Journal Article

Predicting Streamflow Duration From Crowd‐Sourced Flow Observations

Sears, Megan G., Ross, Matthew R. V., Puntenney‐Desmond, Kira C., Peterson, David A., Kampf, Stephanie K., Fairchild, Matthew P., Hammond, John C., Zipper, Sam

2024

Overview

Streamflow duration is important for aquatic ecosystems and assigning stream protection status. This study predicts streamflow duration, represented as the fraction of time with flow each year, using a combination of sensor data and crowd‐sourced visual observations for a study area in northern Colorado, USA. We used 11 stream stage sensors and 177 visual monitoring points to examine how frequently streams should be sampled to compute flow fractions accurately. This showed that the number of visual observations needed to compute accurate flow fractions increases with decreasing flow duration. We then developed random forest models to predict mean annual flow fractions using climate, topographic, and land cover predictors and found that snow persistence, summer precipitation, and drainage area were important predictors. Model performance was best when using sites with ≥10 visual observations. Our model predicts that almost all (98%) of streams in the study region are non‐perennial, about 10% more than the amount of non‐perennial streams in the National Hydrography Dataset. Stream type maps are sensitive to the time period of data collection and to thresholds used to represent perennial versus non‐perennial flow. To improve maps of non‐perennial streams, we recommend moving beyond categorical classification of streams to a continuous variable like flow fraction. These efforts can be best supported with frequent observations in time that span streams with a wide range of flow fractions and drainage area attributes. Plain Language Summary Most small streams in the world are not monitored, so we know little about when they are flowing or dry. Yet, the amount of time streams flow can determine whether they are protected by water quality legislation and streamside management plans. In this study we used visual observations of stream flow/no flow and stream sensors to develop a model that predicts the fraction of time that streams flow. At a study area in northern Colorado, volunteer observers documented stream flow/no flow at 177 stream segments, and we placed sensors in 11 headwater streams at different elevations. We found that streams needed to be visited approximately weekly to determine how long they flow each year. Streams that rarely flow needed to be visited more often than those that flow most of the time. Our model shows that most (98%) of the streams in the study area do not flow continuously. The amount of time that streams flow is sensitive to changing climate and water demands. Ongoing monitoring of these streams will help us track and predict the range of flow conditions that are possible throughout the vast networks of small streams that feed larger rivers and lakes. Key Points Predicted April–September fraction of time with flow using sensors, crowd‐sourced observations, and statistical models in Colorado streams Snow persistence, summer precipitation, and drainage area are dominant predictors of flow fractions in the Northern Colorado study area Developing a reliable model of flow fraction requires sampling diverse streams that span the full spectrum of flow fractions (0–1)

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Aquatic ecosystems / Bats / Cellular telephones / Climate / Climate change / Colorado / Continuity (mathematics) / Creeks & streams / Crowdsourcing / crowd‐sourced / Data collection / Drainage / Drainage area / Duration / Flow duration / flow fraction / Flow mapping / Headwaters / Hydrographic data / Hydrography / intermittency / Lakes / Land cover / Legislation / model validation / Monitoring / Perennial streams / Precipitation / Rivers / Sensors / snow / snow persistence / Stream discharge / Stream flow / streamflow / streams / summer / Summer precipitation / Surface water / topography / Visual observation / water / Water quality