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Environmental flow is an important indicator of river health as it maintains the natural flow pattern of riverine ecosystem. Although numerous researches for analyzing the hydrological alterations are there, still insightful investigation of site specific knowledge should be required for riverine ecosystem protection. In this study, the objective is to analyze the hydrological status of the Sone river basin in Bihar region, India. This study also focuses to develop a flow duration curve (FDC) to show the time duration–frequency of low-flow events. The hydrological status of the basin was analyzed using indicators of hydrologic alteration (IHA). Low flows were estimated using period of record flow duration curve (POR FDC), and design environmental flow was assessed for 10-year and 100-year return period using stochastic flow duration curve (stochastic FDC). Daily discharge data collected from Koelwar station of Sone river for 1990–2020 period were used for the hydrological analysis. Depending on the quantitative and qualitative assessment of the hydrological alterations, it was found that the hydrological status of the river basin is in a \"very altered\" state. The POR FDC analyzed 7-day mean discharge values (7dQ) appropriate for determining low flows, and discharge values corresponding to 95% probability of exceedance (Q95) were considered as low flow for 7dQ. Stochastic FDCs generated 7-day mean flow duration curves for 10-year (7Q10) and 100-year (7Q100) recurrence intervals. Discharge values corresponding to 95% probability of exceedance for 7Q10 range from 120 to 125 cumec and those for 7Q100 range from 135 to 140 cumec. The methodology proposed in this work to design environmental flow considering the effects of hydrological alteration can help in making the long-term strategies to protect the riverine ecosystem in Sone river basin.

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided.

Hydrological dynamics in glacierized catchments of the Alps are shaped by temperature‐driven processes, including snow and ice melt as well as precipitation, leading to diel streamflow cycles that vary in intensity within‐ and among‐the seasons. During the summer melt period, the amplitude of these diel cycles increases due to diminished snow storage and the emergence of efficient subglacial drainage systems. Accurately modeling these sub‐daily cycles remains difficult, due to a lack of high‐resolution meteorological input data for melt simulations and due to challenges in parameterizing meltwater routing through dynamic glacial systems. This research develops an approach for downscaling daily streamflow timeseries to sub‐daily timescales (daily flow duration curves) in alpine glacierized catchments influenced by snow and ice melt runoff. We adapt a maximum entropy framework (POME) to the specificities of glacial systems, that we calibrate on a 45‐year data set of 15‐min discharge records from seven glacier‐fed catchments in the southwestern Swiss Alps. The calibrated method is then applied to the outputs of a semi‐lumped hydrological model that simulates daily discharge and provides hydrological variables such as snow depth and ice melt to inform the downscaling, and the results are evaluated against observed discharge. Our results reveal that a sigmoid function effectively represents seasonally varying daily flow duration curves in glacierized catchments and highlight the influence of climate warming on sub‐daily flow dynamics over recent decades. This downscaling method offers a robust tool for reconstructing sub‐daily discharge in catchments with limited data, opening new perspectives for hydrological modeling at finer scales.

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.

Studying 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. Plain Language Summary River hydrographs are key inputs for understanding long term Earth surface processes. Due to the limited lengths of observational streamflow records, various techniques were previously developed with limited capabilities to generate representative long hydrographs. Through a novel integrated approach, we are able to construct robust high‐resolution hydrographs on multi‐century timescales, based on developing a linkage between hydroclimatic forces and watershed characteristics within a stochastic framework. We used this methodology to generate a 300‐year high‐resolution hydrograph with satisfactory correlation with the observed FDC. Due to the stochastic background of this framework, the deviation between the observed and generated FDCs was estimated to fall within a reasonable range of 6% and 18%. This framework was extended to provide hourly runoff projections for several future climatic models. Median projections for the near‐term period 2040–2069 demonstrated less deviation from reference data set compared to those for the more distant future 2070–2099. This study represents a scientific shift for long‐term simulations through re‐constructing past, simulating present, or projecting future hydrographs. Key Points Introducing a novel framework designed to generate statistically robust hydrographs on multi‐century timescales for long‐term simulations Integrating a weather generator and a disaggregation technique within a rainfall runoff model to achieve high‐temporal resolution hydrographs Utilizing multiple climate models to evaluate the impacts of climate change on hourly runoff responses

Paved surfaces, increased precipitation intensities in addition to limited capacity in the sewer systems, cause a higher risk of combined sewer overflows (CSOs). Sustainable drainage systems (SUDS) offer an alternative approach to mitigate CSO by managing the stormwater locally. Seven SUDS scenarios, developed based on the concept of effective impervious area reduction, have been implemented in the Grefsen catchment using the Mike Urban model. This study evaluated the hydrological performance of two SUDS controls (i.e. green roof (GR) and rain garden (RG)) modules of the model and the effect of the SUDS scenarios on the CSOs using event-based and continuous simulations. The Nash–Sutcliffe efficiency (NSE) along with flow duration curves (FDCs) has been used for evaluating the model performance. Event-based evaluations revealed the superior performance of the RG in reducing CSOs for larger precipitation events, while GRs were proven to have beneficial outcomes during smaller events. The study illustrated another way of assessing the continuous simulations by employing the FDCs. The FDCs were assessed against a discharge threshold at the outlet (which authorities can set as design criteria) of the catchment in terms of the extent, each scenario reduced occurrence and duration of outflow that invokes flow in the overflow pipe.

A flow-duration curve (FDC) shows the relationship between magnitude and frequency of daily streamflows over a specific time period. Artificial intelligence methods e.g. Support Vector Machines for Regression (SVR) and Artificial Neural Network (ANN) are useful techniques in the prediction of FDCs in ungagged basins. Regional analysis of FDCs were performed through SVR, ANN and Nonlinear Regression (NLR) using streamflow with durations of 0.02, 0.10, 0.20, 0.50 and 0.90% as dependent variables and six watershed characteristics chosen as effective independent variables on 33 selected watersheds in the Namak-Lake basin located in central zone of Iran. The results shows that the most important watershed characteristics are weighted average height, area, rangeland area, drainage density, permeable formation, and average stream slope. SVR has higher accuracy with relative root mean squared error (RMSEr) of 9.37 to 1.45 and Nash-Sutcliff criterion (NSE) of 0.54 to 0.91 than ANN with RMSEr with 9.42 to 3.79 and NSE of 0.39 to 0.86 and NLR with RMSEr with 18.04 to 3.38 and NSE of 0.53 to 0.79. In general, SVR is proposed to be used to estimate FDCs.

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.

It is essential to know the streamflow behavior in hydrological basins for appropriate water resource planning and management. In Colombia, where there is a considerable water resource potential, there is a need to generate hydrological modeling for many ungauged catchments. Thus, this study presents the regionalization of flow duration curves (FDC) in Colombia. Daily flow time series from 655 gauging stations were used to define homogenous hydrological regions, considering geological, topographic, and climatic information. Fifteen hydrological regions were delimited by cluster analysis using the K-means algorithm, all of which exhibited high spatial heterogeneity. Multiple linear regressions were used to estimate characteristic dimensionless flows as a function of each basin's attributes. A set of equations that allow the reconstruction of simulated dimensionless FDC for each cluster was determined, and regression (R2) values of 0.5–0.9 were obtained. The percentage error of the mean, maximum, and minimum discharge of the simulated FDC compared with observed values were approximately 9, 30, and 50%, respectively.

Although eco-flows (ecosurplus (ES) and ecodeficit (ED)) based on the flow duration curve (FDC) have been used to assess hydrologic alterations in recent years, they are defective, and their limitations have never been clarified. In this study, the causes of these limitations were analyzed, and the ES and ED on different time scales were redefined using the discharge hydrograph (DH). The monthly ES was redefined as the surplus monthly runoff exceeding the 75th percentile DH divided by its maximum ecological water requirement, and the monthly ED was redefined as the deficient monthly runoff below the 25th percentile DH divided by its minimum ecological water requirement. The seasonal eco-flows were the sum of three corresponding monthly eco-flows, and the annual eco-flows were the sum of twelve consecutive monthly eco-flows. The daily discharge time series from 1956 to 2015 at Xiaolangdi Station was used to test the new method. The results showed that the inconsistencies of eco-flows on different time scales based on FDCs were closely related to the loss of time-dependent information in FDCs. The correlation coefficients and Fréchet distance between Shannon Index (SI) and annual eco-flows showed that DHs performed better than FDCs in assessing eco-flows. Annual EDs calculated from DHs changed synchronously with SI in most years. This novel method for calculating eco-flows are useful for assessing river health in the future.