Uncertainty in hydrological signatures

Information about rainfall–runoff processes is essential for hydrological analyses, modelling and water-management applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Uncertainties in the observed data – including measurement inaccuracy and representativeness as well as errors relating to data management – propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty. <br><br> We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall–runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment. <br><br> We found that the uncertainties were often large (i.e. typical intervals of ±10–40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes

Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales

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

Spatial sensitivity of river flooding to changes in climate and land cover through explainable AI

Explaining the spatially variable impacts of flood-generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning-informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments

When good signatures go bad: Applying hydrologic signatures in large sample studies

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

Uncertainty in hydrological signatures for gauged and ungauged catchments

Reliable information about hydrological behavior is needed for water‐resource management and scientific investigations. Hydrological signatures quantify catchment behavior as index values, and can be predicted for ungauged catchments using a regionalization procedure. The prediction reliability is affected by data uncertainties for the gauged catchments used in prediction and by uncertainties in the regionalization procedure. We quantified signature uncertainty stemming from discharge data uncertainty for 43 UK catchments and propagated these uncertainties in signature regionalization, while accounting for regionalization uncertainty with a weighted‐pooling‐group approach. Discharge uncertainty was estimated using Monte Carlo sampling of multiple feasible rating curves. For each sampled rating curve, a discharge time series was calculated and used in deriving the gauged signature uncertainty distribution. We found that the gauged uncertainty varied with signature type, local measurement conditions and catchment behavior, with the highest uncertainties (median relative uncertainty ±30–40% across all catchments) for signatures measuring high‐ and low‐flow magnitude and dynamics. Our regionalization method allowed assessing the role and relative magnitudes of the gauged and regionalized uncertainty sources in shaping the signature uncertainty distributions predicted for catchments treated as ungauged. We found that (1) if the gauged uncertainties were neglected there was a clear risk of overconditioning the regionalization inference, e.g., by attributing catchment differences resulting from gauged uncertainty to differences in catchment behavior, and (2) uncertainty in the regionalization results was lower for signatures measuring flow distribution (e.g., mean flow) than flow dynamics (e.g., autocorrelation), and for average flows (and then high flows) compared to low flows.Key Points:We quantify impact of data uncertainty on signatures and their regionalizationMedian signature uncertainty ±10–40%, and highly variable across catchmentsNeglecting gauging uncertainty causes overconditioning of regionalizationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137249/1/wrcr21917-sup-0001-2015WR017635-s01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137249/2/wrcr21917.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137249/3/wrcr21917_am.pd

Developing water supply reservoir operating rules for large-scale hydrological modelling

Reservoirs are key components of many water supply systems, providing functional capability to manage, and often mitigate, hydrological variability across space and time. The presence and operation of a reservoir controls the downstream flow regime, such that in many locations understanding reservoir operations is crucial to understanding the hydrological functioning of a catchment. Although substantial progress has been made in modelling reservoir operations, several key challenges remain, particularly for large-scale applications including hundreds of reservoirs. In these cases, generic and uncalibrated reservoir operating rules are often applied. However, these rules were developed from global reservoir databases that consist mostly of large irrigation reservoirs and thus are not transferable to smaller reservoirs or those fulfilling other purposes, such as water supply. An alternative option is to use a calibrated, data-driven approach but such techniques require reservoir inflows, outflows and storage data which are rarely available across hundreds of reservoirs. To overcome these problems, here we design a set of simple reservoir operating rules (with only two calibrated parameters) focused on simulating small water supply reservoirs across large scales with various types of open access data (general catchment attributes such as surface area or reservoir capacity, and flows at downstream gauges). Using Great Britain as a case study, we integrate our rules into a national-scale hydrological model and compare hydrological simulations from two modelling scenarios, with and without the new reservoir component. Our simple reservoir operating rules significantly increase model performance in reservoir-impacted catchments, particularly when the rules are calibrated individually at each downstream gauge. We also test the feasibility of using transfer functions (which transform reservoir and catchment attributes into operating rule parameters) to identify a nationally-consistent parameterisation. This works well in ~50 % of the catchments, while nuances in individual reservoir operations limit performance in others. We suggest that our approach should provide a lower benchmark for simulations in catchments containing water supply reservoirs, and that more complex methods should only be considered where they outperform our simple approach

Cognitive behaviour therapy versus counselling intervention for anxiety in young people with high-functioning autism spectrum disorders: a pilot randomised controlled trial

The use of cognitive-behavioural therapy (CBT) as a treatment for children and adolescents with autism spectrum disorder (ASD) has been explored in a number of trials. Whilst CBT appears superior to no treatment or treatment as usual, few studies have assessed CBT against a control group receiving an alternative therapy. Our randomised controlled trial compared use of CBT against person-centred counselling for anxiety in 36 young people with ASD, ages 12–18. Outcome measures included parent- teacher- and self-reports of anxiety and social disability. Whilst each therapy produced improvements inparticipants, neither therapy was superior to the other to a significant degree on any measure. This is consistent with findings for adults

Influence of soil and climate on root zone storage capacity

Root zone storage capacity (Sr) is an important variable for hydrology and climate studies, as it strongly influences the hydrological functioning of a catchment and, via evaporation, the local climate. Despite its importance, it remains difficult to obtain a wellâ founded catchment representative estimate. This study tests the hypothesis that vegetation adapts its Sr to create a buffer large enough to sustain the plant during drought conditions of a certain critical strength (with a certain probability of exceedance). Following this method, Sr can be estimated from precipitation and evaporative demand data. The results of this â climateâ based methodâ are compared with traditional estimates from soil data for 32 catchments in New Zealand. The results show that the differences between catchments in climateâ derived catchment representative Sr values are larger than for soilâ derived Sr values. Using a model experiment, we show that the climateâ derived Sr can better reproduce hydrological regime signatures for humid catchments; for more arid catchments, the soil and climate methods perform similarly. This makes the climateâ based Sr a valuable addition for increasing hydrological understanding and reducing hydrological model uncertainty.Key Points:Plants develop their root systems to survive droughtsModel root zone storage capacity (Sr) can be inferred from climate recordsModel experiment shows that Sr is stronger influenced by climate than by soilPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137190/1/wrcr21890.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137190/2/wrcr21890_am.pd