Flow Duration Curve Research Articles

Flow duration curve prediction: A framework integrating regionalization and copula model

Flow duration curve prediction: A framework integrating regionalization and copula model

Constructing Long‐Term Hydrographs for River Climate‐Resilience: A Novel Approach for Studying Centennial to Millennial River Behavior

AbstractStudying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long‐term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long‐term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi‐century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall‐runoff transformation model. We tested our approach to generate a statistically representative 300‐year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high‐resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long‐term hydrologic modeling.

A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections

In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future high streamflows, while the perturbed simulations demonstrated the potential for substantially higher streamflows under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.

Determination of ecological flow based on probability distributions of annual and monthly flows

ABSTRACT Developing scientifically sound ecological flow is crucial for protecting river ecosystems. However, most of the existing hydrological methods for determining ecological flow make it difficult to consider both inter-annual and intra-annual changes in runoff. To compensate for this shortcoming, this paper proposes an innovative ecological flow determination method based on the probability distributions of annual and monthly flows. Marginal distributions of annual and monthly flows are first fitted, and the Copula function is used to create joint probability distributions of annual and monthly flows. Conditional probabilities for different monthly flow sizes under different annual flow conditions are then calculated based on Bayesian inference. The conditional probabilities are combined with the flow–duration curve-–based method to finalize the ecological flow process considering both intra- and inter-annual runoff changes. A case study was conducted in Jinsha River, China. The results show that the total annual ecological water demand of the Jinsha River is 1.50, 1.25, and 1.04 × 1011 m3 under annual flow scenarios of high, medium, and low flows, respectively, which provide a red line for the development of water resources and hydro-energy resources of the Jinsha River, as well as for the better protection of the natural plant and animal species.

Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

Historical simulation performance evaluation and monthly flow duration curve quantile-mapping (MFDC-QM) of the GEOGLOWS ECMWF streamflow hydrologic model

Global hydrological models are essential for managing water resources and predicting hydrological events. However, the local-scale usability of global models challenges big-data management, communication, adoption, and validation. Validation is the biggest challenge bercause of the need for large-scale data management and model calibration, which requires extensive and often inaccessible observed data. This study assesses the GEOGLOWS-ECMWF Global Hydrologic Model, revealing systematic biases that impact its accuracy. We propose a bias-correction methodology using flow duration curves to align non-exceedance probabilities of simulated and observed streamflow, significantly improving the GEOGLOWS model. Unfortunately, this approach does not inherently improve simulations in ungauged locations. The methodology not only enhances the GEOGLOWS model's accuracy but also stands as a versatile solution applicable across various hydrological models. This bias correction approach provides a tool for improving hydrological predictions and gives users the confidence to use global models for local water resource management and decision-making processes.

Analysis of flow regime classification in the Omo-Gibe River Basin: insights into fluid dynamics in Ethiopia

ABSTRACT The study investigates flow regime in the Omo-Gibe River Basin to address hydrological complexity caused by precipitation and catchment features. Despite employing various methodologies, daily flow data highlight the need for a more comprehensive understanding of flow variability. The study aims to scrutinize flow regime classification, emphasizing the challenges posed by the basin's unique hydrological dynamics, with the ultimate goal of improving water management practices in the region. Using XLSTAT (Excel statistics software), the average base flow index (60.66%), zero flow index (0.25%), coefficient of variation (1.56%), and flashiness index (0.276%) were determined to be the primary hydrological indices that contributed to streamflow characterization. Finally, flow regime classification was described as non-perennial (13%) or perennial (87%) using the shape of the flow duration curve and this hydrological index. However, the magnitude of extreme flow events was judged depending on flow duration curve and calibrated by the flashiness index computed in the study. The study's findings serve as an input for streamflow regionalization and the foundation for future research on the ecology and hydrology of Ethiopia's river basins as well as the management of the water resources throughout the Omo-Gibe River Basin.

Robust and computationally efficient design for run-of-river hydropower

This paper introduces innovative approaches for robust and computationally efficient optimal design of run-of-river hydropower plants. Compared with existing design software, it (1) integrates optimized turbine operations into design optimization instead of following predefined operational rules, and (2) combines this with a regular sampling of the flow duration curve to significantly reduce data inputs. Our rigorous benchmarking demonstrates that (1) operation optimization improves design performance at low computational cost, whilst (2) data input reduction slashes computational costs by over 92% with minimal impact on design recommendations and key robustness analysis insights. Taken together, these innovations make integrated design and operation optimization, complete with in-depth robustness analysis, laptop-accessible. They also reinforce sustainability efforts by minimizing the need for high-performance computing and large associated embodied greenhouse gas emissions.

Spatio-Temporal Distribution of Water Availability in Marshyangdi Basin: Hydrological Model Development

This Study has developed a hydrological model for the Marshyangdi river basin in Western Nepal, showing considerable potential for developing water resources and overall national prosperity. The model is also used to characterise hydrology and assess the availability of water resources across different spatial and temporal dimensions, hence improving our comprehension of water availability and its possible applications. The newly developed hydrological model in the Soil and Water Assessment Tool (SWAT) replicates the mean flows, hydrological pattern, and flow duration curve at the basin and 54 sub-basin outputs. Additionally, the model undergoes calibration and validation, employing Sufi-2 to fine-tune hydrological parameters. Based on the data generated by the model, the annual average precipitation (P) in the Marshyangdi basin is projected to be 1843 mm. Approximately 17.88% of the average yearly rainfall is lost via evapotranspiration; however, this varies significantly across different locations and time periods. Contrasting the two, the mountains have a higher moisture level than the Himalayan regions. The predicted average annual flow volume at the basin outflow is 9467.423 million cubic metres (MCM). Examining water shortages, finding regions with excess water, and developing solutions to address the competing needs of agriculture, industry, urban areas, and ecosystems are advantageous. Therefore, this method may improve the effectiveness of strategic planning and the long-term management of water resources.

Impacts of River Network Connectivity on Flood Signatures and Severity Regulated by Flood Control Projects

The operation of hydraulic projects within plain river networks to mitigate floods can alter river network connectivity patterns, subsequently affecting flood processes. This study employed the MIKE 11 model to simulate flood processes under three different river network connectivity scenarios. Based on the simulations, we propose a method to evaluate flood intensity severity by integrating three flood characteristic indices: Slope of the Flow Duration Curve (SFDC), Rising Climb Index (RCI), and Flashiness Index (FI). These indices assess the overall magnitude of change, the rate of rise, and process fluctuations, respectively. Results indicate that changes in river network connectivity significantly impact RCI and SFDC, more than FI. Compared to the natural river network connectivity mode, changes in urban or watershed river network connectivity resulted in a significant decrease in RCI values by 3–37% or 18–38% across various return periods, with the rate of change in RCI values increasing as the return period lengthened. The impact of urban river network connectivity changes on SFDC within the Changzhou urban area was more pronounced under high-magnitude storm conditions, causing a 61% reduction. Furthermore, changes in watershed river network connectivity had a larger effect on SFDC under low-magnitude storm conditions than under high-intensity storms. Over 80% of the rivers under natural connectivity conditions exhibited flood intensity severity of Level III or higher, particularly in the Chenshu–Qingyang area. The alterations in connectivity significantly decreased flood intensity severity, with 85% to 91% of rivers showing the lowest flood intensity severity of Level I. Under a 100-year rainstorm scenario, flood risk shifted from within the flood protection envelope to outside it in the Changzhou urban area. The results will provide an important scientific basis for regional flood management in plains with dense rivers.

Estimating exploitable groundwater for agricultural use under environmental flow constraints using an integrated SWAT-MODFLOW model

Traditionally, the impact of groundwater abstraction on streamflow and aquifers has been assessed primarily in terms of water availability and depletion, often neglecting critical environmental flow (Eflow) requirements. To address this gap, our study employed an Eflow perspective to evaluate exploitable groundwater for agricultural use at a watershed scale using an integrated modeling approach. We used the SWAT-MODFLOW model, integrating the Soil and Water Assessment Tool (SWAT) and the Modular Finite-Difference Flow Model (MODFLOW), to analyze water balance segments and stream-aquifer interactions in the study region. Eflow for different management classes was determined using the flow duration curve (FDC) approach. The FDC method classifies Environmental Flow Management Classes (EMC) from “A” (natural or minor alteration) to “F” (critically modified). The relationship between the selected EMC and simulated streamflow, under the pressure of agricultural groundwater pumping, served as a constraint in evaluating exploitable groundwater alongside groundwater level fluctuations. As the traditional approach depends on average recharge for determining exploitable groundwater, this study also assessed the spatiotemporal distribution of groundwater recharge. Simulation periods were aligned with agricultural pumping schedules. Results indicated that groundwater pumping significantly impacts streamflow at the start of the irrigation period. Under moderately affected EMC and with an average groundwater level fluctuation of one meter, the exploitable groundwater during the irrigation period can reach up to 2.96 Mm3/day. With this amount of pumping, the average groundwater discharge to streams declined by around 13 %. While groundwater level fluctuations near the watershed outlet were minimal, in the upstream area, levels could decrease by as much as 2.5 m. The groundwater level fluctuation underscores the necessity of accounting for both spatial and temporal factors, particularly in highly affected regions.

Minimum environmental flow assessment: a fuzzy TOPSIS decision-making system for selecting the best approach

The literature has explored various methods for assessing minimum environmental flow. Implementing holistic approaches proves to be prohibitively expensive and impractical for many small and medium projects. Hence, desktop and cost-effective methods are commonly employed without an integrated decision-making system to justify the assessed values. This study introduces a systematic decision-making framework aimed at selecting the most suitable method for assessing the actual needs of river habitats. Employing a fuzzy technique known as the Order Preference Similarity to the Ideal Solution (FTOPSIS), the study considers factors such as physical, thermal, and dissolved oxygen habitat suitability, maximum habitat area, and water demand loss function to determine the most appropriate method among established ones, including the Tennant method, flow duration curve analysis method, wetted perimeter method, and physical habitat simulation method. The results prioritize physical habitat simulation, wetted perimeter by slope method, and flow indices of 70%, 75%, and 80% by flow duration curve analysis method as the optimal approaches for assessing minimum environmental flow. This proposed decision-making system offers a viable platform to explore the applicability of existing cost-effective methods for assessing minimum environmental flow. It also serves as an effective mechanism for reducing negotiations among stakeholders by comprehensively considering all relevant aspects in the environmental management of river ecosystem requirements.

Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures

Hydrological models are evaluated by comparisons with observed hydrological quantities such as streamflow. A model evaluation procedure should account for dominantly epistemic errors in hydrological data such as model input precipitation and streamflow and avoid type-2 errors (rejecting a good model). This study uses quantile random forest (QRF) to develop limits-of-acceptability (LoA) over streamflows that account for uncertainties in precipitation and streamflow values. A significant advantage of this method is that it can be used to evaluate models even at ungauged basins. This method was used to evaluate a hydrological model –Sacramento Soil Moisture Accounting (SAC-SMA) – over the St. Joseph River Watershed (SJRW) for both gauged and hypothetical ungauged scenarios. QRF defined wide LoAs that yielded a large number of models as behavioral, suggesting the need for additional measures to develop a more discriminating inference procedure. The paper discusses why the LoAs defined by QRF were wide, along with some ways to define more discriminating LoAs. To further constrain the model, five streamflow-based signatures (i.e., autocorrelation function, Hurst exponent, baseflow index, flow duration curve, and long-term runoff coefficient) were used. The combination of LoAs over streamflow and streamflow-based signatures helped constrain the set of behavioral models in both the gauged and the ungauged scenarios. Among the signatures used in this study, the Hurst exponent and baseflow index were the most useful ones. All the 1-million models evaluated in this study were eventually rejected as unfit-for-purpose.

Rewetting impact on the hydrological function of a drained peatland in the boreal landscape

There is a growing interest in peatland restoration as a nature-based solution to mitigate hydrological extremes. To counter the impacts of past peatland degradation and ongoing climate change trajectories, governmental authorities propose rewetting of drained peatlands as a key tool to enhance landscape resilience against floods and droughts by improving water storage. Despite a growing body of literature on this topic, the effectiveness of rewetting to enhance peatland hydrological functions remains insufficiently documented, especially in the boreal region. Therefore, this study utilized high temporal resolution groundwater table level and streamflow data to investigate the impact of peatland rewetting using a before-after-control-impact (BACI) approach. This investigation was conducted on a historically drained peatland located at the Trollberget Experimental Area (TEA) in northern Sweden. The primary aim of the experimental study was to examine the impact of rewetting on (1) the groundwater table level response, (2) runoff dynamics, and (3) water storage and hydrological buffer capacity. Our results showed that peatland rewetting led to a significant increase in the groundwater table level by 60 mm compared to the control. Flowdurationcurve(FDC) analysis demonstrated that the low-flow threshold increased by up to 150% at the rewetted sites. Furthermore, our findings suggested that rewetting resulted in an increase in the groundwater table level threshold at which stream runoff is generated. Additionally, our findings showed a noteworthy shift in the monthly runoff coefficient, with an increase during dry months and a decrease during wet periods. Combined, these observations point towards an enhancement in the peatland’s water storage and hydrological buffer capacity as a positive outcome of the rewetting efforts, but also highlight that within the first three years, full hydrological restoration did not occur.

Potential Legacy of SWOT Mission for the Estimation of Flow–Duration Curves

Flow–duration curves (FDCs) provide a compact view of the historical variability of river flows, reflecting climate conditions and the main hydrologic features of river basins. The Surface Water and Ocean Topography (SWOT) satellite mission will enable the estimation of river flows globally, by sensing rivers wider than 100 m with a sampling recurrence from 3 to 21 days. This study investigated the lifetime mission potential for FDC estimation through the comparison between remotely-sensed and empirical FDCs. We employed the Global Runoff Data Center dataset and derived SWOT-like river flows by selecting gauging stations of rivers wider than 100 m with more than 10-year long daily river flow time series. Overall, 1200 gauged river cross-sections were examined. For each site, we created a set of 24 SWOT-simulated FDCs (i.e., based on different sampling recurrences, mean biases, and random errors) to be compared against their empirical counterparts through the Nash–Sutcliffe efficiency and the mean relative error. Our results show that climate and the sampling recurrence play a key role on the performance of SWOT-based FDCs. Tropical and temperate climates performed the best, whereas arid climates mostly revealed higher uncertainties, especially for high- and low-flows.

Analyzing the adjustment mechanism of daily sediment content under large reservoir operations

Adjustment of daily discharge and sediment content in the Lower Jinsha River Basin has changed dramatically. However, the adjustment mechanism of daily sediment content under reservoir operation remains unclear. The Double Mass Curve (DMS) method was used to divide different periods of daily discharge-sediment content relationship change, and the Flow Duration Curve (FDC) was used to calculate the energy dissipation of streamflow by reservoirs. With the operation of large reservoirs, the average flood discharge and its proportion significantly decreased. With the variation in flow regime, the quantile relationship and Lower boundary relationship of daily discharge and sediment content both showed a downward trend, from 1999 to 2019. Under different periods, adjustment of the cross-flow profile was decreased with larger daily discharge, which was characterized by the ratio of sediment content to the lower boundary. An improved flow duration curve method was proposed to calculate the energy dissipation of streamflow. We discovered a novel model between the relative reduction of sediment content and relative energy dissipation of the daily discharge regime, with a good fitness of 0.97. In this study, the effect of the flow regime constructed on sediment content change was emphasized. It is helpful to evaluate the sediment reduction of the total basin caused by reservoirs.

Optimization of the SWAT+ model to adequately predict different segments of a managed streamflow hydrograph

ABSTRACT Complete representation of rainfall–runoff responses in complex, large watersheds using a single-objective parameterization approach in watershed models is often unachievable. In this study we present a calibration approach for the SWAT+ model that independently fits model parameters for different flow segments of the hydrograph. The approach is demonstrated for the Feather River, California, USA, using daily streamflow from the Lake Oroville Reservoir outlet gage. Results show that when model parameters were independently fitted for different flow segments the KGE, NSE, PBIAS, and RSR values improved to 0.96, 0.99, −3.3, and 0.10, respectively, compared to 0.72, 0.66, −9.30, and 0.53, respectively, achieved under a multiobjective and full hydrograph (average hydrograph) calibration. The results highlight that when considering the average hydrograph and flow duration curves, a more balanced representation of both poorly and well-performing segments is achieved, emphasizing the importance of segment-specific parameterization and multi-objective evaluation for accurately representing different flow conditions.

Baseflow from Snow and Rain in Mountain Watersheds

After peak snowmelt, baseflow is the primary contributor to streamflow in snow-dominated watersheds. These low flows provide important water for municipal, agricultural, and recreational purposes once peak flows have been allocated. This study examines the correlation between peak snow water equivalent (SWE), post-peak SWE precipitation, and baseflow characteristics, including any yearly lag in baseflow. To reflect the hydrologic processes that are occurring in snow-dominated watersheds, we propose using a melt year (MY) beginning with the onset of snowmelt contributions (the first deviation from baseflow) and ending with the onset of the following year’s snowmelt contributions. We identified the beginning of an MY and extracted the subsequent baseflow values using flow duration curves (FDCs) for 12 watersheds of varying sizes across Colorado, USA. Based on the findings, peak SWE and summer rain both dictate baseflow, especially for the larger watersheds evaluated, as identified by higher correlations with the MY-derived baseflow. Lags in the correlation between baseflow and peak SWE are best identified when low-snow years are investigated separately from high-snow years. The MY is a different and more effective approach to calculating baseflow using FDCs in snow-dominated watersheds in Colorado.

Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes

The objective of this study was to conduct a detailed analysis of the available flow series in the Chiriquí Viejo River basin in Panama. This paper examines the patterns of variation within these series and calculates various hydrological indexes indicative of the region’s hydrology. Utilizing advanced hydrological indexes within the Chiriquí Viejo River basin in Panama, which spans an area of 1376 km2 and supports an estimated population of 100,000 inhabitants, analytical methods were employed to compute indexes such as the Daily Flow Variation Index (QVAR), the Slope of the Flow Duration Curve (R2FDC), the Hydrological Regulation Index (IRH), and the average duration of low (DLQ75) and high (DHQ25) flow pulses. The results indicate moderate flow variability (QVAR of 0.72) and a Hydrological Regulation Index (IRH) of 2.32, signifying a moderate capacity for flow regulation. Notably, low flow events (DLQ75) lasted approximately 3.73 days, while high flow events (DHQ25) lasted around 4.08 days. The study highlights a significant capacity to respond to extreme events, with maximum annual flows reaching 80.25 m3/s and minimum flows dropping to 3.01 m3/s. Despite the significant contribution of the basin to hydroelectric power generation and other economic activities, there is an observed need for sustainable management that accommodates hydrological fluctuations and promotes resource conservation. The conclusions indicate that these findings are critical for future planning and conservation strategies in the region, emphasizing the importance of integrating multidisciplinary approaches for Hydro-Social Sustainability. This novel and holistic approach underscores the interdependence between hydrological dynamics, socio-economic activities, and environmental sustainability, aiming to ensure the long-term resilience of the Chiriquí Viejo basin and its communities.

Distribution‐Based Model Evaluation and Diagnostics: Elicitability, Propriety, and Scoring Rules for Hydrograph Functionals

AbstractDistribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility.