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Studying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long‐term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long‐term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi‐century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall‐runoff transformation model. We tested our approach to generate a statistically representative 300‐year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high‐resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long‐term hydrologic modeling. Plain Language Summary River hydrographs are key inputs for understanding long term Earth surface processes. Due to the limited lengths of observational streamflow records, various techniques were previously developed with limited capabilities to generate representative long hydrographs. Through a novel integrated approach, we are able to construct robust high‐resolution hydrographs on multi‐century timescales, based on developing a linkage between hydroclimatic forces and watershed characteristics within a stochastic framework. We used this methodology to generate a 300‐year high‐resolution hydrograph with satisfactory correlation with the observed FDC. Due to the stochastic background of this framework, the deviation between the observed and generated FDCs was estimated to fall within a reasonable range of 6% and 18%. This framework was extended to provide hourly runoff projections for several future climatic models. Median projections for the near‐term period 2040–2069 demonstrated less deviation from reference data set compared to those for the more distant future 2070–2099. This study represents a scientific shift for long‐term simulations through re‐constructing past, simulating present, or projecting future hydrographs. Key Points Introducing a novel framework designed to generate statistically robust hydrographs on multi‐century timescales for long‐term simulations Integrating a weather generator and a disaggregation technique within a rainfall runoff model to achieve high‐temporal resolution hydrographs Utilizing multiple climate models to evaluate the impacts of climate change on hourly runoff responses

In order to predict and control the impacts of floods in torrents, it is important to verify the simulation accuracy of the most used hydrological models. The performance verification is particularly needed for applications in watersheds with peculiar climatic and geomorphological characteristics, such as the Mediterranean torrents. Moreover, in addition to the accuracy, other factors affect the choice of software by stakeholders (users, modellers, researchers, etc.). This study introduces a “performance matrix”, consisting of several evaluation parameters weighted by stakeholders’ opinions. The aim is to evaluate the accuracy of the flood prediction which is achieved by different models, as well as the pros and cons of software user experience. To this aim, the performances and requisites of four physical-based and conceptual models (HEC-HMS, SWMM, MIKE11 NAM and WEC-FLOOD) have been evaluated, by predicting floods in a midsized Mediterranean watershed (Mèsima torrent, Calabria, Southern Italy). In the case study, HEC-HMS and MIKE 11 NAM were the best computer models (with a weighted score of 4.45 and 4.43, respectively), thanks to their low complexity and computation effort, as well as good user interface and prediction accuracy. However, MIKE11 NAM is not free of charge. SWMM showed a lower prediction accuracy, which put the model in third place of the four models. The performance of WEC-FLOOD, although not being as good as for the other tested models, can be considered overall acceptable in comparison to the other well-consolidated models, considering that WEC-FLOOD is in the early stage of development. Overall, the proposal of the performance matrix for hydrological models may represent a first step in building a more complete evaluation framework of the hydrological and hydraulic commercial models, in order to give indications to allow potential users to make an optimal choice.

Reflecting the shape and drainage characteristics of the watershed, time–area histogram (TAH) seems to be the most important parameter for derivation of the transformation hydrograph. In this study, a semi-distributed instantaneous unit hydrograph was established and applied to the steep 103 km²-Galazchai watershed in Iran to improve the results of the rainfall–runoff modelling. Towards this attempt, twenty-three runoff producing events with wide different characteristics were used for the analysis. The direct surface runoff hydrographs (DSRHs) were obtained and consequently compared for the study area using Clark’s instantaneous unit hydrograph (IUH) and through applying different TAHs calculated based on channel profile, dimensionless TAH, average velocity and spatially distributed travel time methods. A weight grid developed from kinematic wave travel time equation for overland flow was prepared and used as input to derive the spatial TAH of the watershed. The results indicated that the different TAHs had noticeable impacts on the estimated hydrographs. The results also proved that the spatial TAH method performed well with efficiency criteria of 0.75 and 0.69 in calibration and validation steps. The implemented method also offered the advantages of flexibility, efficiency and physically powerful links to the spatial data set and GIS software.

This paper presents methodologies for the development of stormwater quality control measures based on the derived probability distribution approach. These stormwater control measures, such as the fraction of pollutant removed from storage facilities, are closed-form analytical models and can be effectively used to evaluate pollutant loads to receiving waters. In this study, a simple form of rainfall-runoff transformation with lumped parameters is first extended to take into account the spatial variations in model parameters. Second, the infiltration process is further incorporated to the rainfall-runoff transformation. This study demonstrates that analytical models can be developed with various levels of complexity based on different hydrologic considerations. The performance of the analytical models is evaluated in a case study, and the results indicate that, with an appropriately formulated rainfall-runoff transformation, analytical stormwater runoff models are capable of providing comparable results to continuous simulation models in the evaluation of the long-term performance of storage facilities.

Climate change and modification of the urban environment increase the frequency and the negative effects of flooding, increasing the interest of researchers and practitioners in this topic. Usually, flood frequency analysis in urban areas is indirectly carried out by adopting advanced hydraulic models to simulate long historical rainfall series or design storms. However, their results are affected by a level of uncertainty which has been extensively investigated in recent years. A major source of uncertainty inherent to hydraulic model results is linked to the imperfect knowledge of the rainfall input data both in time and space. Several studies show that hydrological modelling in urban areas requires rainfall data with fine resolution in time and space. The present paper analyses the effect of rainfall knowledge on urban flood modelling results. A mathematical model of urban flooding propagation was applied to a real case study and the maximum efficiency conditions for the model and the uncertainty affecting the results were evaluated by means of generalised likelihood uncertainty estimation (GLUE) analysis. The added value provided by the adoption of finer temporal and spatial resolution of the rainfall was assessed.

An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment set-up is based on (i) a large set of catchments in France, (ii) the GRP rainfall–runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they use (log, Box–Cox and log–sinh) to account for heteroscedasticity and the evolution of the other properties of the predictive distribution with the discharge magnitude. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box–Cox transformation can be a reasonable choice for flood forecasting.

Changes in active layer thickness (ALT) over Arctic and permafrost regions have an important impact on rainfall-runoff transformation. General warming is observed across Svalbard Archipelago and corresponds to increases in ground temperatures. Permafrost thaw and changes in ALT due to climate warming alter how water is routed and stored in catchments, and thus impact both surface and subsurface processes. The overall aim of the present study is to examine the relationships between temporal changes of active layer depth and hydrological model parameters, together with variation in the catchment response. The analysis was carried out for the small unglaciated catchment Fuglebekken, located in the vicinity of the Polish Polar Station Hornsund on Spitsbergen. For hydrological modelling, the conceptual rainfall-runoff HBV (Hydrologiska Byråns Vattenbalansavdelning) model was used. The model was calibrated and validated on runoff within subperiods. A moving window approach (3 weeks long) was applied to derive temporal variation of parameters. Model calibration, together with an estimation of parametric uncertainty, was carried out using the Shuffled Complex Evolution Metropolis algorithm. This allowed the dependence of HBV model parameters on ALT to be analysed. Also, we tested the influence of model simplification, correction of precipitation, and initial conditions on the modelling results.

This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255–6967 km²) and two different periods (May–August and September–November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May–August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall–runoff transformation.

Considering network topologies and structures of the artificial neural network (ANN) used in the field of hydrology, one can categorize them into two different generic types: feedforward and feedback (recurrent) networks. Different types of feedforward and recurrent ANNs are available, but multilayer perceptron type of feedforward ANN is most commonly used in hydrology for the development of wavelet coupled neural network (WNN) models. This study is conducted to compare performance of the various wavelet based feedforward artificial neural network (ANN) models. The feedforward ANN types used in the study include the multilayer perceptron neural network (MLPNN), generalized feedforward neural network (GFFNN), radial basis function neural network (RBFNN), modular neural network (MNN) and neuro-fuzzy neural network (NFNN) models. The rainfall-runoff data of four catchments located in different hydro-climatic regions of the world is used in the study. The discrete wavelet transformation (DWT) is used in the present study to decompose input rainfall data using db8 wavelet function. A total of 220 models are developed in this study to evaluate the performance of various feedforward neural network models. Performance of the developed WNN models is compared with their counterpart simple models developed without applying wavelet transformation (WT). The results of the study are further compared with - multiple linear regression (MLR) model which suggest that the WNN models outperformed their counterpart simple models. The hybrid wavelet models developed using MLPNN, the GFFNN and the MNN models performed best among the six selected data driven models explored in the study. Moreover, performance of the three best models is found to be similar and thus the hybrid wavelet GFFNN and the MNN models can be considered as an alternative to the most commonly used hybrid WNN models developed using MLPNN. The study further reveals that the wavelet coupled models outperformed their counterpart simple models only with the parsimonious input vector.

The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models inherit the disadvantages of containing more complex structure and enhanced simulation time as a result of use of increased multiple input sub-series obtained by the wavelet transformation (WT). So, the identification of dominant wavelet sub-series containing significant information regarding the hydrological system and subsequent use of those dominant sub-series only as input is crucial for the development of wavelet-coupled ANN models. This study is therefore conducted to evaluate various approaches for selection of dominant wavelet sub-series and their effect on other critical issues of suitable wavelet function, decomposition level and input vector for the development of wavelet-coupled rainfall-runoff models. Four different approaches to identify dominant wavelet sub-series, ten different wavelet functions, nine decomposition levels, and five different input vectors are considered in the present study. Out of four tested approaches, the study advocates the use of relative weight analysis (RWA) for the selection of dominant input wavelet sub-series in the development of wavelet-coupled models. The db8 and the dmey (Discrete approximation of Meyer) wavelet functions at level nine were found to provide the best performance with the RWA approach.