Conceptual Rainfall-Runoff Research Articles

Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context

Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context

Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics

The ease and efficiency with which conceptual rainfall runoff (CRR) models can be calibrated, as well as issues related to the uniqueness of their parameters, has received significant attention in literature. While several studies have tried to gain a better understanding of the underlying factors affecting these issues by examining the features of model error surfaces, this has generally been done in an ad-hoc fashion using lower-dimensional representations of higher-dimensional surfaces. In this paper, it is suggested that exploratory landscape analysis (ELA) metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion throughout the surface. This enables key error surface features of CRR models to be compared in a consistent, efficient and easily communicable fashion for models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics, and calibration data set lengths). Results from the application of ELA metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that increasing model complexity results in an increase in relative error surface roughness and relative optima dispersion and that, while increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that for the models considered in this study, optimisation efficiency is likely to decrease with increasing model complexity and catchment wetness, while optimisation difficulty is likely to increase and parameter uniqueness likely to decrease with model complexity and catchment dryness. While implications for choice of model complexity will need further work, this study highlights the potential value of the proposed approach to understanding the calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models.

A Review of Event-Based Conceptual Rainfall-Runoff Models: A Case for Australia

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments.

Lithologic Controls on Parameters of Conceptual Rainfall–Runoff Model and Runoff Characteristics: A Case Study of the Xinanjiang Model

Lithologic Controls on Parameters of Conceptual Rainfall–Runoff Model and Runoff Characteristics: A Case Study of the Xinanjiang Model

Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin

Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin

Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework

Study regionA total of 11,913 sub-catchments in Peru and transboundary catchments with neighboring countries in South America. Study focusThis paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) model and a river-routing (RAPID) model in thousands of river reaches. We identify 43 hydrological zones (with 122 sub-zones) to run the hybrid hydrological modeling framework (ARNO/VIC+RAPID) with previously calibrated and validated parameters with 43 fluviometric stations for 1981–2020. Simulated flow series show a higher performance at daily scale (KGE ≥ 0.75, NSEsqrt ≥ 0.65, MARE ≤ 1, and −25% ≤ PBIAS ≤ 25%) for catchments located at the Pacific coast and the Andes-Amazon transition, and good representation (R≥0.75) of seasonal and interannual variability. New hydrological insights for the regionIncreasing hydrological hazards such as floods highlight the importance of a systematic hydrological analysis and modeling at national level in gauged and ungauged catchments in Peru. This study introduces a new hydrological dataset of simulated daily flow series. The results are helpful for short-term flood risk scenario simulations and long-term water resource planning as the outcomes can provide valuable information for hydrologists and water resource managers in Peruvian regions with limited or no access to in-situ networks.

A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source

Hydrological modelling at a catchment scale was conducted to investigate the impact of climate change and land-use change individually and in combination with the available streamflow in the Painkalac catchment using an eWater Source hydrological model. This study compares the performance of three inbuilt conceptual models within eWater Source, such as the Australian water balance model (AWBM), Sacramento and GR4J for streamflow simulation. The three-model performance was predicted by bivariate statistics (Nash–Sutcliff efficiency) and univariate (mean, standard deviation) to evaluate the efficiency of model runoff predictions. Potential evapotranspiration (PET) data, daily rainfall data and observed streamflow measured from this catchment are the major inputs to these models. These models were calibrated and validated using eight objective functions while further comparisons of these models were made using objective functions of a Nash–Sutcliffe efficiency (NSE) log daily and an NSE log daily bias penalty. The observed streamflow data were split into three sections. Two-thirds of the data were used for calibration while the remaining one-third of the data was used for validation of the model. Based on the results, it was observed that the performance of the GR4J model is more suitable for the Painkalac catchment in respect of prediction and computational efficiency compared to the Sacramento and AWBM models. Further, the impact of climate change, land-use change and combined scenarios (land-use and climate change) were evaluated using the GR4J model. The results of this study suggest that the higher climate change for the year 2065 will result in approximately 45.67% less streamflow in the reservoir. In addition, the land-use change resulted in approximately 42.26% less flow while combined land-use and higher climate change will produce 48.06% less streamflow compared to the observed flow under the existing conditions.

Parameter Optimization of a Conceptual Rainfall-Runoff Model in the Coastal Urban Region

Sunwoo, W.; Lee, G.; Nguyen, H.H.; Shin, H., and Jun, K.S., 2021. Parameter optimization of a conceptual rainfall-runoff model in the coastal urban region. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 261–265. Coconut Creek (Florida), ISSN 0749-0208. The rainfall-runoff simulation based on local conditions is crucial for flood monitoring and mitigation. Although numerous rainfall-runoff models were widely used, only few studies have been applied in coastal regions in South Korea. The major reasons are that model parameters are incorrectly determined, and the peak flooding is overestimated due to extreme rainfall events. In this study, a conceptual rainfall-runoff model, the Probability Distributed Model (PDM), was employed to simulate discharge with an incorporation of combined satellite-, model-, and ground-based soil moisture as an input data. The combined soil moisture data was generated using two renormalization strategies. In this study, we aimed to compare the performances of two optimization techniques associated in the PDM model, including a traditional non-linear and a newly metaheuristics algorithms, for the discharge prediction. The simulation was conducted in a coastal urban region, Hongseong, using the optimized parameter sets for flood events. The simulated results from the PDM model revealed that parameters obtained with the metaheuristics algorithm indicated a superior performance when comparing against the observed runoff data. Moreover, parameter sets obtained by the metaheuristics algorithm and non-linear technique showed significant differences. The method of this study can provide more improvements in rainfall-runoff simulation results for flood management.

Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling

One of the frequently adopted hybridizations within the scope of rainfall-runoff modeling rests on directing various outputs simulated from the conceptual rainfall-runoff (CRR) models to machine learning (ML) techniques. In those coupled model exercises, after the parameter calibrations of the CRR models are made, their specific outputs constitute auxiliary inputs for the ML model training. However, in this parallel hybridization comprising two consecutive processes, performing the cascade calibration of CRR and ML models increases the computational complexity. Moreover, the mutual interaction between the parameters governing CRR and ML models is also not considered. In this study, to cope with the handicaps mentioned, artificial neural networks (ANN) and support vector regression (SVR) were separately embedded into a monthly lumped CRR model. The dynamic water balance model (dynwbm) was preferred as the CRR model. Then, all free parameters within these nested hybrid models were calibrated simultaneously. The ML parts within the nested schemes manipulate various output variants derived with three conceptual parameters for monthly runoff simulation. These new hybrid models equipped with an automatic calibration algorithm were applied at several locations in the Gediz River Basin of western Turkey. The performance measures regarding mean and high flows indicated that the nested hybrid models outperformed the standalone models (i.e., dynwbm, ANN, and SVR) and coupled model variants. Thus, the credibility of a novel modeling strategy, which takes advantage of the supplementary strengths of a conceptual model and different ML techniques, was demonstrated.

Quantifying the Individual Contributions of Climate Change, Dam Construction, and Land Use/Land Cover Change to Hydrological Drought in a Marshy River

Hydrological drought for marshy rivers is poorly characterized and understood. Our inability to quantify hydrological drought in marshy river environments stems from the lack of understanding how wetland loss in a river basin could potentially change watershed structure, attenuation, storage, and flow characteristics. In this study, hydrological drought in a marshy river in far Northeast China at a higher latitude was assessed with a streamflow drought index (SDI). A deterministic, lumped, and conceptual Rainfall–Runoff model, the NAM (Nedbor Afstromnings Model), was used to quantify the individual contributions of climate change, land use/land cover (LULC) change, and river engineering to hydrological drought. We found that in the last five decades, the frequency of hydrological droughts has been 55% without considering LULC change and reservoir construction in this wetland-abundant area. The frequency of hydrological drought increased by 8% due to land use change and by 19% when considering both the impacts of LULC change and a reservoir construction (the Longtouqiao Reservoir). In addition to the more frequent occurrence of hydrological droughts, human activities have also increased drought intensity. These findings suggest that LULC and precipitation changes play a key role in hydrological drought, and that the effect can be significantly modified by a river dam construction.

Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications

The Shuffled Complex Evolution (SCE-UA) method developed at the University of Arizona is a global optimization algorithm, initially developed by [1] for the calibrationof conceptual rainfall-runoff (CRR) models. SCE-UA searches for the global optimumof a function by evolving clusters of samples drawn from the parameter space, via a systematiccompetitive evolutionary process. Being a general purpose global optimization algorithm, it has found widespread applications across a diverse range of science and engineering fields. Here, we recount the history of the development of the SCE-UA algorithm and its later advancements. We also present a survey of illustrative applications of the SCE-UA algorithm and discuss its extensions to multi-objective problems and touncertainty assessment. Finally, we suggest potential directions for future investigation.

Regionalization of Conceptual Rainfall-Runoff Model Parameters for Predicting Stream Flows of Ungauged Catchments in the Upper Blue Nile Basin

Water resources development and research significantly suffered from lack of stream low data. Regionalization of model parameters was found veryuseful in filling such data gaps. We therefore regionalized the parameters of the HBV model so that the model could be used in ungauged catchments ofthe Upper Blue Nile (UBN) basin. Although we collected stream flow data for 76 stations from the Ministry of Water, Irrigation and Electricity, our dataquality assessment indicated that only 20 stations were suitable for calibrating the HBV model. We calibrated the model using hydro-climate data of 6 years and validated the calibrated model for independent data of 4 years. The calibrated model reproduced the overall pattern and base flow of the catchments. However, it noticeably missed several peak flows. The values of the calibrated parameters varied with the characteristics of the catchments. We therefore developed a multiple regression relationship between the parameters and catchment characteristics for the basin. The relationship was statistically significant and therefore could be used to apply the HBV model for un-gauged catchments in UBN for water balance studies, climate change impact assessment, and recharge estimation. However, additional work specifically improved data sets were needed to improve the regionalization results for peak flows.

Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations

Conceptual lumped rainfall-runoff models are frequently used for various environmental problems. To put them into practice, both the model calibration method and data series of the area-averaged precipitation and air temperature are needed. In the case when data from more than one measurement station are available, first the catchment-averaged meteorological data series are usually obtained by some method, and then they are used for calibration of a lumped rainfall-runoff model. However, various optimization methods could easily be applied to simultaneously calibrate both the aggregation weights attributed to various meteorological stations to obtain a lumped meteorological data series and the rainfall-runoff model parameters. This increases the problem dimensionality but allows the optimization procedure to choose the data that are most important for the rainfall-runoff process in a particular catchment, without a priori assumptions. We test the idea using two conceptual models, HBV and GR4J, and three mutually different, relatively recently proposed Evolutionary Computation and Swarm Intelligence optimization algorithms, that are applied to three catchments located in Poland and northwestern USA. We consider two cases: with and without the model error correction applied to the rainfall-runoff models. It is shown that for the calibration period, joint optimization of the weights used to aggregate the meteorological data and the parameters of the rainfall-runoff model improves the results. However, the results for the validation period are inconclusive and depend on the model, error correction, optimization algorithm, and catchment.

From marginal to conditional probability functions of parameters in a conceptual rainfall–runoff model: an event-based approach

ABSTRACTA parameter estimation strategy for a conceptual rainfall–runoff (CRR) model applied to a storm sewer system in an urban catchment (Chassieu, Lyon, France) is proposed on the basis of event-by-event Bayesian local calibrations. The marginal distribution formed by locally-estimated parameters is divided into conditional functions, clustering the event-based parameters based on their transferability to similar rainfall events. The conditional functions showed to be consistent with an observed bimodality in the marginal representation, reflecting two different hydrological conditions mainly related to the magnitude of the rainfall intensities (high or low). The improvements achieved by expressing the parameter probability functions into a conditional form are shown in terms of accuracy (Nash-Sutcliffe efficiency criterion), precision (average relative interval length) and reliability (percentage of coverage) for simulating flow rate in 255 and 110 calibration/verification events.

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

Various methods are used in the literature for calibration of conceptual rainfall-runoff models. However, very rarely the question on the relation between the number of model runs (or function calls) and the quality of solutions found is asked. In this study two lumped conceptual rainfall-runoff models (HBV and GR4J with added snow module) are calibrated for five catchments, located in temperate climate zones of USA and Poland, by means of three modern variants of Evolutionary Computation and Swarm Intelligence optimization algorithms with four different maximum numbers of function calls set to 1000, 3000, 10,000 and 30,000. At the calibration stage, when more than 10,000 function calls is used, only marginal improvement in model performance has been found, irrespective of the catchment or calibration algorithm. For validation data, the relation between the number of function calls and model performance is even weaker, in some cases the longer calibration, the poorer modelling performance. It is also shown that the opinion on the model performance based on different popular hydrological criteria, like the Nash-Sutcliffe coefficient or the Persistence Index, may be misleading. This is because very similar, largely positive values of Nash-Sutcliffe coefficient obtained on different catchments may be accompanied by contradictory values of the Persistence Index.

An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches

Land Use and Land Cover (LULC) changes intrinsically lead to various hydrological impacts especially on the outflow rate of the watersheds. This research investigated LULC changes and its effect on outlet runoff by detecting LULC changes location and severity via an inverse method for the Little River Watershed, USA. For this purpose, the Clark’s conceptual Rainfall-Runoff model was applied to the delineated sub-watersheds of the basin to generate different outflows by altering the Storage Coefficient (SC) of the model for each sub-watershed as the representative of LULC. Then, the relation between SC values and outflow time series was simulated by Artificial Intelligence (AI) based models of artificial neural network and least square support vector machine. In this way, in order to ignore redundant information and reduce the dimension of input vector, Wavelet-Entropy (WE) values of the outflow sub-series were computed and used as the inputs of the AI model to compute SCs of the sub-watersheds as the outputs. The trained multi-output AI model as an inverse method could be then used to predict the SC values (as representative of LULC) of the sub-watersheds using the observed time series of runoff at the outlet. The obtained results showed that the proposed inverse method could reliably detect not only the location but also the severity of LULC changes by prediction of SC values in the coming future years. For validation of the method, a comparison was also performed between the obtained results and recorded changes via normalized difference vegetation index (NDVI) and land use classification, extracted from Landsat images. The comparison approved the ability of the proposed method for LULC change detection in a way that deforestation and cropland increasing of the sub-watersheds from 1990 to 2013 were aligned with the SC reduction e.g., 26% decrease of SC for downstream sub-watershed versus 53% decrease and 21% increase of forest and crop lands, respectively.

The Impact of the Variability of Precipitation and Temperatures on the Efficiency of a Conceptual Rainfall-Runoff Model

Abstract The main objective of the paper is to understand how the model’s efficiency and the selected climatic indicators are related. The hydrological model applied in this study is a conceptual rainfall-runoff model (the TUW model), which was developed at the Vienna University of Technology. This model was calibrated over three different periods between 1981-2010 in three groups of Austrian catchments (snow, runoff, and soil catchments), which represent a wide range of the hydroclimatic conditions of Austria. The model’s calibration was performed using a differential evolution algorithm (Deoptim). As an objective function, we used a combination of the Nash-Sutcliffe coefficient (NSE) and the logarithmic Nash-Sutcliffe coefficient (logNSE). The model’s efficiency was evaluated by Volume error (VE). Subsequently, we evaluated the relationship between the model’s efficiency (VE) and changes in the climatic indicators (precipitation ΔP, air temperature ΔT). The implications of findings are discussed in the conclusion.

Comparative Study of Evolutionary Algorithms for the Automatic Calibration of the Medbasin-D Conceptual Hydrological Model

The calibration of a hydrological model is an important task for obtaining accurate runoff simulation results for a specific watershed. Several optimisation algorithms have been applied during the last years for the automatic calibration of conceptual rainfall-runoff (CRR) models. The aim of this study is to compare the effectiveness and the efficiency of three evolutionary algorithms, namely the Shuffled Complex Evolution (SCE), the Genetic Algorithms (GA) and the Evolutionary Annealing-Simplex (EAS), for the calibration of the Medbasin-D daily CRR model. An improved calibration approach of Medbasin-D is presented, including a batch-processing module which enables the implementation of coupled simulation-optimisation routines. The enhanced Medbasin calibration module is employed in a watershed of the island of Crete (Greece), using several objective functions in order to test the optimisation algorithms under different hydrological flow conditions. The results reveal that, in terms of effectiveness, SCE and EAS performed equally well, while GA provided slightly worse optimal solutions. However, GA was computationally more efficient than SCE and EAS. Despite the discrepancies among the optimisation runs, the simulated hydrographs had a very similar response for the optimal parameter sets obtained by the same calibration criteria, indicating that all tested optimisation methods produce equally successful results with Medbasin-D model. Additionally, the selected objective function seems to have a more decisive effect on the final simulation outcomes.

Towards an Efficient Rainfall–Runoff Model through Partitioning Scheme

Partitioning Scheme (PS) is one of the strategies that could play a constructive role in improving the performance of conceptual rainfall–runoff (CRR) models. The main objective of this paper is to develop a Rainfall Runoff-Partitioning Scheme (RR-PS) with the ability of distinguishing different flow regimes in a basin and simulating each regime separately. The model utilizes a combination of PS and “regular” procedures and is equipped with Fuzzy C-Means (FCM) and Seasonal Partitioning (SP) to recognize different flow regimes. In addition, to avoid excessive increase of the model parameters caused by PS, sensitivity analysis is used to recognize the sensitive parameters. The PS part of integrated model is only run for the “sensitive” parameters and the “regular” part of model is implemented for the “less-sensitive” parameters. Data from three different scale basins in USA and Iran are used to evaluate the models. A HBV-Light (Hydrologiska Byråns Vattenbalansavdelning-Light model) based CRR model (Improved HBV- IHBV) is developed in FORTRAN (Formula Translation) with several modifications for testing the procedures. The results show that in all cases IHBV-based models using PS method perform better than the regular IHBV model. Overall, IHBV-SP demonstrates better performance than the other PS based models. Further improvement is reached by adopting user-defined seasons in IHBV-SP through optimization.

Performance of conceptual and black-box models in flood warning systems

AbstractFlood forecasting is a core of flood forecasting and flood warning system which can be implemented by both conceptual rainfall–runoff (CRR) model and black-box rainfall–runoff (BBRR) model. Dynamic artificial neural network (DANN) as an innovative BBRR model and HEC-HMS as a traditional CRR model were used for flood forecasting. The aim of this paper is to compare the efficiency of HEC-HMS and DANN for the determination of flood warning lead-time (FWLT) in a steep urbanized watershed. A framework is proposed to compare the performance of the models based on four criteria: type and quantity of required input data by each model, flood simulation performance, FWLT and expected lead-time (ELT). Finally, the results show that FWLT and ELT were estimated longer by DANN than by HEC-HMS model. In brief, because of less required data by BBRR model and its longer ELT, future research should be focused on better verification of it.