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We assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models. Plain Language Summary Reservoir operations greatly influence streamflow in heavily dammed watersheds, and hence incorporating a realistic reservoir component in watershed hydrological models to simulate the impacts is important. Recent efforts have greatly advanced generic reservoir operation model development. We couple various generic reservoir operation models with a rainfall‐runoff model to develop watershed hydrological models. We use a comprehensive evaluation method with state‐of‐the‐art metrics to examine the performance of the coupled watershed models in terms of simulated watershed outflows, model parameterization, and simulated internal variables. Fully coupled watershed models based on recently developed reservoir operation models obtain significantly improved representations of the watershed system (i.e., reservoir operation + natural rainfall‐runoff processes) compared to models that ignore reservoir operations or use simplified representations of reservoirs. Key Points Generic reservoir models with transparent structures are fully coupled with rainfall‐runoff models for streamflow simulations Fully coupled models may be reliably used in large‐scale hydrological models without losing physical significance Coupled models are evaluated based on ability to represent distributional properties of observed flows using state‐of‐the art metrics

The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are widely employed as valuable tools in watershed management. For this specific study, two modelling approaches were employed: the Soil and Water Assessment Tool (SWAT) model and a set of eight artificial intelligence (AI) models. The AI models consisted of seven data-driven approaches, namely k -nearest neighbour regression, support vector regression, linear regression, artificial neural networks, random forest regression, XGBoost, and Histogram-based Gradient Boost regression. Additionally, a deep learning model known as Long Short-Term Memory (LSTM) was also utilized. The study focused on monthly streamflow modelling in the Murredu River basin, with a calibration period from 1999 to 2003 and a validation period from 2004 to 2005, spanning a total of 7 years from 1999 to 2005. The results indicated that all nine models were generally suitable for simulating the rainfall-runoff process, with the LSTM model demonstrating exceptional performance in both the calibration ( R 2 is 0.97 and NSE is 0.96) and validation ( R 2 is 0.97 and NSE is 0.92) periods. Its high coefficient of determination ( R 2 ) and Nash–Sutcliffe efficiency (NSE) values indicated its superior ability to accurately model the rainfall-runoff relationship. While the other models also produced satisfactory results, the findings suggest that selecting the most efficient model, such as the LSTM model, could significantly contribute to the effective management and planning of sustainable water resources in the Murredu watershed.

Accurate catchment level water resource assessment is the base for integrated river basin management. Due to the complexity in model structure and requirement of a large amount of input data for semi-distributed/distributed models, the conceptual models are gaining much attention in catchment modelling these days. The present study compares the performance of three conceptual models, namely GR4J, Australian Water Balance Model (AWBM) and Sacramento for runoff simulation. Four small catchments and one medium catchment in the upper Godavari river basin are selected for this study. Gap-filled daily rainfall data and potential evapotranspiration (PET) measured from the same catchment or adjacent location are the major inputs to these models. These models are calibrated using daily Nash–Sutcliffe efficiency (NSE) with bias penalty as the objective function. GR4J, AWBM and Sacramento models have four, eight and twenty-two parameters, respectively, to optimise during the calibration. Various statistical measures such as NSE, the coefficient of determination, bias and linear correlation coefficient are computed to evaluate the efficacy of model runoff predictions. From the obtained results, it is found that all the models provide satisfactory results at the selected catchments in this study. However, it is found that the performance of GR4J model is more appropriate in terms of prediction and computational efficiency compared to AWBM and Sacramento models.

The requirements for hydrological models have increased considerably during the previous decades to cope with the resolution of extensive remotely sensed data sets and a number of demanding applications. Existing models exhibit deficiencies such as overparameterization, the lack of an effective technique to integrate the spatial heterogeneity of physiographic characteristics, and the nontransferability of parameters across scales and locations. A multiscale parameter regionalization (MPR) technique is proposed as a way to address these issues simultaneously. Using this technique, parameters at a coarser scale, in which the dominant hydrological processes are represented, are linked with their corresponding ones at a finer resolution in which input data sets are available. The linkage is done with upscaling operators such as the harmonic mean, among others. Parameters at the finer scale are regionalized through nonlinear transfer functions which link basin predictors with global parameters to be determined through calibration. MPR was compared with a standard regionalization (SR) method in which basin predictors instead of model parameters are first aggregated. Both methods were tested in a basin located in Germany using a distributed hydrologic model. Results indicate that MPR is superior to SR in many respects, especially if global parameters are transferred from coarser to finer scales. Furthermore, MPR, as opposed to SR, preserves the spatial variability of state variables and conserves the mass balance with respect to a control scale. Cross‐validation tests indicate that the transferability of the global parameters to ungauged locations is possible.

The primary merit of data-driven-based runoff models is their capability to handle various inputs, including hydrological, land use, and geographical data, allowing for flexibility regarding different environmental conditions and landscapes. Physics-based models provide a comprehensive framework for understanding runoff processes, offering physical realism and transferability advantages. In contrast, they may require more expertise and complicated numerical operations compared to data-driven models. The present study aims to improve the predictive capability of data-driven models by including the advantages of physics-based models in the model's structure and preprocessing input features. To achieve this goal, associated environmental factors adopted in theoretical models, having more rigorous physical interpretation for runoff predictions, are thoroughly examined, especially for the features associated with topographic descriptors. The topological distribution inherent in the input data space is analyzed to improve predictive accuracy. The proposed artificial intelligence (AI) model, which incorporates a classification algorithm for preprocessing input features prior to training a model based on the recurrent neural network, exhibits outstanding performance in runoff discharge prediction. The main contribution of this study is to establish a robust runoff model that retains the original superiority of the data-driven model while extending its capability to capture hydrological processes and underlying physical influences in predicting hydrological responses from river basins.

The performance of conceptual catchment runoff models may highly depend on the specific choice of calibration methods made by the user. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two well-known families of Evolutionary Algorithms that are widely used for calibration of hydrological and environmental models. In the present paper, five DE and five PSO optimization algorithms are compared regarding calibration of two conceptual models, namely the Swedish HBV model (Hydrologiska Byrans Vattenavdelning model) and the French GR4J model (modèle du Génie Rural à 4 paramètres Journalier) of the Kamienna catchment runoff. This catchment is located in the middle part of Poland. The main goal of the study was to find out whether DE or PSO algorithms would be better suited for calibration of conceptual rainfall-runoff models. In general, four out of five DE algorithms perform better than four out of five PSO methods, at least for the calibration data. However, one DE algorithm constantly performs very poorly, while one PSO algorithm is among the best optimizers. Large differences are observed between results obtained for calibration and validation data sets. Differences between optimization algorithms are lower for the GR4J than for the HBV model, probably because GR4J has fewer parameters to optimize than HBV.

A precise estimation of seasonal runoff and accurate quantification of discharge components is imperative for understanding the hydroclimatic regimes in mountainous regions. This study aimed to investigate daily discharge processes and seasonal runoff composition by employing a temperature-index Snowmelt Runoff Model (SRM) using in-situ hydro-meteorological data and limited field observations with a combination of remote sensing data in the debris-covered and clean-ice glaciers. This analysis showed that meltwater production was reduced by 26.5% considering clean-ice and debris-cover ice scenarios necessitating the importance of incorporating debris cover and debris thickness information in temperature-index and snowmelt runoff models. The simulation of daily discharge shows satisfactory agreement with the coefficient of determination (0.89–0.91) and the Nash–Sutcliffe Efficiency (0.85–0.88) for the calibration (2001–02) and validation (2003–10) periods, respectively. Decadal analysis of supraglacial debris-covered area changes shows a 0.37% increase per year on average exhibiting negligible effect on glacier melting and associated flow regimes. Analysis of MODIS snow cover data revealed that the seasonal snow cover varies between 80% in winter and 30% in summer. Negative trends in the snow cover were observed during winter and slightly increasing trends during summers indicated a decreasing influence of westerlies and a strengthening of the Indian summer monsoon system over the region.

Surface runoff models are essential for designing water and soil protection measures. However, they often exhibit uncertainty in both parameterization and results. Typically, uncertainty is evaluated by comparing model realizations with measured data. However, this approach is constrained by limited data availability, preventing comprehensive uncertainty assessment. To overcome this limitation, we employed the generalized likelihood uncertainty estimation (GLUE) methodology to conduct sensitivity and uncertainty analyses on a series of surface runoff models. These models were based on an ensemble of artificial rainfall experiments comprising 77 scenarios with similar settings. We utilized the rainfall-runoff-erosion model SMODERP2D to simulate the experiments and employed Differential Evolution, a heuristic optimization method, to generate sets of behavioural models for each experiment. Additionally, we evaluated the sensitivity and uncertainty with respect to two variables; water level and surface runoff. Our results indicate similar sensitivity of water level and surface runoff to most parameters, with a generally high equifinality. The ensemble of models revealed high uncertainty in bare soil models, especially under dry initial soil water conditions where the lag time for runoff onset was the largest (e.g. runoff coefficient ranged between 0–0.8). Conversely, models with wet initial soil water conditions exhibited lower uncertainty compared to those with dry initial soil water content (e.g. runoff coefficient ranged between 0.6 – 1). Models with crop cover showed a multimodal distribution in water flow and volume, possibly due to variations in crop type and growth stages. Therefore, distinguishing these crop properties could reduce uncertainty. Utilizing an ensemble of models for sensitivity and uncertainty analysis demonstrated its potential in identifying sources of uncertainty, thereby enhancing the robustness and generalizability of such analyses.