Jaime R. Goode, Robert J. Hawley, Robert H. Lewis, Bethany Mulhall
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.
{"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}
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
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.
供水预测中的不确定性归因对于提高预测技能和增强季节性水资源管理规划的信心至关重要。我们建立了一个框架来量化部分预报不确定性，并将其划分为：（1）积雪量量化方法；（2）预报后降水量的变化；（3）径流模型误差。我们利用两个末端成员空间分辨率的积雪观测数据，即简单的雪枕指数和机载积雪观测站提供的 50 米分辨率全流域雪水当量 (SWE) 地图，在图鲁姆河和默塞德河流域（美国加利福尼亚州）利用统计径流模型演示了不确定性框架。贝叶斯预测模拟表明，在暖雪干旱期间，当冬季降水中仍有较低比例的雪水当量时，统计供水预测的技能会出现非线性下降。在冬季降水量较低的干雪干旱期间，预测能力也会出现类似的下降。在研究区域，在雪主导地位逐渐消失的过程中，使用雪枕数据进行预测的不确定性增加速度是使用全流域 SWE 地图进行类似预测的 1.9 倍。在所有测试的气候条件下，用全流域 SWE 数据取代雪枕指数可将统计预测的不确定性平均降低 39%。将供水预测的不确定性归因于可减少的误差源，揭示了在未来气候变暖的情况下提高预测可靠性的机会。
{"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}
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