Data and code for: Modeling Seasonal Effects of River Flow on Water Temperatures in an Agriculturally Dominated California River

This resource contains the data and scripts used for: Asarian, J.E., Robinson, C., Genzoli, L. 2023. Modeling Seasonal Effects of River Flow on Water Temperatures in an Agriculturally Dominated California River. Water Resources Research, e2022WR032915. https://doi.org/10.1029/2022WR032915. Abstract from the article: Low streamflows can increase vulnerability to warming, impacting coldwater fish. Water managers need tools to quantify these impacts and predict future water temperatures. Contrary to most statistical models’ assumptions, many seasonally changing factors (e.g., water sources and solar radiation) cause relationships between flow and water temperature to vary throughout the year. Using 21 years of air temperature and flow data, we modeled daily water temperatures in California’s snowmelt-driven Scott River where agricultural diversions consume most summer surface flows. We used generalized additive models to test time-varying and nonlinear effects of flow on water temperatures. Models that represented seasonally varying flow effects with intermediate complexity outperformed simpler models assuming constant relationships between water temperature and flow. Cross-validation error of the selected model was ≤1.2 °C. Flow variation had stronger effects on water temperatures in April–July than in other months. We applied the model to predict effects of instream flow scenarios proposed by regulatory agencies. Relative to historic conditions, the higher instream flow scenario would reduce annual maximum temperature from 25.2 °C to 24.1 °C, reduce annual exceedances of 22 °C (a cumulative thermal stress metric) from 106 to 51 degree-days, and delay onset of water temperatures >22 °C during some drought years. Testing the same modeling approach at nine additional sites showed similar accuracy and flow effects. These methods can be applied to streams with long-term flow and water temperature records to fill data gaps, identify periods of flow influence, and predict temperatures under flow management scenarios. The files are organized into 6 folders: R_Scripts, SourceDataFiles, CompiledData, WorkingFiles, Outputs, and OtherStudies. Details of file are provided in the README.txt file.本数据集包含用于以下研究的数据与代码脚本：Asarian JE、Robinson C、Genzoli L于2023年发表于《水资源研究（Water Resources Research）》的论文《农业主导型加州河流中径流对水温的季节效应建模》（论文编号：e2022WR032915，DOI：10.1029/2022WR032915）。 论文摘要：河道低径流会加剧水体升温风险，对冷水鱼类生存造成不利影响。水利管理者亟需可量化此类影响并预测未来水温的工具。与多数统计模型的假设相悖，诸多随季节变化的因素（如水源、太阳辐射）会导致径流与水温之间的关联随全年时段动态改变。本研究依托21年的气温与径流观测数据，对以融雪补给为主的美国加州斯科特河开展日尺度水温建模——该流域夏季地表径流的大部分被农业引水工程消耗。研究采用广义可加模型（Generalized Additive Models）检验了径流对水温的时变非线性效应。相较于假定水温与径流间关联恒定的简单模型，采用中等复杂度表征季节变化径流效应的模型表现更优。优选模型的交叉验证误差≤1.2 ℃。4月至7月期间，径流变化对水温的影响强度显著高于其他月份。研究团队将该模型应用于监管机构提出的河道内径流调控情景预测：相较于历史基准条件，较高河道内径流情景可将年最高水温从25.2 ℃降至24.1 ℃，将年累计超过22 ℃的度日数（累积热应力指标）从106降至51，还可推迟部分干旱年份中水温超过22 ℃的起始时间。本研究方法可推广应用于具备长期径流与水温观测记录的河流，用于填补数据空白、识别径流影响时段以及预测径流管理情景下的水温变化。 数据集文件分为6个文件夹：R脚本（R_Scripts）、源数据文件（SourceDataFiles）、整合数据（CompiledData）、工作文件（WorkingFiles）、输出结果（Outputs）及其他研究相关文件（OtherStudies）。文件详细说明见README.txt文件。