JAWRA Journal of the American Water Resources Association最新文献

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Rapid geomorphic assessment walkabouts as a tool for stream mitigation monitoring 作为溪流缓解监测工具的快速地貌评估徒步旅行

JAWRA Journal of the American Water Resources Association Pub Date : 2024-07-09 DOI: 10.1111/1752-1688.13222

Jaime R. Goode, Robert J. Hawley, Robert H. Lewis, Bethany Mulhall

{"title":"Rapid geomorphic assessment walkabouts as a tool for stream mitigation monitoring","authors":"Jaime R. Goode, Robert J. Hawley, Robert H. Lewis, Bethany Mulhall","doi":"10.1111/1752-1688.13222","DOIUrl":"https://doi.org/10.1111/1752-1688.13222","url":null,"abstract":"Monitoring of compensatory stream mitigation projects conventionally relies on spatially discrete geometric data and habitat assessments collected from representative reaches. Project success is evaluated by extrapolating site‐scale metrics such as rapid bioassessment protocol (RBP) scores and time‐series changes in width‐to‐depth ratios to adjacent reaches. For example, an excellent RBP score at one location is used to infer excellent habitat in nearby reaches. This paper compares spatially discrete and continuous monitoring data from 38 km of restored stream length on a stream mitigation project in central Kentucky to document how conventional site‐level metrics may not represent conditions in adjacent reaches, particularly on projects plagued by post‐construction geomorphic instability (e.g., headcut migration, propagation of bank erosion, and chute cutoff formation). Over a 5‐year monitoring period, rapid visual assessment walkabouts documented project‐scale geomorphic process trajectories that were not captured by conventional site‐specific monitoring. Early detection of geomorphic instability from this rapid monitoring approach facilitated cost‐effective and tailored adaptive management (e.g., planting of live stakes to arrest bank erosion). Full‐census walkabouts can thereby help to improve mitigation credit valuation, enhance long‐term habitat protection, and facilitate successful steam restoration outcomes.","PeriodicalId":507874,"journal":{"name":"JAWRA Journal of the American Water Resources Association","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sources of seasonal water supply forecast uncertainty during snow drought in the Sierra Nevada 内华达山脉雪旱期间季节性供水预测不确定性的来源

JAWRA Journal of the American Water Resources Association Pub Date : 2024-07-03 DOI: 10.1111/1752-1688.13221

Elijah N. Boardman, C. Renshaw, Robert K. Shriver, Reggie Walters, Bruce McGurk, Thomas H. Painter, J. Deems, K. Bormann, Gabriel M. Lewis, E. Dethier, A. Harpold

{"title":"Sources of seasonal water supply forecast uncertainty during snow drought in the Sierra Nevada","authors":"Elijah N. Boardman, C. Renshaw, Robert K. Shriver, Reggie Walters, Bruce McGurk, Thomas H. Painter, J. Deems, K. Bormann, Gabriel M. Lewis, E. Dethier, A. Harpold","doi":"10.1111/1752-1688.13221","DOIUrl":"https://doi.org/10.1111/1752-1688.13221","url":null,"abstract":"Uncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate.","PeriodicalId":507874,"journal":{"name":"JAWRA Journal of the American Water Resources Association","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0