Modelling the rainfall–runoff process of the Mara River basin using the Soil and Water Assessment Tool

The delicate balance between human utilization and sustaining its pristine biodiversity in the Mara River basin (MRB) is being threatened because of the expansion of agriculture, deforestation, human settlement, erosion and sedimentation and extreme flow events. This study assessed the applicability of the Soil and Water Assessment Tool (SWAT) model for long‐term rainfall–runoff simulation in MRB. The possibilities of combining/extending gage rainfall data with satellite rainfall estimates were investigated. Monthly satellite rainfall estimates not only overestimated but also lacked the variability of observed rainfall to substitute gage rainfall in model simulation. Uncertainties related to the quality and availability of input data were addressed. Sensitivity and uncertainty analysis was reported for alternative model components and hydrologic parameters used in SWAT. Mean sensitivity indices of SWAT parameters in MRB varied with and without observed discharge data. The manual assessment of individual parameters indicated heterogeneous response among sub‐basins of MRB. SWAT was calibrated and validated with 10 years of discharge data at Bomet (Nyangores River), Mulot (Amala River) and Mara Mines (Mara River) stations. Model performance varied from satisfactory at Mara Mines to fair at Bomet and weak at Mulot. The (Nash–Sutcliff efficiency, coefficient of determination) results of calibration and validation at Mara Mines were (0.68, 0.69) and (0.43, 0.44), respectively. Two years of moving time window and flow frequency analysis showed that SWAT performance in MRB heavily relied on quality and abundance of discharge data. Given the 5.5% area contribution of Amala sub‐basin as well as uncertainty and scarcity of input data, SWAT has the potential to simulate the rainfall runoff process in the MRB. Copyright © 2012 John Wiley & Sons, Ltd.     

Modelling streamflow trends for a watershed with limited data: case of the Litani basin,Lebanon

Abstract

Streamflow variability in the Upper and Lower Litani basin, Lebanon was modelled as there is a lack of long-term measured runoff data. To simulate runoff and streamflow, daily rainfall was derived using a stochastic rainfall generation model and monthly rainfall data. Two distinct synthetic rainfall models were developed based on a two-part probabilistic distribution approach. The rainfall occurrence was described by a Markov chain process, while the rainfall distribution on wet days was represented by two different distributions (i.e. gamma and mixed exponential distributions). Both distributions yielded similar results. The rainfall data were then processed using water balance and routing models to generate daily and monthly streamflow. Compared with measured data, the model results were generally reasonable (mean errors ranging from 0.1 to 0.8?m³/s at select locations). Finally, the simulated monthly streamflow data were used to investigate discharge trends in the Litani basin during the 20th century using the Mann-Kendall and Sen slope nonparametric trend detection methods. A significant drying trend of the basin was detected, reaching a streamflow reduction of 0.8 and 0.7 m³/s per decade in January for the Upper and Lower basin, respectively.

Editor D. Koutsoyiannis; Associate editor Sheng Yue

Citation Ramadan, H.H., Beighley, R.E., and Ramamurthy, A.S., 2012. Modelling streamflow trends for a watershed with limited data: case of the Litani basin, Lebanon. Hydrological Sciences Journal, 57 (8), 1516–1529.     

Lumped surface and sub‐surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

Developing models to predict on‐site soil erosion and off‐site sediment transport at the agricultural watershed scale represent an on‐going challenge in research today. This study attempts to simulate the daily discharge and sediment loss using a distributed model that combines surface and sub‐surface runoffs in a small hilly watershed (< 1 km²). The semi‐quantitative model, Predict and Localize Erosion and Runoff (PLER), integrates the Manning–Strickler equation to simulate runoff and the Griffith University Erosion System Template equation to simulate soil detachment, sediment storage and soil loss based on a map resolution of 30 m × 30 m and over a daily time interval. By using a basic input data set and only two calibration coefficients based, respectively, on water velocity and soil detachment, the PLER model is easily applicable to different agricultural scenarios. The results indicate appropriate model performance and a high correlation between measured and predicted data with both Nash–Sutcliffe efficiency (Ef) and correlation coefficient (r²) having values > 0.9. With the simple input data needs, PLER model is a useful tool for daily runoff and soil erosion modeling in small hilly watersheds in humid tropical areas. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimation of extreme floods of the River Meuse using a stochastic weather generator and a rainfall–runoff model / Estimation des crues extrêmes de la Meuse à l'aide d'un générateur stochastique de variables météorologiques et d'un modèle pluie–débit

Abstract

A stochastic weather generator has been developed to simulate long daily sequences of areal rainfall and station temperature for the Belgian and French sub-basins of the River Meuse. The weather generator is based on the principle of nearest-neighbour resampling. In this method rainfall and temperature data are sampled simultaneously from multiple historical records with replacement such that the temporal and spatial correlations are well preserved. Particular emphasis is given to the use of a small number of long station records in the resampling algorithm. The distribution of the 10-day winter maxima of basin-average rainfall is quite well reproduced. The generated sequences were used as input for hydrological simulations with the semi-distributed HBV rainfall–runoff model. Though this model is capable of reproducing the flood peaks of December 1993 and January 1995, it tends to underestimate the less extreme daily peak discharges. This underestimation does not show up in the 10-day average discharges. The hydrological simulations with the generated daily rainfall and temperature data reproduce the distribution of the winter maxima of the 10-day average discharges well. Resampling based on long station records leads to lower rainfall and discharge extremes than resampling from the data over a shorter period for which areal rainfall was available.     

Multiple suppression by 2D filtering in the parabolic τ–p domain: a wave-equation-based method1

The filter for wave-equation-based water-layer multiple suppression, developed by the authors in the x-t, the linear τ-p, and the f-k domains, is extended to the parabolic τ-2 domain. The multiple reject areas are determined automatically by comparing the energy on traces of the multiple model (which are generated by a wave-extrapolation method from the original data) and the original input data (multiples + primaries) in τ-p space. The advantage of applying the data-adaptive 2D demultiple filter in the parabolic τ-p domain is that the waves are well separated in this domain. The numerical examples demonstrate the effectiveness of such a dereverberation procedure. Filtering of multiples in the parabolic τ-p domain works on both the far-offset and the near-offset traces, while the filtering of multiples in the f-k domain is effective only for the far-offset traces. Tests on a synthetic common-shot-point (CSP) gather show that the demultiple filter is relatively immune to slight errors in the water velocity and water depth which cause arrival time errors of the multiples in the multiple model traces of less than the time dimension (about one quarter of the wavelet length) of the energy summation window of the filter. The multiples in the predicted multiple model traces do not have to be exact replicas of the multiples in the input data, in both a wavelet-shape and traveltime sense. The demultiple filter also works reasonably well for input data contaminated by up to 25% of random noise. A shallow water CSP seismic gather, acquired on the North West Shelf of Australia, demonstrates the effectiveness of the technique on real data.     

Sensitivity of hydrological models to uncertainty in rainfall input

Abstract

Different approaches used in hydrological modelling are compared in terms of the way each one takes the rainfall data into account. We examine the errors associated with accounting for rainfall variability, whether in hydrological modelling (distributed vs lumped models) or in computing catchment rainfall, as well as the impact of each approach on the representativeness of the parameters it uses. The database consists of 1859 rainfall events, distributed on 500 basins, located in the southeast of France with areas ranging from 6.2 to 2851 km². The study uses as reference the hydrographs computed by a distributed hydrological model from radar rainfall. This allows us to compare and to test the effects of various simplifications to the process when taking rainfall information (complete rain field vs sampled rainfall) and rainfall–runoff modelling (lumped vs distributed) into account. The results appear to show that, in general, the sampling effect can lead to errors in discharge at the outlet that are as great as, or even greater than, those one would get with a fully lumped approach. We found that small catchments are more sensitive to the uncertainties in catchment rainfall input generated by sampling rainfall data as seen through a raingauge network. Conversely, the larger catchments are more sensitive to uncertainties generated when the spatial variability of rainfall events is not taken into account. These uncertainties can be compensated for relatively easily by recalibrating the parameters of the hydrological model, although such recalibrations cause the parameter in question to completely lose physical meaning.

Citation Arnaud, P., Lavabre, J., Fouchier, C., Diss, S. & Javelle, P. (2011) Sensitivity of hydrological models to uncertainty of rainfall input. Hydrol. Sci. J. 56(3), 397–410.     

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.     

Estimation of discharges of water flows and debris floods in a small watershed

Floods in small mountainous watersheds cover a wide spectrum of flow. They can range from clear water flows and hyperconcentrated flows to debris floods and debris flows, and calculation of the peak discharge is crucial for predicting and mitigating such hazards. To determine the optimal approach for discharge estimation, this study compared water flow monitoring hydrographs to investigate the performance of five hydrological models that incorporate different runoff yields and influx calculation methods. Two of the models performed well in simulating the peak discharge, peak time, and total flow volume of the water flood. The ratio (γ) of the monitored debris flood discharge (Q_d) to the simulated water flow discharge (Q_w) was investigated. Qualitatively, γ initially increased with Q_w but then decreased when Q_w exceeded a certain threshold, which corresponded to rainfall of 95 and 120 mm in a 6- and 24-h event with a normal distribution of precipitation, respectively. The decrease might be attributable to a threshold of sediment availability being reached, beyond which increased flow rate is not matched by increased sediment input in the large watershed. Uncertainty of hydrological calculation was evaluated by dividing the catchment into sub-basins and adopting different rainfall time steps as input. The efficiency of using a distributed simulation exhibited marginal improvement potential compared with a lumped simulation. Conversely, the rainfall time step input significantly affected the simulation results by delaying the peak time and decreasing the peak discharge. This research demonstrates the applicability of a discharge estimation method that combines a hydrological water flow simulation and an estimation of γ. The results were verified on the basis of monitored flow densities and videos obtained in two watersheds with areas of 2.34 and 32.4 km².     

Application and evaluation of a snowmelt runoff model in the Tamor River basin,Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree‐day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash–Sutcliffe metric ~0.84, annual volume bias < 3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002–2006 period is estimated to be 29.7 ± 2.9% (which includes 4.2 ± 0.9% from snowfall that promptly melts), whereas 70.3 ± 2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000–5500 m range contributes the most to basin runoff, averaging 56.9 ± 3.6% of all snowmelt input and 28.9 ± 1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree‐day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds. Copyright © 2013 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.     

Distributed hydrological modelling of a heavily glaciated Alpine river basin

Abstract

A glacier submodel was successfully integrated into the distributed hydrological model WaSiM-ETH to simulate the discharge of a heavily glaciated drainage basin. The glacier submodel comprises a distributed temperature index model including solar radiation to simulate the melt rate of glaciated areas. Meltwater and rainfall are transformed into glacier discharge by using a linear reservoir approach. The model was tested on a high-alpine sub-basin of the Rhone basin (central Switzerland) of which 48% is glaciated. Continuous discharge simulations were performed for the period 1990–1996 and compared with hourly discharge observations. The pronounced daily and annual fluctuations in discharge were simulated well. The obtained efficiency criterion, R², exceeds 0.89 for all years. The good performance of the glacier submodel is also demonstrated by integrating it into the hydrological model PREVAH.     

Development and parameterization of an infiltration model accounting for water depth and rainfall intensity

Experimental work has clearly shown that the effective hydraulic conductivity (K_e) or effective infiltration rate (f_e) on the local scale of a plot cannot be considered as constant but are dependent on water depth and rainfall intensity because non‐random microtopography‐related variations in hydraulic conductivity occur. Rainfall–runoff models generally do not account for this: models assume that excess water is uniformly spread over the soil surface and within‐plot variations are neglected. In the present study, we propose a model that is based on the concepts of microtopography‐related water depth‐dependent infiltration and partial contributing area. Expressions for the plot scale K_e and f_e were developed that depend on rainfall intensity and runon from upslope (and thus on water depth). To calibrate and validate the model, steady state infiltration experiments were conducted on maize fields on silt loam soils in Belgium, with different stages and combinations of rainfall intensity and inflow, simulating rainfall and runon. Water depth–discharge and depth–inundation relationships were established and used to estimate the effect of inundation on K_e. Although inflow‐only experiments were found to be unsuitable for calibration, the model was successfully calibrated and validated with the rainfall simulation data and combined rainfall–runon data (R²: 0.43–0.91). Calibrated and validated with steady state infiltration experiments, the model was combined with the Green–Ampt infiltration equation and can be applied within a two‐dimensional distributed rainfall–runoff model. The effect of water depth–dependency and rainfall intensity on infiltration was illustrated for a hillslope. Copyright © 2012 John Wiley & Sons, Ltd.     

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 uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation

This work proposes a risk analysis model to evaluate the risk of underestimating the predicted peak discharge, i.e. the exceedance of probability due to the uncertainties in rainfall information (rainfall depth, duration, and storm pattern) and the parameters of the rainfall-runoff model (Sacramento Soil Moisture Accounting model, SAC-SMA) during the flooding prevention and warning operation. The proposed risk analysis model is combined with the multivariate Monte Carlo simulation method and the Advance First-Order Second-Moment method (AFOSM). The observed rainfall and discharge measured at Yu-feng Basin study area in Shihmen reservoir watershed is used in the model development and application. The results of the model application indicate that the proposed risk analysis model can analyze the sensitivity of the uncertainty factors for the predicted peak discharge and evaluates the variation of the probability of exceeding the predicted peak discharge with respect to the rainfall depth and storm duration. In addition, the result of risk analysis for a real rainstorm event, Typhoon Morakot, shows that the proposed model successfully explores the risk of underestimating the predicted peak discharge using SAC-SMA and forecasted rainfall information and provides a probabilistic forecast of the peak discharge.     

The effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment

Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution and errors. When using these rainfall datasets as input for hydrological models, their errors and uncertainties propagate through the hydrological system. The aim of this study is to investigate the effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment, the 6.5‐km² Hupsel Brook experimental catchment. We used five distinct rainfall data sources: two automatic raingauges (one in the catchment and another one 30 km away), operational (real‐time and unadjusted) and gauge‐adjusted ground‐based C‐band weather radar datasets and finally a novel source of rainfall information for hydrological purposes, namely, microwave link data from a cellular telecommunication network. We used these data as input for the, a recently developed rainfall‐runoff model for lowland catchments, and intercompared the five simulated discharges time series and groundwater time series for a heavy rainfall event and a full year. Three types of rainfall errors were found to play an important role in the hydrological simulations, namely: (1) Biases, found in the unadjusted radar dataset, are amplified when propagated through the hydrological system; (2) Timing errors, found in the nearest automatic raingauge outside the catchment, are attenuated when propagated through the hydrological system; (3) Seasonally varying errors, found in the microwave link data, affect the dynamics of the simulated catchment water balance. We conclude that the hydrological potential of novel rainfall observation techniques should be assessed over a long period, preferably a full year or longer, rather than on an event basis, as is often done. Copyright © 2016 The Authors. Hydrological Processes. Published by John Wiley & Sons Ltd.     

Coupled infiltration and filtration behaviours of concrete porous pavement for stormwater management

Cementitious porous pavement (CPP) is a structural low‐impact development material for rainfall–runoff management. Both infiltration and filtration are critical functions for CPP stormwater quality and quantity control. In this study, three groups of CPP specimens exposed to rainfall–runoff for 4 years and experienced with different maintenance intervals (6, 12 and 48 months, respectively) were used to examine CPP infiltration and filtration performance. Particle mass strained on CPP surface, saturated infiltration rate I_f, temporal infiltration rate I(t), suspended sediment concentration (SSC) and turbidity (τ) were measured to evaluate the process of filtration/infiltration. I(t), SSC and τ were examined less than 50 mg/l of the suspended particle loading. It was found that the CPP surface cleaning methods used in the past 4 years, namely, high pressure wash followed by vacuuming with one atmosphere (100 kPa), were effective, and a 12‐month maintenance interval was verified suitable to maintain the pore structure an acceptable infiltration rate for stormwater management. It was also found that CPP infiltration and filtration process affect each other, and the two properties are coupled in urban stormwater quality and quantity control. On the basis of the experimental measurements, the temporal infiltration rate of the cleaned CPP under a certain particle loading could be simulated by a first‐order nonlinear rational model, and effluent turbidity–SSC relationship was found following a power law. Copyright © 2012 John Wiley & Sons, Ltd.     

Nonlinear bacterial load‐streamflow response on a marine beach

Bacterial concentration (Escherichia coli) is used as the key indicator for marine beach water quality in Hong Kong. For beaches receiving streamflow from unsewered catchments, water quality is mainly affected by local nonpoint source pollution and is highly dependent on the bacterial load contributed from the catchment. As most of these catchments are ungauged, the bacterial load is generally unknown. In this study, streamflow and the associated bacterial load contributed from an unsewered catchment to a marine beach, Big Wave Bay, are simulated using a modelling approach. The physically based distributed hydrological model, MIKE‐SHE, and the empirical watershed water quality model (Hydrological Simulation Program – Fortran) are used to simulate streamflow and daily‐averaged E. coli concentration/load, respectively. The total daily derived loads predicted by the model during calibration (June–July 2007) and validation (July–October 2008) periods agree well with empirical validation data, with a percentage difference of 3 and 2%, respectively. The simulation results show a nonlinear relationship between E. coli load and rainfall/streamflow and reveal a source limiting nature of nonpoint source pollution. The derived load is further used as an independent variable in a multiple linear regression (MLR) model to predict daily beach water quality. When compared with the MLR models based solely on hydrometeorological input variables (e.g. rainfall and salinity), the new model based on bacterial load predicts much more realistic E. coli concentrations during rainstorms. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessing the influence of afforestation with Eucalyptus globulus on hydrological response from a small catchment in northwestern Spain using the HBV hydrological model

Land use change as conversion pasture to forest produces several changes on hydrological cycle. In this paper, we analyse the effects on stream discharge of afforestation of a small watershed devoted to pasture using the HBV hydrological model. Streamflow data obtained over the first 10 years after planting were employed to evaluate the capacity of HBV model to simulate hydrological behaviour of catchment after afforestation. Obtained results indicate that the estimation of streamflow was accurate as reflected by statistics (R² = 0.90, NSC = 0.89 and PBIAS = 0.34). Afterwards, streamflow under pasture land use (if afforestation had not occurred) was simulated using hydrometeorological data collected during the period of study and model parameters optimized previously, together with two parameters, pcorr and cevpfo, that were adjusted for pasture conditions. The HBV model results indicate that afforestation produced a water yield reduction around 2000 mm (22% of total stream discharge) during the first 10 years of planting growth. The differences between forest and pasture land cover are increasing in all seasons year by year. The greatest streamflow reduction was observed in wet period (autumn and winter) with 76% of total reduction. In summer, streamflow reduction represents only 3% of total, however, represents 24.7% of discharge in this season. Streamflow reduction was related to increase of rainfall interception (mainly in wet periods) and the increase of evapotranspiration by plantation in dry periods. Copyright © 2013 John Wiley & Sons, Ltd.     

Coupling the Xinanjiang model to a kinematic flow model based on digital drainage networks for flood forecasting

The Xinanjiang model, which is a conceptual rainfall‐runoff model and has been successfully and widely applied in humid and semi‐humid regions in China, is coupled by the physically based kinematic wave method based on a digital drainage network. The kinematic wave Xinanjiang model (KWXAJ) uses topography and land use data to simulate runoff and overland flow routing. For the modelling, the catchment is subdivided into numerous hillslopes and consists of a raster grid of flow vectors that define the water flow directions. The Xinanjiang model simulates the runoff yield in each grid cell, and the kinematic wave approach is then applied to a ranked raster network. The grid‐based rainfall‐runoff model was applied to simulate basin‐scale water discharge from an 805‐km² catchment of the Huaihe River, China. Rainfall and discharge records were available for the years 1984, 1985, 1987, 1998 and 1999. Eight flood events were used to calibrate the model's parameters and three other flood events were used to validate the grid‐based rainfall‐runoff model. A Manning's roughness via a linear flood depth relationship was suggested in this paper for improving flood forecasting. The calibration and validation results show that this model works well. A sensitivity analysis was further performed to evaluate the variation of topography (hillslopes) and land use parameters on catchment discharge. Copyright © 2009 John Wiley & Sons, Ltd.