormwater best management practices., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Urban-use pesticides are of increasing concern as they are widely used and have been linked to toxicity of aquatic organisms. To assess the occurrence and treatment of these pesticides in stormwater runoff, an approach combining field sampling and watershed-scale modeling was employed. Stormwater samples were collected at four locations in the lower San Diego River watershed during a storm event and analyzed for fipronil, three of its degradation products, and eight pyrethroids. All 12 compounds were detected with frequency ranging from 50 to 100%. Field results indicate pesticide pollution is ubiquitous at levels above toxicity benchmarks and that runoff may be a major pollutant source to urban surface waters. A watershed-scale stormwater model was developed, calibrated using collected data, and evaluated for pesticide storm load and concentrations under several management scenarios. Modeling results show that enhanced stormwater control measures, such as biochar-amended biofilters, reduce both pesticide storm load and toxicity benchmark exceedances, while conventional biofilters reduce the storm load but provide minimal toxicity benchmark exceedance reduction. Consequently, biochar amendment has the potential to broadly improve water quality at the watershed scale, particularly when meeting concentration-based metrics such as toxicity benchmarks. This research motivates future work to demonstrate the reliability of full-scale enhanced stormwater control measures to treat pollutants of emerging concern.

Acute water shortages for large metropolitan regions are likely to become more frequent as climate changes impact historic precipitation levels and urban population grows. California and Los Angeles County have just experienced a severe four year drought followed by a year of high precipitation, and likely drought conditions again in Southern California. We show how the embedded preferences for distant sources, and their local manifestations, have created and/or exacerbated fluctuations in local water availability and suboptimal management. As a socio technical system, water management in the Los Angeles metropolitan region has created a kind of scarcity lock-in in years of low rainfall. We come to this through a decade of coupled research examining landscapes and water use, the development of the complex institutional water management infrastructure, hydrology and a systems network model. Such integrated research is a model for other regions to unpack and understand the actual water resources of a metropolitan region, how it is managed and potential ability to become more water self reliant if the institutions collaborate and manage the resource both parsimoniously, but also in an integrated and conjunctive manner. The Los Angeles County metropolitan region, we find, could transition to a nearly water self sufficient system.

The Colorado Water Plan estimates as much as 0.8 million irrigated acres may dry up statewide from agricultural to municipal and industrial transfers. To help mitigate this loss, new sources of water are being explored in Colorado. One such source may be produced water. Oil and gas production in 2016 alone produced over 300 million barrels of produced water. Currently, the most common method of disposal of produced water is deep well injection, which is costly and has been shown to cause induced seismicity. Treating this water to agricultural standards eliminates the need to dispose of this water and provides a new source of water. This research explores which counties in Colorado may be best suited to reusing produced water for agriculture based on a combined index of need, quality of produced water, and quantity of produced water. The volumetric impact of using produced water for agricultural needs is determined for the top six counties. Irrigation demand is obtained using evapotranspiration estimates from a range of methods, including remote sensing products and ground-based observations. The economic feasibility of treating produced water to irrigation standards is also determined using an integrated decision selection tool (iDST). We find that produced water can make a substantial volumetric impact on irrigation demand in some counties. Results from the iDST indicate that while costs of treating produced water are higher than the cost of injection into private disposal wells, the costs are much less than disposal into commercial wells. The results of this research may aid in the transition between viewing produced water as a waste product and using it as a tool to help secure water for the arid west., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Stormwater best management practices (BMPs) are implemented to reduce microbial pollution in runoff, but their removal efficiencies differ. Enhanced BMPs, such as those with media amendments, can increase removal of fecal indicator bacteria (FIB) in runoff from 0.25-log 10 to above 3-log 10 ; however, their implications for watershed-scale management are poorly understood. In this work, a computational model was developed to simulate watershed-scale bacteria loading and BMP performance using the Ballona Creek Watershed (Los Angeles County, CA) as a case study. Over 1400 scenarios with varying BMP performance, percent watershed area treated, BMP treatment volume, and infiltrative capabilities were simulated. Incremental improvement of BMP performance by 0.25-log 10 , while keeping other scenario variables constant, reduces annual bacterial load at the outlet by a range of 0-29%. In addition, various simulated scenarios provide the same FIB load reduction; for example, 75% load reduction is achieved by diverting runoff from either 95% of the watershed area to 25 000 infiltrating BMPs with 0.5-log 10 removal or 75% of the watershed area to 75 000 infiltrating BMPs with 1.5-log 10 removal. Lastly, simulated infiltrating BMPs provide greater FIB reduction than noninfiltrating BMPs at the watershed scale. Results provide new insight on the trade-offs between BMP treatment volume, performance, and distribution.

Post-fire runoff has the potential to be a large source of contaminants to downstream areas. However, the magnitude of this effect in urban fringe watersheds adjacent to large sources of airborne contaminants is not well documented. The current study investigates the impacts of wildfire on stormwater contaminant loading from the upper Arroyo Seco watershed, burned in 2009. This watershed is adjacent to the Greater Los Angeles, CA, USA area and has not burned in over 60 years. Consequently, it acts as a sink for regional urban pollutants and presents an opportunity to study the impacts of wildfire. Pre- and post-fire storm samples were collected and analyzed for basic cations, trace metals, and total suspended solids. The loss of vegetation and changes in soil properties from the fire greatly increased the magnitude of storm runoff, resulting in sediment-laden floods carrying high concentrations of particulate-bound constituents. Post-fire concentrations and loads were up to three orders of magnitude greater than pre-fire values for many trace metals, including lead and cadmium. A shift was also observed in the timing of chemical delivery, where maximum suspended sediment, trace metal, and cation concentrations coincided with, rather than preceded, peak discharge in the post-fire runoff, amplifying the fire's impacts on mass loading. The results emphasize the importance of sediment delivery as a primary mechanism for post-fire contaminant transport and suggest that traditional management practices that focus on treating only the early portion of storm runoff may be less effective following wildfire. We also advocate that watersheds impacted by regional urban pollutants have the potential to pose significant risk for downstream communities and ecosystems after fire.

Contaminant loading associated with stormwater runoff from recently burned areas is poorly understood, despite the fact that it has the potential to affect downstream water quality. The goal of the present study is to assess regional patterns of runoff and contaminant loading from wildfires in urban fringe areas of southern California. Postfire stormwater runoff was sampled from five wildfires that each burned between 115 and 658 km(2) of natural open space between 2003 and 2009. Between two and five storm events were sampled per site over the first one to two years following the fires for basic constituents, metals, nutrients, total suspended solids, and polycyclic aromatic hydrocarbons (PAHs). Results were compared to data from 16 unburned natural areas and six developed sites. Mean copper, lead, and zinc flux (kg/km(2)) were between 112- and 736-fold higher from burned catchments and total phosphorus was up to 921-fold higher compared to unburned natural areas. Polycyclic aromatic hydrocarbon flux was four times greater from burned areas than from adjacent urban areas. Ash fallout on nearby unburned watersheds also resulted in a threefold increase in metals and PAHs. Attenuation of elevated concentration and flux values appears to be driven mainly by rainfall magnitude. Contaminant loading from burned landscapes has the potential to be a substantial contribution to the total annual load to downstream areas in the first several years following fires., (Copyright © 2012 SETAC.)

Mercury (Hg) stored in vegetation and soils is known to be released to the atmosphere during wildfires, increasing atmospheric stores and altering terrestrial budgets. Increased erosion and transport of sediments is well-documented in burned watersheds, both immediately post-fire and as the watershed recovers; however, understanding post-fire mobilization of soil Hg within burned watersheds remains elusive. The goal of the current study is to better understand the impact of wildfire on soil-bound Hg during the immediate post-fire period as well as during recovery, in order to assess the potential for sediment-driven transport to and within surface waters in burned watersheds. Soils were collected from three southern California watersheds of similar vegetation and soil characteristics that experienced wildfire. Sampling in one of these watersheds was extended for several seasons (1.5 years) in order to investigate temporal changes in soil Hg concentrations. Laboratory analysis included bulk soil total Hg concentrations and total organic carbon of burned and unburned samples. Soils were also fractionated into a subset of grain sizes with analysis of Hg on each fraction. Low Hg concentrations were observed in surface soils immediately post-fire. Accumulation of Hg coincident with moderate vegetative recovery was observed in the burned surface soils 1 year following the fire, and mobilization was also noted during the second winter (rainy) season. Hg concentrations were highest in the fine-grained fraction of unburned soils; however, in the burned soils, the distribution of soil-bound Hg was less influenced by grain size. The accelerated accumulation of Hg observed in the burned soils, along with the elevated risk of erosion, could result in increased delivery of organic- or particulate-bound Hg to surface waters in post-fire systems.

Although previous investigations have concluded that reactive oxygen metabolites contribute to reperfusion arrhythmias, the experimental models employed also had a significant amount of tissue injury, which may have contributed to the observed electrophysiologic effects. We studied whether exposure of the intact heart to a reactive oxygen metabolite at doses that are not associated with histologic evidence of cell necrosis would alter myocardial refractoriness, suggesting that subtle and reversible oxidative stress could alter myocardial electrophysiologic properties and perhaps contribute to ventricular arrhythmias. Isolated rabbit hearts were perfused for 30 min with low doses of hydrogen peroxide (H2O2), either 10(-5), 5 x 10(-6), or 10(-6)-M H2O2 versus vehicle alone; followed by a 30-min washout period without H2O2. Infusion of H2O2 for 30 min decreased ventricular epicardial effective refractory period (ERP) in a dose-dependent manner compared to saline controls (delta ERP). The delta ERP versus time curves during the last 10 min of H2O2 infusion were different (p less than 0.01) for each of the three H2O2 doses. Creatine phosphokinase and reversible oxidized glutathione release occurred during 10(-5)-M H2O2 infusion, but not with lower H2O2 doses. Exposure of the intact heart to low concentrations of H2O2, in a range that caused subtle oxidative injury, decreased ventricular ERP in a dose-dependent manner. Thus, H2O2 generation could contribute to ventricular arrhythmias, even in settings of sublethal and potentially reversible oxidative injury.