Conceptual Rainfall-runoff Model 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

Error correction method based on deep learning for improving the accuracy of conceptual rainfall-runoff model

Due to the complex runoff and concentration situation, flood forecasting for small and medium-sized catchments is very difficult. To improve the accuracy of flood forecasting, this study constructs a distributed model for flood forecasting based on the Xainanjiang (XAJ) model and the North China (NC) model respectively, and takes the deep learning model including LSTM and transformer to compare. Taking the Podi Basin and Shibazi Basin as study cases. LSTM, Transformer, and LSTPencoder models were used to correct the error of distributed models, and the differential evolution (DE) algorithm was used to optimize the model parameters. Taking the observed rainfall and distributed model results as input, the residuals of the simulation flow are fitted to improve the accuracy of the distributed model. The research results show that the NC model performs better than the XAJ, LSTM, and transformer models. Compared with the XAJ model, the average Nash-Sutcliffe coefficient of the NC model for the two catchments increased by 25.7 % and 5.87 % respectively. The performance of the NC+LSTPencoder model is better than other models. Compared with the NC model, the average Nash-Sutcliffe coefficient of the NC+LSTPencoder model for two catchments increased by 89.7 % and 1.12 % respectively, and the Root Mean Square Errors (RMSE) of the two catchments reduced by 79.1 % and 63.4 % respectively. The model proposed in this paper has strong correction ability, which has important significance for improving the accuracy of flood forecasts.

Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach

Accurately characterizing the spatial variability of tension water storage capacity (TWC) within a catchment is challenging due to limited in-situ hydrologic data availability. Conventional conceptual rainfall-runoff models typically rely on an empirically specified TWC distribution. However, this empirical distribution lacks a physical foundation and fails to effectively redistribute critical hydrologic components, such as local capacity and contributing area, to real-world contexts. To overcome this limitation, the topographic wetness index (TWI) and its generalized form (GTWI) are introduced to bridge local topographic information with hydrologic components. Four TWC distribution curves are contrived based on the empirical parabolic distribution, empirical linear distribution, TWI, and GTWI, respectively. The effects of these alternate TWC distributions on streamflow are investigated within the framework of the HYdrologic MODel (HYMOD) across 460 Australian catchments. The results illustrate that the GTWI-based HYMOD (GTHYMOD) outperforms other models in terms of daily streamflow, with high Kling-Gupta Efficiency (KGE) attained in 74.8% of the study catchments during the validation period. The eastern coast of Australian catchments presents a superior streamflow performance compared to that in the western coast. GTHYMOD demonstrates its superiority in characterizing spatial variability, an aspect HYMOD lacked. This study has the potential to refine the empirical TWC distribution from a physical perspective and advance our comprehension of underlying hydrologic behaviors.

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.

Performance assessment of daily GR conceptual rainfall-runoff models in the Upper Benue River (Cameroon) using airGR packages

Performance assessment of daily GR conceptual rainfall-runoff models in the Upper Benue River (Cameroon) using airGR packages

What controls the tail behaviour of flood series: rainfall or runoff generation?

Abstract. Many observed time series of precipitation and streamflow show heavy-tail behaviour. For heavy-tailed distributions, the occurrence of extreme events has a higher probability than for distributions with an exponentially receding tail. If we neglect heavy-tail behaviour we might underestimate the magnitude of rarely observed, high-impact events. Robust estimation of upper-tail behaviour is often hindered by the limited length of observational records. Using long time series and a better understanding of the relevant process controls can help with achieving more robust tail estimations. Here, a simulation-based approach is used to analyse the effect of precipitation and runoff generation characteristics on the upper tail of flood peak distributions. Long, synthetic precipitation time series with different tail behaviour are produced by a stochastic weather generator. These are used to force a conceptual rainfall–runoff model. In addition, catchment characteristics linked to a threshold process in the runoff generation are varied between model runs. We characterize the upper-tail behaviour of the simulated precipitation and discharge time series with the shape parameter of the generalized extreme value (GEV) distribution. Our analysis shows that runoff generation can strongly modulate the tail behaviour of flood peak distributions. In particular, threshold processes in the runoff generation lead to heavier tails. Beyond a certain return period, the influence of catchment processes decreases and the tail of the rainfall distribution asymptotically governs the tail of the flood peak distribution. Beyond which return period this is the case depends on the catchment storage in relation to the mean annual rainfall amount.

Enhancing flood event predictions: Multi-objective calibration using gauge and satellite data

Rainfall-runoff models are widely used for predicting watershed runoff. The accuracy of the predictions heavily relies on effective model calibration, which is majorly influenced by the employed objective functions that measure the agreement between predictions and real-world observations. In this study, we propose a novel multi-objective calibration approach with the aim of improving the flood event predictions of a conceptual rainfall-runoff model applied to a catchment in the Austrian Alps. To achieve this, we include a total of five carefully selected objective functions into calibration. Three of these objective functions can be regarded as more classical and are roughly based on comparing absolute deviations, square of the residuals, as well as bias, correlation and variability of the flow. Another objective function relies on remote observations of the snow covered area, for which the MODIS Terra (MOD10A1.061) and Aqua (MYD10A1.061) datasets are used. The final objective function measures the differences in peak magnitude and timing for the largest events in the observation period. To showcase the proposed approach’s effectiveness, we compare the calibration and validation results with those obtained by a model that was only calibrated for two runoff objective functions and to carefully selected benchmark cases. The presented study reveals that our approach enhances the model’s capacity to depict snow cover dynamics, a crucial factor in accurately predicting snow-driven flooding events. Additionally, the results underscore that incorporating an error metric targeting magnitude and timing errors of large flood events can enhance the model’s ability to predict peak runoff. The results of this study further suggest that in general a more robust model can be obtained by incorporating additional error metrics that specifically target physical states and individual parts of the runoff curve into model calibration.

Effect of mountainous rainfall on uncertainty in flood model parameter estimation

Abstract Explaining the significant variability of rainfall in orographically complex mountainous regions remains a challenging task even for modern raingauge networks. To address this issue, a real-time spatial rainfall field estimation model, called WREPN (WRF Rainfall-Elevation Parameterized Nowcasting), has been developed, incorporating the influence of mountain effect based on ground raingauge networks. In this study, we examined the effect of mountainous rainfall estimates on the uncertainty of flood model parameter estimation. As a comparison, an inverse distance weighting technique was applied to ground raingauge data to estimate the spatial rainfall field. To convert the spatial rainfall fields into flood volumes, we employed the ModClark model, a conceptual rainfall–runoff model with distributed rainfall input. Bayesian theory was applied for parameter estimation to incorporate uncertainty analysis. The ModClark model demonstrated good flood reproducibility regardless of the estimation method for spatial rainfall fields. Parameter estimation results indicated that the WREPN spatial rainfall field, which accounted for the influence of the mountain effect, led to lower curve numbers due to higher estimated rainfall compared to the IDW spatial rainfall field, while the concentration time and storage coefficient showed minimal differences.

Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling

Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.

Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets

Data assimilation (DA) techniques that integrate remotely sensed observations and in-situ measurements into hydrological models have been extensively implemented, but few studies have applied multisource actual evapotranspiration (ETa) products to evaluate its impacts on the DA performances through a large number of basins with different catchment conditions. This study performs a DA experiment to assess the influence of remotely sensed ETa products on streamflow simulations over 149 Catchment Attributes and Meteorology for Large-sample Studies basins across the contiguous United States, and the relationships between the DA results and multiple catchment characteristics are further analyzed. In this experiment, three satellite ETa products from the GLEAM, REA-ET, and GLDAS are assimilated into a conceptual rainfall-runoff model (i.e., HYMOD) using the ensemble Kalman filter. The results show that assimilating the three ETa products provides relatively small improvements compared to the open loop (OL) scenario in approximately 48%-58% of the study basins, and around 14%-19% of these basins exhibit an increase in Nash-Sutcliffe Efficiency over 0.01. Specifically, the GLEAM ETa shows the greatest improvements in runoff, followed by the GLDAS and REA-ET products. The OL performance plays a significant role in runoff improvements, greater improvements are achieved for basins with poor OL runoff simulations. The assimilation efficiency is revealed to be relevant to the accuracy of the satellite ETa products. While the catchment conditions such as the aridity index, mean slope, the fraction of forest, and catchment area are found to have a limited effect on the spatial pattern of ET assimilation performances.

Performance Evaluation of Rainfall-Runoff Models for Predictions of Inflows to Bhumibol Reservoir in Thailand

The Bhumibol reservoir in the Ping River basin is the largest reservoir in the Kingdom of Thailand. This reservoir has contributed to economic development of the country by supplying increased electricity and irrigation water demands as well as flood mitigation in riparian areas along the Ping and the Chao Phraya River. The prediction of inflows to the reservoir is crucial for the optimal management of water for irrigation, power generation and flood control. Properly customized rainfall-runoff models of the catchment could provide the basis for predicting the inflows to the reservoir. Hence, five lumped conceptual rainfall-runoff models were developed for the Ping River basin to simulate daily inflows to the Bhumibol reservoir. The rainfall-runoff models are Australian Water Balance Model (AWBM), Sacramento Soil Moisture Accounting Model, Simplified Hydrolog Model (SIMHYD), Soil Moisture Accounting and Routing Model (SMAR) and Tank Model. The evaluation of the performances of these models showed that all models are capable of predicting inflows. However, the SIMHYD, Sacramento, AWBM and Tank models perform better than SMAR model. Hence, these models could be employed for prediction of inflows to the reservoir with acceptable accuracy.

DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling

DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling

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.

An inverse-problem approach to detect outliers in rainfall measurements of ground gauges for robust reservoir flood control operation

This study proposes an approach to detect and remove systematic outliers in rainfall measurement of ground gauges in the watershed of a reservoir. This method utilizes a conceptual rainfall-runoff model with the downstream observed reservoir inflow to supervise the detection and cleanse of upstream rain measurements. The analysis is based on a nonlinear optimization with the objective designed as only removing outliers which leads to relevant improvement in runoff simulation to satisfy required precision. The minimum acceptable precision in hydrological simulation is specified based on the objective of reservoir flood control operation. This method can be utilized to purify historical rain data for hydrological studies, as well as to estimate the current floodwater detained in the watershed for more robust flood management in real-time. A realistic case study along with synthetically designed experiments are demonstrated to verify the effectiveness of the proposed methods.

Bridging the gap from hydrological to biogeochemical processes using tracer-aided hydrological models in a tropical montane ecosystem

How simple or complex catchment models need to be is still unclear particularly for tracer-aided models that go beyond hydrograph fitting. Here, we take advantage of a sub-daily hydrometric and tracer data set from a tropical montane (páramo) experimental catchment to fill this knowledge gap. We evaluated six conceptual rainfall-runoff model structures with different complexity that represent the perceptual understanding of the catchment's hydrological functioning. The models solved conservative and reactive tracer mass balances to simulate streamflow, stable isotopes, and DOC concentrations and were assessed for performance and parsimony using three calibration targets (discharge only, discharge and stable isotopes, and discharge and DOC), resulting in 18 model configurations. Although all models satisfied the discharge calibration (KGE > 0.8), differences arose when considering tracer transport. The more complex models outperformed the simpler ones in terms of goodness-of-fit and parsimony, indicating that the catchment streamflow response consisted of quick near-surface flow and tracer contributions, more mixed flow through the two main soil types (Andosols and Histosols), as well as flow from the well-mixed shallow fractured rock (up to 20 m depth). The bedrock flow pathway contributed up to ∼25% of total flow during baseflow conditions. This study provides a benchmark experiment to identify hydrological and biogeochemical behavior in tropical montane catchments using relatively parsimonious tracer-aided hydrological models.

Calibration of conceptual rainfall-runoff models by selected differential evolution and particle swarm optimization variants

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

(Digital Presentation) Evaluation of Two Methods of Bias Correction of Precipitations on Improvement of Hydrological Simulation:Case of Chiffa Basin-Northern Algeria-

This work aim to assess the “basic-quantile”and“gamma-mapping” bias correction methods in order to improve hydrological simulations of the Chiffa watershed. The results are provided by the conceptual rainfall-runoff GR2M model in the R environment, coupled with the outputs of rainfall data simulations from the RCA4 model of CORDEX-AFRICA forced by two global circulation models (MPI-ESM-LR and CRNM-CM5) under two Representative Concentration Pathways (RCP) RCP4.5 and RCP 8.5 .The validation of GR2M hydrological model on the Chiffa basin using Nash criteria made it possible to simulate future flows over the period 2074-2099. The results showed that the future flows simulated from raw rainfall data are not in adequacy with the evolution of future rainfall simulations whereas the flows simulated from the corrected rainfall data shown better results, which highlights the importance of bias correction for hydrological impact studies. Finally, the regional climate models simulate a 9% decrease in winter, and a 6% increase in spring under RCP4.5 by the end of the 21st century Figure 1