Conceptual Rainfall-runoff Model Research Articles

Regionalization of Tank Model Using Landscape Metrics of Catchments

To address the challenges inherent in accessing spatiotemporal hydrological data, water resources professionals have developed various regionalization tools. The present study examines the possibility that changes in landscape metrics including mean shape index, mean perimeter-area ratio, mean patch size and patch density of land use/ land cover could result in variations in the optimized parameters of the conceptual rainfall-runoff Tank model. Data from 30 catchments that are geographically distributed in Germany was used to develop the procedure. Regression analysis-based modeling indicated that four out of twelve model parameters (r2 ≥ 0.40) can be explained by changes in catchment geometrics along with a set of landscape metrics of land use/land cover. They include: coefficient of infiltration flow (r2 = 0.48, p < 0.03), intermediate flow (r2 = 0.77, p < 0.02), water storage level for sub-surface flow (r2 = 0.57, p < 0.05) and water storage level for intermediate flow (r2 = 0.85, p < 0.01). Despite developing fairly reliable regression models, uncertainty analysis also revealed that uncertainty induced unreliability of the regionalized models is of significant importance.

Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input

High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward.

Comparing single- and two-segment statistical models with a conceptual rainfall-runoff model for river streamflow prediction during typhoons

This study examined various regression-based techniques and an artificial neural network used for streamflow forecasting during typhoons. A flow hydrograph was decomposed into two segments, rising and falling limbs, and the individual segments were modeled using statistical techniques. In addition, a conceptual rainfall–runoff model, namely the Public Works Research Institute (PWRI)-distributed hydrological model, and statistical models were compared. The study area was the Tsengwen Reservoir watershed in Southern Taiwan. The data used in this study comprised the observed watershed rainfalls, reservoir inflows, typhoon characteristics, and ground weather data. The forecast horizons ranged from 1 to 12 h. A series of assessments, including statistical analyses and simulations, was conducted. According to the improvements in errors, among single-segment statistical models, the multilayer perceptron achieved superior prediction accurary compared with the regression-based methods. However, the pace regression was the most favorable according to an evaluation of model complexity and accuracy. To examine the robustness of the results for forecast horizons varying from 1 to 12 h, statistical significance tests were performed for the single- and two-segment models. The prediction ability of the two-segment models was superior to that of the single-segment models. In addition, Typhoon Sinlaku in 2008 was considered in a comparison between the conceptual PWRI model output and that of the developed statistical models. The results showed that the PWRI model yielded the least favorable results.

Alteration of hydrologic indicators for Korean catchments under CMIP5 climate projections

The change of hydrological regimes may cause impacts on human and natural system. Therefore, investigation of hydrologic alteration induced by climate change is essential for preparing timely proper adaptation to the changes. This study employed 24 climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 scenario. The climate projections were downscaled at a station-spacing for seven Korean catchments by a statistical downscaling method that preserves a long-term trend in climate projections. Using an ensemble of future hydrologic projections simulated by three conceptual rainfall-runoff models (GR4J, IHACRES, and Sacramento models), we calculated Hydrologic Alteration Factors (HAFs) to investigate degrees of variations in Indicators of Hydrologic Alteration (IHAs) derived from the hydrologic projections. The results showed that the seven catchments had similar trend in terms of the HAFs for the 24 IHAs. Given that more frequent severe floods and droughts were projected over Korean catchments, sound water supply strategies are definitely required to adapt to the alteration of streamflow. A wide range of HAFs between rainfall-runoff models for each catchment was detected by large variations in the magnitude of HAFs with the hydrologic models and the difference could be the hydrologic prediction uncertainty. There were no-consistent tendency in the order of HAFs between the hydrologic models. In addition, we found that the alterations of hydrologic regimes by climate change are smaller as the size of catchment is larger. Copyright © 2016 John Wiley & Sons, Ltd.

Linking tracers, water age and conceptual models to identify dominant runoff processes in a sparsely monitored humid tropical catchment

Assessing catchment runoff response remains a key research frontier because of limitations in current observational techniques to fully characterize water source areas and transit times in diverse geographical environments. Here, we report a study that combines empirical data with modelling to identify dominant runoff processes in a sparsely monitored humid tropical catchment. The analysis integrated isotope tracers into conceptual rainfall–runoff models of varying complexity (from 5 to 11 calibrated parameters) that are able to simulate discharge and tracer concentrations and track the evolving age of stream water exiting the catchment. The model structures can be seen as competing hypotheses of catchment functioning and were simultaneously calibrated against uncertain streamflow gaugings and a 2-year daily isotope rainfall–runoff record. Comparison of the models was facilitated using global parameter sensitivity analysis and the resulting effect on calibration. We show that a variety of tested model structures reproduced water and tracer dynamics in stream, but the simpler models failed to adequately reproduce both. The resulting water age distributions of the tested models varied significantly with little similarity between the stream water age and stored water age distributions. The sensitivity analysis revealed that only some of the more complex models (from eight parameters) could be better constrained to infer more plausible water age distributions and catchment storage estimates. These models indicated that the age of water stored in the catchment is generally older compared with the age of water fluxes, with evapotranspiration age being younger compared with streamflow. However, the water age distributions followed a similar temporal behaviour dominated by climatic seasonality. Stream water ages increased during the dry season (greater than 1 year) and decreased with increased streamflow (a few weeks old) during the wet season. We further show that the ratios of the streamwater age to stored water age distribution and the water age distribution of actual evapotranspiration to the stored water age distribution from constrained models could potentially serve as useful hydrological indicators of catchment functioning. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow

ABSTRACTThe rainfall–runoff process is governed by parameters that can seldom be measured directly for use with distributed models, but are rather inferred by expert judgment and calibrated against historical records. Here, a comparison is made between a conceptual model (CM) and an artificial neural network (ANN) for their ability to efficiently model complex hydrological processes. The Sacramento soil moisture accounting model (SAC-SMA) is calibrated using a scheme based on genetic algorithms and an input delay neural network (IDNN) is trained for variable delays and hidden layer neurons which are thoroughly discussed. The models are tested for 15 ephemeral catchments in Crete, Greece, using monthly rainfall, streamflow and potential evapotranspiration input. SAC-SMA performs well for most basins and acceptably for the entire sample with R2 of 0.59–0.92, while scoring better for high than low flows. For the entire dataset, the IDNN improves simulation fit to R2 of 0.70–0.96 and performs better for high flows while being outmatched in low flows. Results show that the ANN models can be superior to the conventional CMs, as parameter sensitivity is unclear, but CMs may be more robust in extrapolating beyond historical record limits and scenario building.EDITOR M.C. Acreman; ASSOCIATE EDITOR not assigned

Multivariate Drought Assessment Considering the Antecedent Drought Conditions

Several drought indices have been developed based on a single variable or multiple variables using very complex calculations. Antecedent conditions are quite significant for analyzing physical processes involved in the conceptual rainfall-runoff modeling and for proper assessment of drought. However, not much attention has been paid to these conditions in the development of drought indices. Hence, we developed an alternative index for drought assessment, i.e., the antecedent condition-based multivariate drought index (AMDI), by taking into consideration all of the forms of drought, including meteorological, agricultural, and hydrological drought, in combination with the antecedent drought conditions. By comparing the AMDI with the standardized precipitation index (SPI) and reconnaissance drought index (RDI), it was revealed that in most cases, the drought trend was more or less the same. However, some discrepancies were also observed. Moreover, by considering additional factors, i.e., the antecedent soil moisture conditions and balance, an approximately 6 % difference in the drought frequency was observed compared to that of the SPI and RDI results, leading to a significant and proper drought assessment. The AMDI was also identified as a multi-scalar, multivariate index, which aggregates the effects of multiple drought forms by maintaining the continuity during month-to-month transitions. Hence, we concluded that the AMDI could be considered as an alternative tool for significant drought assessment.

A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.

Have applications of continuous rainfall–runoff simulation realized the vision for process‐based flood frequency analysis?

AbstractKeith Beven was amongst the first to propose and demonstrate a combination of conceptual rainfall–runoff modelling and stochastically generated rainfall data in what is known as the ‘continuous simulation’ approach for flood frequency analysis. The motivations included the potential to establish better links with physical processes and to avoid restrictive assumptions inherent in existing methods applied in design flood studies. Subsequently, attempts have been made to establish continuous simulation as a routine method for flood frequency analysis, particularly in the UK. The approach has not been adopted universally, but numerous studies have benefitted from applications of continuous simulation methods. This paper asks whether industry has yet realized the vision of the pioneering research by Beven and others. It reviews the generic methodology and illustrates applications of the original vision for a more physically realistic approach to flood frequency analysis through a set of practical case studies, highlighting why continuous simulation was useful and appropriate in each case. The case studies illustrate how continuous simulation has helped to offer users of flood frequency analysis more confidence about model results by avoiding (or exposing) bad assumptions relating to catchment heterogeneity, inappropriateness of assumptions made in (UK) industry‐standard design event flood estimation methods, and the representation of engineered or natural dynamic controls on flood flows. By implementing the vision for physically realistic analysis of flood frequency through continuous simulation, each of these examples illustrates how more relevant and improved information was provided for flood risk decision‐making than would have been possible using standard methods. They further demonstrate that integrating engineered infrastructure into flood frequency analysis and assessment of environmental change are also significant motivations for adopting the continuous simulation approach in practice. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Hydro-Meteorological Drought Projections into the 21-st Century for Selected Polish Catchments

The nature of drought conditions is estimated using a range of indices describing different aspects of drought events. Three drought indices are evaluated, namely the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI), using observed hydroclimatic data and applying them to hydro-meteorological projections into the 21st century. The first two indices are evaluated using only meteorological variables and from this point of view, are better suited to meteorological drought projections than the third index, SRI, which is based on catchment discharge and represents hydrological drought. We assess information contained in those indices and their suitability to catchment scale climate projection drought assessment in ten selected Polish catchments, representing different hydro-climatic conditions, which are used as a case study. Projections of climatic variables (precipitation and temperature) are obtained from the EURO-CORDEX initiative derived from seven climate models at a grid resolution of 12.5 km for the time period 1971–2100. Future runoff projections for the catchments are obtained using a conceptual rainfall-runoff model (HBV). The results of analyses of indices based on observations in the reference period show consistent estimates for most of the catchments. Hydro-meteorological climate model projections for three periods, including the reference period 1971–2000, and two 30-year periods, near-future 2021–2050 and far-future 2071–2100, are used to estimate changes of future drought conditions in the catchments studied. The results show a substantial variation of temporal drought patterns over the catchments and their dependence on projected precipitation and temperature variables and the type of indices applied. Of the three indices studied, only SPEI projections indicate drier conditions in the catchments in the far-future period. The other two indices, SPI and SRI, indicate wetter climates in the future.

A flexible and efficient multi-model framework in support of water management

Abstract. Flexible, fast and accurate water quantity models are essential tools in support of water management. Adjustable levels of model detail and the ability to handle varying spatial and temporal resolutions are requisite model characteristics to ensure that such models can be employed efficiently in various applications. This paper uses a newly developed flexible modelling framework that aims to generate such models. The framework incorporates several approaches to model catchment hydrology, rivers and floodplains, and the urban drainage system by lumping processes on different levels. To illustrate this framework, a case study of integrated hydrological-hydraulic modelling is elaborated for the Grote Nete catchment in Belgium. Three conceptual rainfall-runoff models (NAM, PDM and VHM) were implemented in a generalized model structure, allowing flexibility in the spatial resolution by means of an innovative disaggregation/aggregation procedure. They were linked to conceptual hydraulic models of the rivers in the catchment, which were developed by means of an advanced model structure identification and calibration procedure. The conceptual models manage to emulate the simulation results of a detailed full hydrodynamic model accurately. The models configured using the approaches of this framework are well-suited for many applications in water management due to their very short calculation time, interfacing possibilities and adjustable level of detail.

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables.The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

Hydropower Plant Site Assessment by Integrated Hydrological Modeling, Gene Expression Programming and Visual Basic Programming

The integrated hydrological modeling techniques were used to create a conceptual rainfall-runoff model for Hurman River watershed. The HEC-HMS and water modeling system (WMS) were used to generate the required runoff data at any location within the watershed based on the flow duration curve (FDC) analysis. The modeling results showed a good agreement between the observed and the simulated runoff data. The model accuracy was confirmed through four well statistical indicators, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), mean error (ME) and root mean square error (RMSE). The watershed was divided into 130 sub-basins by adding outlet at each 1 km distance along the stream network and runoff was simulated by using daily rainfall data. The 130 flow duration curves were estimated. Gene expression programming was used to develop a mathematical expression based on the results of rainfall-runoff model to generate flow duration curve at any location along the stream network. A Visual Basic Computer Program was developed with visual interface in Microsoft Excel software to run-of-the river type hydropower plant site assessment. A searching algorithm to select optimum site to install the hydropower project was developed according to the power or head criteria. The results showed that 1 MW as input power criteria revealed optimum hydroelectricity generation compared to other alternative design criteria.

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.

Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall‐runoff models

AbstractHydrologic models have potential to be useful tools in planning for future climate variability. However, recent literature suggests that the current generation of conceptual rainfall runoff models tend to underestimate the sensitivity of runoff to a given change in rainfall, leading to poor performance when evaluated over multiyear droughts. This research revisited this conclusion, investigating whether the observed poor performance could be due to insufficient model calibration and evaluation techniques. We applied an approach based on Pareto optimality to explore trade‐offs between model performance in different climatic conditions. Five conceptual rainfall runoff model structures were tested in 86 catchments in Australia, for a total of 430 Pareto analyses. The Pareto results were then compared with results from a commonly used model calibration and evaluation method, the Differential Split Sample Test. We found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that models may be more capable under changing climatic conditions than previously thought. Of the 282[347] cases of apparent model failure under the split sample test using the lower [higher] of two model performance criteria trialed, 155[120] were false negatives. We discuss potential causes of remaining model failures, including the role of data errors. Although the Pareto approach proved useful, our aim was not to suggest an alternative calibration strategy, but to critically assess existing methods of model calibration and evaluation. We recommend caution when interpreting split sample results.

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.

A step towards considering the spatial heterogeneity of urban key features in urban hydrology flood modelling

Summary Some of the major challenges in modelling rainfall–runoff in urbanised areas are the complex interaction between the sewer system and the overland surface, and the spatial heterogeneity of the urban key features. The former requires the sewer network and the system of surface flow paths to be solved simultaneously. The latter is still an unresolved issue because the heterogeneity of runoff formation requires high detailed information and includes a large variety of feature specific rainfall–runoff dynamics. This paper discloses a methodology for considering the variability of building types and the spatial heterogeneity of land surfaces. The former is achieved by developing a specific conceptual rainfall–runoff model and the latter by defining a fully distributed approach for infiltration processes in urban areas with limited storage capacity dependent on OpenStreetMaps (OSM). The model complexity is increased stepwise by adding components to an existing 2D overland flow model. The different steps are defined as modelling levels. The methodology is applied in a German case study. Results highlight that: (a) spatial heterogeneity of urban features has a medium to high impact on the estimated overland flood-depths, (b) the addition of multiple urban features have a higher cumulative effect due to the dynamic effects simulated by the model, (c) connecting the runoff from buildings to the sewer contributes to the non-linear effects observed on the overland flood-depths, and (d) OSM data is useful in identifying pounding areas (for which infiltration plays a decisive role) and permeable natural surface flow paths (which delay the flood propagation).

Detection of land use/cover change effect on watershed’s response in generating runoff using computational intelligence approaches

Quantifying the potential impacts of changing land use/cover in watersheds especially via the rainfall-runoff modeling remains among the more challenging problems in hydrology. This study presents a new method in detection, quantification and prediction of long term land use/cover change of the South Fork Eel River watershed, based on linear conceptual rainfall-runoff modeling and computational intelligence tools. The methodology was presented via a two-phase modeling procedure: event-based and continuous-based modeling phases. In the first phase, a linear conceptual rainfall-runoff model was proposed based on unequal cascade of reservoirs in which the drainage basin was divided into several sub-basins in a sequence based on the stream gauge locations and drainage network. This model includes two parameters of linear reservoir and channel lag times which were computed using relations based on the watershed’s geomorphology. The parameters were calibrated using observed rainfall-runoff events via genetic algorithm. In the second phase, calibrated lag times of events during each month were averaged to produce monthly time series of lag time to be used for training of classic artificial neural network and hybrid Wavelet-ANN for prediction of land use/cover change effect on the watershed’s response in generating runoff. Results indicate the good capability of such two-phase modeling procedure in quantification and prediction of land use change effects on hydrological response of the watershed. Results show, autoregressive and seasonal patterns of lag time, evapotranspiration and temperature are the most important factors which could affect rainfall-runoff modeling of the watershed. The land use change effect results show a slight improvement in the vegetation cover during long period for the South Fork Eel River watershed.

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.

Parameter Uncertainties in a Conceptual Rainfall-runoff Model and Implications on Surface Water Management and Planning Decisions

This paper investigates the uncertainties arising from parameter identification in a conceptual rainfall-runoff model and implications on surface water management and planning decisions. A conceptual rainfall-runoff model, the Probability Distributed Rainfall-Runoff Model (PDM), is applied within the Dove River catchment (UK) using 1km2 resolution radar rainfall as inputs and 15minutes resolution gauged flow data for calibration and validation. In most conceptual/lumped models, some parameters lack physical basis and cannot be inferred from direct measurements. The DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm developed by Vrugt et al. [1] is employed for Bayesian inference to automatically calibrate the model against observed flow data. Probabilistic flow predictions are determined based on the resulting posterior parameter distributions, which reflect the residual model parameter uncertainty. These uncertainties associated with model parameters are propagated through a reservoir management model to assess its impacts on reservoir performance in maintaining adequate supply demand balance. The impact of using various reservoir operational rules on the characteristics of uncertainty propagation and associated impact on predicted supply demand balance are investigated. The results in this study suggest that adaptive management of reservoir operational rules can be used to achieve optimum balance between environmental impact, drought reliable supply and operation cost.