Terri S. Hogue

Wildfires are increasingly reshaping landscapes across the U.S., disrupting hydrogeologic processes such as runoff, infiltration, and sediment transport—posing major challenges for streamflow prediction and water resource management. Traditional conceptual and physically based hydrologic models often struggle to capture these disturbance-driven dynamics. In this study, we explore the potential of long short-term memory (LSTM) networks, a type of recurrent neural network, to simulate post-fire streamflow across 1,082 fire-affected basins spanning the contiguous U.S.—representing the first near-continental-scale application of LSTMs for wildfire-related hydrologic prediction. Three LSTM models were trained on different temporal splits of fifteen-year datasets containing wildfire events: one using pre-fire data, one using post-fire data, and one using the full dataset. Models were evaluated on unseen basins in both pre- and post-fire windows. Results show that the model trained on the full dataset consistently outperformed the others, underscoring the importance of temporally diverse training data that include disturbance events. Importantly, LSTMs demonstrated strong generalization across disturbed and undisturbed environments, highlighting their ability to learn hydrologic patterns beyond the constraints of traditional process-based modeling frameworks. Feature importance analysis revealed that topographic variables (e.g., elevation and slope) were most influential, followed by soil/geologic and vegetation characteristics, while fire-specific indicators (e.g., burn severity) ranked surprisingly low. This suggests that the LSTMs internalized key controls on streamflow response without heavy reliance on the explicit disturbance metrics included. To further isolate the model’s learned response to wildfire, simulations were performed with synthetic unburned conditions for each disturbed basin and compared against burned scenarios. Spatial analysis by EPA Level II ecoregion revealed that in the Southeastern U.S., Ozark/Appalachian Forests, and Mediterranean California, the model identified a persistent, multi-year increase in streamflow-lasting up to three years after wildfire. These regions share ecological characteristics such as high vegetation biomass, seasonal climate regimes, and terrain-driven hydrologic gradients that collectively amplify post-fire reductions in evapotranspiration and enhance runoff generation. In contrast, no significant streamflow change was detected in the Western Cordillera, South Central Prairies or Cold Desert ecoregions, where water-limited climates and lower fuel loads results in a dual-action response of hydrologic buffering and constrained post-fire increases in water yield. Together, these findings demonstrate that LSTMs can detect regionally coherent hydrologic responses to wildfire even in the absence of strong dependence on explicit disturbance features, highlighting the promise of AI-driven, data-centric approaches for modeling hydrologic change in an era of increasing disturbances. As wildfires and other extreme events become more frequent, integrating machine learning into hydrologic prediction frameworks offers a powerful pathway toward adaptive water resource management and improved resilience across diverse ecohydrologic settings.

Stream water temperature (SWT) is a key water quality indicator impacting both ecosystems and infrastructure. There is growing evidence that SWT is affected by wildfire, especially given the abrupt land cover changes stemming from wildfire severity and extent. Although wildfire impacts are of increasing concern for various parts of the hydrologic cycle in the United States (U.S.) and globally, the effects of wildfire on SWT are highly variable and not well understood. This knowledge gap is exacerbated by the limited collection of pre- and post-fire SWT observations, with the largest U.S. study to date having used only a few dozen sites. We posit that SWT modeling over unmonitored watersheds which have previously experienced wildfire could substantially expand the available data on SWT response to wildfire. To assess wildfire impacts over a larger scale (hundreds to thousands of sites), we validated a previously developed statistically-based SWT model called TempEst 2 on available pre/post-fire observations. After validation, TempEst 2 is used to model SWT response to wildfires across the Western U.S. where pre/post-fire observations are unavailable.Preliminary results, assessed at monthly resolution to reduce noise, show that the TempEst 2 model captures ~70% of spatial variance in the pre- to post-fire change in August mean daily maximum SWT. Preliminary results also suggest that application of the model to several hundred burned watersheds in the forested mountains of the Western U.S. has the potential to enable characterization of general trends in SWT response. More broadly, analysis of the spatial trends in wildfire SWT response and how they relate to burn severity and watershed conditions will improve the scientific community’s ability to project SWT response to future wildfire-related land cover change.

Stream Temperature Seasonal Thermal Regime Data

Stormwater management is a multiobjective decision-making process. Integrating life-cycle costs (LCC) and a benefit analysis with hydrologic modeling can enhance planning-level analyses by expanding available information for decision makers. In this study, two urbanized watersheds located in Los Angeles, California, and Denver, Colorado, are modeled using an integrated decision support tool for green to grey stormwater infrastructure. The primary goals are to compare capital costs to LCC and to explore the preference of green versus grey solutions using varying management criteria and a hydrologic benefit analysis. Results show that optimizing on capital costs alone produces qualitatively different results than optimizing on LCC, which are substantially higher. Additionally, a shift in relative cost order between stormwater control measure (SCM) types, when comparing capital costs and LCC, may misguide decision makers on which SCMs minimize cost while achieving regulatory compliance. Watershed-specific management criteria relevant to Ballona Creek (Los Angeles) and Berkeley neighborhood (Denver) result in a preference for greener solutions given hydrologic process benefits. The optimal solutions for these basins are determined by evaluating costs to the community and environment, comparing the hydrologic performance of varying SCMs, and ensuring the priorities of the individual watershed are met. Using a framework that integrates stormwater modeling, LCC, and a benefit analysis advances decision-relevant information for urban planners and water resource managers.

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.

As we move into an era of increased urbanization, stormwater practitioners are charged with creating multibenefit solutions through the installation of stormwater control measures (SCMs). Two drivers facilitate the accrual of benefits in SCMs: hydrologic or water quality process and vegetation. This study investigated the feasibility of incorporating benefits beyond water quality and quantity control into the SCM planning process. A critical review was used to determine which benefits would be assessed with hydrologic and water quality modeling or a complementary conceptual framework. Drawing on common themes in the literature, the conceptual framework of the 4 Cs (community, context, connectivity, and canopy) was created to assess vegetation-based benefits. To demonstrate the coupled benefit assessment, a case study was performed in a neighborhood of Denver, Colorado. Results from hydrologic and water quality modeling show that vegetated swales provide the most preferred solution. From the vegetation-based benefit perspective, we find that the modeled area of the vegetated swales is only 0.2% by area, which is likely too small to have a measurable benefit effect at the neighborhood scale. We show how the 4 Cs can be used to leverage existing greenness to maximize the potential vegetation-based benefit of swales and consider how existing sociodemographic and vegetation trends can be leveraged to make benefit-driven decisions.

The objective of this research is to develop a module for the design of best management practices based on percent pollutant removal. The module is a part of the site-scale integrated decision support tool (i-DSTss) that was developed for stormwater management. The current i-DSTss tool allows for the design of best management practices based on flow reduction. The new water quality module extends the capability of the i-DSTss tool by adding new procedures for the design of best management practices based on treatment performance. The water quality module can be used to assess the treatment of colloid/total suspended solid and dissolved pollutants. We classify best management practices into storage-based (e.g., pond) and infiltration-based (e.g., bioretention and permeable pavement) practices for design purposes. Several of the more complex stormwater tools require expertise to build and operate. The i-DSTss and its component modules including the newly added water quality module are built on an accessible platform (Microsoft Excel VBA) and can be operated with a minimum skillset. Predictions from the water quality module were compared with observed data, and the goodness-of-fit was evaluated. For percent total suspended solid removal, both R² and Nash–Sutcliffe efficiency values were greater than 0.7 and 0.6 for infiltration-based and storage-based best management practices, respectively, demonstrating a good fit for both types of best management practices. For percent total phosphorous and Escherichia. coli removal, R² and Nash–Sutcliffe efficiency values demonstrated an acceptable fit. To enhance usability of the tool by a broad range of users, the tool is designed to be flexible allowing user interaction through a graphical user interface.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.