Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model

Abstract

The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time.     

Sensitivity and fuzzy uncertainty analyses in the determination of SCS-CN parameters from rainfall–runoff data

Spatial and seasonal variations of curve number (CN) and initial abstraction ratio (λ) in a watershed can result in inaccurate runoff volume estimations when using the US Natural Resources Conservation Service (SCS-CN) method with constant values for these parameters. In this paper, parameters of CN and λ are considered as calibration parameters and the sensitivity of estimated runoff to these parameters using the SCS-CN method is scrutinized. To incorporate the uncertainty associated with CN and λ, fuzzy linear regression (FLR) is applied to derive the relationships of CN and λ with rainfall depth (P) by employing a large dataset of storm events from four watersheds in Iran. Results indicate that the proposed approach provides more accuracy in estimation of runoff volume compared to the SCS method with constant values of CN and λ, and gives a straightforward technique for evaluating the hydrological effects of CN, λ, and P on runoff volume.     

Artificial neural networks for daily rainfall—runoff modelling

Abstract

The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A linear multiple-input single-output (MISO) model coupled with the ANN is shown to provide a better representation of the rainfall-runoff relationship in such large size catchments compared with linear and nonlinear MISO models. The present model provides a systematic approach for runoff estimation and represents improvement in prediction accuracy over the other models studied herein.     

Laboratory examination of linearity in rainfall–runoff relationships

ABSTRACT

Certain rainfall–runoff models, e.g. the unit hydrograph, assume linear relationships between the variables. These are proportionality of runoff discharges to (net) rainfall depth and linear summations of discharges resulting from (net) rainfalls during different time intervals or over different sectors of a watershed. This study examines the validity of these assumptions by use of an extensive two-dimensional laboratory experimentation. The results indicate that proportionality would be found under high rainfall intensity through a long duration. Spatial summations would more likely yield correct discharges in cases where rainfall duration is equal to, or is longer than, the time of concentration. Temporal summations may yield correct discharges when rainfall duration is longer than one half of the time of concentration. Here, the time of concentration is determined at the beginning of gradual approach of the discharge towards the equilibrium state.     

Rainfall–runoff modelling of railway embankment steep slopes

Abstract

A distributed 1D rainfall–runoff model is presented. It consists of the Saint Venant continuity and momentum equations for overland flow and a modified Green-Ampt model for the infiltration on railway embankment steep slopes. The model is applied to adjacent 10-m-wide erosion control experimental plots with different percentages of grass cover. A relationship between the 2-day antecedent rainfall and initial moisture content was established and used to predict the saturated hydraulic conductivity (Ks). Average values of Ks for 0, 50 and 100% grass cover were found to be 0.1, 1.19 and 2.56 mm/h, respectively. For the majority of cases, the model simulated runoff with acceptable accuracy, 68% having Nash-Sutcliffe efficiency (NSE) values above 0.50. The average NSE value varied between 0.60 and 0.80, with 0% grass-covered plots yielding the highest values. As expected, the runoff volume decreased with increasing percentage of grass cover.

Citation Sajjan, A.K., Gyasi-Agyei, Y., and Sharma, R.H., 2013. Rainfall–runoff modelling of railway embankment steep slopes. Hydrological Sciences Journal, 58 (5), 1162–1176.

Editor D. Koutsoyiannis     

Contribution to hydrological modelling of small Amazonian catchments: application of rainfall–runoff models to simulate flow duration curves

Abstract

The objective of this study is to analyse three rainfall–runoff hydrological models applied in two small catchments in the Amazon region to simulate flow duration curves (FDCs). The simple linear model (SLM) considers the rainfall–runoff process as an input–output time-invariant system. However, the rainfall–runoff process is nonlinear; thus, a modification is applied to the SLM based on the residual relationship between the simulated and observed discharges, generating the modified linear model (MLM). In the third model (SVM), the nonlinearity due to infiltration and evapotranspiration is incorporated into the system through the sigmoid variable gain factor. The performance criteria adopted were a distance metric (δ) and the Nash-Sutcliffe coefficient (R²) determined between simulated and observed flows. The good results of the models, mainly the MLM and SVM, showed that they could be applied to simulate FDCs in small catchments in the Amazon region.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Blanco, C.J.C., Santos, S.S.M., Quintas, M.C., Vinagre, M.V.A., and Mesquita, A.L.A., 2013. Contribution to hydrological modelling of small Amazonian catchments: application of rainfall–runoff models to simulate flow duration curves. Hydrological Sciences Journal, 58 (7), 1–11.     

Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River

Abstract

Climate and soil characteristics vary considerably around the Lake Victoria basin resulting in high spatial and temporal variability in catchment inflows. However, data for estimating the inflows are usually sparsely distributed and error-prone. Therefore, modelled estimates of the flows are highly uncertain, which could explain early difficulties in reproducing the lake water balance. The aim of this study was to improve the estimates of catchment flow to Lake Victoria. The WASMOD model was applied to the Nzoia River, one of the major tributaries to Lake Victoria. Uncertainty was assessed within the GLUE framework. During calibration, log-transformation was performed on both simulated and observed flows. The results showed that, despite its simple structure, WASMOD produces acceptable results for the basin. For a Nash-Sutcliffe efficiency (NS) threshold of 0.6, the percentage of observations bracketed by simulations (POBS) was 74%, the average relative interval length (ARIL) was 0.93, and the maximum NS value was 0.865. The residuals were shown to be homoscedastic, normally distributed and nearly independent. When the NS threshold was increased to 0.8, POBS decreased to 54% with an improvement of ARIL to 0.49, highlighting the effect of the subjective choice of likelihood threshold.

Citation Kizza, M., Rodhe, A., Xu, C.-Y. & Ntale, H. K. (2011) Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River. Hydrol. Sci. J. 56(7), 1210–1226.     

Joint frequency analysis and uncertainty estimation of coupled rainfall–runoff series relying on historical and simulated data

ABSTRACT

Joint frequency analysis and quantile estimation of extreme rainfall and runoff (ERR) are crucial for hydrological engineering designs. The joint quantile estimation of the historical ERR events is subject to uncertainty due to the errors that exist with flow height measurements. This study is motivated by the interest in introducing the advantages of using Hydrologic Simulation Program-Fortran (HSPF) simulations to reduce the uncertainties of the joint ERR quantile estimations in Taleghan watershed. Bivariate ERR quantile estimation was first applied on P_AMS-Q_SIM pairs and the results were compared against the historical rainfall–runoff data (P_AMS-Q_obs). Student’s t and Frank copulas with respectively Gaussian-P3 and Gaussian-LN3 marginal distributions well suited to fit the P_AMS-Q_obs and P_AMS-Q_SIM pairs. Results revealed that confidence regions (CRs) around the p levels become wider for P_AMS-Q_obs compared to P_AMS-Q_SIM, indicating the lower sampling uncertainties of HSPF simulations compared to the historical observations for bivariate ERR frequency analysis.     

Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting

Two methods for generating streamflow forecasts in a Sahelian watershed, the Sirba basin, were compared. The direct method used a linear relationship to relate sea-surface temperature to annual streamflow, and then disaggregated on a monthly time scale. The indirect method used a linear relationship to generate annual precipitation forecasts, a temporal disaggregation to generate daily precipitation and the SWAT (Soil and Water Assessment Tool) model to generate monthly streamflow. The accuracy of the forecasts was assessed using the coefficient of determination, the Nash-Sutcliffe coefficient and the Hit score, and their economic value was evaluated using the cost/loss ratio method. The results revealed that the indirect method was slightly more effective than the direct method. However, the direct method achieved higher economic value in the majority of cost/loss situations, allowed for predictions with longer lead times and required less information.     

Regional scale rainfall–runoff modeling using VARX–MGARCH approach

Multivariate time series modeling approaches are known as useful tools for describing, simulating, and forecasting hydrologic variables as well as their changes over the time. These approaches also have temporal and cross-sectional spatial dependence in multiple measurements. Although the application of multivariate linear and nonlinear time series approaches such as vector autoregressive with eXogenous variables (VARX) and multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models are commonly used in financial and economic sciences, these approaches have not been extensively used in hydrology and water resources engineering. This study employed VARX and VARX–MGARCH approaches in modeling mean and conditional heteroscedasticity of daily rainfall and runoff records in the basin of Zarrineh Rood Dam, Iran. Bivariate diagonal VECH (DVECH) model, as a main type of MGARCH, shows how the conditional variance–covariance and conditional correlation structure vary over the time between residuals series of the fitted VARX. For this purpose, five model fits, which consider different combinations of twofold rainfall and runoff, including both upstream and downstream stations, have been investigated in the present study. The VARX model, with different orders, was applied to the daily rainfall–runoff process of the study area in each of these model fits. The Portmanteau test revealed the existence of conditional heteroscedasticity in the twofold residuals of fitted VARX models. Therefore, the VARX–DVECH model is proposed to capture the heteroscedasticity existing in the daily rainfall–runoff process. The bivariate DVECH model indicated both short-run and long-run persistency in the conditional variance–covariance matrix related to the twofold innovations of rainfall–runoff processes. Furthermore, the evaluation criteria for the VARX–DVECH model revealed the improvement of VARX model performance.     

DEM-based numerical modelling of runoff and soil erosion processes in the hilly–gully loess regions

For sake of improving our current understanding on soil erosion processes in the hilly–gully loess regions of the middle Yellow River basin in China, a digital elevation model (DEM)-based runoff and sediment processes simulating model was developed. Infiltration excess runoff theory was used to describe the runoff generation process while a kinematic wave equation was solved using the finite-difference technique to simulate concentration processes on hillslopes. The soil erosion processes were modelled using the particular characteristics of loess slope, gully slope, and groove to characterize the unique features of steep hillslopes and a large variety of gullies based on a number of experiments. The constructed model was calibrated and verified in the Chabagou catchment, located in the middle Yellow River of China and dominated by an extreme soil-erosion rate. Moreover, spatio-temporal characterization of the soil erosion processes in small catchments and in-depth analysis between discharge and sediment concentration for the hyper-concentrated flows were addressed in detail. Thereafter, the calibrated model was applied to the Xingzihe catchment, which is dominated by similar soil erosion processes in the Yellow River basin. Results indicate that the model is capable of simulating runoff and soil erosion processes in such hilly–gully loess regions. The developed model are expected to contribute to further understanding of runoff generation and soil erosion processes in small catchments characterized by steep hillslopes, a large variety of gullies, and hyper-concentrated flow, and will be beneficial to water and soil conservation planning and management for catchments dealing with serious water and soil loss in the Loess Plateau.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments?

Landscape differences induced by urbanization have prompted hydrologists to define a fuzzy boundary between rural- and urban-specific hydrological models. We addressed the validity of establishing this boundary, by testing two rural models on a large sample of 175 French and United States (US) urbanized catchments, and their 175 rural neighbours. The impact of urbanization on the hydrological behaviour was checked using four metrics. Using a split-sample test, we have compared the performances, parameter distributions, and internal fluxes of GR4H and IHACRES, two conceptual and continuous models running at the hourly time step. Both model structures are based on soil moisture accounting reservoirs (infiltration, runoff, and actual evapotranspiration) and quick flow/slow flow routing components, with no consideration of any specific feature related to urbanization. Results showed: (a) Except for the ratio of streamflow flashiness to precipitation flashiness, the range of hydrological signature metrics in rural catchments encompassed the specificities of urbanized ones. Overall, the urbanized catchments showed higher ratios of mean streamflow to mean precipitation (median values: 0.39 vs. 0.27) and streamflow flashiness to precipitation flashiness (0.13 vs. 0.03), besides lower baseflow index (0.42 vs. 0.62) and shorter characteristic response time (3 vs. 14 hr). (b) The performances of GR4H revealed no significant distinction between rural and urbanized catchments in terms of Kling–Gupta Efficiency (KGE), whereas IHACRES better simulated urbanized catchments, especially during summer. (c) With respect to differences in urbanization level, the GR4H and IHACRES parameters showed different distributions. The differences in parameters were consistent with the differences in hydrological behaviour, which is promising for a model-based assessment of the impact of urbanization. (d) The models agreed less in reproducing the internal fluxes over the urbanized catchments than over the rural ones. These results demonstrate the flexibility of conceptual models to handle the specificities of urbanized catchments.     

Comparison of two rainfall–runoff models: effects of conceptualization on water budget components

Rainfall–runoff models with different conceptual structures for the hydrological processes can be calibrated to effectively reproduce the hydrographs of the total runoff, while resulting in water budget components that are essentially different. This finding poses an open question on the reliability of rainfall–runoff models in reproducing hydrological components other than those used for calibration. In an effort to address this question, we use data from the Glafkos catchment in western Greece to calibrate and compare the ENNS model, a research-oriented lumped model developed for the river Enns in Austria developed for the river Enns in Austria, with the operational MIKE SHE model. Model performance is assessed in the light of the conceptual/structural differences of the modelled hydrological processes, using indices calculated independently for each year, rather than for the whole calibration period, since the former are stricter. We show that even small differences in the representation of hydrological processes may impact considerably on the water budget components that are not measured (i.e. not used for model calibration). From all water budget components, direct runoff exhibits the highest sensitivity to structural differences and related model parameters.

EDITOR M.C. Acreman

ASSOCIATE EDITOR S. Huang     

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

ABSTRACT

The 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     

An enhanced rainfall–runoff model with coupled canopy interception

The translation of rainfall to runoff is significantly affected by canopy interception. Therefore, a realistic representation of the role played by vegetation cover when modelling the rainfall–runoff system is essential for predicting water, sediment, and nutrient transport on hillslopes. Here, we developed a new mathematical model to describe the dynamics of interception, infiltration, and overland flow on canopy-covered sloping land. Based on the relationship between rainfall intensity and the maximum interception rate, the interception process was modelled under two simplified scenarios (i.e., r_e ≤ Int_m and r_e > Int_m). Parameterization of the model was based on consideration of both vegetation condition and soil properties. By analysing the given examples, we found that Int_m reflects the capacity of the canopy to store the precipitation, k reveals the ability of the canopy to retain the intercepted water, and the processes of infiltration and runoff generation are impacted dramatically by Int_m and k. To evaluate the model, simulated rainfall experiments were conducted in 2 years (2016 and 2017) across six cultivation plots at Changwu State Key Agro-Ecological Experimental Station of the Chinese Loess Plateau. The parameters were obtained by fitting the unit discharge (simulated rainfall experiments in 2016) using the least squares method, and estimation formulas for parameters pertaining to vegetation/soil factors (measured in 2016) were constructed via multiple nonlinear regressions. By matching the simulated results and unit discharge (simulated rainfall experiments in 2017), the validity of the model was verified, and a reasonable precision (average R² = .86 and average root mean square error = 6.45) was obtained. The model developed in this research creatively incorporates the canopy interception process to complement the modelling of rainfall infiltration and runoff generation during vegetation growth and offers an improved hydrological basis to analyse matter transport during rainfall events.     

A Bayesian method for missing rainfall estimation using a conceptual rainfall–runoff model

The estimation of missing rainfall data is an important problem for data analysis and modelling studies in hydrology. This paper develops a Bayesian method to address missing rainfall estimation from runoff measurements based on a pre-calibrated conceptual rainfall–runoff model. The Bayesian method assigns posterior probability of rainfall estimates proportional to the likelihood function of measured runoff flows and prior rainfall information, which is presented by uniform distributions in the absence of rainfall data. The likelihood function of measured runoff can be determined via the test of different residual error models in the calibration phase. The application of this method to a French urban catchment indicates that the proposed Bayesian method is able to assess missing rainfall and its uncertainty based only on runoff measurements, which provides an alternative to the reverse model for missing rainfall estimates.     

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

Abstract

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. Koutsoyiannis

Citation 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.     

A wavelet based approach for combining the outputs of different rainfall–runoff models

The rainfall–runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall–runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall–runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall–runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance.     

Basin-scale analysis of rainfall and runoff in Peru (1969–2004): Pacific,Titicaca and Amazonas drainages

Abstract

We present a procedure for estimating Q95 low flows in both gauged and ungauged catchments where Q95 is the flow that is exceeded 95% of the time. For each step of the estimation procedure, a number of alternative methods was tested on the Austrian data set by leave-one-out cross-validation, and the method that performed best was used in the final procedure. To maximise the accuracy of the estimates, we combined relevant sources of information including long streamflow records, short streamflow records, and catchment characteristics, according to data availability. Rather than deriving a single low flow estimate for each catchment, we estimated lower and upper confidence limits to allow local information to be incorporated in a practical application of the procedure. The components of the procedure consist of temporal (climate) adjustments for short records; grouping catchments into eight seasonality-based regions; regional regressions of low flows with catchment characteristics; spatial adjustments for exploiting local streamflow data; and uncertainty assessment. The results are maps of lower and upper confidence limits of low flow discharges for 21 000 sub-catchments in Austria.