Conceptual Rainfall-runoff Model Research Articles

Multiple criteria rainfall–runoff model calibration using a parallel genetic algorithm in a cluster of computers / Calage multi-critères en modélisation pluie–débit par un algorithme génétique parallèle mis en œuvre par une grappe d'ordinateurs

Genetic algorithms are among of the global optimization schemes that have gained popularity as a means to calibrate rainfall–runoff models. However, a conceptual rainfall–runoff model usually includes 10 or more parameters and these are interdependent, which makes the optimization procedure very time-consuming. This may result in the premature termination of the optimization process which will prejudice the quality of the results. Therefore, the speed of optimization procedure is crucial in order to improve the calibration quality and efficiency. A hybrid method that combines a parallel genetic algorithm with a fuzzy optimal model in a cluster of computers is proposed. The method uses the fuzzy optimal model to evaluate multiple alternatives with multiple criteria where chromosomes are the alternatives, whilst the criteria are flood performance measures. In order to easily distinguish the performance of different alternatives and to address the problem of non-uniqueness of optimum, two fuzzy ratios are defined. The new approach has been tested and compared with results obtained by using a two-stage calibration procedure. The current single procedure produces similar results, but is simpler and automatic. Comparison of results between the serial and parallel genetic algorithms showed that the current methodology can significantly reduce the overall optimization time and simultaneously improve the solution quality.

Ensemble predictions of runoff in ungauged catchments

A new approach to regionalization of conceptual rainfall‐runoff models is presented on the basis of ensemble modeling and model averaging. It is argued that in principle, this approach represents an improvement on the established procedure of regressing parameter values against numeric catchment descriptors. Using daily data from 127 catchments in the United Kingdom, alternative schemes for defining prior and posterior likelihoods of candidate models are tested in terms of accuracy of ungauged catchment predictions. A probability distributed model structure is used, and alternative parameter sets are identified using data from each of a number of gauged catchments. Using the models of the 10 gauged catchments most similar to the ungauged catchment provides generally the best results and performs significantly better than the regression method, especially for predicting low flows. The ensemble of candidate models provides an indication of uncertainty in ungauged catchment predictions, although this is not a robust estimate of possible flow ranges, and frequently fails to encompass flow peaks. Options for developing the new method to resolve these problems are discussed.

An empirical method for estimating future flood risks for flood warnings

Abstract. Since medium and long-term precipitation forecasts are still not reliable enough, rough estimates of the degree of the extremity of forthcoming flood events that might occur in the course of dangerous meteorological situations approaching a basin could be useful to decision-makers as additional information for flood warnings. One approach to answering such a problem is to use real-time data on the soil moisture conditions in a catchment in conjunction with estimates of the extremity of the future rainfall and experience with the basin's behaviour during historical floods. A scenario-based method is proposed for such a future flood risk estimation, based on an a priori evaluation of the extremity of hypothetical floods generated by combinations of synthetic extreme precipitation and previously observed antecedent pre-flood basin saturations. The Hron river basin, located in central Slovakia, was chosen as the pilot basin in the case study. A time series of the basin's average daily precipitation was derived using spatial interpolation techniques. A lumped HBV-type daily conceptual rainfall-runoff model was adopted for modelling runoff. Analysis of the relationship of the modelled historical pre-flood soil moisture and flood causing-precipitation revealed the independence of both quantities for rainfall durations lasting 1 to 5 days. The basin's average annual maximum 1 to 5 day precipitation depths were analysed statistically and synthetic extreme precipitation scenarios associated with rainfall depths with return periods of 5, 20, 50 and 100 years, durations of 1 to 5 days and temporal distribution of extreme rainfall observed in the past were set up for runoff simulation. Using event-based flood simulations, synthetic flood waves were generated for random combinations of the rainfall scenarios and historical pre-flood soil moisture conditions. The effect of any antecedent basin saturation on the extremity of floods was quantified empirically and critical values of the basin saturation leading to floods with a higher return period than the return period of precipitation were identified. A method for implementing such critical values into flood risk warnings in a hydrological forecasting and warning system in the basin was suggested.

Identification of rainfall–runoff models for operational applications / Identification de modèles pluie–débit pour des applications opérationnelles

The problem of selecting appropriate objective functions for the identification of a lumped conceptual rainfall–runoff model is investigated, focusing on the value of the model in an operational setting. A probability-distributed soil moisture model is coupled with a linear parallel routing scheme, and conditioned on rainfall–runoff observations from three catchments in the southeast of England. Using an abstraction control problem, which requires accurate simulation of the intermediate flow range, it is shown that using the traditional RMSE fit criterion, produces operationally sub-optimal predictions. This is true in the identification period, when applied to a testing period, and to proxy catchment data. Using a second case study of the Leaf River in Mississippi (USA), where the focus changes to predicting flood peaks over a specified threshold, also suggests that the relevant flood threshold should govern the objective function choice. It is concluded that, due to limitations in the structure of the employed model, it would be counter-productive to try to achieve a good all-round representation of the rainfall–runoff processes, and that a more empirical approach to identification may be preferred for specific forecasting problems. This leaves us with the question of how far hydrological realism should be sacrificed in favour of purpose-driven objective functions.

Using SSURGO data to improve Sacramento Model a priori parameter estimates

As it transitions to smaller scale, distributed hydrologic modeling approaches, the National Weather Service (NWS) is improving methods of estimating parameters for the Sacramento Soil Moisture Accounting model (SAC-SMA). This is the major hydrologic model used for flood forecasting at most of the 13 river forecasting centers throughout the United States. A physically based approach based on the nationally available State Soil Geographic Database (STATSGO) has been developed (Koren, V.I., Smith, M., Wang, D., Zhang, Z., 2000. Use of soil property data in the derivation of conceptual rainfall–runoff model parameters. Proceedings of the 15th Conference on Hydrology, AMS, Long Beach, CA, pp. 103–106; Koren, V., Smith, M., Duan, Q., 2003. Use of a priori parameter estimates in the derivation of spatially consistent parameter sets of rainfall–runoff models. In: Duan, Q., Sorooshian, S., Gupta, H., Rosseau, H., Turcotte, H. (Eds.), Calibration of Watershed Models, Water Science and Applications 6, AGU, pp. 239–254), leading to objective, spatially consistent parameter estimates. This paper shows that a better representation of basin physical properties and potential improvements in hydrologic simulation performance can be obtained by basing parameter estimates on a finer-scale database of soils data, the Soil Survey Geographic Database (SSURGO), combined with high-resolution land use/land cover data. Results also suggest that an intermediate level of improvement may be obtained by combining detailed land cover data with STATSGO to refine current parameter estimates. This latter finding is significant because the SSURGO data are not yet available for the entire country.

Enhancing gradient-based parameter estimation with an evolutionary approach

Traditionally, the calibration of groundwater models has depended on gradient-based local optimization methods. These methods provide a reasonable degree of success only when the objective function is smooth, second-order differentiable, and satisfies the Lipschitz's condition. For complicated and highly nonlinear objective functions it is almost impractical to satisfy these conditions simultaneously. Research in the calibration of conceptual rainfall-runoff models, has shown that global optimization methods are more successful in locating the global optimum in the region of multiple local optima. In this study, a global optimization technique, known as shuffle complex evolution (SCE), is coupled to the gradient-based Lavenberg–Marquardt algorithm (GBLM). The resultant hybrid global optimization algorithm (SCEGB) is then deployed in parallel testing with SCE and GBLM to solve several inverse problems where parameters of a nonlinear numerical groundwater flow model are estimated. Using perfect (i.e. noise-free) observation data, it is shown SCEGB and SCE are successful at identifying the global optimum and predicting all model parameters; whereas, the commonly applied GBLM fails to identify the optimum. In subsequent inverse simulations using observation data corrupted with noise, SCEGB and SCE again outperform GBLM by consistently producing more accurate parameter estimates. Finally, in all simulations the hybrid SCEGB is seen to be equally effective as SCE but computationally more efficient.

Integration of artificial neural networks with conceptual models in rainfall-runoff modeling

A hybrid form of rainfall-runoff models that integrates artificial neural networks (ANNs) with conceptual models is proposed in this study. Based on this integrated approach, the spatial variation of rainfall, the heterogeneity of watershed characteristics and their impacts on runoff can be investigated by the development of a semi-distributed form of conceptual rainfall-runoff models. As a result, in each subcatchment, the runoff generation and water budget among different runoff components including surface runoff and groundwater can be simulated with consideration of the spatially distributed model parameters and rainfall inputs. In the runoff routing, instead of a linear superposition of the routed runoff from all subcatchments in the formation of total runoff output at the entire watershed outlet as traditionally performed in a semi-distributed form of conceptual models, artificial neural networks as effective tools in nonlinear mapping are employed to explore nonlinear transformations of the runoff generated from the individual subcatchments into the total runoff at the entire watershed outlet. The feasibility of this new approach has been demonstrated in the study based on the selection of three different types of conceptual rainfall-runoff models. The verification results from the three conceptual models indicate that the approach of integrating artificial neural networks with conceptual models presented in this paper shows promise in rainfall-runoff modeling.

A comparison of regionalisation methods for catchment model parameters

Abstract. In this study we examine the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. We calibrate 11 parameters of a semi-distributed conceptual rainfall-runoff model to daily runoff and snow cover data of 320 Austrian catchments in the period 1987-1997 and verify the model for the period 1976-1986. We evaluate the predictive accuracy of the regionalisation methods by jack-knife cross-validation against daily runoff and snow cover data. The results indicate that two methods perform best. The first is a kriging approach where the model parameters are regionalised independently from each other based on their spatial correlation. The second is a similarity approach where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its physiographic attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils and geology). For the calibration period, the median Nash-Sutcliffe model efficiency ME of daily runoff is 0.67 for both methods as compared to ME=0.72 for the at-site simulations. For the verification period, the corresponding efficiencies are 0.62 and 0.66. All regionalisation methods perform similar in terms of simulating snow cover.

Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure

Genetic Algorithm (GA) is globally oriented in searching and thus useful in optimizing multiobjective problems, especially where the objective functions are ill-defined. Conceptual rainfall–runoff models that aim at predicting streamflow from the knowledge of precipitation over a catchment have become a basic tool for flood forecasting. The parameter calibration of a conceptual model usually involves the multiple criteria for judging the performances of observed data. However, it is often difficult to derive all objective functions for the parameter calibration problem of a conceptual model. Thus, a new method to the multiple criteria parameter calibration problem, which combines GA with TOPSIS (technique for order performance by similarity to ideal solution) for Xinanjiang model, is presented. This study is an immediate further development of authors' previous research (Cheng, C.T., Ou, C.P., Chau, K.W., 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. Journal of Hydrology, 268, 72–86), whose obvious disadvantages are to split the whole procedure into two parts and to become difficult to integrally grasp the best behaviors of model during the calibration procedure. The current method integrates the two parts of Xinanjiang rainfall–runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrated the intrinsic phenomenon of observed data in integrity. Comparison of results with two-step procedure shows that the current methodology gives similar results to the previous method, is also feasible and robust, but simpler and easier to apply in practice.

Regional Analysis of Conceptual Rainfall Runoff Models for Runoff Simulation In Ungauged Catchments: The Case Of Upper Ewaso Ngiro Drainage Basin in Kenya

Two rainfall-runoff models were calibrated and regionalized using data from upper Ewaso Ng'iro drainage basin in Kenya. The two models were Nash Cascade-Diskin Infiltration (NCDI) model and Nash Cascade-Green Ampt Infiltration (NCGAI) model. Both models have physical and conceptual parameters. The former were derived from catchment characteristics by overlaying land cover and soil types within the framework of GIS. The latter were optimized using Schuffled Complex Evolution (SCE-UA) optimization algorithm. The conceptual parameters so determined were correlated with catchment physical parameters in a regionalisation process. This involved, in the case of suction at the wetting front and hydraulic conductivity, deriving the parameter values from soil texture. The remaining conceptual parameters were regionalised through stepwise optimization and adjustment of boundary conditions. The parameters realised were correlated with catchment characteristics through regression analysis in which values of R2 ranging from 0.92 to 0.99 were obtained. The regionalised models were then used to simulate runoff from the five catchments upon which satisfactory results were realised.

Managing releases from small upland reservoirs for downstream recharge in semi-arid basins (Northeast of Tunisia)

This paper describes the methodology used to estimate the efficiency of a small upland reservoir that is aimed at artificial groundwater recharge. A conceptual rainfall–runoff model was used to estimate the surface runoff of the ungauged catchment associated with the reservoir. Then, the water balance of the reservoir was computed. The model was calibrated and tested by comparing the simulated water volume of the reservoir to the observed one. The simulated reservoir water balance indicated high losses by evaporation. Another conceptual model was used to estimate the recharge rate during artificial recharge operations. These operations consisted of releasing water from the reservoir to the natural downstream bed of the wadi channel. The model was calibrated by comparing the simulated streamflow and the simulated water table level to the observed ones. The water balance of the artificial recharge operations indicated a high recharge rate. Simulations of artificial recharge were carried out to analyze the effect of the small upland reservoir on the water table. The simulation of an additional water release of 51% of the volume actually released over a decade indicated an additional rise of the water table of about 7.5 m.

Selection of conceptual models for regionalisation of the rainfall-runoff relationship

This study attempts to identify relationships between suitable conceptual rainfall-runoff model structures and catchment types, and demonstrates an objective procedure for selection of model structures for use in model regionalisation studies. 12 conceptual model structures have been tested on 28 UK catchments. The model structures are combinations of three soil moisture accounting modules (the catchment wetness index model structure, a modified Penman model structure, and a probability distributed soil moisture model), and four routing models (two linear reservoirs in parallel, an adaptation that represents macropore flow, three linear reservoirs in parallel, and a model that includes leakage from the groundwater store). The 28 catchments were selected to cover a range of hydrological types based on known catchment characteristics (designed combinations of catchment size, baseflow index, and annual rainfall are used). The important points in objectively selecting model structures for regionalisation are proposed as: performance in calibration and validation, trade-off between high and low flow performance, and model complexity (here represented as the number of parameters). Although, the results in this study provide no evidence of relationships between catchment type and preferred model structure, the study indicates that out of the 12 trial model structures, the following four may be the most suitable for regionalisation across UK catchments: the modified Penman model with two parallel linear routing reservoirs, and the probability distributed soil moisture model with two parallel linear routing reservoirs, three parallel linear routing reservoirs, or the macropore adaptation.

Parallelisation of a distributed hydrologic model

In the solution of streamflow problems, fully distributed hydrologic models (DHMs), which are based directly on governing equations, offer distinct advantages over conceptual rainfall-runoff models, which are derived from empirical observations. However, the primary problem associated with DHMs is that they consume more computational resources than other models, and as a result, they have not been as popular as their capabilities would imply. A parallel DHM software system for solving streamflow prediction problems has been implemented and analysed, and an investigation has been conducted of: the efficiency and scalability of the algorithm; load balancing among processors; interprocessor communication; disk performance. The load balancing algorithms show great promise for the kind of problem addressed. The software exhibits substantial parallel speedup, but the degree of speedup is limited by I/O costs.

Development and Application of a Rainfall-runoff Model:-A Proposal of Storage Channel Concept-

This study presents an effort to develop a new conceptual rainfall-runoff model based on a proposal of storage channel concept. The model implicitly solves the flow equations and was successfully calibrated and validated by applying it to a lowland catchment in the central province of Vietnam for comparison with the observed data. Moreover, the applicability of the model was also tested by comparing its results to those computed by a well-known model in Japan, the ordinal Tank model developed by Sugawara. From both the performance statistics and the hydrographs of the model results, it has been found that the model integrating a proper discharge control device provides satisfactory estimation of runoff discharge. The full model structure has totally twelve parameters to be calibrated. Sensitivity analysis for these parameters shows that they contribute effects to both the magnitude and the shape of the runoff hydrographs. Discharge control devices installed at the channel's outlets have a high advantage when considering the effects of time-varying boundary conditions at the channel′s outlets, in contrast some traditional lumped rainfall-runoff models using fixed discharge coefficients may not. The use of water control devices has made the model possible to consider the effect of tidal regime in lowland catchments while also play an important part in the model toward the shape, peak and timing of the runoff hydrographs as well as express the nonlinearity of the rainfall-runoff process. In short, the newly developed model has several good advantages over lumped hydrological models. Furthermore, the new model is basically able to analyze both runoff and flow movement in river basins that include mountainous, hilly and lowland tidal areas.

Avaliação de um Método de Assimilação de Erros nas Previsões de Vazões de Curto Prazo Afluentes ao Reservatório da UHE Camargos

ABSTRACT Hydrological forecasting is an important tool in the planning and operational control of water resources systems. The purpose of data assimilation or updating procedures, as applied to short-term streamflow forecasting is to improve accuracy and provide more reliable results. In this paper, streamflow forecasting is accomplished by means of a conceptual rainfall-runoff model, named Rio Grande, which has been applied to simulate the inflows of the Rio Grande river basin to the reservoir of the Camargos hydropower plant, in the Brazilian state of Minas Gerais. This model consists of three main modules: the Production Module, which is responsible for the runoff formation over the sub-basin area, the Concentration Module, which is responsible for the flow concentration at the outlet of the sub-basin, and the Propagation Module, which is responsible for flow propagation through the river channel. A stochastic model for the simulation of errors in each sub-basin is proposed, in order to update the short-term forecasts of inflows to the Camargos reservoir. Data assimilation is based on the correction of simulated discharges by adding to them the forecasts of the stochastic model errors. The stochastic models considered here are of the p-th order autoregressive AR(p)-type and/or of the p-th and q-th order autoregressive and moving average ARMA(p,q)-type. The analysis of the results shows that the application of that procedure to the upstream control stations closest to the reservoir may considerably improve short-term inflow forecasting. Key-words: Hydrological forecasting; reservoir.

Dual state–parameter estimation of hydrological models using ensemble Kalman filter

Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model.

Estimating groundwater recharge using the SMAR conceptual model calibrated by genetic algorithm

Proper groundwater management of a contaminated aquifer requires the accurate estimation of groundwater recharge, which carries the contaminated load. There are many direct and indirect methods and sophisticated models for estimating recharge. However, most of them require many field data or model parameters, which limit their actual field application. To overcome this limitation, the Soil Moisture Accounting and Routing (SMAR), a conceptual rainfall-runoff model, is employed. The SMAR model has the potential for estimating recharge using only rainfall, evaporation and groundwater level data. However, for an aquifer having prominent horizontal groundwater flow, this model cannot be used directly. For this reason a horizontal flow component is added to this model. Model parameters are calibrated by the Genetic Algorithm (GA) optimization technique. Sensitivity of calibrated parameters to model efficiency and estimated recharge, and parameter interdependence are investigated. This model is applied to 11 observation locations in four catchment areas of Miyakojima Island, Japan, where groundwater nitrate contamination is a threat. The effectiveness of the model is evaluated using the model efficiency ( R 2), the mean of the sum of square errors (MSE), plots of observed versus estimated groundwater levels, scatter plots of observed versus estimated groundwater levels, measure of timing of the peaks, and the correlation between monthly rainfall and monthly estimated recharge. All show that this technique is very efficient for estimation of recharge. Model efficiency ( R 2) up to 92%, minimum MSE 0.32 m 2/day, average relative error of timing of the peaks 4.13%, and coefficient of determination ( r 2) up to 0.92 are obtained for the study area. The estimated recharge is 45% of the mean annual rainfall and agrees with other finding. It is thus concluded that the SMAR model could be a viable alternative since it can estimate dependable recharge with a minimum of input data.

Modelling stream flow for use in ecological studies in a large, arid zone river, central Australia

AbstractAustralian arid zone ephemeral rivers are typically unregulated and maintain a high level of biodiversity and ecological health. Understanding the ecosystem functions of these rivers requires an understanding of their hydrology. These rivers are typified by highly variable hydrological regimes and a paucity, often a complete absence, of hydrological data to describe these flow regimes. A daily time‐step, grid‐based, conceptual rainfall–runoff model was developed for the previously uninstrumented Neales River in the arid zone of northern South Australia. Hourly, logged stage data provided a record of stream‐flow events in the river system. In conjunction with opportunistic gaugings of stream‐flow events, these data were used in the calibration of the model. The poorly constrained spatial variability of rainfall distribution and catchment characteristics (e.g. storage depths) limited the accuracy of the model in replicating the absolute magnitudes and volumes of stream‐flow events. In particular, small but ecologically important flow events were poorly modelled. Model performance was improved by the application of catchment‐wide processes replicating quick runoff from high intensity rainfall and improving the area inundated versus discharge relationship in the channel sections of the model. Representing areas of high and low soil moisture storage depths in the hillslope areas of the catchment also improved the model performance. The need for some explicit representation of the spatial variability of catchment characteristics (e.g. channel/floodplain, low storage hillslope and high storage hillslope) to effectively model the range of stream‐flow events makes the development of relatively complex rainfall–runoff models necessary for multisite ecological studies in large, ungauged arid zone catchments. Grid‐based conceptual models provide a good balance between providing the capacity to easily define land types with differing rainfall–runoff responses, flexibility in defining data output points and a parsimonious water‐balance–routing model. Copyright © 2004 John Wiley & Sons, Ltd.

Evaluation of two hybrid metric-conceptual models for simulating phosphorus transfer from agricultural land in the river enborne, a lowland UK catchment

Across Europe, elevated phosphorus (P) concentrations in lowland rivers have made them particularly susceptible to eutrophication. This is compounded in southern and central UK by increasing pressures on water resources, which may be further enhanced by the potential effects of climate change. The EU Water Framework Directive requires an integrated approach to water resources management at the catchment scale and highlights the need for modelling tools that can distinguish relative contributions from multiple nutrient sources and are consistent with the information content of the available data. Two such models are introduced and evaluated within a stochastic framework using daily flow and total phosphorus concentrations recorded in a clay catchment typical of many areas of the lowland UK. Both models disaggregate empirical annual load estimates, derived from land use data, as a function of surface/near surface runoff, generated using a simple conceptual rainfall–runoff model. Estimates of the daily load from agricultural land, together with those from baseflow and point sources, feed into an in-stream routing algorithm. The first model assumes constant concentrations in runoff via surface/near surface pathways and incorporates an additional P store in the river-bed sediments, depleted above a critical discharge, to explicitly simulate resuspension. The second model, which is simpler, simulates P concentrations as a function of surface/near surface runoff, thus emphasising the influence of non-point source loads during flow peaks and mixing of baseflow and point sources during low flows. The temporal consistency of parameter estimates and thus the suitability of each approach is assessed dynamically following a new approach based on Monte-Carlo analysis.

Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model

A conceptual rainfall-runoff model was applied to 95 catchments in the Rhine basin for the purpose of modeling of the effect of land use change on the runoff. An approach to calibrate the model by associating the model parameters with the physical catchment characteristics was implemented. Land use, soil type, catchment size, and topographic structure were used as the bases for regionalization of the model parameters. Parameter values were initially associated with these catchment characteristics and were finally transferred into their catchment scale values using a transfer function, whose form was assumed a priori. A simultaneous model calibration was performed for a number of catchments with contrasting catchment characteristics. An optimization algorithm was used for the calibration with an objective function defined as the normalized sum of the squared differences between the modeled and the observed runoff. The regionalized model thus obtained was then used to model the resulting runoff for different land use scenarios generated in the model area. The results obtained suggest that increased urbanization leads to an increase in the lower peak runoff resulting from summer storm, while the increase in the higher peaks resulting from winter rainfall is very little. On the other hand, a considerable reduction of both the peak runoff and the total runoff volume resulted from intensified afforestation.