A simple rainfall–runoff model based on hydrological units applied to the Teba catchment (south‐east Spain)

A hydrological model (YWB, yearly water balance) has been developed to model the daily rainfall–runoff relationship of the 202 km² Teba river catchment, located in semi‐arid south‐eastern Spain. The period of available data (1976–1993) includes some very rainy years with intensive storms (responsible for flooding parts of the town of Malaga) and also some very dry years. The YWB model is in essence a simple tank model in which the catchment is subdivided into a limited number of meaningful hydrological units. Instead of generating per unit surface runoff resulting from infiltration excess, runoff has been made the result of storage excess. Actual evapotranspiration is obtained by means of curves, included in the software, representing the relationship between the ratio of actual to potential evapotranspiration as a function of soil moisture content for three soil texture classes. The total runoff generated is split between base flow and surface runoff according to a given baseflow index. The two components are routed separately and subsequently joined. A large number of sequential years can be processed, and the results of each year are summarized by a water balance table and a daily based rainfall runoff time series. An attempt has been made to restrict the amount of input data to the minimum. Interactive manual calibration is advocated in order to allow better incorporation of field evidence and the experience of the model user. Field observations allowed for an approximate calibration at the hydrological unit level. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

Conceptual modelling to assess how the interplay of hydrological connectivity,catchment storage and tracer dynamics controls nonstationary water age estimates

Although catchment storage is an intrinsic control on the rainfall–runoff response of streams, direct measurement remains a major challenge. Coupled models that integrate long‐term hydrometric and isotope tracer data are useful tools that can provide insights into the dynamics of catchment storage and the volumes of water involved. In this study, we use a tracer‐aided hydrological model to characterize catchment storage as a dynamic control on system function related to streamflow generation, which also allows direct estimation of the nonstationarity of water ages. We show that in a wet Scottish upland catchment dominated by runoff generation from riparian peats (histosols) with high water storage, nonstationarity in water age distributions is only clearly detectable during more extreme wet and dry periods. This is explained by the frequency and longevity of hydrological connectivity and the associated relative importance of flow paths contributing younger or older waters to the stream. Generally, these saturated riparian soils represent large mixing zones that buffer the time variance of water age and integrate catchment‐scale partial mixing processes. Although storage simulations depend on model performance, which is influenced by input variability and the degree of isotopic damping in the stream, a longer‐term storage analysis of this model indicates a system that is only sensitive to more extreme hydroclimatic variability. Copyright © 2014 John Wiley & Sons, Ltd.     

Curve‐Number/Green–Ampt mixed procedure for streamflow predictions in ungauged basins: Parameter sensitivity analysis

A procedure combining the Soil Conservation Service‐Curve Number (SCS‐CN) method and the Green–Ampt (GA) infiltration equation was recently developed to overcome some of the drawbacks of the classic SCS‐CN approach when estimating the volume of surface runoff at a sub‐daily time resolution. The rationale of this mixed procedure, named Curve Number for Green–Ampt (CN4GA), is to use the GA infiltration model to distribute the total volume of the net hyetograph (rainfall excess) provided by the SCS‐CN method over time. The initial abstraction and the total volume of rainfall given by the SCS‐CN method are used to identify the ponding time and to quantify the hydraulic conductivity parameter of the GA equation. In this paper, a sensitivity analysis of the mixed CN4GA parameters is presented with the aim to identify conditions where the mixed procedure can be effectively used within the Prediction in Ungauged Basin perspective. The effects exerted by changes in selected input parameters on the outputs are evaluated using rectangular and triangular synthetic hyetographs as well as 100 maximum annual storms selected from synthetic rainfall time series. When applied to extreme precipitation events, which are characterized by predominant peaks of rainfall, the CN4GA appears to be rather insensitive to the input hydraulic parameters of the soil, which is an interesting feature of the CN4GA approach and makes it an ideal candidate for the rainfall excess estimation at sub‐daily temporal resolution at ungauged sites. Copyright © 2013 John Wiley & Sons, Ltd.     

Sources of uncertainty in the NRCS CN model: Recognition and solutions

The Natural Resources Conservation Service Curve Number model is one of the most recognizable procedures in the field of rainfall–run‐off estimation. It has been widely applied for different purposes in hydrological models. In spite of its widespread use, some uncertainties have not even clarified and must be examined for its proper application. Particularly, choosing the most representative rainfall–run‐off events, and the coefficient λ that relates the parameters of the model (curve number CN and initial abstraction I_a). In this research, an advanced analysis is developed to evaluate the influence of λ for a set of representative watersheds of the Agricultural Research Service of the United Stated Department of Agriculture. They are characterized by different soil properties, land uses, and climatic conditions. Finally, 2 novel methodologies for the selection of the most representative rainfall–run‐off events and for the adaptation of coefficient λ are included, based on the pattern of rainfall distribution.     

Method for calibrating a theoretical model in karst springs: an example for a hydropower station in South China

A theoretical, dimensionless rainfall–runoff model was used to simulate the discharge of Wulongdong spring in western Hubei province, South China. The single parameter (time constant τ) in the model is easy to obtain by fitting the recession rate of the observed hydrographs. The model was scaled by simply matching the total annual flow volume of the model to the observed value. Annual distribution of actual evapotranspiration was embedded in the model input to calculate the accumulated deficit of soil moisture before each rain event. Hourly precipitation input data performed better than daily data, defining τ of 0.85 days and returning a Nash–Sutcliffe efficiency of 0.89 and the root mean square error of 0.07. This model offers an effective way to simulate the discharge of karst springs that respond sensitively to rainfall events. The model parameters of a successful simulation can be used to estimate the recharge area and indicate the intrinsic response time of the basin. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of genetic algorithms and shuffled complex evolution approach for calibrating distributed rainfall–runoff model

Three methods, Shuffled Complex Evolution (SCE), Simple Genetic Algorithm (SGA) and Micro‐Genetic Algorithm (µGA), are applied in parameter calibration of a grid‐based distributed rainfall–runoff model (GBDM) and compared by their performances. Ten and four historical storm events in the Yan‐Shui Creek catchment, Taiwan, provide the database for model calibration and verification, respectively. The study reveals that the SCE, SGA and µGA have close calibration results, and none of them are superior with respect to all the performance measures, i.e. the errors of time to peak, peak discharge and the total runoff volume, etc. The performances of the GBDM for the verification events are slightly worse than those in the calibration events, but still quite satisfactory. Among the three methods, the SCE seems to be more robust than the other two approaches because of the smallest influence of different initial random number seeds on calibrated model parameters, and has the best performance of verification with a relatively small number of calibration events. Copyright © 2009 John Wiley & Sons, Ltd.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments

Abstract

The SWAT model was tested to simulate the streamflow of two small Mediterranean catchments (the Vène and the Pallas) in southern France. Model calibration and prediction uncertainty were assessed simultaneously by using three different techniques (SUFI-2, GLUE and ParaSol). Initially, a sensitivity analysis was conducted using the LH-OAT method. Subsequent sensitive parameter calibration and SWAT prediction uncertainty were analysed by considering, firstly, deterministic discharge data (assuming no uncertainty in discharge data) and secondly, uncertainty in discharge data through the development of a methodology that accounts explicitly for error in the rating curve (the stage?discharge relationship). To efficiently compare the different uncertainty methods and the effect of the uncertainty of the rating curve on model prediction uncertainty, common criteria were set for the likelihood function, the threshold value and the number of simulations. The results show that model prediction uncertainty is not only case-study specific, but also depends on the selected uncertainty analysis technique. It was also found that the 95% model prediction uncertainty interval is wider and more successful at encompassing the observations when uncertainty in the discharge data is considered explicitly. The latter source of uncertainty adds additional uncertainty to the total model prediction uncertainty.

Editor D. Koutsoyiannis; Associate editor D. Gerten

Citation Sellami, H., La Jeunesse, I., Benabdallah, S., and Vanclooster, M., 2013. Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean watersheds. Hydrological Sciences Journal, 58 (8), 1635?1657.     

The role of hydrograph indices in parameter estimation of rainfall–runoff models

A reliable prediction of hydrologic models, among other things, requires a set of plausible parameters that correspond with physiographic properties of the basin. This study proposes a parameter estimation approach, which is based on extracting, through hydrograph diagnoses, information in the form of indices that carry intrinsic properties of a basin. This concept is demonstrated by introducing two indices that describe the shape of a streamflow hydrograph in an integrated manner. Nineteen mid‐size (223–4790 km²) perennial headwater basins with a long record of streamflow data were selected to evaluate the ability of these indices to capture basin response characteristics. An examination of the utility of the proposed indices in parameter estimation is conducted for a five‐parameter hydrologic model using data from the Leaf River, located in Fort Collins, Mississippi. It is shown that constraining the parameter estimation by selecting only those parameters that result in model output which maintains the indices as found in the historical data can improve the reliability of model predictions. These improvements were manifested in (a) improvement of the prediction of low and high flow, (b) improvement of the overall total biases, and (c) maintenance of the hydrograph's shape for both long‐term and short‐term predictions. Copyright © 2005 John Wiley & Sons, Ltd.     

Error analysis for the evaluation of model performance: rainfall–runoff event time series data

This paper provides a procedure for evaluating model performance where model predictions and observations are given as time series data. The procedure focuses on the analysis of error time series by graphing them, summarizing them, and predicting their variability through available information (recalibration). We analysed two rainfall–runoff events from the R‐5 data set, and evaluated 12 distinct model simulation scenarios for these events, of which 10 were conducted with the quasi‐physically‐based rainfall–runoff model (QPBRRM) and two with the integrated hydrology model (InHM). The QPBRRM simulation scenarios differ in their representation of saturated hydraulic conductivity. Two InHM simulation scenarios differ with respect to the inclusion of the roads at R‐5. The two models, QPBRRM and InHM, differ strongly in the complexity and number of processes included. For all model simulations we found that errors could be predicted fairly well to very well, based on model output, or based on smooth functions of lagged rainfall data. The errors remaining after recalibration are much more alike in terms of variability than those without recalibration. In this paper, recalibration is not meant to fix models, but merely as a diagnostic tool that exhibits the magnitude and direction of model errors and indicates whether these model errors are related to model inputs such as rainfall. Copyright © 2004 John Wiley & Sons, Ltd.     

Multi‐criterial validation of TOPMODEL in a mountainous catchment

The need for powerful validation methods for hydrological models including the evaluation of internal stages and spatially distributed simulations has often been emphasized. In this study a multi‐criterial validation scheme was used for validation of TOPMODEL, a conceptual semi‐distributed rainfall–runoff model. The objective was to test TOPMODEL's capability of adequately representing dominant hydrological processes by simple conceptual approaches. Validation methods differed in the type of data used, in their target and in mode. The model was applied in the humid and mountainous Brugga catchment (40 km²) in south‐west Germany. It was calibrated by a Monte Carlo method based on hourly runoff data. Additional information for validation was derived from a recession analysis, hydrograph separation with environmental tracers and from field surveys, including the mapping of saturated areas. Although runoff simulations were satisfying, inadequacies of the model structure compared with the real situation with regard to hydrological processes in the study area were found. These belong mainly to the concept of variable contributing areas for saturation excess overland flow and their dynamics, which were overestimated by the model. The simple TOPMODEL approach of two flow components was found to be insufficient. The multi‐criterial validation scheme enables not only to demonstrate limitations with regard to process representation, but also to specify where and why these limitations occur. It may serve as a valuable tool for the development of physically sound model modifications. Copyright © 1999 John Wiley & Sons, Ltd.     

Snow hydrology signatures for model identification within a limits‐of‐acceptability approach

Discharge simulation from snow‐dominated catchments seems to be an easy task. Any spatially explicit precipitation–runoff model coupled to a temperature‐index snow model generally yields simulations that mimic well the observed daily discharges. The robustness of such models is, however, questionable: in the presence of strong annual discharge cycles, small model residuals do not guarantee high explanatory power of the underlying model. This paper proposes a methodology for snow hydrological model identification within a limits‐of‐acceptability framework, where acceptable model simulations are the ones that reproduce a set of signatures within an a priori specified range. The signatures proposed here namely include the relationship between the air temperature regime and the discharge regime, a new snow hydrology signature that can be readily transferred to other Alpine settings. The discriminatory power of all analysed signatures is assessed with a new measure of their discriminatory power in the model prediction domain. The value of the proposed snow hydrology signatures and of the limits‐of‐acceptability approach is demonstrated for the Dischma river in Switzerland, whose discharge shows a strong temporal variability of hydrologic forcing conditions over the last 30 years. The signature‐based model identification for this case study leads to the surprising conclusion that the observed discharge data contains a multi‐year period that cannot be reproduced with the model at hand. This model‐data mismatch might well result from a yet to be identified problem with the discharge observations, which would have been difficult to detect in a classical residual‐based model identification approach. Overall, the detailed results for this case study underline the robustness of the limits‐of‐acceptability approach in the presence of error‐prone observations if it is applied in combination with relatively robust signatures. Future work will show whether snow hydrology signatures and their limits‐of‐acceptability can be regionalized to ungauged catchments, which would make this model selection approach particularly powerful for Alpine environments. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling the hydrological response of mediterranean catchments,Prades, Catalonia. The use of distributed models as aids to hypothesis formulation

This paper attempts to extend the physical arguments underlying the distributed TOPMODEL concepts in an application to the strongly seasonal contributing area responses in two adjacent small mediterranean catchments in the Prades region of Catalonia, Spain. A perceptual model of hydrological response in these catchments is used to suggest possible modifications of the model in a hypothesis testing framework, including an attempt to modify the topographic index approach to reflect the expansion of the effective area of subsurface flow during the wetting-up sequence. It is found that slight improvements in modelling efficiency are possible but that different model parameter distributions are appropriate for different parts of the record. The model was much more successful for the catchment producing the higher runoff volumes. © 1997 John Wiley & Sons, Ltd.     

Application of wavelet‐based multi‐model Kalman filters to real‐time flood forecasting

This paper presents the application of a multimodel method using a wavelet‐based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real‐time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet‐based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state‐estimates, each of which is weighted by its possibility that is also determined on‐line, are combined to form an optimal estimate. Validations conducted for the Wu‐Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time‐varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall–runoff process in the Wu‐Tu watershed. Copyright © 2004 John Wiley & Sons, Ltd.     

Assessing the effectiveness of imperviousness on stormwater runoff in micro urban catchments by model simulation

Imperviousness, considered as a critical indicator of the hydrologic impacts of urbanization, has gained increasing attention both in the research field and in practice. However, the effectiveness of imperviousness on rainfall–runoff dynamics has not been fully determined in a fine spatiotemporal scale. In this study, 69 drainage subareas <1 ha of a typical residential catchment in Beijing were selected to evaluate the hydrologic impacts of imperviousness, under a typical storm event with a 3‐year return period. Two metrics, total impervious area (TIA) and effective impervious area (EIA), were identified to represent the impervious characteristics of the selected subareas. Three runoff variables, total runoff depth (TR), peak runoff depth (PR), and lag time (LT), were simulated by using a validated hydrologic model. Regression analyses were developed to explore the quantitative associations between imperviousness and runoff variables. Then, three scenarios were established to test the applicability of the results in considering the different infiltration conditions. Our results showed that runoff variables are significantly related to imperviousness. However, the hydrologic performances of TIA and EIA were scale dependent. Specifically, with finer spatial scale and the condition heavy rainfall, TIA rather than EIA was found to contribute more to TR and PR. EIA tended to have a greater impact on LT and showed a negative relationship. Moreover, the relative significance of TIA and EIA was maintained under the different infiltration conditions. These findings may provide potential implications for landscape and drainage design in urban areas, which help to mitigate the runoff risk. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of the Kalman filter to the Nash model

The Nash model was used for application of the Kalman filter. The state vector of the rainfall–runoff system was constituted by the IUH (instantaneous unit hydrograph) estimated by the Nash model and the runoff estimated by the Nash model using the Kalman filter. The initial values of the state vector were assumed as the average of 10% of the IUH peak values and the initial runoff estimated from the average IUH. The Nash model using the Kalman filter with a recursive algorithm accurately predicted runoff from a basin in Korea. The filter allowed the IUH to vary in time, increased the accuracy of the Nash model and reduced physical uncertainty of the rainfall–runoff process in the river basin. © 1998 John Wiley & Sons, Ltd.     

Predicting catchment flow in a semi‐arid region via an artificial neural network technique

A model of rainfall–runoff relationships is an essential tool in the process of evaluation of water resources projects. In this paper, we applied an artificial neural network (ANN) based model for flow prediction using the data for a catchment in a semi‐arid region in Morocco. Use of this method for non‐linear modelling has been demonstrated in several scientific fields such as biology, geology, chemistry and physics. The performance of the developed neural network‐based model was compared against multiple linear regression‐based model using the same observed data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modelling appears to be a promising technique for the prediction of flow for catchments in semi‐arid regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate. Copyright © 2004 John Wiley & Sons, Ltd.     

RELATIONSHIPS BETWEEN CATCHMENT ATTRIBUTES AND HYDROLOGICAL RESPONSE CHARACTERISTICS IN SMALL AUSTRALIAN MOUNTAIN ASH CATCHMENTS

Sixteen small catchments in the Maroondah region of Victoria, Australia were analysed using rainfall, temperature and streamflow time series with a rainfall–runoff model whose parameters efficiently characterize the hydrological response of a catchment. A set of catchment attributes for each of these catchments was then compared with the associated set of hydrological response characteristics of the catchments as estimated by the model. The time constant governing quickflow recession of streamflow (τ_q) was related to the drainage network and catchment area. The time constant governing slowflow recession of streamflow (τ_s) was related to the slope and shape of the catchment. The parameter governing evapotranspirative losses ( f ) was related to catchment gradient and vegetative water use. Forestry activities in the catchments changed evapotranspirative losses and thus total volume of streamflow, but did not affect the rate of streamflow recession.