Effects of the catchment runoff coefficient on the performance of TOPMODEL in rainfall–runoff modelling

Effects of the catchment runoff coefficient on the performance of TOPMODEL in simulating catchment rainfall–runoff relationships are investigated in this paper, with an aim to improve TOPMODEL's simulation efficiency in catchments with a low runoff coefficient. Application of TOPMODEL in the semi‐arid Yihe catchment, with an area of 2623 km² in the Yellow River basin of China, produced a Nash–Sutcliffe model efficiency of about 80%. To investigate how the catchment runoff coefficient affects the performance of TOPMODEL, the whole observed discharge series of the Yihe catchment is multiplied with a larger‐than‐unity scale factor to obtain an amplified discharge series. Then TOPMODEL is used to simulate the amplified discharge series given the original rainfall and evaporation data. For a set of different scale factors, TOPMODEL efficiency is plotted against the corresponding catchment runoff coefficient and it is found that the efficiency of TOPMODEL increases with the increasing catchment runoff coefficient before reaching a peak (e.g. about 90%); after the peak, however, the efficiency of TOPMODEL decreases with the increasing catchment runoff coefficient. Based on this finding, an approach called the discharge amplification method is proposed to enhance the simulation efficiency of TOPMODEL in rainfall–runoff modelling in catchments with a low runoff coefficient. Copyright © 2004 John Wiley & Sons, Ltd.     

Fuzzy computing based rainfall–runoff model for real time flood forecasting

This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.     

Comparative study of neural network,fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains

This paper compares artificial neural network (ANN), fuzzy logic (FL) and linear transfer function (LTF)‐based approaches for daily rainfall‐runoff modelling. This study also investigates the potential of Takagi‐Sugeno (TS) fuzzy model and the impact of antecedent soil moisture conditions in the performance of the daily rainfall‐runoff models. Eleven different input vectors under four classes, i.e. (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff and (iv) rainfall, runoff and antecedent moisture content are considered for examining the effects of input data vector on rainfall‐runoff modelling. Using the rainfall‐runoff data of the upper Narmada basin, Central India, a suitable modelling technique with appropriate model input structure is suggested on the basis of various model performance indices. The results show that the fuzzy modelling approach is uniformly outperforming the LTF and also always superior to the ANN‐based models. Copyright © 2010 John Wiley & Sons, Ltd.     

Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study

A rainfall‐runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall‐runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km²) in the Nile basin is selected here as a case study. The catchment is classified into seven subcatchments, and the mean areal precipitation over those subcatchments is computed as a main input to the ANN model. The available daily data (1992–99) are divided into two sets for model calibration (1992–96) and for validation (1997–99). The results of the ANN model are compared with one of physical distributed rainfall‐runoff models that apply hydraulic and hydrologic fundamental equations in a grid base. The results over the case study area and the comparative analysis with the physically based distributed model show that the ANN technique has great potential in simulating the rainfall‐runoff process adequately. Because the available record used in the calibration of the ANN model is too short, the ANN model is biased compared with the distributed model, especially for high flows. Copyright © 2005 John Wiley & Sons, Ltd.     

Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering

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

Delineating runoff processes and critical runoff source areas in a pasture hillslope of the Ozark Highlands

The identification of runoff contributing areas would provide the ideal focal points for water quality monitoring and Best Management Practice (BMP) implementation. The objective of this study was to use a field‐scale approach to delineate critical runoff source areas and to determine the runoff mechanisms in a pasture hillslope of the Ozark Highlands in the USA. Three adjacent hillslope plots located at the Savoy Experimental Watershed, north‐west Arkansas, were bermed to isolate runoff. Each plot was equipped with paired subsurface saturation and surface runoff sensors, shallow groundwater wells, H‐flumes and rain gauges to quantify runoff mechanisms and rainfall characteristics at continuous 5‐minute intervals. The spatial extent of runoff source areas was determined by incorporating sensor data into a geographic information‐based system and performing geostatistical computations (inverse distance weighting method). Results indicate that both infiltration excess runoff and saturation excess runoff mechanisms occur to varying extents (0–58% for infiltration excess and 0–26% for saturation excess) across the plots. Rainfall events that occurred 1–5 January 2005 are used to illustrate the spatial and temporal dynamics of the critical runoff source areas. The methodology presented can serve as a framework upon which critical runoff source areas can be identified and managed for water quality protection in other watersheds. Copyright © 2008 John Wiley & Sons, Ltd.     

Estimating daily inflows of large lakes using a water‐balance‐based runoff coefficient scaling approach

Daily river inflow time series are highly valuable for water resources and water environment management of large lakes. However, the availability of continuous inflow data for large lakes is still relatively limited, especially for large lakes situated within humid plain regions with tens or even hundreds of tributaries. In this study, we choose the fifth largest freshwater Lake Chaohu in China as our study area to introduce a new approach to reconstruct historical daily inflows at ungauged subcatchments of large lakes. This approach makes use of water level, lake surface rainfall, evaporation from the lake, and catchment rainfall observations. Rainfall–runoff relationship at a reference catchment was analysed to select rainfall input and estimate run‐off coefficient firstly, and the run‐off coefficient was then transferred to ungauged subcatchments to initially estimate daily inflows. Run‐off coefficient was scaled to adjust daily inflows at ungauged subcatchments according to water balance of the lake. This approach was evaluated using sparsely measured inflows at eight subcatchments of Lake Chaohu and compared with the commonly used drainage area ratio method. Results suggest that the inflow time series reconstructed from this approach consistent well to corresponding observations, with mean R² and Nash–Sutcliffe efficiency values of 0.69 and 0.6, respectively. This approach outperforms drainage area ratio method in terms of mean R² and Nash–Sutcliffe efficiency values. Accuracy of this approach holds well when the number of water‐level station being used decreased from four to one.     

A systematic approach to the input determination for neural network rainfall–runoff models

Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs are removed. Then an adequate ANN model, which only includes highly relevant inputs, is constructed. Unlike the trial‐and‐error procedure, the proposed approach is more systematic and avoids unnecessary trials. To demonstrate the effectiveness of the proposed approach, an application to actual typhoon events is presented. The results show that the proposed ANN model, which is constructed by the proposed approach, has advantages over those obtained by the trial‐and‐error procedure. The proposed ANN model has a simpler architecture, needs less training time, and performs better. The proposed ANN model is recommended as an alternative to existing rainfall–runoff ANN models. Copyright © 2007 John Wiley & Sons, Ltd.     

Dynamic-stochastic models of rainfall and snowmelt runoff formation

Abstract

Possibilities for the development of dynamic-stochastic models of runoff formation with random inputs are discussed. Two models are described: the first allows the calculation of the statistical distribution of the maximum discharges of rainfall floods, and the second the statistical distribution of snowmelt flood volumes. Meteorological inputs are generated by the Monte- Carlo method. Physically-based models are used for the transformation of input data into runoff. The various models are applied to observation data from two watersheds.     

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.     

Rainfall–runoff trends in the south‐eastern USA: 1938–2005

An extensive dataset (230 precipitation gauges and 79 stream gauges) was used to analyse rainfall–runoff relationships in 10 subregions of a 482000 km² area in the south‐eastern USA (Maryland, Virginia, North Carolina, South Carolina and Georgia). The average annual rainfall and runoff for this study area between 1938 and 2005 were 1201 and 439 mm, respectively. Average runoff/rainfall ratios during this period varied between 0·24 in the southernmost Coastal Plain subregion to 0·64 in the Blue Ridge Province. Watershed elevation and relief are the principal determinants governing the conversion of rainfall to runoff. Temporal rainfall variation throughout the south‐eastern USA ranges from ～40% above and below normal while the variation for runoff is higher, from ? 75% to + 100%. In any given year there can exist a ± 25–50% error in predicted runoff deviation using the annual rainfall–runoff regression. Fast Fourier Transform and autoregressive spectral analysis revealed dominant cyclicities for rainfall and runoff between 14 and 17 years. Secondary periodicities were typically between 6–7 and 10–12 years. The inferred cyclicity may be related to ENSO and/or Central North Pacific atmospheric phenomena. Mann–Kendall analyses indicate that there were no consistent statistically significant temporal trends with respect to south‐eastern US rainfall and runoff during the study period. The results of U‐tests similarly indicated that rainfall between 1996 and 2005 was not statistically higher or lower than during earlier in the study period. Copyright © 2008 John Wiley & Sons, Ltd.     

A rational runoff coefficient for a revisited rational formula

ABSTRACT

The Rational Formula (RF) is probably the most frequently applied equation in practical hydrology to compute the peak discharge, due to its simplicity and effective compromise between theory and data availability. Thus, after more than a century, the estimation of peak discharge through the RF is still an important and challenging issue in hydrology. The RF assumes response linearity and sometimes assumes that the return period does not depend on the runoff coefficient and neglects the time to ponding and the antecedent moisture condition. Moreover, the RF requires the critical duration of rainfall and the runoff coefficient to be estimated, both of which are highly controversial. This paper proposes an advanced RF that makes it possible to derive the peak discharge at the hillslope scale, where the above RF assumptions are mostly relaxed. Physically based runoff coefficient tables, which are not affected by subjectivity, are presented and application of the derived procedure is performed.     

A stochastic framework to assess the performance of flood warning systems based on rainfall‐runoff modeling

Nowadays, Flood Forecasting and Warning Systems (FFWSs) are known as the most inexpensive and efficient non‐structural measures for flood damage mitigation in the world. Benefit to cost of the FFWSs has been reported to be several times of other flood mitigation measures. Beside these advantages, uncertainty in flood predictions is a subject that may affect FFWS's reliability and the benefits of these systems. Determining the reliability of advanced flood warning systems based on the rainfall–runoff models is a challenge in assessment of the FFWS performance which is the subject of this study. In this paper, a stochastic methodology is proposed to provide the uncertainty band of the rainfall–runoff model and to calculate the probability of acceptable forecasts. The proposed method is based on Monte Carlo simulation and multivariate analysis of the predicted time and discharge error data sets. For this purpose, after the calibration of the rainfall–runoff model, the probability distributions of input calibration parameters and uncertainty band of the model are estimated through the Bayesian inference. Then, data sets of the time and discharge errors are calculated using the Monte Carlo simulation, and the probability of acceptable model forecasts is calculated by multivariate analysis of data using copula functions. The proposed approach was applied for a small watershed in Iran as a case study. The results showed using rainfall–runoff modeling based on real‐time precipitation is not enough to attain high performance for FFWSs in small watersheds, and it seems using weather forecasts as the inputs of rainfall–runoff models is essential to increase lead times and the reliability of FFWSs in small watersheds. Copyright © 2013 John Wiley & Sons, Ltd.     

A statistical methodology for assessing the long‐term effects of antecedent drought conditions on stream runoff: applications to the Piedmont Province,southeastern United States

A method was developed to investigate the long‐term (months‐to‐years) effects (both magnitude and duration) of antecedent rainfall upon subsequent runoff coefficients (RCs) or runoff/rainfall ratios. The method was applied to a four‐state region (Georgia, South Carolina, North Carolina, and Virginia) within the southeastern Piedmont Province of the United States and incorporated a 59‐year data set of 19 United States Geological Survey stream gages and 57 National Climatic Data Center rain gages. The method was designed to facilitate statistical comparisons [Mann–Whitney rank sum tests] between various groups of normalized runoff coefficients (NRCs) representing 6–36 month periods which differed in terms of antecedent rainfall conditions. The results of this study show that under all subsequent rainfall conditions, with the exception of excess rainfall, a 1‐year period of antecedent drought lowered NRCs for at least 1 year following the drought. The principal finding of this study is that a year‐long drought period within the southeastern Piedmont Province lowers NRCs by ～25% during the following year when rainfall returns to normal. In most cases, RCs are significantly lower during the second year following a drought than they would be when anteceded by normal rainfall; however, the effects of drought wane during this period. This is a statistical and regional method that can be modified to other study areas; however, it cannot be used to predict storm‐event rainfall–runoff relationships for any specific basin. Copyright © 2010 John Wiley & Sons, Ltd.     

Rainfall–runoff modelling for estimating Latonyanda River flow contributions to Luvuvhu River downstream of Albasini Dam

Rainfall–runoff modelling was conducted to estimate the flows that Latonyanda River contribute to Luvuvhu River downstream of Albasini Dam. The confluence of Latonyanda and Luvuvhu Rivers is ungauged. The contributed flows compensate for upstream water abstractions and periodic lack of releases from Albasini Dam. The flow contributions from tributaries to Luvuvhu River are important for ecosystem sustenance, meeting downstream domestic and agricultural water demand and ecological water requirements particularly in Kruger National Park. The upper Latonyanda River Quaternary Catchment (LRQC), with streamflow gauging station number A9H027 was delineated and used for rainfall–runoff modelling. The simulation was done using Mike 11 NAM rainfall–runoff model. Calibration and verification runs of Mike 11 NAM rainfall–runoff model were carried out using data for periods of 4 and 2 years, respectively. The model was calibrated using shuffled complex evolution optimizer. The model efficiency was tested using coefficient of determination (R²), root mean square error (RMSE), overall water balance error (OWBE) and percentage bias (PBIAS). The model parameters obtained from the upper LRQC were transferred and used together with rainfall and evaporation data for 40 years period in the simulation of runoff for the LRQC. The flows that Latonyanda River contribute to Luvuvhu River were computed by subtracting irrigation abstractions and runoff drained to Tshakhuma Dam from the simulated runoff time series of the LRQC. The observed and the simulated runoff showed similar trends and measures of performances for both calibration and verification runs fell within acceptable ranges. The pairs of values obtained for R², RMSE, OWBE and PBIAS for calibration and verification were 0.86 and 0.73, 0.21 and 0.2, 2.1 and 1.3, and 4.1 and 3.4, respectively. The simulated runoff for LRQC correlated well with the areal rainfall showing that the results are reasonable. The mean and maximum daily flow contributions from the Latonyanda River are 0.91 and 49 m³/s respectively. The estimation of these ungauged flows makes it possible to plan and manage the water requirements for the downstream users.     

Response analysis of rainfall–runoff processes using wavelet transform: a case study of the alpine meadow belt

Rainfall–runoff processes appear to be highly nonlinear in Bayinbluk watersheds of the northwestern China. In this study, the time‐scale wavelet transform has been used for the analysis of this nonstationary system. The Haar and Morlet wavelet transform were used to analyse the rainfall–runoff conversion relationship. Wavelet power spectrum and change point methods are also employed to analyse rainfall rates and runoffs measured at daily to half‐hourly sampling rate. The four experimental sites (Luoto, Haer, Kuce and Shengl) are located in the Tianshan Mountains (Xinjiang province, China). Correlation analysis and wavelet transform are first applied to runoff process in different underlying surfaces. Wavelet analyses of rainfall rates and runoffs also give meaningful information on the temporal variability of the rainfall–runoff relationship. Change point and wavelet power spectrum analysis provide simple interpretation of energy distribution between different scales. The results indicate that wavelet transform is a good method for analysing the nonlinear relationship of temporal–spatial responses between rainfall and runoff. This method allowed quantification of the processes affecting runoff and provided an insight into their implications in surface water management. Copyright © 2011 John Wiley & Sons, Ltd.     

Storm‐event rainfall–runoff modelling approach for ungauged sites in Taiwan

This work develops a top‐down modelling approach for storm‐event rainfall–runoff model calibration at unmeasured sites in Taiwan. Twenty‐six storm events occurring in seven sub‐catchments in the Kao‐Ping River provided the analytical data set. Regional formulas for three important features of a streamflow hydrograph, i.e. time to peak, peak flow, and total runoff volume, were developed via the characteristics of storm event and catchment using multivariate regression analysis. Validation of the regional formulas demonstrates that they reasonably predict the three features of a streamflow hydrograph at ungauged sites. All of the sub‐catchments in the study area were then adopted as ungauged areas, and the three streamflow hydrograph features were calculated by the regional formulas and substituted into the fuzzy multi‐objective function for rainfall–runoff model calibration. Calibration results show that the proposed approach can effectively simulate the streamflow hydrographs at the ungauged sites. The simulated hydrographs more closely resemble observed hydrographs than hydrographs synthesized using the Soil Conservation Service (SCS) dimensionless unit hydrograph method, a conventional method for hydrograph estimation at ungauged sites in Taiwan. Copyright © 2008 John Wiley & Sons, Ltd.     

Measurements of the initiation of post‐wildfire runoff during rainstorms using in situ overland flow detectors

Overland flow detectors (OFDs) were deployed in 2012 on a hillslope burned by the 2010 Fourmile Canyon fire near Boulder, Colorado, USA. These detectors were simple, electrical resistor‐type instruments that output a voltage (0–2·5 V) and were designed to measure and record the time of runoff initiation, a signal proportional to water depth, and the runoff hydrograph during natural convective rainstorms. Initiation of runoff was found to be spatially complex and began at different times in different locations on the hillslope. Runoff started first at upstream detectors 56% of the time, at the mid‐stream detectors 6%, and at the downstream detectors 38% of the time. Initiation of post‐wildfire runoff depended on the time‐to‐ponding, travel time between points, and the time to fill surface depression storage. These times ranged from 0·5–54, 0·4–1·1, and 0·2–14 minutes, respectively, indicating the importance of the ponding process in controlling the initiation of runoff at this site. Time‐to‐ponding was modeled as a function of the rainfall acceleration (i.e. the rate of change of rainfall intensity) and either the cumulative rainfall at the start of runoff or the soil–water deficit. Measurements made by the OFDs provided physical insight into the spatial and temporal initiation of post‐wildfire runoff during unsteady flow in response to time varying natural rainfall. They also provided data that can be telemetered and used to determine critical input parameters for hydrologic rainfall–runoff models. Copyright © 2015 John Wiley & Sons, Ltd.     

Temporally weighted average curve number method for daily runoff simulation

The modified Soil Conservation Service curve number (CN) method is widely used in long‐term continuous models to predict daily surface runoff. However, it has been shown that this method gives poor results in reproducing peak flows in high rainfall periods. This is because there is an inaccuracy stemming from the model algorithm as it adjusts the daily runoff curve number as a function of soil moisture content at the end of the previous day. This paper proposes an alternative daily based curve number technique that can provide better prediction of daily runoff during the high flow season. The proposed method uses the temporally weighted average curve number (TWA‐CN) to estimate daily surface runoff, while considering the effect of rainfall during a given day as well as the antecedent soil moisture condition. To test the applicability of the TWA‐CN method, it was incorporated with the long‐term, continuous simulation watershed models SWAT and SWAT‐G. Simulations were conducted for the Miho River watershed located in the middle of South Korea. The graphical displays and statistics of the determination coefficient (R²) and the Nash–Sutcliffe model efficiency (NSE) of the observed and simulated daily runoff indicated that the modified SWAT with the TWA‐CN method may provide better runoff prediction (R² = 0·837, NSE = 0·833) than the original SWAT (R² = 0·815, NSE = 0·824). Likewise, the determination coefficient (R² = 0·816) and the Nash–Sutcliffe efficiency (NSE = 0·834) for the modified SWAT‐G are also higher than the original version (R² = 0·782, NSE = 0·825). It is expected that the improved capability in predicting surface runoff using the suggested CN estimate method will provide a sound contribution to the accurate simulations of water yield. Copyright © 2008 John Wiley & Sons, Ltd.     

Characteristics of surface runoff and throughflow in a purple soil of Southwestern China under various rainfall events

In order to harvest runoff to palliate water disaster as well as effectively manage irrigation and fertilizer application in the studied region, it is necessary to better understand the runoff processes. A newly designed runoff collection system for a plot scale was used to partition runoff under contrasting rainfall events into surface flow and subsurface flow to obtain characteristics of surface runoff and throughflow in a purple soil (Regosols in FAO taxonomy, Entisol in USDA taxonomy) of Sichuan, China. Under small rainfall (shower and drizzle), only surface runoff was observed. It is noted that, under shower, particularly with antecedent dry soil conditions, the highest peak surface runoff significantly lagged behind that of rainfall, because air‐locked soil pores of the top layer appeared temporally. Under rainstorm and downpour, surface runoff and throughflow both commenced and showed hysteresis. The hydrograph of surface runoff better resembled that of rainfall than throughflow did. The durations of throughflow discharge of post‐rainfall‐end were near the same (within 24 h) under various rainfalls and rather dependent upon the soil properties than the rainfall characteristics. Throughflow is about 60–90% of total runoff, and especially significant in a ploughed layer under downpour. The chloride concentration of throughflow was over twice that of surface runoff and rainfall, implying that throughflow contains more nutrients than surface runoff. Presumably, surface runoff was primarily governed by an infiltration‐excess or saturated excess‐infiltration mechanism under unsaturated or saturated soil conditions. Therefore, the management of water and fertilizer, and the harvesting of water flow in the ploughed soil layer, should be emphasized in this region. Copyright © 2005 John Wiley & Sons, Ltd.