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The Soil Conservation Service Curve Number (SCS-CN) method is popular for predicting surface runoff due to its simplicity, ease of application, and widespread acceptance. However, it has limitations, such as the neglect of storm duration, a lack of guidance on antecedent moisture conditions, and the assumption of a constant initial abstraction coefficient (λ = 0.2), leading to uncertainty. Its reliance on static land use classifications and empirical assumptions limits its accuracy across diverse geographic regions and complex hydrological scenarios, particularly under extreme weather conditions. Furthermore, selecting the most suitable watershed CN values remains a subject of global debate. Moreover, the model is widely applied beyond its originally intended purpose. Its basic assumptions, flexibility in dealing with different hydrological conditions, and susceptibility to variables including soil type, land use, and antecedent moisture conditions have all drawn criticism for the method. To overcome the original curve number method limitations, many studies have been made on improving the SCS-CN method. Despite these advancements, significant gaps remain, particularly in the method's applicability across diverse geographic regions and its accuracy in extreme weather events. This paper revisits the popular SCS-CN method, its history, development of methodology, limitations, and refinements that occurred to the original method with the progress of science and technology. It also explores the need for further research to improve its applicability, highlighting opportunities for more robust, flexible runoff estimation models. [ABSTRACT FROM AUTHOR]

The solution to inverse problems is crucial for model calibration and to provide a good basis for model results to be reliable. This paper is based on a recently proposed conceptual framework for the development and application of mathematical models that require the solution of forward and inverse problems. The focus of this paper is on the discussion of some terminology related to the results of forward problems and their reanalysis, on the use of the proposed framework to revise and generalise some methods of solutions of the inverse problem, and to provide a non-standard insight in some aspects about the Bayesian approach to model calibration. [ABSTRACT FROM AUTHOR]

In the context of global research in snow-affected regions, research in the Australian Alps has been steadily catching up to the more established research environments in other countries. One area that holds immense potential for growth is hydrological modelling. Future hydrological modelling could be used to support a range of management and planning issues, such as to better characterise the contribution of the Australian Alps to flows in the agriculturally important Murray-Darling Basin despite its seemingly small footprint. The lack of recent hydrological modelling work in the Australian Alps has catalysed this review, with the aim to summarise the current state and to provide future directions for hydrological modelling, based on advances in knowledge of the Australian Alps from adjacent disciplines and global developments in the field of hydrologic modelling. Future directions proffered here include moving beyond the previously applied conceptual models to more physically based models, supported by an increase in data collection in the region, and modelling efforts that consider non-stationarity of hydrological response, especially that resulting from climate change. [ABSTRACT FROM AUTHOR]

The identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high‐altitude watersheds in Southeast Asia. Global Circulation Model (GCM)‐Coupled Model Intercomparison Project Phase 6 (CMIP6)‐based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM‐CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best‐suited GCM‐CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best‐suited GCM‐CMIP6 (i.e., CMCC‐ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset‐retreat (MOR)‐based best‐suited GCM to evaluate its usability. A multi‐model simulation approach has been carried out for runoff estimation based on observed datasets to find a better‐suited hydrological model. The proposed overall methodology is tested in a hydrological extreme‐prone ungauged watershed (i.e., Ranikhola). CMCC‐ESM2 and SSP2‐4.5 has been identified as best‐suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049). [ABSTRACT FROM AUTHOR]

Isotope‐enabled models provide a means to generate robust hydrological simulations. However, daily isotope‐enabled rainfall‐runoff models applied to larger spatial scales (>100 km2) require more input data than conventional non‐isotope models in the form of precipitation isotope time series, which are difficult to generate even with point station measurements. Spatially distributed isotope data can be circumvented by isotope‐enabled climate models. Here, we evaluate the hydrological simulations of the J2000‐isotope enabled hydrological model driven with data from corrected and un‐corrected isotope‐enabled global and regional climate models (isotope‐enabled global spectral model [IsoGSM] and isotope‐enabled regional spectral model [IsoRSM], respectively) compared with 1 year of measured reference station and a yearly average precipitation isotope input for a pilot site, the data‐scarce sub‐humid Eerste River catchment in South Africa. The models driven by all input products performed well for upstream and downstream discharge gauges with Nash Sutcliffe efficiency (NSE) from 0.58 to 0.85 and LogNSE of 0.66 to 0.93. The simulated δ2H stream isotopes using the reference J2000‐iso and J2000‐isoRSM were good for the main river with a stream Kling Gupta efficiency (KGE) of between 0.4–0.9 and the top 100 Monte Carlo simulations varying by around 5‰ for δ2H. For smaller tributaries the model was unable to capture the measured stream isotopes due to biased precipitation isotope inputs. Adjusting the J2000‐iso with a bias corrected IsoRSM improved the stream and groundwater isotope simulation and outperformed the model driven by an average yearly precipitation isotope input. Differences in simulated hydrological processes were only evident between the models when evaluating percolation with unrealistic simulations for the standard J2000 model. While the regional climate model is computationally more intensive than its global counterpart, it provided better stream isotope simulations and improvements to simulated percolation. Our results indicate that isotope‐enabled climate models can provide useful input data in data scarce regions for hydrological models, where improved water management to address climate change impacts is needed. [ABSTRACT FROM AUTHOR]

Streamflow information is crucial for effectively managing water resources. The declining number of active gauging stations in many rivers is a global concern, necessitating the need for reliable streamflow estimates. Deep learning techniques offer potential solutions, but their application in southern Africa remains largely underexplored. To fill this gap, this study evaluated the predictive performance of gated recurrent unit (GRU) and long short-term memory (LSTM) networks using two headwater catchments of the Steelpoort River, South Africa, as case studies. The model inputs included rainfall, maximum, and minimum temperature, as well as past streamflow, which was utilized in an autoregressive sense. The inclusion of streamflow in this way allowed for the incorporation of simulated streamflow values into the look-back window for predicting the streamflow of the testing set. Two modifications were required to the GRU and LSTM architectures to ensure physically consistent predictions, including a change in the activation function of the GRU/LSTM cells in the final hidden layer, and a non-negative constraint that was used in the dense layer. Models trained using commercial weather station data produced reliable streamflow estimates, while moderately accurate predictions were obtained using freely available gridded weather data. HIGHLIGHTS Assessment of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks for streamflow prediction in southern Africa reveals similar predictive performance.; By incorporating simulated streamflow into the look-back window (LBW) and modifying the GRU and LSTM architectures, this study presents a novel daily streamflow prediction method.; These models show potential for both long-term data gap filling in streamflow records and for short-term daily forecasts, that could be beneficial for flood risk management and planning.; LBWs of 10 to 30 days were found to be suitable for achieving accurate predictions with both GRU and LSTM models.;

Productive agricultural supply chains require the support of functional ecosystems, but intense agricultural practices change local hydrological systems (e.g. river diversion). In this study, the impact of farm dams was assessed for the Verlorenvlei catchment, a sensitive ecosystem currently under a state of hydrological change in South Africa. We developed a new module for the Jena Adaptable Modelling System (JAMS)/J2000 rainfall–runoff model to assess the streamflow impact from the points of abstraction, losses during storage and irrigation. The model achieved a satisfactory streamflow calibration with efficiencies Nash Sutcliffe Efficiency (NSE, logNSE) of 0.52 and 0.51. The irrigated area reduced simulated streamflow by 12 to 19%. The results from the study agree with remote sensed evapotranspiration, measured lake surface water levels and streamflow, but uncertainty remains in the total simulated dam evaporation. While many catchments lack the data required for a detailed irrigation impact assessment, this approach considers total water use, dam storage to area relationships and general farming practices. [ABSTRACT FROM AUTHOR]

The hyetograph represents the temporal spread of rainfall intensity occurring at a point or over a watershed during a storm. The importance of regionally derived/developed hyetographs and the pooled sets of categorical seasonal curves on intensity-duration, intensity-depth, and depth-duration are of multifarious conveniences and importance. Twenty-one years of daily and sub-daily rainfall records (2000–2020) regained via satellite-observed precipitation products were examined and used to retrieve a valid understanding towards annual, monthly, daily, and hourly based variability of rainfall across six different stations. An attempt was made to compare the shapes of synthesized seasonal rain mass curves with that of the historic Soil Conservation Service (SCS) mass curve. The results indicate that the location-specific patterns and trends of curves do not align closely with any historical SCS curves or theoretical curves prevalent in the literature and commonly adopted. It has been observed that region-specific rainfall and its temporal distributions exhibit unique trends, not necessarily conforming to the standard SCS-based curves categorized as Types I, Ia, II, and III. This emphasizes the need to rely more on region-specific curves rather than instinctively adopting a standard set of curves. HIGHLIGHTS Region-specific hyetographs reveal distinct rainfall intensity patterns.; Historical Soil Conservation Service curve shapes do not align with regional trends.; Emphasizes the need for region-specific curves over standardized ones.; Advocates for site-specific mass curves and hyetographs.;

The solution to inverse problems is crucial for model calibration and to provide a good basis for model results to be reliable. This paper is based on a recently proposed conceptual framework for the development and application of mathematical models that require the solution of forward and inverse problems. The focus of this paper is on the discussion of some terminology related to the results of forward problems and their reanalysis, on the use of the proposed framework to revise and generalise some methods of solutions of the inverse problem, and to provide a non-standard insight in some aspects about the Bayesian approach to model calibration.

Realistic projections of the future climate and how this translates to water availability is crucial for sustainable water resource management. However, data availability constrains the capacity to simulate streamflow and corresponding hydrological processes. Developing more robust hydrological models and methods that can circumvent the need for large amounts of hydro‐climatic data is crucial to support water‐related decisions, particularly in developing countries. In this study, we use natural isotope tracers in addition to hydro‐climate data within a newly developed version of the spatially‐distributed J2000iso as an isotope‐enabled rainfall‐runoff model simulating both water and stable isotope (δ2H) fluxes. We pilot the model for the humid tropical San Carlos catchment (2500 km2) in northeastern Costa Rica, which has limited time series, but spatially distributed data. The added benefit of simulating stable isotopes was assessed by comparing different amounts of observation data using three model calibration strategies (i) three streamflow gauges, (ii) three gauges with stream isotopes and (iii) isotopes only. The J2000iso achieved a streamflow Kling–Gupta efficiency (KGE) of 0.55–0.70 across all the models and gauges, but differences in hydrological process simulations emerged when including stable water isotopes in the rainfall‐runoff calibration. Hydrological process simulation varied between the standard J2000 rainfall‐runoff model with a high simulated surface runoff proportion of 37% as opposed to the isotope version with 84%–89% simulated baseflow or interflow. The model solutions that used only isotope data for calibration exhibited differences in simulated interflow, baseflow and model performance but captured bulk water balances with a reasonable match between the simulated and observed hydrographs. We conclude that J2000iso has shown the potential to support water balance modelling for ungauged catchments using stable isotope, satellite and global reanalysis data sets. [ABSTRACT FROM AUTHOR]

Runoff prediction is crucial for effective water resource management and risk mitigation. However, predicting these catchment responses is challenging due to their unique characteristics and the randomness of hydrological processes. This manuscript explores two different types of modelling frameworks (deterministic and stochastic) and aims to answer questions regarding the reliance on stochastic simulation based on deterministic simulations, the suitability of simple deterministic models, and the influence of catchment characteristics on the results. A simple deterministic rainfall‐runoff model (with only one model parameter) was used to feed the Brisk Local Uncertainty Estimator for Hydrological Simulations and Predictions (Bluecat) framework, exploring the whole range of values of the model parameter. Our findings showed that Bluecat enhanced the Kling‐Gupta Efficiency (KGE) outcomes in arid and semi‐arid regions as well as high‐altitude catchments. Additionally, using the mean of the confidence band of the stochastic simulation as the simulated discharge, rather than the median, resulted in improved KGE values for all catchments. Hysteresis between S‐KGE (stochastic KGE) and D‐KGE (deterministic KGE) was observed, indicating a non‐monotonic relationship between the two variables, and therefore, S‐KGE optimisation can be achieved even when D‐KGE is not optimal. Bluecat showed exceptional performance extended to arid and semi‐arid regions, as well as high‐altitude areas, making it a promising alternative for rainfall‐runoff simulations in these challenging locations. [ABSTRACT FROM AUTHOR]

The established watershed's hydrologic simulation process, involving the calibration and validation of a model through spatiotemporal streamflow observations, has recently been adapted to encompass ungauged watersheds. The research introduces a methodology for simulating ungauged hydrosystems, suggesting the calibration and validation of regional rainfall‐runoff models using large‐scale hydrological models (LSMs) in the absence of in situ streamflow measurements. For doing so, LSM's 30‐years simulated discharges at various basins and subbasins of the case study River Basin District are assessed for their accuracy against the limited available observations. Subsequently, the rectified time series tunes the optimal parametrization of a regional lumped hydrologic model that is triggered by precipitation and temperature data from a partially dense meteorological network. The validated regional model is then applied in selective ungauged neighbouring subbasins employing the regionalization concept used in LSMs, that is in basins with similar physiographic characteristics to those where the regional model was set up. The performance of the research is assessed using statistical metrics. In cases where observations are available, the results show very satisfactory indexes (e.g., coefficient of determination: 0.654 < R2 < 0.949, Kling‐Gupta efficiency: 0.759 < KGE <0.790, Nash–Sutcliffe efficiency: 0.649 < NSE <0.745 for all the cases). In completely ungauged basins and in an order of magnitude index, the comparison of the simulated discharges with those from the LSM also indicates the satisfactory performance of the proposed approach. To sum up, the methodology underscores the alternate use of large and regional scale hydrological modelling coupling as an asset across various basin scales globally, offering reliable water quantity information. [ABSTRACT FROM AUTHOR]

This study investigates the use of the Tropical Rainfall Measurement Mission's (TRMM) rainfall data for predicting water flows and flood events in three catchments on the island of Java, Indonesia, namely, Ciliwung, Citarum and Bengawan Solo. The Shetran model was used for rainfall-runoff simulations, with rainfall input obtained from measured rain gauges (hourly and daily) and TRMM (3-hourly and daily). The daily Nash Sutcliffe Efficiency (NSE) values for the model calibration period were 0.75, 0.70 and 0.85 using rain gauge data and 0.44, 0.44 and 0.75 using the TRMM rainfall data. For the validation period, the NSE values were 0.71, 0.62 and 0.89 using rain gauge data and 0.26, 0.61 and 0.58 for the TRMM data. The Critical Success Index for predicting flooding events was improved using rain gauge data compared to using TRMM data. The results demonstrate that rain gauge data are systematically superior to TRMM rainfall data when used for simulating discharges and predicting flooding events. These findings suggest that rain gauge data are preferred for flood early warning systems in tropical rainfall regimes and that if TRMM or similar satellite rainfall data are used, the evaluated flood risks should be treated with extreme caution. HIGHLIGHTS This study investigates the quality of the Tropical Rainfall Measurement Mission's (TRMM) rainfall data using the Shetran hydrological model.; Two rainfall products (observed rainfall and TRMM rainfall) have been used to find out which one has the greatest potential to predict flooding.; The observed rainfall measurement data produce a better simulation discharge than the one using TRMM rainfall data.;

The purpose of the study was to use hierarchical clustering and Thiessen polygon algorithms to identify the significant rain gauge stations for flood forecasting at Sardar Sarovar Dam. Rainfall data from 2010 to 2018 was utilized to analyze the catchment region between Omkareshwar Dam and Sardar Sarovar Dam. The study identified two clusters of rain gauge stations with similar rainfall patterns and divided the study area into seventeen regions using Thiessen polygons. The land use map showed that the study area was mostly covered by crop lands, and the soil map divided the area into three types of sedimentary claystone soil. A hydrological model, Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS), was used for rainfall-runoff modeling, and the computed runoff was compared with observed gauge discharge and inflow of Sardar Sarovar Dam. Regression analysis was performed to assess the performance of the model, and the results showed a good correlation between observed rainfall and estimated runoff values for 2012 and 2016. The study concludes that the existing rain gauge network is sufficient for flood forecasting, and the developed model along with the Thiessen polygon method can provide more accurate predictions of flow. The study highlights the importance of selecting suitable rain gauge stations for reliable flood forecasting in flood-prone basins. The study findings can be useful for future runoff prediction and flood forecasting in the study area.

This study aimed to develop a hydrologic model for the central region (central catchments) located between Dokan and Al-Dibis dams in the Lesser Zab River (LZR) watershed, in Iraq. The hydrologic structure of the study area was prepared based on the DEM layer with 12.5 m spatial resolution by using the GIS environment, and then the HEC-HMS software was used for simulating the main hydrological processes like the infiltration losses, transformation, channel routing, and the baseflow contribution by using the SCS-CN, SCS-UH, Muskingum, and the Recession methods respectively. The corrections of the CN parameter due to the effects of the slope and initial abstraction were used and the resulting CN values for the entire LZR watershed were ranging from 56 to 100. This study concluded the effectivity of using the GIS environment and HEC-HMS software in the continuous rainfall-runoff modelling and achieved very good performance with R2 and NSE criteria of 0.9115 and 0.9 under the calibration phase, while 0.925 and 0.91 values were achieved for the same criteria under the validation phase respectively, also the CN was the most sensitive parameter in the proposed hydrologic model. [ABSTRACT FROM AUTHOR]

The NRCS-CN method is widely used to estimate runoff depth. It is based on a key parameter called the Curve Number (CN), which is used to estimate the runoff coefficients of rainfall-runoff events. Since both storm and catchment characteristics for each event are dynamically variable, treating the CN of a basin as constant seems to be an unrealistic assumption. To take this variability into account, we have developed an empirical equation that estimates the event-based runoff coefficients using a correction factor obtained from the 24-h and maximum daily intensity of rainfall as well as from a minimum CN. The proposed CN method was tested in both calibration and validation stages using an event-based rainfall-runoff dataset from the Kuhsookhteh Watershed, Iran. The results showed good performance for both the calibration and validation datasets, with NS = 0.75 and R2 = 0.77 for the calibration set, and acceptable with NS = 0.6 and R2 = 0.61 for validation set. This study also highlights a variable threshold in form of the slope intercept equation which its initial abstraction could be seen as a descriptor of the loss component. The results for storm events with multiple continuous rainfalls show that the basin infiltration capacity reaches its minimal value and excess rainfall is expected to be controlled by the interaction between potential retention and λ. The obtained runoff coefficient from this methodology was encouraging however such relations for long rainy events should be tested in more catchments. [ABSTRACT FROM AUTHOR]

Many sources of error in hydroclimatic data can affect hydrological modelling, yet the impact of streamflow data quality is poorly quantified. This work aims to investigate whether inconsistencies found in streamflow time series commonly available for hydrological studies (typically in national streamflow archives) have an impact on the efficiency and the parameter estimates of rainfall–runoff models. Hydroclimatic data were gathered at the hourly time step over the period 1998–2018 for a set of 30 catchments in France. Hydrological modelling was carried out with the lumped conceptual GR5H (standing for modèle du Génie Rural à 5 paramètres Horaire, i.e. Hourly 5-parameter rural engineering model) model. A typology of "realistic" suspicious streamflow was established to set up several error models in order to corrupt the data. Our results suggest that common suspicious streamflow data do not have a strong impact on model efficiency and parameter estimates overall, but may be an important source of instability and lack of robustness when working on a single catchment. [ABSTRACT FROM AUTHOR]

This work aims to improve the feature selection for data-driven rainfall–runoff models by assessing the significance of each input variable in the learning process and analysing it from a physical point of view. For this purpose, a set of 14 experiments was carried out in two watersheds of the Santa Lucía Chico basin, Uruguay. A random forest model was trained and tested for daily discharge prediction in each of them using different input variables. A feature importance analysis was carried out for each model, using a non-model-biased method (Shapely additive explanations). Results showed that the most relevant variables were lagged discharges of one and two days, along with seven-day accumulated rainfall, which is interpreted as a proxy of the soil moisture condition of the watershed. The temperature was also relevant and was proven to represent the effect of the whole set of climatic variables (relative humidity, solar radiation, wind speed). [ABSTRACT FROM AUTHOR]

The semi-distributed approaches proposed in the past for simulating rainfall–runoff (RR) still require extensive hydro-meteorological data for their complex calibration process. Therefore, research efforts are needed to develop novel and innovative semi-distributed RR models that require a minimum amount of data and effort. In this study, four semi-distributed RR models are proposed using a simple lumped model in a distributed sense by gradually enforcing spatial distribution in terms of hydro-meteorological and physiographical features in a basin. Only rainfall, runoff, and temperature data from three contrasting basins were employed in developing the proposed models. The results show that semi-distributed models performed better than the lumped model, and the accuracy increased with gradual enforcement of the spatial variations in various data. Further, the results suggest that incorporation of the basin's physiographic features is the most important aspect in developing efficient semi-distributed RR models followed by hydrological and climatic information in a basin. [ABSTRACT FROM AUTHOR]

Accounting for the variability of hydrological processes and climate conditions between catchments and within catchments remains a challenge in rainfall-runoff modelling. Among the many approaches developed over the past decades, multi-model approaches provide a way to take into account the uncertainty linked to the choice of model structure and its parameter estimates. Semi-distributed approaches make it possible to account explicitly for spatial variability while maintaining a limited level of complexity. However, these two approaches have rarely been used together. Such a combination would allow us to take advantage of both methods. The aim of this work is to answer the following question: What is the possible contribution of a multi-model approach within a variable spatial framework compared to lumped single models for streamflow simulation? To this end, a set of 121 catchments with limited influence in France was assembled, with precipitation, potential evapotranspiration and streamflow data at the hourly time step over the period 1998-2018. The semi-distribution set-up was kept simple by considering a single downstream catchment defined by an outlet, and one or more upstream sub-catchments. The multi-model approach was implemented with 13 rainfall-runoff model structures, three calibration options and two spatial frameworks, for a total of 78 distinct modelling options. A simple average method was used to combine the various simulated streamflow at the outlet of the catchments and sub-catchments. The most efficient lumped model on a given catchment was taken as the benchmark for model evaluation. Overall, the semi-distributed multi-model approach yields better performance than the different lumped models considered individually. The gain is mainly brought about by the multi-model set-up, with the spatial framework providing a benefit on more occasional basis. These results, based on a large catchment set, evince the benefits of using a multi-model in a variable spatial framework to simulate streamflow. [ABSTRACT FROM AUTHOR]

Despite the progress made by numerous contributions in recent decades on uncertainty in hydrological simulation, there are still knowledge gaps in estimating uncertainty sources, especially associated with precipitation. The aim of this study was to determine the precipitation uncertainty through an error model based on the skew normal distribution function and to evaluate the effect of its propagation towards the simulated flow with the TETIS distributed hydrological model in a poorly instrumented tropical Andean basin. The results show the performance of the hydrological model is more sensitive to the location of the meteorological station used than to the number of stations employed in a real case with scarce information. Implementing the Bayesian approach for the study of uncertainty in input data such as precipitation is essential for its quantification, improving the knowledge of how this source of error propagates to the results of the hydrological simulation. [ABSTRACT FROM AUTHOR]

Global hydrological alterations are being driven by climate change. However, while modelling tools have been instrumental in identifying these changes, their calibration is often dependent solely on streamflow data, which limits the ability to simulate important hydrological processes with the required level of certainty. Constraining hydrological process simulations is required to identify robust model parameters, reducing model uncertainty and providing a baseline model for climate‐related risk assessments. In this study, stable isotope measurements of rainwater, groundwater and stream water (δ2H and δ18O), together with an end member mixing analysis (EMMA) (as a post‐processing step) were integrated with the Jena adaptative modelling system (JAMS)/J2000 rainfall‐runoff model, named J2000iso. The J2000iso models of δ2H and δ18O were able to achieve a streamflow Nash–Sutcliffe efficiency (NSE) of 0.75 and 0.72 and an isotope mixing Kling–Gupta efficiency (KGE) of 0.51 and 0.60, respectively. Compared with the base version, the J2000iso had a much smaller variability with 56% less ensemble variance in the NSE, 13% more simulated interflow which resulted in a better soil‐moisture simulation at an observation point, and lower simulated flow component uncertainty. The J2000iso δ2H model was more robust than the base version during a subsequent validation in terms of KGE and Bias with 0.60 and 0.002 compared with 0.51 and −0.14 for the base version. The EMMA provided a means to improve the hydrograph separation ability of the JAMS/J2000. As many catchments around the world are still ungauged and are affected by anthropogenic activities, the development and enhancement of isotope‐enabled modelling provides a means to improve the simulation of key hydrological processes. [ABSTRACT FROM AUTHOR]

Yağış-akış modelleri kapsamında ele alınan modeller içerisinden kavramsal modeller havza dinamiğini atanan parametreler yardımıyla benzeştirmeye çalışırken, kapalı kutu modelleri ise fiziksel süreci dikkate almadan veri işleme esaslı uygulanmaktadır. Her iki yöntemin de birbirine göre avantajlı ve dezavantajlı yönleri bulunmaktadır. Örneğin kavramsal modellerin bazı parametreleri doğrusal tanımlandıklarında simülasyonlarda yanlılıklar gözlenebilmektedir. Diğer yandan, kapalı kutu modelleri tutarlı bir simülasyon için gecikmeli yağış değerlerine ihtiyaç duymaktadır. Bu nedenle çalışmada her iki yaklaşımın iyi yönlerini birleştiren hibrit bir model yapısının ortaya konması amaçlanmıştır. Bu kapsamda, dinamik su bütçesi adı verilen kavramsal bir yağış-akış modelinin doğrusal davranış gösteren yeraltısuyu depolama elemanı yerine destek vektör makinesi eklenerek beş parametreli hibrit bir model oluşturulmuştur. Destek vektör makinesi ilavesi ile doğrusal olmayan haritalama yetisi kazanan model Balıkesir’in İkizcetepeler Baraj Havzası’nda uygulanmıştır. Hibrit modelin kavramsal modele kıyasla kalibrasyon ve validasyon dönemlerinde sırasıyla %21 ve %14 daha düşük hata performansı vermesi istatistiksel açıdan anlamlı bulunmuştur.

This study explores the application of long short-term memory (LSTM) networks to simulate runoff at the national scale of Denmark using data from 301 catchments. This is the first LSTM application on Danish data. The results were benchmarked against the Danish national water resources model (DK-model), a physically based hydrological model. The median Kling-Gupta Efficiency (KGE), a common metric to assess performance of runoff predictions (optimum of 1), increased from 0.7 (DK-model) to 0.8 (LSTM) when trained against all catchments. Overall, the LSTM outperformed the DK-model in 80% of catchments. Despite the compelling KGE evaluation, the water balance closure was modelled less accurately by the LSTM. The applicability of LSTM networks for modelling ungauged catchments was assessed via a spatial split-sample experiment. A 20% spatial hold-out showed poorer performance of the LSTM with respect to the DK model. However, after pre-training, that is, weight initialisation obtained from training against simulated data from the DK-model, the performance of the LSTM was effectively improved. This formed a convincing argument supporting the knowledge-guided machine learning (ML) paradigm to integrate physically based models and ML to train robust models that generalise well.

Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments. HIGHLIGHTS Evaluated applicability of satellite rainfall products for rainfall - runoff modelling in Vamsadhara and Mahanadi river basins.; GPM-IMERG and CHIRPS data could be reliably used with LSTM and ELM models for rainfall -runoff modelling and streamflow forecasting.; Deep learning models with the CHIRPS products can lead to a new horizon to provide flood forecasting in flood-prone catchments.;

Observational data scarcity often limits the potential of rainfall-runoff modelling around the globe. In ungauged catchments, earth-observations or reanalysis products could be used to replace missing ground-based station data. However, performance of different datasets needs to be thoroughly tested, especially at finer temporal resolutions such as hourly time steps. This study evaluates the performance of ERA5 -Land and COSMO-REA6 precipitation reanalysis products (PRPs) using 16 meso-scale catchments located in Slovenia, Europe. These two PRPs are firstly compared with a gridded precipitation dataset that was constructed based on ground observational data. Secondly, a comparison of the temperature data of these reanalysis products with station-based air temperature data is conducted. Thirdly, several data combinations are defined and used as input for the rainfall-runoff modelling using the GR4H model. A special focus is on the application of an additional snow module. Both tested PRPs underestimate, for at least 20%, extreme rainfall events that are the driving force of natural hazards such as floods. In terms of air temperature both tested reanalysis products show similar deviations from the observational dataset that was catchment-specific. Additionally, air temperature deviations are smaller in winter compared to summer. In terms of rainfall-runoff modelling, the ERA5-Land yields slightly better performance than COSMO-REA6. If a re-calibration with PRP has been carried out, the performance is similar compared to the simulations where station-based data was used as input. Model recalibration proves to be essential in providing relatively sufficient rainfall-runoff modelling results. Hence, tested PRPs could be used as an alternative to the station-based based data in case that precipitation or air temperature data are lacking, but model calibration using discharge data would be needed to improve the performance. [ABSTRACT FROM AUTHOR]

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We propose to evaluate the impact of rainfall–runoff model (RRM) structural uncertainty on climate model evaluation, performed within a process-oriented framework using the RRM. Structural uncertainty is assessed with an ensemble approach using three conceptual RRMs (HBV, IHACRES and GR4J). We evaluate daily precipitation and temperature from 11 regional climate models forced by five general circulation models (GCM–RCMs), issued from EURO-CORDEX. The assessment was performed over the reference period (1970–2000) for five catchments situated in northern Tunisia. Seventeen discharge performance indexes were used to explore the representation of hydrological processes. The three RRMs performed well over the reference period, with Nash–Sutcliffe efficiency values ranging from 0.70 to 0.90 and bias close to 0%. The ranking of GCM–RCMs according to hydrological performance indexes is more meaningful before the bias correction, which considerably reduces the differences between GCM- and RCM-driven hydrological simulations. Our results illustrate a strong similarity between the different RRMs in terms of raw GCM–RCM performances over the reference period for the majority of performance indexes, in spite of their different model structures. This proves that the structural uncertainty induced by RRMs does not affect GCM–RCM evaluation and ranking, which contributes to consolidate the RRM as a standard tool for climate model evaluation. HIGHLIGHTS Structural uncertainty induced using rainfall–runoff models does not affect climate model evaluation and ranking performed using hydrological modelling.; Importance of considering a wide set of hydrological performance indexes to evaluate climate model performance.; Climate models that rank well on high-flow performance indexes rank poorly on low-flow performance indexes and vice versa.;

Rainfall-Runoff Models were developed by using the daily total precipitation (P) and daily mean discharge (Q) values on Aksu Stream and future discharges were predicted. The performances of numerical methods to explain the Rainfall-Runoff process were compared. Two kinds of Artificial Intelligence (AI) methods, including, Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS); as well as Heuristic Regression (HR) method, including Multivariate Adaptive Regression Splines (MARS) and Support Vector Machine (SVM), were used to describe the process. The performance of the methods was determined by using the Root Mean Square Error (RMSE), Correlation Coefficient (R) and Mean Absolute Error (MAE) coefficients. Although the performance of AI methods was very close, the lowest error value was obtained by ANFIS method. For the other methods, the highest performance was observed with MARS. It has been concluded that MARS method, which is HR method have higher performance than AI methods. [ABSTRACT FROM AUTHOR]

In the dry zone of Sri Lanka experiencing hydrological and weather extremes, non-availability of validated scientific data on stream-flows and groundwater stocks for sufficient time periods, and the presence of large numbers of small and large tanks that divert the water from the streams, make water resource planning a very complex challenge. An integrated modelling exercise was carried out: first using a hydrological simulation model to assess the annual and seasonal water flows of Yan Oya basin of Anuradhapura district and its seven sub-watersheds for 30 years and their dependability; then using a water balance model to analyze the extent to which the current water demands of five selected cascades are being met from the supplies available; and assessing the scope for augmenting the existing storage capacity of the tanks to reduce the irrigation deficits. This article presents the results of the study with regard to: the basin flows and their variability; and water balance scenarios for five selected cascades, with and without modifications in tank storage capacity. The article also discusses how the results from such integrated modelling studies should be used by irrigation planners for taking water management decisions for the basin with decentralized storages like tanks. HIGHLIGHTS Water resources planning around small systems is complex.; Non-availability of scientific data on stream-flows, and the presence of large number of small water systems are major challenges.; SWAT modelling was done to assess the yield of Yan Oya basin.; The rainfall-runoff model estimated for the basin showed linear relationship.; Analysis of five cascades with 30 tanks showed that in 16 cases, supply deficit existed.;

Conditions of the formation of key elements of the water balance, such as precipitation and runoff, and relations between them in the mountain catchment area are very complicated, conditioned both by the climatic factor and the physiographic characteristics of the catchment area. The aim of the study is to determine relations between precipitation and runoff in the Kłodzka Valley (KV) located in mountain areas of south-western Poland. Analyzes were based on precipitation in KV and discharges of the Nysa Kłodzka River and its tributaries, recorded in hydrological years 1974–2013. The bivariate Archimedean copulas were used to describe the degree of synchronicity between these variables. The study area shows a considerable variability in the conditions of transformation of precipitation into runoff. It is conditioned both by the pluvial regime and the physical-geographical characteristics of the catchment area. As a result, sub-catchments with diversified hydrological activity and their role in the formation of water resources of the entire KV were identified. Among them, the Biała Lądecka River sub-catchment was found to be the most hydrologically active, and the sub-catchment of Bystrzyca Dusznicka River the most inert, despite e.g. quite similar synchronicity of precipitation compared to the average precipitation in KV. At the same time, the KV rivers are characterized by different types of runoff regime and characteristic of the water balance structure. The methodology presented can be useful in determining dependencies between selected elements of the water balance and evaluation of water resources availability in source areas of mountain rivers. [ABSTRACT FROM AUTHOR]

Aim of the study: Aim of the study is to provide a comparative assessment of event based (EBA4SUB) and continuous (COSMO4SUB) modelling approaches for rainfall-runoff modeling for small and ungauged basins, focusing on the influence of the Antecedent Moisture Condition (AMC) of the soil on the estimated design peak discharge. Material and methods: The event based approach is the EBA4SUB software. It consists in selecting a design rainfall event, in estimating the rainfall excess and in transforming it into the direct hydrograph. The continuous approach is the COSMO4SUB framework. It consists in generating a long synthetic rainfall time series at sub-daily resolution that feeds a continuous rainfall–runoff model. Then, a discharge time series is determined, providing the estimation of the runoff and the related peak discharge. Results and conclusions: Results show the critical role of antecedent moisture condition (AMC) and how subjective the event-based approach is for determining the design hydrograph and peak discharge. In the event-based approach, AMC is arbitrarily selected by the analyst, while in the continuous modelling it is automatically determined using the synthetic rainfall input. Our findings indicate that the event-based approach systematically leads to a considerable overestimation of floods if AMC III (wet soil) is assumed or to a slight underestimation of floods if AMC II (average condition for soil humidity) is selected.

Hydrological dam safety analyses should assess the frequency curve of maximum reservoir water levels in flood events by routing a large set of inflow hydrographs. Therefore, stochastic bivariate analyses are used. Hydrograph shapes obtained by using hydrometeorological simulations are required for each flood peak-hydrograph volume pair. Hydrograph shapes depend on hyetograph shapes. A sensitivity analysis is required to select the appropriate hyetograph shape, focusing on the influence of the hyetograph time step on the hydrograph shape. In this study, the Cuerda del Pozo Dam in central Spain is selected as a case study.

Predicting inter‐catchment groundwater flow (IGF) is essential because IGF greatly affects stream water discharge and water chemistry. However, methods for estimating sub‐annual IGF and clarifying its mechanisms using minimal data are limited. Thus, we quantified the sub‐annual IGF and elucidated its driving factors using the short‐term water balance method (STWB) for three forest headwater catchments in Japan (named here catchment A, B and As). Our previous study using the chloride mass balance indicated that annual IGF of catchment A (49.0 ha) can be negligible. Therefore, we calculated the daily evapotranspiration (ET) rate using the Priestley–Taylor expression and the 5‐year water balance in catchment A (2010–2014). The sub‐annual IGF of the three catchments was then calculated by subtracting the ET rate from the difference between rainfall and stream discharge during the sub‐annual water balance periods selected using the STWB. The IGF rates of catchment B (7.0 ha), which is adjacent to catchment A, were positive in most cases, indicating that more groundwater flowed out of the catchment than into it, and exhibited positive linear relationships with rainfall and stream discharge. This suggested that as the catchments became wetter, more groundwater flowed out of catchment B. Conversely, the IGF rates of catchment As (5.3 ha), included in catchment A, were negative in most cases, indicating that more groundwater flowed into the catchment than out from it, and exhibited negative linear relationships with rainfall and stream discharge. Given the topography of the catchments studied, infiltration into the bedrock was the probable reason for the IGF outflow from catchment B. We hypothesized that in catchment As, the discrepancy between the actual hydrological boundary and the surface topographic boundary could have caused an IGF inflow. This study provides a useful tool for determining an IGF model structure to be incorporated into rainfall‐runoff models. [ABSTRACT FROM AUTHOR]

• Five precipitation datasets were evaluated on 714 basins across Brazil. • CHIRPS, MERRA-2 and IMERG excelled in hydrological simulations. • Hydrologic modelling presented KGE > 0.5 for 78 % of the catchments. • The worst hydrological performances were in the most arid hydrographic regions. • Parameter sensitivity varies with catchment climate and aridity. Satellite-based and reanalysis precipitation products are widely adopted as complementary information to in situ measurements for estimating river discharge using hydrological modelling. However, there is still a notable research gap in the literature associated with assessing the accuracy of satellite-based or reanalysis products in different tropical and sub-tropical catchments at large-sampling hydrological modelling with sensitivity analysis. We investigated the accuracy of precipitation, hydrological model performance and parameter sensitivity related to seven precipitation data sets based on satellite and reanalysis products, i.e., CHIRPS, TRMM, GLDAS, IMERG, MERRA-2, PERSIANN-CDR, and ERA5 over 714 contrasting tropical and subtropical catchments located in Brazil. We used the Génie Rural Journalier 4 (GR4J) hydrological model to simulate the hydrological processes of the different catchments with two approaches for the calibration: using measured ground-based precipitation data (approach I) or using each individual satellite/reanalysis precipitation products (approach II) to calibrate the models. The results showed that the precipitation products tend to overestimate precipitation, with the exception of ERA5 and MERRA-2. CHIRPS is the only product that produces unbiased precipitation estimates for most catchments. The model calibration using each precipitation product individually improved the hydrological model performance. CHIRPS, IMERG, and MERRA-2 showed good accuracy in terms of both, precipitation estimation and hydrological simulation performance in the calibration period. In the validation period, the best products in terms of KGE were CHIRPS, IMERG and TRMM (KGE > 0.64). The errors in precipitation products are better compensated via hydrological modelling in wet regions. The model parameter sensitivity varies according to precipitation input, climate, and catchment aridity. Overall, all seven products exhibited their worst hydrological performance in arid regions. This study helps to improve our understanding of the catchment response in tropical and subtropical regions while also providing key insights into the reliability of satellite/reanalysis rainfall products for streamflow simulation. This study is valuable in hydrometeorological applications, climate change assessment, water resources and disaster management, especially in regions with a relatively sparse density of precipitation stations. [ABSTRACT FROM AUTHOR]

Flooding impacts can be reduced through application of suitable hydrological and hydraulic tools to define flood zones in a specific area. This article proposes a risk matrix technique which is applied on a case study of Taibah and Islamic universities catchment in Medina, Kingdom of Saudi Arabia (KSA). The analysis is based on integration of the hydrologic model hydraulic models to delineate the flood inundation zones. A flood risk matrix is developed based on the flood occurrence probability and the associated inundation depth. The risk matrix criterion is classified according to the degree of risks as high, moderate and low. The case study has indicted low to moderate risk for flood frequencies of 5 years return periods and moderate to high risk may exist for flood with rerun period of 50 and 100 years. The results are projected on a two-dimensional satellite images that shows the geographical locations exposed to flooding. A quantitative summary of the results have been presented graphically to estimate the magnitude of the inundation areas that can assess the degree of damage and its economic aspects. The developed flood risk matrix tool is a quantitative tool to assess the damage which is crucial for decision makers.

The active rock glacier "Innere Ölgrube" and its catchment area (Ötztal Alps, Austria) are assessed using various hydro(geo)logical tools to provide a thorough catchment characterization and to quantify temporal variations in recharge and discharge components. During the period from June 2014 to July 2018, an average contribution derived from snowmelt, ice melt and rainfall of 35.8%, 27.6% and 36.6%, respectively, is modelled for the catchment using a rainfall‐runoff model. Discharge components of the rock glacier springs are distinguished using isotopic data as well as other natural and artificial tracer data, when considering the potential sources rainfall, snowmelt, ice melt and longer stored groundwater. Seasonal as well as diurnal variations in runoff are quantified and the importance of shallow groundwater within this rock glacier‐influenced catchment is emphasized. Water derived from ice melt is suggested to be provided mainly by melting of two small cirque glaciers within the catchment and subordinately by melting of permafrost ice of the rock glacier. The active rock glacier is characterized by a layered internal structure with an unfrozen base layer responsible for groundwater storage and retarded runoff, a main permafrost body contributing little to the discharge (at the moment) by permafrost thaw and an active layer responsible for fast lateral flow on top of the permafrost body. Snowmelt contributes at least 1/3rd of the annual recharge. During droughts, meltwater derived from two cirque glaciers provides runoff with diurnal runoff variations; however, this discharge pattern will change as these cirque glaciers will ultimately disappear in the future. The storage‐discharge characteristics of the investigated active rock glacier catchment are an example of a shallow groundwater aquifer in alpine catchments that ought to be considered when analysing (future) river runoff characteristics in alpine catchments as these provide retarded runoff during periods with little or no recharge. [ABSTRACT FROM AUTHOR]

A number of rainfall-runoff models have been developed for hydraulic and hydrological engineering with an emphasis on reproducing river discharge time series. Physicallybased rainfall-runoff models have recently reached a certain level of achievement following the advancement of computers and the development of various geographical and meteorological datasets. However, it has been pointed out that the current physically-based models do not properly reflect observed hillslope water dynamics. The present paper proposes a methodology to examine the capability of a depth-discharge constitutive equation for physicallybased rainfall-runoff modelling to simulate hillslope water dynamics. An application of the methodology suggested that 1) the targeted constitutive equation was capable of representing the depth-discharge relationship on hillslopes under the assumed conditions, 2) the runoff simulations with the constitutive equation described hillslope water flows, at least in the downward direction, and 3) there was a possibility that the parameters in the constitutive equation was determined from the internal structure of hillslope water dynamics. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the unusable water in the Sebou basin (Morocco) with a view to transferring them to the Bouregreg basin. To this end, a hydrological model of Belksiri Basin (7744 km2, downstream part of Sebou) was elaborated. Then, several simulations were carried out to estimate the flow resulting from rainfall, with different return periods, in the Belksiri catchment. The model validation showed good results (NSE = 0.70, R2 = 0.78). As for simulations of flow provoked by different return periods of the rainfall depth, the results showed that the peak flow increases from 916 m3/s for the return period T = 2–6744 m3/s for T = 100. This study clearly showed the possibility of the flow and route optimization related to the North to South Water Transfer Project (NSWTP) in Morocco. Indeed, the transfer flow can be increased from some 45 m3/s to 200 m3/s. [ABSTRACT FROM AUTHOR]

Rainfall-runoff process, most important component of the hydrologic cycle, has a significant place in hydrologic analysis and engineering of water resources. Recently, artificial intelligence methods which are in demand due to bringing successful outcomes to complex problems are preferred in modelling of hydrological events as in many fields. In this study, monthly average streamflow values that belongs to two stations which is inside of the boundary of the Tigris basin, is tried to determine by using precipitation data which has been obtained from meteorological observation stations. Rainfall-runoff relationship was evaluated by setting up for used stations with Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Gene Expression Programming (GEP) and Multiple Linear Regression (MLR). It was seen that GEP performed better performance compared to other methods between observed streamflow and estimated streamflow at one of the used stations. At another station, it was observed that ANFIS is rather successful at predict the streamflow with high sensitive. The outcomes not only corroborate the feasibility of the intelligence methods, but also show the usability of GEP that is a mathematical method to determine the rainfall-runoff relationship. [ABSTRACT FROM AUTHOR]

Ringlet Reservoir in Cameron Highlands impounds water mainly from four main rivers namely Sg. Telom, Sg. Habu, Sg. Ringlet and Sg. Bertam. Due to the absence of gauge flow data, MIKE NAM rainfall runoff model was used to simulate inflow for short term and long term prediction. Peak flow is sensitive towards any changes in Umax, TG, CQOF, CKBF, CKIF and CK1,2. All parameters except CK1,2 are sensitive in calculation of total volume. Model was calibrated for the period from 1999 to 2006 and validated for the period from 2010 to 2012 at two streamflow locations. The model is reliable to simulate flow satisfactorily especially during flood events. Model shows good agreement between the simulated and observed flow in terms of low flow, peak flow and total volume. Good calibration results were achieved for all scenarios, with NSE > 0.66, RSR < 0.6, R2 > 0.74 and PBIAS (%) < 15%.