Stream water temperature (SWT) affects both societal and ecosystem functions, with impacts ranging from dis solved oxygen to algal blooms to lotic species mortality. The role of SWT in water chemistry and ecology has motivated rapid development in SWT modeling. Large-domain models support evaluation of current and future SWT conditions and impacts of disturbances or restorations across varied spatiotemporal scales, particularly for ungaged watersheds (no SWT observations). In the last decade, daily/kilometer-resolution, ungaged SWT models have been developed for the contiguous United States (CONUS), but do not support forecasting. Existing SWT forecasting models are constrained by lower spatial/temporal resolution or are applicable to smaller domains. Thus, there is a need for higher-resolution, large-domain forecasting to investigate future thermal conditions at scale. We present the first CONUS-scale, kilometer-resolution, forecasting-capable, daily, ungaged SWT model: "temperature estimation: near-term expected temperatures" (TempEst-NEXT). TempEst-NEXT can account for changing surface conditions (e.g., urbanization) and climate and is demonstrated for historical prediction and 1-16 day forecasting. TempEst-NEXT is also compatible with NOAA's NextGen National Water Model frame work. Forecast-forced historical tests (two-day lead) for mean daily SWT show a median RMSE of 2.3 degrees C (2.1 degrees C with estimated weather inputs), R2 of 0.92, and bias of 2.0% for ungaged watersheds, with similar perfor mance in real-time forecasts (1-16 day lead). Unlike comparable SWT models, TempEst-NEXT forecasts do not require local calibration, supporting real-time decision-making for water-resource management, such as reser voir releases, where long-term observations are unavailable. Broadly, TempEst-NEXT enables efficient, flexible, large-scale prediction of stream thermal regimes.

Stream temperature (ST) is a key driver of water quality and ecosystem health, and the analysis and forecasting thereof benefit from the availability of high spatiotemporal resolution ST datasets. However, such datasets are limited spatially and temporally across the CONUS, particularly for small, remote streams. Available models are limited in domain (regional), spatial resolution (>= 10 km), temporal resolution (monthly), or a combination thereof. We address these limitations by developing a satellite remote sensing-based spatial-statistical model, TempEst 2 ("stream TEMPerature ESTimation, version 2"), to estimate daily mean and maximum temperatures at 1 km resolution for ungaged streams of any size across the CONUS. This contrasts with an earlier version, TempEst 1, which used a random forest algorithm at monthly resolution. TempEst 2 also improves over TempEst 1 in interpretability and computational efficiency. In TempEst 2, the streams used for model training and testing cover a wide range of urban and rural land uses, climates, and geographies. TempEst 2 requires minimal input data and tolerates sparse training gage networks, supporting generalization outside the CONUS. For CONUS applications, TempEst 2 is trained on 1,316 USGS ST gages. Quantitative performance impacts from locally-sparse gage networks are reported. Model performance is evaluated using cross-validation, walk-forward validation, and extrapolation validations over regions and elevation. TempEst 2 estimates daily mean temperatures with a median cross-validation gage RMSE of 2.0 degrees C, NSE of 0.91, and bias of 0.10%. TempEst 2 supports the development and analysis of high-resolution, large-domain ST datasets across the CONUS.

The water temperature of streams in montane catchments is a key harbinger of ecosystem health and water resource quality for nearby and downstream communities, a dependency that is ever increasing and sensitive to change, in the western United States and worldwide. In recent decades, representative snowfall-dominated, montane catchments such as the Sagehen Experimental Forest (hereafter, “Sagehen”), located in the eastern Sierra Nevada mountains of California, have been studied to better understand how disturbances ranging from climate-induced events, i.e., drought, wildfire, and extreme precipitation events; to human-caused events, i.e., forestry experimentation, affect stream flow and stream water temperature (SWT). Sagehen, like many catchments in the mountain West, experiences cold, wet winters and warm, dry summers, with both the quantity and timing of snow and rain being vitally important for sustaining spring and summer streamflow and buffering SWT for ecosystem resiliency. Alarmingly, climate projections for Sagehen indicate an earlier snowmelt season and more rain-on-snow events, both of which are likely to result in unknown consequences. Additionally, rising global temperatures may exacerbate the risk of hard-to-predict disturbances (i.e., wildfires and insect infestations) and resulting impacts on hydrologic systems. Stream hydrologic response, including SWT, to such events remains poorly understood due to limitations such as lack of field data and/or lack of years-long records.To address this knowledge gap, we leverage a 12-year dataset of streamflow and SWT observations collected across Sagehen to first calibrate and then compare statistical and machine learning models for SWT. Currently, performance metrics using TempEst-NEXT, a CONUS-scale, statistical SWT forecasting model show a RMSE of 4.09°C, R2 of 0.88, NSE of 0.43 and percent bias (PBIAS) of 48% for mean daily SWT. Performance metrics for the machine-learning neural network model using daily SWT, air temperature and snow input show a strong validation period RMSE of 0.71°C, R2 of 0.98, NSE of 0.98, and PBIAS of 0.11%. Using this unique dataset, which encompasses both dry and wet periods, droughts and extreme precipitation events, as well as forest treatments, we consider the following objectives: 1) examine how climatic factors have influenced SWT response during the period of record and how response may change in the future using climate scenarios, and 2) identify what, if any, physical patterns can be discerned from observations, modeling results, and model comparisons. Preliminary analysis of daily SWT in Sagehen shows that summer 2020 had the highest daily mean SWT for the 12-year record, followed by summer 2021, and 2022. In terms of SWT variability, preliminary analysis has identified a possible relation between SWT variability and slope-face, where SWT is most buffered on the main stem, followed by the north-facing, then south-facing tributaries. Pending model analysis and cross-comparison is expected to illuminate differences in model prediction of SWT for the Sagehen basin for both the near-term and the future. Broadly, this research is expected to provide new insights on the evolution of hydrology in a montane catchment as it responds to climate variability and disturbance events.

Restoration of urban rivers must simultaneously design for ecological habitat while accounting for altered flow regimes associated with urban runoff, flood protection, and industrial/wastewater discharge. The goal of this study was to use ecological flow targets to guide channel restoration of the Los Angeles (LA) River across potential future flow regimes. Using a one-dimensional hydraulic model, we simulated a range of channel cross section configurations subject to different flow management decisions (wastewater reuse, low-flow [LF] treatment, and baseflow augmentation). Hydraulic results were assessed relative to ecohydraulic targets for desirable aquatic species in the LA River (willow, steelhead trout, and Santa Ana sucker). Results suggest that, along the mainstem of the LA River, restoration designs that include narrow LF channels may support Santa Ana sucker habitat and steelhead migration if management decisions decrease instream flows (e.g., by reusing treated wastewater). However, the same channel design and management decisions may not provide conditions needed to propagate floodplain vegetation such as willows. In tributary reaches, flows are too low to support habitat conditions for Santa Ana sucker or steelhead but may be able to support riparian habitat if a soft-bottom LF channel and active floodplain are present. In general, results illustrate the trade-offs between water management goals and habitat requirements for target species.

The influence of anthropogenic activity and land cover alteration on stream temperatures has major ecological implications, such as limiting fish survival. While these ecological impacts have been extensively studied at varying spatial scales for major rivers, our understanding of the range and complexity of this relationship across climates and geographies for smaller rivers (less than similar to 60 m wide) remains limited. This is in part because although such rivers comprise a vast majority (similar to 97%) of total stream length, most smaller rivers lack routine temperature observations despite extensive gage networks. While existing gage networks in many regions are sufficient to support satellite remote sensing-based modeling of small stream temperatures, most large regions including the contiguous United States do not have modeled data products at high spatial and temporal resolution for small rivers. In this study, we developed a statistical model, TempEst ("temperature estimation"), to estimate monthly average water temperatures for smaller rivers and evaluated the model with rivers ranging from 3 m to 1000 m (1 km) in width, using land cover, topography, and satellite-based land surface temperature. The rivers chosen for development of the model are representative of a range of urban and rural land uses and cover eight US Environmental Protection Agency Level I ecoregions. The model was calibrated and validated using observed stream temperatures from the United States Geological Survey. TempEst is able to predict monthly average temperature for streams of any size with an overall median RMSE of about 1.5 degrees C and bias of 0%. Model accuracy has a consistent trend with training gauge network density, with median RMSE increasing to about 2.0 degrees C in more sparsely-gauged regions. As a simple demonstration, we used TempEst to estimate thermal suitability conditions for cutthroat trout in Rocky Mountain streams in the United States. The developed model, available as open-source R (model) and Google Earth Engine (data retrieval) scripts, will facilitate the study of stream temperature behaviors at higher resolutions than previously available across the contiguous United States and can be easily adapted to support global river systems.

Seasonal regimes of stream temperatures are important for ecological health as well as for societal water use. Seasonal regimes can be captured in the annual temperature cycle (the mean temperature for each day of the year) or in summary statistics such as seasonal mean temperatures, the former of which is the focus of this work. The annual temperature cycle is often characterized as a sine function, which performs satisfactorily for most streams. However, the sine function is unable to capture major seasonal variations, particularly for colder, drier, and high-elevation regions. Seasonal summary statistics are effective for classification but do not capture the full time series, preventing the use of lost time-series information, and lack context for the comparison of trends, hindering distinction between different causes of similar seasonal trends. We propose an improved function called the "three-sine model" to describe the stream annual temperature cycle with higher accuracy and demonstrate its use in two case studies. The three-sine model uses a cosine function over the entire year coupled with two seasonal anomaly sine functions. The three-sine model captures the stream annual temperature cycle with eight parameters, reveals distinct spatial trends, and outperforms the sinusoidal model for all elevations and 99% of streams. We conclude that this approach can support improved stream temperature analysis by capturing detailed seasonal trends in context.

The future of the Western United States is threatened by both an increase in wildfire frequency and a decrease in water availability. By reducing fuel loads, wildfire mitigation measures (forest treatments) can offer reduced fire severity and increased annual total runoff (water yield) via reduction in evapotranspiration (ET). While the benefits of forest treatments for fire management are well studied, their impact on ET and water yield remains largely unknown, and existing literature shows conflicting results. Here, we aim to resolve this ambiguity by quantifying the impact of forest treatments on ET and water yield, at spatially localized scales. Using daily average flow rates from sub-basin and basin scale gauges, 100-m LiDAR data, 800-m PRISM precipitation data and 30-m SSEBop ETa data, we analysed the impact of forest treatments on ETa and water yield in the Sagehen Experimental Watershed. Within treated areas of Sagehen, there is a linear relationship between loss of canopy cover and ETa reductions at the 100-m pixel scale when canopy cover loss exceeds 10%. The impact of treatment was highly localized, and across the entire watershed (30 km(2)), treated areas with reduced ETa only made up 4 km(2), similar to 10% of the Sagehen area. At sub-basin and basin scale, the magnitude of year-to-year ETa reduction was <15%, and there was no quantifiable increase in water yield. Instead, precipitation alone explained >= 85% of water yield variability at sub-basin and basin scale. Future forest management practices in the Sierra Nevada are essential for combating wildfire, but our results from Sagehen reveal that even at the subbasin scale (similar to 3 km(2)), 56% thinning treatment by area did not result in increased water yield.

Anthropogenic development has adversely affected river habitat and species diversity in urban rivers, and existing habitats are jeopardized by future uncertainties in water resources management and climate. The Los Angeles River (LAR), for example, is a highly modified system that has been mostly channelized for flood control purposes, has altered hydrologic and hydraulic conditions, and is thermally altered (warmed), which severely limits the habitat suitability for cold water fish species. Efforts are currently underway to provide suitable environmental flows and improve channel hydraulic conditions, such as depth and velocity, for adult fish migration from the Pacific Ocean to upstream spawning areas. However, the thermal responses of restoration alternatives for resident and migrating cold water fish have not been fully investigated. Using a mechanistic model, we simulated the LAR’s water temperature under baseline conditions and future alternative restoration scenarios for migration of the native, anadromous steelhead trout in Southern California and the historically resident Santa Ana sucker. We considered three scenarios: 1) increasing roughness of the low-flow channel, 2) increasing the depth and width of the low-flow channel, and 3) allowing subsurface inflow to the river at a soft bottom reach in the LA downtown area. Our analysis indicates that the maximum weekly average temperature (MaxWAT) in the baseline condition was 28.9°C, suggesting that the current river temperatures would act as a limiting factor during the steelhead migration season and habitat for Santa Ana sucker. The MaxWAT dropped about 3%–28°C after applying all the considered scenarios at the study site, which is 3°C higher than the determined steelhead survival threshold. Our simulations suggest that without consideration of thermal restoration, restoring hydraulic conditions may be insufficient to support cold water fish migration or year-round resident native fish populations, particularly with potential river temperature increases due to climate change.

Flows in urban rivers are increasingly managed to support water supply needs while also protecting and/or restoring instream ecological functions, goals that are often in opposition to each other. Effluent-dominated rivers (i.e., rivers that consist primarily of discharged treated wastewater) pose a particular challenge because changes in effluent discharge may impact river ecology. A functional flows approach, in which metrics from the annual hydrograph correspond to ecological processes, was applied to understand the hydro-ecological implications of wastewater reuse in the Los Angeles River watershed (Los Angeles County, California, USA). The Los Angeles River, like many urban rivers, is dominated by effluent, particularly during dry weather. An hourly hydrologic model was created, calibrated, and validated in EPA SWMM for the Los Angeles River watershed to investigate how increases in wastewater reuse (i.e., decreases in discharge to the river) may impact river flows and subsequently ecology and recreation in the river. Current flows are shown to support freshwater marsh, riparian habitat, fish migration, and wading shorebird habitat, in addition to recreational kayaking. Functional flow metrics were assessed under future management scenarios including reducing discharge to increase recycling at three wastewater treatment plants within the watershed. Both wet-season and dry-season baseflows were most sensitive to increasing wastewater reuse, with an average decrease of 51–56% (0.93 cms) from current baseflows. Sensitivity curves that relate potential changes in wastewater discharge to changes in functional flows show that a 4% decrease in current wastewater discharge may negatively impact habitat for indicator species during the dry season. More opportunity exists for wastewater reuse during the wet season, when current wastewater discharge may be reduced by 24% with minimal impacts to ecology and recreation. The developed approach has the potential to inform similar tradeoff decisions in other urban rivers where flows are dominated by wastewater or stormdrain discharge.

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 (R2 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.

While automatic calibration programs exist for many hydraulic models, no user-friendly and broadly reusable automatic calibration system currently exists for steady-state HEC-RAS models. This study highlights development of Raspy-Cal, an automatic HEC-RAS calibration program based on a genetic algorithm and implemented in Python. It includes a graphical user interface and an interactive command-line interface, as well as libraries readily usable by other programs. As a case study, Raspy-Cal was used to calibrate a model of the Los Angeles River in California and its two major tributaries. We found that Raspy-Cal matched the accuracy of manual calibrations in much less time and without manual intervention, producing a Nash–Sutcliffe Efficiency of 0.89 or greater within several hours when run for 100 iterations. Our analysis showed that the open-source freeware facilitates fast and precise calibration of HEC-RAS models and could serve as a basis for future software development. Raspy-Cal is available online in source and executable form as well as through the Python Package Index.

Earth and Space Science Open Archive This is a preprint and has not been peer reviewed. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v2]Evaluating the impact of substrate temperature on thermal habitat suitability and ecological restoration in shallow urban riversAuthorsRezaAbdiiDJenniferRogersiDAshleyRustJordynWolfandiDDanielPhilippusiDKristineTaniguchi-QuaniDKatieIrvingiDVictoriaHennoniDEricSteiniDTerriHogueSee all authors Reza AbdiiDCorresponding Author• Submitting AuthorColorado School of MinesiDhttps://orcid.org/0000-0003-2901-7415view email addressThe email was not providedcopy email addressJennifer RogersiDSouthern California Coastal Water Research ProjectiDhttps://orcid.org/0000-0002-4515-0753view email addressThe email was not providedcopy email addressAshley RustColorado School of Minesview email addressThe email was not providedcopy email addressJordyn WolfandiDUniversity of PortlandiDhttps://orcid.org/0000-0003-2650-4373view email addressThe email was not providedcopy email addressDaniel PhilippusiDColorado School of MinesiDhttps://orcid.org/0000-0001-8039-2662view email addressThe email was not providedcopy email addressKristine Taniguchi-QuaniDSouthern California Coastal Water Research ProjectiDhttps://orcid.org/0000-0001-8631-5174view email addressThe email was not providedcopy email addressKatie IrvingiDSouthern California Coastal Water Research ProjectiDhttps://orcid.org/0000-0002-6582-7979view email addressThe email was not providedcopy email addressVictoria HennoniDColorado School of MinesiDhttps://orcid.org/0000-0002-5953-4627view email addressThe email was not providedcopy email addressEric SteiniDSouthern California Coastal Water Research ProjectiDhttps://orcid.org/0000-0002-4729-809Xview email addressThe email was not providedcopy email addressTerri HogueColorado School of Minesview email addressThe email was not providedcopy email address