Rainfall-runoff Model Parameters Research Articles

Lithologic control on parameters of conceptual rainfall-runoff model and runoff characteristics: a case study of the Xinanjiang model

Lithologic control on parameters of conceptual rainfall-runoff model and runoff characteristics: a case study of the Xinanjiang model

Lithologic control on parameters of conceptual rainfall-runoff model and runoff characteristics: a case study of the Xinanjiang model

Lithologic control on parameters of conceptual rainfall-runoff model and runoff characteristics: a case study of the Xinanjiang model

Dependence of rainfall-runoff model transferability on climate conditions in Iran

ABSTRACT This study investigates the potential differences between spatial and temporal transferability of the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model parameters in 576 Iranian catchments. Our goal is to determine how parameter transferability is affected by (a) parameter transfer modes (temporal or spatial) and (b) different climate conditions. In the temporal mode, we make decisions in each catchment based on a benchmark baseline that accounts for the seasonality of flows. In the spatial mode, we examine physical similarity and spatial proximity methods. The main conclusions are that: (1) the HBV model struggles to beat the benchmark in lowland catchments; (2) under stationary conditions, rainfall-runoff models have greater temporal transferability than spatial transferability; (3) under non-stationary climate conditions, the number of catchments that perform best in terms of temporal transferability is smaller than in stationary climate conditions; and (4) the rainfall-runoff model has better transferability from drier to wetter conditions.

Reliability analysis for reservoir water supply due to uncertainties in hydrological factors, rainfall-runoff routing and operating rule curves

This study aims at developing a reliability-analysis model (RA_WS_RES) to quantify the effect of variations in uncertainties factors regarding the reservoir inflow and outflow at various 10-day periods on the reliability of water supply from the reservoir. The uncertainty factors considered are classified into three types: the hydrological factors (rainfall, baseflow, and initial water level of the reservoir), the reservoir operation rules for the water-allocation model (RIBASIM), and the parameters of rainfall-runoff model (i.e. Sacramento Soil Moisture Accounting, SAC-SMA). In the proposed RA_WS_RES model, the reliability of the water supply attributed to the uncertainty factors considered can be quantified by collaborating the multivariate Monte Carlo simulation (MMCS) methods and uncertainty-risk (advanced first order and second moment, AFOSM) analysis. Shihmen Reservoir watershed in Northern Taiwan is selected as the study area and four associated demand nodes are regarded as the study nodes; the hourly rainfall data from 1987 to 2014 and other hydrological data (i.e. rainfall, baseflow, and initial water level of the reservoir) as well as the operation rule curves are adopted in the model development and application. The results from the numerical experiences indicate that among the uncertainty factors concerned, the average rainfall depth at the current 10-day period and the range between the lower and critical levels are more sensitive to the estimation of water supply from Shihmen Reservoir. Additionally, the impact of variation in baseflow on the reliability of the water supply from Shihmen Reservoir should be taken into account, especially in the dry season. In addition to rainfall and baseflow, the initial water level should be an important source for the water supply, and its effect gradually reduces with the 10-day period. Furthermore, the reliability of water supply is obviously impacted by the uncertainty in the range between the lower and critical rule level, especially in the dry season, due to its range being adversely related with the water supply. Eventually, the proposed RA_WS_RES model can effectively and reasonably quantify the reliability of water supply attributed to variations in uncertainty factors at different 10-day period under the consideration of climate change.

Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density

Abstract. The setup of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrometric information available in the region. The informative content of the dataset (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method. This study analyses how the performances of regionalisation approaches are influenced by the “information richness” of the available regional dataset, i.e. the availability of potential donors, and in particular by the gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section. The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches. The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors and then re-applied, decreasing the informative content of the dataset. The effect of excluding nested basins and the status of “nestedness” is identified based on the position of the closing section along the river or the percentage of shared drainage area. Moreover, the impact of reducing station density on regionalisation performance is analysed. The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The “output-averaging” approaches, which exploit the information of more than one donor basin and preserve the correlation structure of the parameter, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to the deterioration of the informative content of the set of donors.

Application of TRMM in the Hydrological Analysis of Upper Bengawan Solo River Basin

Rainfall is a major water resource with a significant role in terms of growth, environment concerns, and sustainability. Several human activities demand adequate water supply for drinking, agriculture, domestic, and commercial consumption. The accuracy of any hydrologic study depends heavily on the availability of good-quality precipitation estimates. Most countries are unable to provide sufficient climatic data, including rainfall and observed discharge statistics. This scarcity is a huge obstacle in conducting thorough hydrologic studies over a certain period. For instance, Indonesia, as an archipelagic country, has long been faced with data availability problems. For this reason, Tropical Rainfall Measuring Mission (TRMM), which was developed by NASA, became an alternative solution to rainfall data limitations. However, to be applied in hydrologic investigations, TRMM data require proper estimation and adjustment. The aim of this study was to evaluate the quality of TRMM rainfall data and its application in determining design flood and water availability. Dividing the data into several groups based on its magnitude and multiplying each unit with a correction coefficient are parts of the modification process. Subsequently, objective functions, including false alarm ratio (FAR), probability of detection (POD), and root mean square error (RMSE) were also applied. Rainfall-runoff modeling and design storm analysis at Delingan dam were used to study the TRMM correction performance. Based on the analysis, corrected TRMM showed considerable findings compared to ground station data. Model calibration and verification using corrected TRMM data provide satisfactory model parameters compared to ground station derivatives. The results also disclosed a closer fit of the corrected TRMM to catchment response translated from derived rainfall-runoff model parameters to ground station compared to control. Furthermore, design storm calculated from corrected TRMM reflects an improvement compared to uncorrected TRMM data.

Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters

An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12-hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume.

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

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

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

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

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

ERM model analysis for adaptation to hydrological model errors

Hydrological conditions are changed continuously and these phenomenons generate errors on flood forecasting models and will lead to get unrealistic results. Therefore, to overcome these difficulties, a concept called model updating is proposed in hydrological studies. Real-time model updating is one of the challenging processes in hydrological sciences and has not been entirely solved due to lack of knowledge about the future state of the catchment under study. Basically, in terms of flood forecasting process, errors propagated from the rainfall-runoff model are enumerated as the main source of uncertainty in the forecasting model. Hence, to dominate the exciting errors, several methods have been proposed by researchers to update the rainfall-runoff models such as parameter updating, model state updating, and correction on input data. The current study focuses on investigations about the ability of rainfall-runoff model parameters to cope with three types of existing errors, timing, shape and volume as the common errors in hydrological modelling. The new lumped model, the ERM model, has been selected for this study to evaluate its parameters for its use in model updating to cope with the stated errors. Investigation about ten events proves that the ERM model parameters can be updated to cope with the errors without the need to recalibrate the model.

Conceptual modelling of E. coli in urban stormwater drains, creeks and rivers

Accurate estimation of faecal microorganism levels in water systems, such as stormwater drains, creeks and rivers, is needed for appropriate assessment of impacts on receiving water bodies and the risks to human health. The underlying hypothesis for this work is that a single conceptual model (the MicroOrganism Prediction in Urban Stormwater model – i.e. MOPUS) can adequately simulate microbial dynamics over a variety of water systems and wide range of scales; something which has not been previously tested. Additionally, the application of radar precipitation data for improvement of the model performance at these scales via more accurate areal averaged rainfall intensities was tested. Six comprehensive Escherichia coli (E. coli) datasets collected from five catchments in south-eastern Australia and one catchment in Raleigh, USA, were used to calibrate the model. The MOPUS rainfall-runoff model performed well at all scales (Nash-Sutcliffe E for instantaneous flow rates between 0.70 and 0.93). Sensitivity analysis showed that wet weather urban stormwater flows can be modelled with only three of the five rainfall runoff model parameters: routing coefficient (K), effective imperviousness (IMP) and time of concentration (TOC). The model’s performance for representing instantaneous E. coli fluctuations ranged from 0.17 to 0.45 in catchments drained via pipe or open creek, and was the highest for a large riverine catchment (0.64); performing similarly, if not better, than other microbial models in literature. The model could also capture the variability in event mean concentrations (E = 0.17–0.57) and event loads (E = 0.32–0.97) at all scales. Application of weather radar-derived rainfall inputs caused lower overall performance compared to using gauged rainfall inputs in representing both flow and E. coli levels in urban drain catchments, with the performance improving with increasing catchment size and being comparable to the models that use gauged rainfall inputs at the large riverine catchment. These results demonstrate the potential of the MOPUS model and its ability to be applied to a wide range of catchment scales, including large riverine systems.

Urban hydrologic trend analysis based on rainfall and runoff data analysis and conceptual model calibration

Urban stormwater is a major cause of urban flooding and natural water pollution. It is therefore important to assess any hydrologic trends in urban catchments for stormwater management and planning. This study addresses urban hydrological trend analysis by examining trends in variables that characterize hydrological processes. The original and modified Mann-Kendall methods are applied to trend detection in two French catchments, that is, Chassieu and La Lechere, based on approximately 1 decade of data from local monitoring programs. In both catchments, no trend is found in the major hydrological process driver (i.e., rainfall variables), whereas increasing trends are detected in runoff flow rates. As a consequence, the runoff coefficients tend to increase during the study period, probably due to growing imperviousness with the local urbanization process. In addition, conceptual urban rainfall-runoff model parameters, which are identified via model calibration with an event based approach, are examined. Trend detection results indicate that there is no trend in the time of concentration in Chassieu, whereas a decreasing trend is present in La Lechere, which, however, needs to be validated with additional data. Sensitivity analysis indicates that the original Mann-Kendall method is not sensitive to a few noisy values in the data series.

Development of Rainfall Runoff Modeling in Matlab Environment

The aim of this study is to understand the basic concept and parameters of rainfall-runoff model, and how that concept applies in model as well. And familiar with how to build/develop the models of rainfall-runoff, with more about the practical situations by considering corresponding different parameters according to the seasonal conditions (for example snowfall in winter season and its role according to the temperature), hydrological conditions of site also. Matlab is used as a tool for the developing this model. This type of models are very much useful to calculate the, how much water will enter in to deeper layers of sub-soil surfaces, when rainfall occurs and to calculate the total amount water losses in different forms (for example evaporation from land and transpiration from vegetation and trees) also. Get good experience while working with Matlab to develop the individual group model and while observing the already given scrip for example model to understand as well. While working with this exercise, got good questions about concept and parameters concerned for model development and had good discussion with in the working group and with available assistance/staff.

Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments – A comparative hydrology approach

Summary Daily streamflow information is critical for solving various hydrologic problems, though observations of continuous streamflow for model calibration are available at only a small fraction of the world’s rivers. One approach to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) catchment to an ungauged (receiver) catchment of interest. Central to this approach is the selection of a hydrologically similar donor. No single metric or set of metrics of hydrologic similarity have been demonstrated to consistently select a suitable donor catchment. We design an experiment to diagnose the dominant controls on successful hydrologic model parameter transfer. We calibrate a lumped rainfall–runoff model to 83 stream gauges across the United States. All locations are USGS reference gauges with minimal human influence. Parameter sets from the calibrated models are then transferred to each of the other catchments and the performance of the transferred parameters is assessed. This transfer experiment is carried out both at the scale of the entire US and then for six geographic regions. We use classification and regression tree (CART) analysis to determine the relationship between catchment similarity and performance of transferred parameters. Similarity is defined using physical/climatic catchment characteristics, as well as streamflow response characteristics (signatures such as baseflow index and runoff ratio). Across the entire US, successful parameter transfer is governed by similarity in elevation and climate, and high similarity in streamflow signatures. Controls vary for different geographic regions though. Geology followed by drainage, topography and climate constitute the dominant similarity metrics in forested eastern mountains and plateaus, whereas agricultural land use relates most strongly with successful parameter transfer in the humid plains.

Regional models of flow-duration curves of perennial and intermittent streams and their use for calibrating the parameters of a rainfall–runoff model

One of the main challenges faced by hydrologists and water engineers is the estimation of variables needed for water resources planning and management in ungauged river basins. To this end, techniques for transposing information, such as hydrological regional analyses, are widely employed. A method is presented for regionalizing flow-duration curves (FDCs) in perennial, intermittent and ephemeral rivers, based on the extended Burr XII probability distribution. This distribution shows great flexibility to fit data, with accurate reproduction of flow extremes. The performance analysis showed that, in general, the regional models are able to synthesize FDCs in ungauged basins, with a few possible drawbacks in the application of the method to intermittent and ephemeral rivers. In addition to the regional models, we summarize the experience of using synthetic FDCs for the indirect calibration of the Rio Grande rainfall–runoff model parameters in ungauged basins.Editor D. KoutsoyiannisCitation Costa, V., Fernandes, W., and Naghettini, M., 2013. Regional models of flow-duration curves of perennial and intermittent streams and their use for calibrating the parameters of a rainfall–runoff model. Hydrological Sciences Journal, 59 (2), 262–277.

Considering rating curve uncertainty in water level predictions

Abstract. Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

Hydrological impact of forest fires and climate change in a Mediterranean basin

Forest fire can modify and accelerate the hydrological response of Mediterranean basins submitted to intense rainfall: during the years following a fire, the effects on the hydrological response may be similar to those produced by the growth of impervious areas. Moreover, climate change and global warming in Mediterranean areas can imply consequences on both flash flood and fire hazards, by amplifying these phenomena. Based on historical events and post-fire experience, a methodology to interpret the impacts of forest fire in terms of rainfall-runoff model parameters has been proposed. It allows to estimate the consequences of forest fire at the watershed scale depending on the considered burned area. In a second stage, the combined effect of forest fire and climate change has been analysed to map the future risk of forest fire and their consequence on flood occurrence. This study has been conducted on the Llobregat river basin (Spain), a catchment of approximately 5,000 km2 frequently affected by flash floods and forest fires. The results show that forest fire can modify the hydrological response at the watershed scale when the burned area is significant. Moreover, it has been shown that climate change may increase the occurrence of both hazards, and hence, more frequent severe flash floods may appear.

Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?

This paper investigates the uncertainty of hydrological predictions due to rainfall-runoff model parameters in the context of climate change impact studies. Two sources of uncertainty were considered: (i) the dependence of the optimal parameter set on the climate characteristics of the calibration period and (ii) the use of several posterior parameter sets over a given calibration period. The first source of uncertainty often refers to the lack of model robustness, while the second one refers to parameter uncertainty estimation based on Bayesian inference. Two rainfall-runoff models were tested on 89 catchments in northern and central France. The two sources of uncertainty were assessed in the past observed period and in future climate conditions. The results show that, given the evaluation approach followed here, the lack of robustness was the major source of variability in streamflow projections in future climate conditions for the two models tested. The hydrological projections generated by an ensemble of posterior parameter sets are close to those associated with the optimal set. Therefore, it seems that greater effort should be invested in improving the robustness of models for climate change impact studies, especially by developing more suitable model structures and proposing calibration procedures that increase their robustness.

Calibration of the Parameters of a Rainfall-Runoff Model in Ungauged Basins Using Synthetic Flow Duration Curves as Estimated by Regional Analysis

AbstractThe knowledge of hydrologic variables and related functions at a given river cross section, as well as its time series of daily mean flows, supports the planning, design, and operation of facilities for the use of water resources in gauged river basins. In basins lacking systematic records of water levels and discharges, however, it becomes necessary to develop methodologies that enable the transfer of hydrologic information that is available in other basins. The present study proposes and evaluates a method for the automatic calibration of the parameters of a rainfall-runoff model in ungauged basins, using synthetic long-term flow duration curves (FDCs) estimated by a regional statistical model as the paradigm for the hydrologic processes. In this context, the long-term flow duration curves are used as an instrument to calibrate the parameters of a conceptual rainfall-runoff model. Once the parameters that control the hydrologic synthesis by the rainfall-runoff model are estimated, the continuous...