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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

Due to the fact that one of the important ways of describing the performance of basins is to use the hydrological signatures, the present study investigates the effects of climate change using the hydrological signatures in Azarshahr Chay basin, Iran. To this end, the Canadian Earth system model (CanESM2) is first used to predict future climate change (2030–2059) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Six signature indices were extracted from flow duration curve (FDC) as follows: runoff ratio (RR), high-segment volume (FHV), low-segment volume (FLV), mid-segment slope (FMS), mid-range flow (FMM), and maximum peak discharge (DiffMaxPeak). These signature indices act as sorts of fingerprints representing differences in the hydrological behavior of the basin. The results indicate that the most significant changes in the future hydrological response are related to the FHV and FLV and FMS indices. The BiasFHV index indicates an increase in high discharge rates under the RCP8.5 scenario, compared to the baseline period, and also the RCP2.6 scenario. The mean annual discharge rate, however, is lower than the discharge rate under this scenario. Generally, for the RCP8.5 scenario, the changes in the signature indices in both high discharges and low discharges are significant.

The escalating impacts of global climate change significantly affect regional hydrological systems, particularly in northern areas such as Estonia. This study investigates the hydrological sensitivity of Estonian catchments to climatic variability, focusing on the interplay between surface water and groundwater. Using data from 42 river catchments, it employs various statistical methods in hydrology, emphasizing the autocorrelation function, cross-correlation function, baseflow index, and flow duration curve. The analysis spans the years 2012–2022, integrating hydrological, spatial, and water quality parameters. The research identifies four distinct hydrological behavior clusters: plateau, sandstone upland, carbonate upland, and lowland. Key findings include diverse catchment sensitivities to groundwater recharge, the role of baseflow in streamflow stabilization, the memory effect in catchment responses, and insights from the flow duration curve on flow variability and extremes. The LightGBM model, predicting focus parameters, highlights the critical influence of air temperature and snowpack on streamflow characteristics. This study underscores the diverse hydrological sensitivities of Estonian catchments to hydroclimatic changes, emphasizing the importance of considering catchment-specific characteristics in water resource management and policy-making. Contributing to the broader understanding of hydrological processes, it provides valuable insights for future research and environmental planning in the face of climate variability and change.

Construction of flow duration curves (FDCs) is a challenge for hydrologists as most streams and rivers worldwide are ungauged. Regionalization methods are commonly followed to solve the problem of discharge data scarcity by transforming hydrological information from gauged basins to ungauged basins. As a consequence, regionalization-based FDC predictions are not very reliable where discharge data are scarce quantitatively and/or qualitatively. In such a scenario, it is perhaps more meaningful to use a calibration-free rainfall‒runoff model that can exploit easily available meteorological information to predict FDCs in ungauged basins. This hypothesis is tested in this study by comparing a well-known regionalization-based model, the inverse distance weighting (IDW) model, with the recently proposed calibration-free dynamic Budyko model (DB) in a region where discharge observations are not only insufficient quantitatively but also show apparent signs of observational errors. The DB model markedly outperformed the IDW model in the study region. Furthermore, the IDW model’s performance sharply declined when we randomly removed discharge gauging stations to test the model in a variety of data availability scenarios. The analysis here also throws some light on how errors in observational datasets and drainage area influence model performance and thus provides a better picture of the relative strengths of the two models. Overall, the results of this study support the notion that a calibration-free rainfall‒runoff model can be chosen to predict FDCs in discharge data-scarce regions. On a philosophical note, our study highlights the importance of process understanding for the development of meaningful hydrological models.

This study presents a comprehensive comparison between traditional hydrological models and advanced machine learning (ML) techniques in predicting streamflow dynamics. Traditional models, namely the Soil and Water Assessment Tool (SWAT) and Génie Rural à 4 Paramètres Journalier (GR4J), are juxtaposed against ML models, including Random Forest (RF), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Both SWAT and GR4J demonstrated commendable performance, with GR4J displaying marginally superior predictive accuracy, evidenced by its tighter RMSE values. In the realm of ML, RF exhibited exceptional prowess in integrating diverse climatic features, especially in a scenario integrating comprehensive meteorological data. ANN showcased consistent performance across different input scenarios, emphasising its robustness. LSTM and BiLSTM, tailored for time series data, underscored the importance of precipitation’s temporal dynamics in streamflow predictions. A notable revelation is the significance of choosing appropriate input data, with certain scenarios outperforming others based on the amalgamation of meteorological parameters. The flow duration curve (FDC) analysis further highlighted the model capabilities, with RF and BiLSTM excelling in capturing extreme flows, while traditional models resonated more with medium flow regimes. This research offers vital insights for hydrologists and decision-makers, aiding in informed model selection for streamflow predictions.

The environmental water demand of the Mahabad River in the Urmia Lake basin in Iran was first estimated, using the flow duration curve shifting method (FDC Shifting) in this study. Secondly, the optimal operating model of the reservoir was developed with the goals of decreasing the deficiencies and considering the downstream demands of the reservoir, especially the environmental water demands by employing the simulated annealing (SA) and non-linear programming (NLP) methods. The results of the SA algorithm were compared with those of the NLP model with the indices of reliability, resiliency velocity, vulnerability, and sustainability. Then, the optimum released water values in the current month, the optimum water storage values in the reservoir, reservoir inflows and monthly demands were considered as inputs of the M5 tree model, and the optimum values of released water in the next month were considered as outputs of the M5 model. Finally, the optimum operation rules from the reservoir were developed in the form of if-then linear rules for future uses. One of the main advantages of the M5 tree model is to present two operation rules as if-then rules for all the operating periods with relatively acceptable accuracy. The results showed that the SA-M5 tree model, as a method of data mining, can extract the operation rules with relatively high accuracy.

Renewable energy sources are an extremely important component of human life on today's globe. In Ethiopia, 80 percent of the population lives in rural areas with limited access to modern energy. The primary goal of this research was to use a Geographic Information System to identify suitable potential sites for small-scale hydropower in the Fetam Rivers. There were six prospective intake sites discovered using the digital elevation model (30 × 30 m) by converting to contour and identifying head potential along the river, which were coded according to their proximity to town and ease of access. Stream flow data were checked for consistency, outlier testing, and the construction of flow duration curves for ungauged rivers by transferring using the area ratio approach. While the power generated has been approximated, potential sites for implementation have been ranked based on minimum mean monthly stream discharge, net head availability, utility access, and town distance from the grid. The digital elevation model is one of the key driving forces for studying physical processes of surface resources, according to the findings of the study. The findings of the analyses suggest that the examined regions have a maximum and minimum significant potential for small-scale hydropower for use of energy resources of 8,288.48 and 122.52 kW, respectively. Using multi-criteria analysis of eligible locations, it is possible to rank as well as beyond the 6 chosen sites, with site 3 diversions coming in first and site 5 coming in second, according to the specified criteria.

The Long-term Hydrologic Impact Assessment Model with Asymptotic Curve Number Regression Equation and Water Quality model (L-THIA ACN-WQ) has been developed to simulate streamflow as well as instream water quality using fewer parameters, compared to other watershed models. However, since model input parameters (i.e. hydraulic response unit (HRU) map, stream network, database (DB), etc.) should be built by user manually, it is difficult to use the model for a nonprofessional or environmental policy decision-maker. In addition, it is difficult to analyze model outputs in time and space because the model does not provide geographic information system (GIS) information for the simulation results. To overcome the limitations, an advanced version of L-THIA ACN-WQ system which is based on ArcGIS interface was developed in this study. To evaluate the applicability of the developed system, it was applied to the Banbyeon A watershed in which total maximum daily load (TMDL) has been implemented. The required model input datasets were automatically collected in the system, and stream flow, T-N and T-P pollutant loads were simulated for the watershed. Furthermore, flow duration curve (FDC) and load duration curve (LDC) were generated to analyze hot spot areas in the system through automatic processes included in the system. The system can establish the model input data easily, automatically provide the graphs of FDC and LDC, and provide hot spot areas which indicate high pollutant loads. Therefore, this system can be useful in establishing various watershed management plans.