Conceptual Rainfall-runoff Model Research Articles

Method for testing the accuracy of rainfall–runoff models in predicting peak flow changes due to rainfall changes, in a climate changing context

Summary Catchment hydrological models are widely used in climate change impact studies. The considered climate scenarios often involve increases in rainfall intensities. These increases might go beyond the range of historical events used in model calibration and validation. The hydrological impact results of climate change consequently are under these circumstances highly uncertain. This paper presents a method to test the validity of hydrological models for that type of impact analysis. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. The method is demonstrated, based upon the results of three conceptual rainfall–runoff models (NAM, PDM, VHM) for three catchments in Belgium. It is shown how the performance of the model in predicting impacts of rainfall increases can be integrated in the calibration process and how this method can increase the reliability of climate change impact results on peak flows. The paper addresses the importance of the relation between the soil moisture content and the overland flow coefficient, as it controls the generation of peak flows in the considered rainfall–runoff models.

Calibration of a conceptual rainfall–runoff model using a genetic algorithm integrated with runoff estimation sensitivity to parameters

This study proposes a parameter-calibration method (SA_GA) for the conceptual rainfall–runoff model using a real-value coding genetic algorithm (GA) which takes into account runoff estimation sensitivity to model parameters; this process is carried out using the standardized regression equation. The proposed SA_GA method treats the standardized values of model parameters as the real-value code and adopts a multinomial trial process with a probability of selecting genes for the crossover and mutation resulting from the runoff estimation sensitivity to the model parameters. A 19-parameter conceptual rainfall–runoff model, Sacramento Soil Moisture Accounting (SAC-SMA) model, and seven rainstorm events recorded in the Baj-Hang River watershed of South Taiwan are applied in the model development and validation. The results indicate that SA_GA is superior to a simple genetic algorithm (SGA) as regards the calculation of fitness values associated with the optimal parameters under various GA operators. In addition, by comparing the performance indices of estimated runoff with the calibrated optimal parameters by SA_GA and SGA with the different number of calibration rainstorm events, SA_GA can provide efficient and robust optimal parameters. These parameters not only estimate reliable and accurate runoff, but also capture the varying trends of discharge in time.

Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

AbstractDifferent methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

Performance of a distributed semi-conceptual hydrological model under tropical watershed conditions

Abstract Conceptual rainfall-runoff models at the watershed scale are useful tools for assisting in managing and planning water resources, making it possible to estimate streamflow and to predict hydrologic impacts due to land-use changes. The objective of this study was to calibrate and to validate the LAvras Simulation of Hydrology (LASH Model) (Beskow, 2009) in a Brazilian Tropical Watershed for daily streamflow. LASH is a continuous, distributed, semi-physically based model for simulation of different hydrologic components on a daily basis. The Shuffled Complex Evolution (SCE-UA) global search method was used with the LASH model in order to optimize model parameters that were found to be the most sensitive or not directly measurable. The LASH model was calibrated over a 2-year period, thereafter, the parameters obtained through the calibration were kept constant for the validation step using a different period of time from that analyzed during the calibration. The Nash–Sutcliffe coefficient (CNS) values found were 0.820 and 0.764 during calibration and validation, respectively, whereas, the CNS (log Q) values equal to 0.821 and 0.770 were obtained for the same periods. The SCE-UA method was found to be an efficient algorithm for searching ‘optimal’ model parameter values. It was possible to conclude that the model has a great potential for being applied in generating the long-term streamflow as well as flow-duration curves. Therefore, the model can reliably be applied under tropical conditions of this medium-sized watershed or other similar watersheds, thus making it useful to plan the sustainable development of similar tropical and subtropical watersheds.

Estimation of Continuous Streamflow in Ontario Ungauged Basins: Comparison of Regionalization Methods

Regionalization, a process of transferring hydrological information [i.e., parameters of a conceptual rainfall-runoff model, namely, the McMaster University-Hydrologiska Byrans Vattenbalansavdelning (MAC-HBV)] from gauged to ungauged basins, was applied to estimate continuous flows in ungauged basins across Ontario, Canada. To identify appropriate regionalization methods, different regionalization methods were applied, including the spatial proximity [i.e., kriging, inverse distance weighted (IDW), and mean parameters], physical similarity, and regression-based approaches. Furthermore, an approach coupling the spatial-proximity (IDW) method and the physical similarity approach is proposed. The analysis results show that the coupled regionalization approach as well as the IDW and kriging produce better model performances than the remaining three. Further investigations show that the coupled-regionalization approach provides slightly better performances than the other two spatial proximity methods. In addit...

The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments

This study attempts to assess the uncertainty in the hydrological impacts of climate change using a multi-model approach combining multiple emission scenarios, GCMs and conceptual rainfall–runoff models to quantify uncertainty in future impacts at the catchment scale. The uncertainties associated with hydrological models have traditionally been given less attention in impact assessments until relatively recently. In order to examine the role of hydrological model uncertainty (parameter and structural uncertainty) in climate change impact studies a multi-model approach based on the Generalised Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods is presented. Six sets of regionalised climate scenarios derived from three GCMs, two emission scenarios, and four conceptual hydrological models were used within the GLUE framework to define the uncertainty envelop for future estimates of stream flow, while the GLUE output is also post processed using BMA, where the probability density function from each model at any given time is modelled by a gamma distribution with heteroscedastic variance. The investigation on four Irish catchments shows that the role of hydrological model uncertainty is remarkably high and should therefore be routinely considered in impact studies. Although, the GLUE and BMA approaches used here differ fundamentally in their underlying philosophy and representation of error, both methods show comparable performance in terms of ensemble spread and predictive coverage. Moreover, the median prediction for future stream flow shows progressive increases of winter discharge and progressive decreases in summer discharge over the coming century.

Rainfall-Runoff Modelling Across Southeast Australia: Datasets, Models and Results

This study describes a daily rainfall, potential evaporation and streamflow data set compiled for the important water resources region of southeast Australia, and the application of six commonly used lumped conceptual rainfall-runoff models to estimate daily runoff across the region. The daily climate data set and the daily modelled runoff are available from 1895 to 2008 at 0.05° grid resolution across the region. The modelling exercise indicates that the rainfall-runoff models can generally be calibrated to reproduce the daily observed streamflow (for 232 catchments in the high runoff generation areas), and the regionalisation results indicate that the use of optimised parameter values from a gauged catchment nearby can model runoff reasonably well in the ungauged areas. There are differences between the six models, but they are relatively small when used to describe aggregated results across large regions.

Adopting the downward approach in hydrological model development: the Bradford catchment case study

The paper presents the development of a lumped conceptual rainfall-runoff model [Transformation of rainfall to runoff, Variability across timescales and Model parsimonization (TVM)] and a series of tests on various levels of model structure at different time resolutions. It is applied to the Bradford catchment in the United Kingdom. The TVM model is developed with a flexible structure through various relationships in each module that can be modified depending on the study catchments. Adopting the downward approach, parsimonious models are developed to examine at what level of complexity the model is able to capture runoff variability. The approach aims to compromise between parsimonious and complex alternatives in model development. This study shows that model structure requires data at different aggregation levels of timescales depending on its complexity. It reveals that the absence of the infiltration excess strongly affected all models. The analysis shows that the time resolution of hourly downwards must be used for the study catchment. The investigation of model complexity indicates that the combination of the most complicated model structure and timescale of quarter-hourly is adequate to capture the catchment runoff characteristics. The downward approach in the TVM model helps to gain a deeper understanding of water balance and runoff process in the study catchment. The approach could be applicable to other catchments to obtain parsimonious models. Copyright © 2010 John Wiley & Sons, Ltd.

Using lumped conceptual rainfall–runoff models to simulate daily isotope variability with fractionation in a nested mesoscale catchment

This paper presents 19 months of stable isotope ( δ 2H and δ 18O) data to enhance understanding of water and solute transport at two spatial scales (2.3 km 2 and 122 km 2) in the agricultural Lunan catchment, Scotland. Daily precipitation and stream isotope data, weekly lake and spring isotope data and monthly groundwater isotope data revealed important insights into flow pathways and mixing of water at both scales. In particular, a deeper groundwater flow path significantly contributes to total streamflow (25–50%). Upstream lake isotope dynamics, susceptible to evaporative fractionation, also appeared to have an important influence on the downstream isotope composition. This unique tracer data set facilitated the conceptualization of a lumped catchment-scale flow–tracer model. The incorporation of hydrological, mixing and fractionation processes based on these data improved simulations of the stream δ 2H isotope response at the catchment outlet from 0.37 to 0.56 for the Nash–Sutcliffe statistic. The stable isotope data successfully aided model conceptualization and calibration in the quest for a simple water and solute transport model with improved representation of process dynamics.

Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering

AbstractGiven the structural shortcomings of conceptual rainfall–runoff models and the common use of time‐invariant model parameters, these parameters can be expected to represent broader aspects of the rainfall–runoff relationship than merely the static catchment characteristics that they are commonly supposed to quantify. In this article, we relax the common assumption of time‐invariance of parameters, and instead seek signature information about the dynamics of model behaviour and performance. We do this by using a temporal clustering approach to identify periods of hydrological similarity, allowing the model parameters to vary over the clusters found in this manner, and calibrating these parameters simultaneously. The diagnostic information inferred from these calibration results, based on the patterns in the parameter sets of the various clusters, is used to enhance the model structure. This approach shows how diagnostic model evaluation can be used to combine information from the data and the functioning of the hydrological model in a useful manner. Copyright © 2010 John Wiley & Sons, Ltd.

There are no hydrological monsters, just models and observations with large uncertainties!

Catchments that do not behave in the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall–runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model error, about which little is currently known. In addition, hydrological observation systems are far from perfect, with the principal catchment forcing (rainfall) often subject to large sampling errors. When ignored or treated simplistically, these errors develop into monsters that destroy our ability to model certain catchments. In this paper, these monsters are tackled using Bayesian Total Error Analysis, a framework that accounts for user-specified sources of error and yields quantitative insights into how prior knowledge of these uncertainties affects our ability to infer models and use them for predictive purposes. A case study involving a catchment with an apparent water balance anomaly (a hydrological monstrosity!) illustrates these concepts. It is found that, in the absence of additional information, the rainfall–runoff record is insufficient to explain this anomaly – it could be due to a large export of groundwater, systematic overestimation of catchment rainfall of the order of 40%, or a conspiracy of these factors. There is “no free lunch” in hydrology. The rainfall–runoff record on its own is insufficient to decompose the different sources of uncertainty affecting calibration, testing and prediction, and hydrological monstrosities will persist until additional independent knowledge of uncertainties is obtained. Citation Kuczera, G., Renard, B., Thyer, M. & Kavetski, D. (2010) There are no hydrological monsters, just models and observations with large uncertainties! Hydrol. Sci. J. 55(6), 980–991.

Regionalization of a conceptual rainfall–runoff model based on similarity of the flow duration curve: A case study from the semi-arid Karkheh basin, Iran

Summary The study examines the possibility of simulating time series of streamflows for poorly gauged catchments based on hydrological similarity. The data of 11 gauged catchments (475–2522 km 2 ), located in the mountainous semi-arid Karkheh river basin of Iran, is used to develop the procedure. The well-known HBV model is applied to simulate daily streamflow with parameters transferred from gauged catchment counterparts. Hydrological similarity is defined based on four similarity measures: drainage area, spatial proximity, catchment characteristics and flow duration curve (FDC). The study shows that transferring HBV model parameters based on the FDC similarity criterion produces better runoff simulation compared to the other three methods. Furthermore, it is demonstrated that the FDC based regionalization of HBV model parameters works reasonably well for streamflow simulations in the data limited catchments in the mountainous parts of the Karkheh river basin. In addition, it could be demonstrated that the parameter uncertainty of the model has little impact on the FDC based regionalization approach. The methodology presented in this paper is easy to replicate in other river basins of the world, particularly those facing decline in streamflow monitoring networks and with a limited number of gauged catchments.

Assessing the value of high‐resolution isotope tracer data in the stepwise development of a lumped conceptual rainfall–runoff model

AbstractA study was undertaken in a small agricultural catchment in north east Scotland with the objective of assessing the value of high‐resolution isotope data for integration within a lumped, conceptual hydrological model to improve calibration and aid evaluation. Daily samples of precipitation and stream flow, collected over a year, were analysed for deuterium using new laser spectroscopy technology. The utility of such high‐resolution isotope data was evaluated in relation to the associated uncertainty which was contextualized in relation to uncertainties over hydrometric data and the influence of different sampling resolutions. The simulations were evaluated against model and data errors using auxiliary stream deuterium time series in addition to discharge. The lumped conceptual catchment isotope model (CIM) was developed and adequately reflects flow dynamics and deuterium peaks, but a simple assumption of ‘good mixing’ is not able to fully reproduce the daily stream deuterium dynamic. Using auxiliary data for model evaluation, however, significantly constrained acceptable behavioural parameter sets and therefore reduces the model's degree of freedom. The data indicate that isotopic variability in the stream response is not adequately captured using weekly tracer data. The input resolution of precipitation deuterium concentrations, which were much more variable, proved to be crucial. This approach has provided further assessment of the value of tracers in hydrological modelling, demonstrating their usefulness in terms of model conceptualization, development and calibration, which outweighs the additional uncertainty. Copyright © 2010 John Wiley & Sons, Ltd.

An assessment of modelling capacity to identify the impacts of climate variability on catchment hydrology

The aim is to investigate the consistency or variability of catchment response over time and space and evaluate the predictive error caused by the impacts of climate variability on streamflow. For this purpose, both data- and top-down model-based analyses of the dynamic relation between rainfall and runoff for selected sub-catchments have been undertaken. Data analysis techniques (e.g. trend analysis, deconvolution and baseflow filtering) were used to assess the temporal and spatial variation in the hydrologic response characteristics for each site. The lumped conceptual rainfall–runoff model IHACRES CMD (Catchment Moisture Deficit) version is applied to the sub-catchments to assess the adequacy of the model response in representing the impact of weather patterns on streamflow. Several performance criteria have been used to evaluate the performance of the model in each calibration period using a multi-criteria approach. The IHACRES-3S (3 Storage) model is applied to assess low flow behaviour and capture the timing in the switch between baseflow and no flow periods. Rainfall–runoff model performance characteristics of each sub-catchment are quite related to their incident rainfall regime. Sub-catchments which are located in a lower rainfall regime show poor to average model performance. The reduction in performance in R 2 is due to the poor fitting to the peaks for both large and small streamflow events, with the model underestimating the highest flow peaks, and overestimating smaller peaks. Further work will be needed to assess observed data reliability and improve model performance in order to separate the impacts of climate variations and land use change on hydrological response. An appropriate model structure having a variable partitioning between quick and slow flow components is under consideration and techniques are being used to identify problematic periods and events with high error in the observational data.

Parameter identification of a conceptual rainfall-runoff model for a small urban catchment

Parameter identification of a conceptual rainfall-runoff model for a small urban catchment

Internal validation of conceptual rainfall–runoff models using baseflow separation

Summary An important tool in the management of floods is the use of rainfall–runoff models to predict the arrival of discharge peaks. These models generally use rainfall and potential evapotranspiration rates as input, and relate these to the catchment discharge through a number of conceptual equations. The parameters of these equations are estimated through a comparison of the modeled discharge to the observations. Only one variable, catchment discharge, is thus generally used to calibrate and validate these models. The objective of this paper is to validate the internal model dynamics of two widely used rainfall–runoff models using baseflow estimates. The baseflow time series used in this paper are obtained through the use of a physically-based digital baseflow filter. These models, the Hydrologiska Byrans Vattenbalansavdelning (HBV) model and the Probability Distributed Model (PDM), were calibrated using 1 year of hourly discharge data. The HBV model uses a linear reservoir for the modeling of groundwater flow, while the PDM uses a cubic reservoir for this purpose. In order to assess the impact of the type of reservoir choice, the cubic reservoir in the PDM was also replaced by a linear reservoir. Two different parameter estimation algorithms were used for model calibration. The Shuffled Complex Evolution (SCE-UA) algorithm minimizes the Root Mean Square Error between the model simulations and the observations, while Multistart Weight-Adaptive Recursive Parameter Estimation (MWARPE) uses the Extended Kalman Filter equations in an iterative, Monte-Carlo framework. MWARPE was found to lead to the best discharge simulations, but the differences with the results obtained from the SCE-UA algorithm were relatively small. When only the modeled discharge was analyzed, no clear picture emerged of which model produced the best results. However, it has been found that the replacement of the cubic groundwater reservoir in the PDM by a linear reservoir resulted in a strongly improved model performance with respect to the baseflow. Further, MWARPE consistently led to the best baseflow estimates for the three models, while the HBV model resulted in the best simulations of the baseflow, regardless of the calibration algorithm. The overall conclusion of this paper is that, even though it may be difficult to assess which model and calibration algorithm resulted in the best discharge estimates, the MWARPE calibration algorithm and the HBV model consistently led to the best internal model dynamics.

Modelling the impact of urbanisation on flood runoff volume

In this study, a relatively simple and intuitive extension to an event-based lumped conceptual rainfall–runoff model, the revitalised flood hydrograph model is introduced and shown to improve model performance on a heavily urbanised catchment. The extension does not affect model performance on a rural catchment. The hydrological losses in the model are calculated based on a percentage runoff type model, initially developed for use in rural catchments and relating losses to soil moisture content, soil moisture capacity and rainfall. By assuming a fixed percentage runoff of 70% from the urban (impervious) parts of the catchment, an overall improvement in predictive ability of the extended urban model is observed on the heavily urbanised catchment. Model performance is generally found to be improved more when considering events occurring with low initial soil moisture content than for events associated with higher initial soil moisture content. A sensitivity analysis indicates that the urban loss-model is relatively robust with regard to percentage runoff and imperviousness of the urban area.

Application of Ihacres Conceptual Rainfall-Runoff Model to Simulate Daily Stream Flow in Upper Tana Basin, Kenya

Stream-flow measurements are important for water resources assessment and management. In Kenya, inadequate streamflow data poses a major challenge to water resources assessment and management. Application of rainfall-runoff models to the catchments can be used to generate streamflow data. A lumped conceptual rainfall-runoff model IHACRES was applied to six medium sized catchments in the Upper Tana River Basin of Kenya. The model IHACRES was chosen because it requires only catchment area, streamflow data and a surrogate variable representing evaporation such as temperature. In addition, it has only two parameters to be varied during calibration. Streamflow data for the six catchments spanning six years was used in the present study. Simulated streamflow was compared with the observed streamflow for these catchments and the goodness of fit evaluated. The calibration R2 ranged from 0.57 to 0.85 while the simulation R2 ranged from 0.55 to 0.77 for all catchments. The Nash-Sutcliffe (1970) efficiency ranged from 0.78 to 0.91 for calibration and from 0.77 to 0.88 for simulation. Sensitivity tests were also carried out using results for one of the catchments. IHACRES is capable of simulating daily streamflow given concurrent daily rainfall and temperature data and offers a possibility of infilling missing data or generating a long series of streamflow data for water resources management in the Upper Tana River basin

Groundwater balance estimation in karst by using a conceptual rainfall–runoff model

A conceptual rainfall–runoff model is proposed for the estimation of groundwater balance components including the influences of time-variant catchment boundaries and intercatchment groundwater flows. Karst underground is considered as a lumped system that contains: (1) soil cover, (2) epikarst zone and (3) vadose and phreatic zones. The soil cover receives an average rainfall in catchment as the input into the system. The epikarst zone is composed of a production store and a routing store. The production store contributes to the loss of water in process of evapotranspiration. The routing store contributes to the retention of percolated water and the lateral distribution of groundwater recharge between internal runoff and diffuse infiltration. The vadose and phreatic zones accumulate groundwater in fissures, fractures and vertical shafts, and produce the vadose seepage, vadose flow and shaft flow components of the karst spring discharge. The parameter estimation and calculation procedure assemble the moisture balance and the groundwater-balance approaches. The rainfall–runoff model is divided in two sub-models. The sub-model based on the moisture balance of soil cover and epikarst production store calculates effective rainfalls. The sub-model based on the groundwater balance of vadose and phreatic zone calculates groundwater recharges. The difference between the effective rainfalls and the groundwater recharges represents the contribution of epikarst zone and non-conservative and time-variant components to the groundwater balance. The proposed methodology is applied to the Jadro Spring located near the city of Split in Croatia. The calculated groundwater balance shows that the Jadro Spring aquifer contains a significant storage capacity in the vadose and phreatic zones. During the year, the aquifer may accumulate up to 140 millions m3. The variability of calculated catchment area is explained with the time-variant catchment boundary dependent on groundwater levels and the intercatchment groundwater flows from neighboring catchments. The average catchment area of 396 km2 is estimated by using the average monthly effective rainfalls and the average monthly groundwater recharges.

Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events

Abstract. A conceptual lumped rainfall-runoff flood event model was developed and applied on the Gardon catchment located in Southern France and various single-objective and multi-objective functions were used for its calibration. The model was calibrated on 15 events and validated on 14 others. The results of both the calibration and validation phases are compared on the basis of their performance with regards to six criteria, three global criteria and three relative criteria representing volume, peakflow, and the root mean square error. The first type of criteria gives more weight to large events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent on the type of criteria used. Significant trade-offs are observed between the different objectives: no unique set of parameters is able to satisfy all objectives simultaneously. Instead, the solution to the calibration problem is given by a set of Pareto optimal solutions. From this set of optimal solutions, a balanced aggregated objective function is proposed, as a compromise between up to three objective functions. The single-objective and multi-objective calibration strategies are compared both in terms of parameter variation bounds and simulation quality. The results of this study indicate that two well chosen and non-redundant objective functions are sufficient to calibrate the model and that the use of three objective functions does not necessarily yield different results. The problems of non-uniqueness in model calibration, and the choice of the adequate objective functions for flood event models, emphasise the importance of the modeller's intervention. The recent advances in automatic optimisation techniques do not minimise the user's responsibility, who has to choose multiple criteria based on the aims of the study, his appreciation on the errors induced by data and model structure and his knowledge of the catchment's hydrology.