Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States

Collar, Natalie M.; Saxe, Samuel; Ebel, Brian A.; Boden, Kathryn S.; Rust, Ashley; Hogue, T. S.

doi:10.1016/j.jhydrol.2022.128242

Cited by 13 publications

(11 citation statements)

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“…In our own study, we demonstrate that spatially localized 30‐m SSEBop ET a decreases in response to forest treatment (Figure 3), as expected. Other studies that used the same Landsat driven 30‐m SSEBop data also found decreases in ET a following landcover disturbances (Collar et al, 2022). Thus, on both the local and CONUS scales, SSEBop is capturing at least a portion of expected ET a variability.…”

Section: Discussionmentioning

confidence: 64%

“…Other studies that used the same Landsat driven 30-m SSEBop data also found decreases in ET a following landcover disturbances (Collar et al, 2022). Thus, on both the local and CONUS scales, SSEBop is capturing at least a portion of expected ET a variability.…”

Section: Uncertainty In Ssebop Et Amentioning

confidence: 60%

“…An older summary of paired catchment studies found that timber harvesting can lead to increases in WY as long as 15%–50%, depending on the region, of the forested area is removed (Stednick, 1996). A more recent growing body of literature on other types of disturbances, such as thinning from forest treatment, finds that the effect is variable, leading to increased runoff, decreased runoff or no change in runoff (Biederman et al, 2015; Collar et al, 2022; Goeking & Tarboton, 2020; Kurzweil et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A multi‐scale assessment of forest treatment impacts on evapotranspiration and water yield in the Sierra Nevada

et al. 2023

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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 km2), treated areas with reduced ETa only made up 4 km2, ~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 sub‐basin scale (~3 km2), 56% thinning treatment by area did not result in increased water yield.

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Section: Discussionmentioning

confidence: 64%

Section: Uncertainty In Ssebop Et Amentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

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A multi‐scale assessment of forest treatment impacts on evapotranspiration and water yield in the Sierra Nevada

et al. 2023

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“…Evapotranspiration shifts after wildfire can also contribute to differences in watershed annual yield (i.e., total flow), especially in the western United States (e.g., Blount et al., 2020; Hallema, Sun, et al., 2018; Saxe et al., 2018). There is, however, substantial variability in wildfire effects on annual yield (A. P. Williams et al., 2022; Biederman et al., 2022), and compensatory uptake by unburned downgradient vegetation can reduce measured wildfire effects on streamflow (Collar et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Modeling Post‐Wildfire Hydrologic Response: Review and Future Directions for Applications of Physically Based Distributed Simulation

et al. 2023

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Wildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post‐wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post‐wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under‐represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post‐wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community.

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“…Nesse trabalho, o teste aumentado de Dickey-Fuller indicou que todas covariáveis são estacionárias, ou seja, as séries temporais criadas a partir de processos estocásticos são constantes ao longo do período analisado, não interferindo nas inferências estatísticas dos modelos utilizados (COLLAR et al, 2022;SILVEIRA et al, 2022a).…”

Section: Análise De Estacionariedade Das Séries Temporaisunclassified

Modelagem de vazões máximas e mínimas com base em aprendizado de máquina

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O comportamento hidrológico de diversas bacias hidrográficas tem sido afetado por fatores como uso e cobertura da terra, demanda hídrica e mudanças climáticas, levando ao comprometimento da disponibilidade hídrica ou à ocorrência de eventos extremos de vazão. Esse comportamento pode ser avaliado por meio de testes não-paramétricos, que mostram a tendência de variáveis hidroclimatológicas de interesse para a gestão ambiental. Além dessa análise, em estudos hidrológicos tem-se a necessidade de aumentar os horizontes de previsão da vazão, principalmente em bacias com frequente ocorrência de eventos de alta magnitude. Assim, objetivou-se compreender o comportamento hidrológico e suas possíveis mudanças nos últimos anos na bacia hidrográfica do rio Muriaé, além de modelar a vazão com base em diferentes cenários de dias de antecedência na sua previsão. No Capítulo 1 foram aplicados testes não-paramétricos para a identificação de tendências de mudança no comportamento hidrológico em anos/meses em diferentes níveis de significância, bem como a investigação de fatores que poderiam estar associados às mudanças. Os testes não-paramétricos mostraram tendência negativa dos valores extremos de vazão, principalmente no período de estiagem, com destaque para o mês de outubro. Na análise dos fatores, a precipitação e a demanda hídrica apresentaram indicativos de influência no regime de vazões, diferentemente do uso e cobertura da terra. Os diferentes métodos estatísticos permitiram encontrar resultados satisfatórios sobre o comportamento hidrológico da bacia, enquanto as análises dos fatores climáticos e antrópicos possibilitaram conhecer as possíveis influências nas mudanças de tendência da vazão. No Capítulo 2 foi explorada a utilização de técnicas de Aprendizado de Máquina como o Modelo Linear Generalizado (GLM), as Splines de Regressão Adaptativa Multivariada (MARS) e o Random Forest (RF) na previsão da vazão considerando cenários de dados com um, dois e três dias de antecedência. A modelagem mostrou que todas as técnicas tiveram um desempenho muito bom na previsão das vazões com um dia de antecedência. Por outro lado, houve uma redução do desempenho com o aumento do horizonte de previsão, principalmente no cenário de três dias. Em todos os períodos de antecedência a técnica RF se mostrou superior às demais, mesmo que tenha apresentado subestimação das vazões máximas. A modelagem em diferentes cenários de previsão com Aprendizado de Máquina mostrou a aplicabilidade dos algoritmos na previsão da vazão, principalmente em bacias com ocorrência elevada de eventos máximos de vazão, com possibilidade de serem utilizados nos sistemas de alerta e, assim, reduzir os impactos socioambientais causados por esses eventos. Palavras-chave: Comportamento hidrológico. Modelagem hidrológica. Períodos de antecedência.

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Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States

Cited by 13 publications

References 84 publications

A multi‐scale assessment of forest treatment impacts on evapotranspiration and water yield in the Sierra Nevada

A multi‐scale assessment of forest treatment impacts on evapotranspiration and water yield in the Sierra Nevada

Modeling Post‐Wildfire Hydrologic Response: Review and Future Directions for Applications of Physically Based Distributed Simulation

Modelagem de vazões máximas e mínimas com base em aprendizado de máquina

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