Small streams often lack reliable hydrological data. Environmental agencies play a key role in providing such data; however, these agencies are often challenged by the growingmonitoring needs and lack of funding. Given the spatial mismatch between observed data and small watersheds/headwaters, local volunteers can act as potentially valuable research partners. We examine how CrowdHydrology, a citizen science program that collects stream stage and stream temperature observations, improves a hydrologic model of the Boyne River, Michigan, USA. Volunteers provided observations at four calibration sites with different interarrival times of the observations. We tested whether stream stage and stream temperature observations (measured by volunteers) improved the performance of a Soil and Water Assessment Tool (SWAT) model of the Boyne River. Observations were integrated into the model using the ensemble Kalman filter. This framework allowed us to integrate observation error, track the variability of model parameters, and simulate daily streamflow and stream temperature across the watershed. Measures of daily model performance included the Nash‐Sutcliffe efficiency, modified Nash‐Sutcliffe efficiency (Ef‐mod), refined index of agreement (dr), and relative bias (Bias). For all calibration sites, estimates of streamflow improved after data assimilation compared to simulations based on initial/default SWAT parameters. Different measures of model performance emerged based on the interarrival times of the observations. Results demonstrate that observations collected by local volunteers, with a certain temporal resolution, can improve SWAT hydrological models and capture central tendency.

中文翻译：

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通过同化众包数据改进水文模型

小溪流通常缺乏可靠的水文数据。环境机构在提供此类数据方面发挥着关键作用；然而，这些机构往往面临着日益增长的监测需求和缺乏资金的挑战。鉴于观测数据与小流域/源头之间的空间不匹配，当地志愿者可以充当潜在有价值的研究合作伙伴。我们研究了 CrowdHydrology（一个收集河流水位和河流温度观测值的公民科学计划）如何改进美国密歇根州博因河的水文模型。志愿者在四个校准站点提供了观测，观测的到达间隔时间不同。我们测试了河流阶段和河流温度观测（由志愿者测量）是否提高了博因河土壤和水评估工具 (SWAT) 模型的性能。使用集成卡尔曼滤波器将观察结果集成到模型中。该框架使我们能够整合观测误差，跟踪模型参数的可变性，并模拟流域内的每日流量和河流温度。日常模型性能的测量包括 Nash-Sutcliffe 效率、修正的 Nash-Sutcliffe 效率 (Ef-mod)、精炼的一致性指数 (dr) 和相对偏差 (Bias)。对于所有校准站点，与基于初始/默认 SWAT 参数的模拟相比，数据同化后的流量估计值有所提高。根据观测的到达间隔时间出现了不同的模型性能度量。结果表明，当地志愿者收集的具有一定时间分辨率的观测可以改进SWAT水文模型并捕捉集中趋势。