Search for: All records

To manage water resources and forecast river flows, hydrologists seek to understand how water moves from precipitation, through watersheds, into river channels. However, we lack fundamental information on the spatial distribution and physical controls on global hydrologic processes. This information is needed to provide theoretical support for large-domain model simulations. Here, to address this issue, we present a global, searchable database of 400 research watersheds with published descriptions of dominant hydrologic flow pathways. This knowledge synthesis approach leverages decades of grant funding, fieldwork effort and local expertise. We use the database to test longstanding hypotheses about the roles of climate, biomes and landforms in controlling hydrologic processes. We show that aridity predicts the depth of water flow pathways and that terrain and biomes predict the prevalence of lateral flow pathways. These new data and search capabilities support efficient hypothesis testing to investigate emergent patterns that relate landscape organization to hydrologic function. 

Post-fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post-fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post-fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration-excess overland flow and decrease in saturation-excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post-fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well-performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post-fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post-fire hydrologic hazards after wildfire occurs. 

Abstract Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow. 

Hydrologic signatures are quantitative metrics that describe streamflow statistics and dynamics. Signatures have many applications, including assessing habitat suitability and hydrologic alteration, calibrating and evaluating hydrologic models, defining similarity between watersheds and investigating watershed processes. Increasingly, signatures are being used in large sample studies to guide flow management and modelling at continental scales. Using signatures in studies involving 1000s of watersheds brings new challenges as it becomes impractical to examine signature parameters and behaviour in each watershed. For example, we might wish to check that signatures describing flood event characteristics have correctly identified event periods, that signature values have not been biassed by data errors, or that human and natural influences on signature values have been correctly interpreted. In this commentary, we draw from our collective experience to present case studies where naïve application of signatures fails to correctly identify streamflow dynamics. These include unusual precipitation or flow regimes, data quality issues, and signature use in human-influenced watersheds. We conclude by providing guidance and recommendations on applying signatures in large sample studies. 

Abstract Freshwater salinization of rivers is occurring across the globe because of nonpoint source loading of salts from anthropogenic activities such as agriculture, urbanization, and resource extraction that accelerate weathering and release salts. Multidecadal trends in river salinity are well characterized, yet our understanding of annual regimes of salinity in rivers draining diverse central and western U.S. landscapes and their associated catchment attributes is limited. We classified annual salinity regimes in 242 stream locations through dynamic time warping and fuzzy c‐medoids clustering of salinity time series. We found two dominant regimes in salinity characterized by an annualsummer–fall peakorspring decline. Using random forest regression, we found that precipitation amount, stream slope, and soil salinity were the most important predictors of salinity regime classification. Advancing our understanding of salinity regimes in rivers will improve our ability to predict and mitigate the effects of salinization in freshwater ecosystems through management interventions.