In this study, we use stable isotopes of water to quantify the flow pathways delivering water to the tributaries, mainstem river and groundwater basin underlying urban San Diego. Information about sources of stormflow and recharge are necessary to maintain the health of waterways and aquifers, but studies of these processes are scarce in urban, semi-arid regions.
Isotopic methods have been used in experimental or natural watersheds, but also have potential to quantify urban water cycling behaviour and water sources. We sampled baseflow, precipitation, and hourly stormflow from eight events with a range of antecedent conditions, and used end member mixing analysis to determine stormflow and groundwater sources.
New hydrological insights for the region
Our results show that hydrologic connectivity controls stormflow sources. After a dry summer, and early in storm events, connectivity of pre-event water with the channel is low, so only new precipitation reaches the river. In wetter conditions, connectivity is higher and pre-event surface water mixes with infiltration-origin groundwater. Deeper groundwater composition mimics stormflow, a mix of stagnated river water and infiltration-origin water. The close connection between streamflow and groundwater implies that improving groundwater quality requires improvements to surface water quality. Average uncertainty in source fractions was ±8.0 %, suggesting that despite complex water pathways in urban, semi-arid environments, isotopic sampling is valuable for quantifying water sources.
We present a Matlab toolbox to calculate hydrologic signatures, which are metrics that quantify streamflow dynamics. Signatures are widely used for catchment characterisation, hydrologic model evaluation, and assessment of instream habitat, but standardisation across applications and advice on signature selection is lacking. The toolbox provides accessible, standardised signature calculations, with clear information on methodological decisions and recommended parameter values. The toolbox implements three categories of signatures: basic signatures that describe the five components of a natural streamflow regime, signatures from benchmark papers, and an extended set of process-based signatures. The toolbox is designed for ease of use, including documentation, workflow scripts and example data to demonstrate implementation procedures, and visualisation options. We demonstrate the accuracy and robustness of the signature calculations by applying reproducible workflows to large streamflow datasets. The modular design of the toolbox allows for flexibility and easy future expansion. The toolbox is available from https://github.com/TOSSHtoolbox/TOSSH (https://doi.org/10.5281/zenodo.4451846).
Hydrologic signatures are metrics that quantify aspects of streamflow response. Linking signatures to underlying processes enables multiple applications, such as selecting hydrologic model structure, analysing hydrologic change, making predictions in ungauged basins, and classifying watershed function. However, many lists of hydrologic signatures are not process‐based, and knowledge about signature‐process links has been scattered among studies from experimental watersheds and model selection experiments. This review brings together those studies to catalogue more than 50 signatures representing evapotranspiration, snow storage and melt, permafrost, infiltration excess, saturation excess, groundwater, baseflow, connectivity, channel processes, partitioning, and human alteration. The review shows substantial variability in the number, type, and timescale of signatures available to represent each process. Many signatures provide information about groundwater storage, partitioning, and connectivity, whereas snow processes and human alteration are underrepresented. More signatures are related to the seasonal scale than the event timescale, and land surface processes (ET, snow, and overland flow) have no signatures at the event scale. There are limitations in some signatures that test for occurrence but cannot quantify processes, or are related to multiple processes, making automated analysis more difficult. This review will be valuable as a reference for hydrologists seeking to use streamflow records to investigate a particular hydrologic process or to conduct large‐sample analyses of patterns in hydrologic processes.
Hydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated 5 snow signatures. Considering inter-annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter-annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (1) snow melt dynamics and (2) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter-catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (1) snow melt rate and/or snow melt affected area over the snow melt season and (2) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.
A catchment’s hydrological response is controlled by climatic forcing and by the landscape through which water moves. Yet when we compare large samples of catchments, we often find climate to be the only good predictor of the hydrological response and a lot of variability is left unexplained. This contradicts extensive evidence from field and regional studies which shows the importance of catchment form (e.g. geology) on catchment hydrological processes, particularly on baseflow processes. We hypothesize that this is due to limitations in (a) the catchment attributes we use to inform our analyses and (b) the hydrological signatures we use to describe the hydrological response. To test these hypotheses we use a large sample of catchment data across the contiguous United States. By reviewing literature from several U.S. regions, we show that region‐specific knowledge is underutilized in large sample studies. To organize the findings from these regions we propose and apply a framework based on standardized perceptual models. Informed by these perceptual models, we use both available and newly calculated catchment attributes to show that baseflow signature predictions can be improved regionally. Multiple baseflow signatures are needed to better distinguish between different baseflow sources, such as the subsurface, surface water bodies, and snow. We conclude with pointing at potential future directions and argue that we should aim at a more systematic and hydrologically motivated selection of catchment attributes and hydrological signatures.
Soil moisture is a key modifier of runoff generation from rainfall excess, including during extreme precipitation events associated with Atmospheric Rivers (ARs). This paper presents a new, publicly available dataset from a soil moisture monitoring network in Northern California’s Russian River Basin, designed to assess soil moisture controls on runoff generation under AR conditions. The observations consist of 2‐min volumetric soil moisture at 19 sites and 6 depths (5, 10, 15, 20, 50, and 100 cm), starting in summer 2017. The goals of this monitoring network are to aid the development of research applications and situational awareness tools for Forecast‐Informed Reservoir Operations at Lake Mendocino. We present short analyses of these data to demonstrate their capability to characterize soil moisture responses to precipitation across sites and depths, including time series analysis, correlation analysis, and identification of soil saturation thresholds that induce runoff. Our results show strong inter‐site Pearson’s correlations (>0.8) at the seasonal timescale. Correlations are strong (>0.8) during events with high antecedent soil moisture and during drydown periods, and weak (<0.5) otherwise. High event runoff ratios are observed when antecedent soil moisture thresholds are exceeded, and when antecedent runoff is high. Although local heterogeneity in soil moisture can limit the utility of point source data in some hydrologic model applications, our analyses indicate three ways in which soil moisture data are valuable for model design: (1) sensors installed at 6 depths per location enable us to identify the soil depth below which evapotranspiration and saturation dynamics change, and therefore choose model soil layer depths, (2) time series analysis indicates the role of soil moisture processes in controlling runoff ratio during precipitation, which hydrologic models should replicate, and (3) spatial correlation analysis of the soil moisture fluctuations helps identify when and where distributed hydrologic modelling may be beneficial.
Hydrologic signatures are quantitative metrics or indices that describe statistical or dynamical properties of hydrologic data series, primarily streamflow. Hydrologic signatures were first used in eco‐hydrology to assess alterations in flow regime, and have since seen wide uptake across a variety of hydrological fields. Their applications include extracting biologically relevant attributes of streamflow data, monitoring hydrologic change, analyzing runoff generation processes, defining similarity between watersheds, and calibrating and evaluating hydrologic models. Hydrologic signatures allow us to extract meaningful information about watershed processes from streamflow series, and are therefore seeing increasing use in emerging information‐rich areas such as global‐scale hydrologic modeling, machine learning, and large‐sample hydrology. This overview paper describes the background and development of hydrologic signature theory, reviews hydrologic signature use across a variety of applications, and discusses ongoing hydrologic signature research including current challenges.
Soil moisture is an important variable in hydrological studies, but has been little used for model evaluation due to its high sensitivity to local conditions. We explore the possibility to derive hydrological signatures from soil moisture data that could overcome this limitation and be helpful for model evaluation. A set of eight hydrological signatures was built, encompassing long‐term to short‐term time scales. These signatures were tested according to robustness, representativeness and discriminatory power, using in situ data sets from New Zealand, including national network and experimental watershed data. Field capacity, type of soil moisture distribution, and starting dates of seasonal transitions typically meet the criteria, subject to uniform sensor depths and homogeneous land uses. Durations of seasonal transitions and event‐based signatures showed higher variability and lower discriminatory power. In general, long‐term signatures are more robust, more representative of large areas, and have a high discriminatory power, thus showing a good potential for use in diagnostic evaluation of regional models.
Increased climate variability is driving changes in water storage across the contiguous United States (CONUS). Observational estimates of these storage changes are important for validation of hydrological models and predicting future water availability. We estimate CONUS terrestrial water storage anomalies (TWSA) from 2007–2017 using Global Positioning System (GPS) displacements, constrained by lower‐resolution TWSA observations from Gravity Recovery and Climate Experiment (GRACE) satellite gravity—a combination that provides higher spatiotemporal resolution than previous estimates. The relative contribution of seasonal, interannual, and subseasonal TWSA varies widely across CONUS watersheds, with implications for regional water security. Separately, we find positive correlation between TWSA and the El Niño/Southern Oscillation in the southeastern Texas‐Gulf and South Atlantic‐Gulf watersheds and an unexpected negative correlation in the southwest. In the western United States, atmospheric rivers (ARs) drive a large fraction of subseasonal TWSA, with the top 5% of ARs contributing 73% of total AR‐related TWSA increases.