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Determining rainfall runoff responses of catchments to unprecedented climate conditions is an issue which has largely eluded the hydrologic community for many years. Conceptual rainfall runoff models are used globally to predict runoff for regional water resources management and planning. However, obtaining parameter values suitable for future climate conditions requires approaches that consider conditions beyond historical periods. This paper takes advantage of data from 207 Australian catchments to determine model parameters that most closely produce expected rainfall runoff coefficients (ratio of runoff to rainfall) for a wide range of environmental conditions. This was done for two popular rainfall runoff models, GR4J and Sacramento. In a two‐step process, parameters were first selected that could adequately reproduce observed runoff coefficients across the 207 catchments. Acceptable parameter sets were stored in a library from which, in the second step, parameters were selected for each individual catchment according to various goodness‐of‐fit metrics. Performance of this calibration approach was compared with a classical optimization employed for each catchment (DELO—Differential Evolution Local Optimization). The study found performance trade‐offs using the parameter library based calibration compared to DELO for metrics such as Nash‐Sutcliffe Efficiency and percentage bias. The library‐based calibration exhibited behavior that more closely aligned with expectations under perturbed climate conditions, compared to DELO parameters. Results also showed tolerable estimates of rainfall runoff coefficient using DELO parameters at many sites when rainfall is reduced by no more than 25%. However, there is a high risk of under‐ or over‐estimating runoff coefficients at larger reductions.

Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall–runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swapping training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall–runoff modeling.Graphic abstract

The soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil‐atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall‐runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km2. Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m2. The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field‐mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall‐runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most “representative” field, previously determined through the temporal stability analysis, can reproduce the catchment‐mean soil moisture with a determination coefficient, R2, higher than 0.96 and a root‐mean‐square error, RMSE, equal to 2.38%. For the “nonrepresentative” fields the accuracy in terms of RMSE decreased, but similar R2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know an index of soil moisture temporal pattern to be incorporated in conceptual rainfall‐runoff models. The obtained results can address the soil moisture monitoring network design from which a reliable soil moisture temporal pattern at the catchment scale can be derived.

Continuous simulation for design flood estimation is increasingly becoming a viable alternative to traditional event‐based methods. The advantage of continuous simulation approaches is that the catchment moisture state prior to the flood‐producing rainfall event is implicitly incorporated within the modeling framework, provided the model has been calibrated and validated to produce reasonable simulations. This contrasts with event‐based models in which both information about the expected sequence of rainfall and evaporation preceding the flood‐producing rainfall event, as well as catchment storage and infiltration properties, are commonly pooled together into a single set of “loss” parameters which require adjustment through the process of calibration. To identify the importance of accounting for antecedent moisture in flood modeling, this paper uses a continuous rainfall‐runoff model calibrated to 45 catchments in the Murray‐Darling Basin in Australia. Flood peaks derived using the historical daily rainfall record are compared with those derived using resampled daily rainfall, for which the sequencing of wet and dry days preceding the heavy rainfall event is removed. The analysis shows that there is a consistent underestimation of the design flood events when antecedent moisture is not properly simulated, which can be as much as 30% when only 1 or 2 days of antecedent rainfall are considered, compared to 5% when this is extended to 60 days of prior rainfall. These results show that, in general, it is necessary to consider both short‐term memory in rainfall associated with synoptic scale dependence, as well as longer‐term memory at seasonal or longer time scale variability in order to obtain accurate design flood estimates. Key Points Short‐ and long‐term memory in precipitation is important for flood modelling There are advantages to using a continuous rainfall‐runoff modelling framework Influence of catchment and climate‐specific factors on antecedent precipitation

In forested, seasonally dry watersheds, winter rains commonly replenish water storage deficits in the vadose zone before recharging underlying hillslope groundwater systems that sustain streamflow. However, the relative inaccessibility of the subsurface limits our understanding of how groundwater recharge is moderated by vadose zone storage deficits generated by plant‐water uptake. Here, we compare groundwater recharge inferred from the storage‐discharge relationship with independent, distributed estimates of deficits across 12 undisturbed California watersheds. We find accrued dry season deficits primarily driven by evapotranspiration insufficiently explain inter‐annual variability in the amount of precipitation required to generate groundwater recharge due to continued deficit accumulation between wet season storms. Tracking the deficit at the storm event‐scale, however, reveals a characteristic response in groundwater to increasing rainfall not captured in the seasonal analysis that may improve estimates of the rainfall required to generate recharge and streamflow on a per‐storm basis. Our findings demonstrate the potential for existing public data sets to better capture water partitioning within the subsurface and thus improve the prediction of rainfall‐runoff behavior and summer water availability in rainfall‐dominated, seasonally dry basins using a combined deficit‐recharge approach.

This paper discusses the notion of similarity often used in the regionalization studies of hydrological models. We compare two different visions of similarity: the apparent similarity defined on the basis of observable catchment properties, and behavioral similarity judged through the use of hydrological models. These two visions are generally assumed to be merged in regionalization studies: Catchments having apparently similar physical characteristics are assumed to have a similar hydrological behavior. In this paper, we wished to test the validity of this assumption. To this aim, we defined behavioral (hydrological) similarity on the basis of model parameter transferability. Then pools of hydrologically similar catchments are compared with pools of apparently physically similar catchments, as identified on the basis of physiographic catchment descriptors. The overlap between the two pools of similar catchments is analyzed, making it possible to judge the efficiency of the physical similarity measure and to identify hydrologically similar catchments in an ungauged context. The results show that the overlap between the two pools is significant for only 60% of the catchments. For the other catchments, two major reasons were identified as contributing to the lack of overlap: (1) these catchments often have a quite specific hydrological behavior and (2) the role of the underground properties of the catchment on its hydrological behavior was not found to be accurately described by the available physical descriptors, meaning that more relevant catchment descriptors should be sought to better describe the geological and lithological context in hydrological terms.

This study investigated the performance of the adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), and support vector regression (SVR) models for rainfall–runoff modeling using gauged and satellite rainfall, and their fusions in the Gilgel Abay watershed, Ethiopia. Afterward, simple average ensemble (SAE), weighted average ensemble (WAE), and neural network ensemble (NNE) techniques were applied to combine the outputs of individual models under three scenarios. The performance of the models was evaluated using Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE). The results demonstrated that the ANFIS model outperformed all the other single models with validation stage NSE values of 0.864 and 0.875, and RMSE values of 23.58 and 21.84 m3/s for gauge and fusion rainfall data, respectively. Among the physical-based models, SWAT gave better modeling performance with the validation stage NSE values of 0.81 and 0.821 for gauge and fusion rainfall data, respectively. Moreover, an ensemble of artificial intelligence and physical-based models greatly improved the overall modeling performance. The NNE improved the performance of single models up to 15.7 and 21.2 5% for fusion and satellite-based rainfall modeling, respectively.

In this paper, the Kolar River watershed, Madhya Pradesh is taken as the study area. This study area is located in Narmada River in Central India. The data set consists of monthly rainfall of three meteorological stations, Ichhawar, Brijesh Nagar, and Birpur rainfall stations from 2000 to 2018, runoff data at Birpur and temperature data of Sehore district. In this paper, radial basis function neural network models have been studied for generation of rainfall–runoff modeling along with wavelet input and without wavelet input to the RBF neural network. A total of 15 models were developed in this experiment based on various combinations of inputs and spread constant of RBF model. The evaluation criteria for the best models selected are based on R2, AARE, and MSE. The best predicting model among the networks is model 8, which has input of R(t-1), R(t-2), R(t-3), R(t-4), and Q(t-1). For the RBFNN model, the maximum value of R2 is 0.9567 and the lowest values of AARE and MSE are observed. Similarly, for the WRBFNN model, the maximum value of R2 is 0.9889 and the lowest values of AARE and MSE are observed. WRBF performs better than RBF with any data processing techniques which shows the proposed model possesses better predictive capability.

ABSTRACT Rainfall and runoff are considered the main components of the hydrological cycle, and their forecasting is of great significance in water resource management, particularly for reservoir operation. Developing an accurate model to capture the dynamic connection between rainfall and runoff remains problematic and challenging in water resource management due to the nonstationary characteristics of hydrologic processes and the effects of noise. In this study, data‐driven techniques, such as the group method of data handling (GMDH), extreme learning machine (ELM), and two hybrids of artificial neural network (ANN) with Cuckoo search algorithm (ANN + Cuckoo) and genetic algorithm (ANN + GA) were used to model the rainfall–runoff relationship. For a comprehensive analysis, four scenarios were examined based on the different input combinations to test and select the best scenario and best model performance. The results indicated that the performance of ELM and GMDH in predicting runoff was more accurate than that of ANN + Cuckoo and ANN + GA. Although the GMDH predicts runoff with higher accuracy, ELM provides reliable performance in simulating both low and high values. The models' performance can be ranked based on the testing data in the following order: GMDH, ELM, ANN + GA, and ANN + CUKOO. The root mean squared error (RMSE) was recorded as 56.7 and 69.7 m3/s for the GMDH and ELM models, respectively. These low RMSE values highlight the potential of these models in effectively addressing the challenges associated with the complexity of rainfall–runoff simulations. Moreover, the results demonstrate that the machine learning models could be used as a simple, rapid, and inexpensive approach for timely and reliable runoff prediction that is expected to benefit reservoir management.

As the world is changing, mainly due to climate change, extreme events such as floods and droughts are becoming more frequent and severe. Considering this, the predictive modeling of flow in small mountain catchments that are particularly vulnerable to flooding is critical. Rainfall data sources such as rain gauges, meteorological radars, and satellites provide data to the hydrological model with a lag. Only numerical weather predictions can achieve this in advance, but their estimates are often subject to considerable uncertainty. This article aims to verify whether Global Environmental Multiscale numerical precipitation prediction can be successfully applied for event-based rainfall–runoff hydrological modeling. These data were verified for use in two aspects: the flow modeling and determination of antecedent moisture conditions. The results indicate that GEM data can be satisfactorily used for hydrological modeling, and particularly good simulation results are obtained when significant rainfall occurs. In addition, these data can be used to correctly estimate the AMC groups for each sub-catchment in advance, which is one of the key elements flowing into the amount of projected outflow in the catchment. It is worth noting that, according to the literature review conducted by the article’s author, this is the first published attempt to use GEM data directly in applied hydrological applications.