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1.
A hydrology–sediment modelling framework based on the model Topkapi-ETH combined with basin geomorphic mapping is used to investigate the role of localized sediment sources in a mountain river basin (Kleine Emme, Switzerland). The periodic sediment mobilization from incised areas and landslides by hillslope runoff and river discharge is simulated in addition to overland flow erosion to quantify their contributions to suspended sediment fluxes. The framework simulates the suspended sediment load provenance at the outlet and its temporal dynamics, by routing fine sediment along topographically driven pathways from the distinct sediment sources to the outlet. We show that accounting for localized sediment sources substantially improves the modelling of observed sediment concentrations and loads at the outlet compared to overland flow erosion alone. We demonstrate that the modelled river basin can shift between channel-process and hillslope-process dominant behaviour depending on the model parameter describing gully competence on landslide surfaces. The simulations in which channel processes dominate were found to be more consistent with observations, and with two independent validations in the Kleine Emme, by topographic analysis of surface roughness and by sediment tracing with 10 Be concentrations. This research shows that spatially explicit modelling can be used to infer the dominant sediment production process in a river basin, to inform and optimize sediment sampling strategies for denudation rate estimates, and in general to support sediment provenance studies. © 2020 John Wiley & Sons, Ltd.  相似文献   

2.
Climate change has significant impacts on water availability in larger river basins. The present study evaluates the possible impacts of projected future daily rainfall (2011–2099) on the hydrology of a major river basin in peninsular India, the Godavari River Basin, (GRB), under RCP4.5 and RCP8.5 scenarios. The study highlights a criteria-based approach for selecting the CMIP5 GCMs, based on their fidelity in simulating the Indian Summer Monsoon rainfall. The nonparametric kernel regression based statistical downscaling model is employed to project future daily rainfall and the variable infiltration capacity (VIC) macroscale hydrological model is used for hydrological simulations. The results indicate an increase in future rainfall without significant change in the spatial pattern of hydrological variables in the GRB. The climate-change-induced projected hydrological changes provide a crucial input to define water resource policies in the GRB. This methodology can be adopted for the climate change impacts assessment of larger river basins worldwide.  相似文献   

3.
Tropical river basins are experiencing major hydrological alterations as a result of climate variability and deforestation. These drivers of flow changes are often difficult to isolate in large basins based on either observations or experiments; however, combining these methods with numerical models can help identify the contribution of climate and deforestation to hydrological alterations. This paper presents a study carried out in the Tapaj?s River (Brazil), a 477,000 km2 basin in South‐eastern Amazonia, in which we analysed the role of annual land cover change on daily river flows. Analysis of observed spatial and temporal trends in rainfall, forest cover, and river flow metrics for 1976 to 2008 indicates a significant shortening of the wet season and reduction in river flows through most of the basin despite no significant trend in annual precipitation. Coincident with seasonal trends over the past 4 decades, over 35% of the original forest (140,000 out of 400,000 km2) was cleared. In order to determine the effects of land clearing and rainfall variability to trends in river flows, we conducted hindcast simulations with ED2 + R, a terrestrial biosphere model incorporating fine scale ecosystem heterogeneity arising from annual land‐use change and linked to a flow routing scheme. The simulations indicated basin‐wide increases in dry season flows caused by land cover transitions beginning in the early 1990s when forest cover dropped to 80% of its original extent. Simulations of historical potential vegetation in the absence of land cover transitions indicate that reduction in rainfall during the dry season (mean of ?9 mm per month) would have had an opposite and larger magnitude effect than deforestation (maximum of +4 mm/month), leading to the overall net negative trend in river flows. In light of the expected increase in future climate variability and water infrastructure development in the Amazon and other tropical basins, this study presents an approach for analysing how multiple drivers of change are altering regional hydrology and water resources management.  相似文献   

4.
Abstract

Important characteristics of an appropriate river basin model, intended to study the effect of climate change on basin response, are the spatial and temporal resolution of the model and the rainfall input. The effects of input and model resolution on extreme discharge of a large river basin are assessed to give some indication on appropriate resolutions. A simple stochastic rainfall model and a river basin model with uniform parameters and multiple rainfall input have been developed and applied to the River Meuse basin in northwestern Europe. The results show that the effect of model resolution on extreme river discharge is much greater than that of input resolution. The highest model resolution seems to be quite accurate in determining extreme discharge. Although the results should be interpreted with caution, they may give some indication of appropriate input and model resolutions for the determination of extreme discharge of a large river basin.  相似文献   

5.
A back‐propagation algorithm neural network (BPNN) was developed to synchronously simulate concentrations of total nitrogen (TN), total phosphorus (TP) and dissolved oxygen (DO) in response to agricultural non‐point source pollution (AGNPS) for any month and location in the Changle River, southeast China. Monthly river flow, water temperature, flow travel time, rainfall and upstream TN, TP and DO concentrations were selected as initial inputs of the BPNN through coupling correlation analysis and quadratic polynomial stepwise regression analysis for the outputs, i.e. downstream TN, TP and DO concentrations. The input variables and number of hidden nodes of the BPNN were then optimized using a combination of growing and pruning methods. The final structure of the BPNN was determined from simulated data based on experimental data for both the training and validation phases. The predicted values obtained using a BPNN consisting of the seven initial input variables (described above), one hidden layer with four nodes and three output variables matched well with observed values. The model indicated that decreasing upstream input concentrations during the dry season and control of NPS along the reach during average and flood seasons may be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data are available, the methodology developed here can easily be applied to other case studies. The BPNN model is an easy‐to‐use modelling tool for managers to obtain rapid preliminary identification of spatiotemporal water quality variations in response to natural and artificial modifications of an agricultural drainage river. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

6.
Climate change is one of the main drivers of river warming worldwide. However, the response of river temperature to climate change differs with the hydrology and landscape properties, making it difficult to generalize the strength and the direction, of river temperature trends across large spatial scales and various river types. Additionally, there is a lack of long‐term and large‐scale trend studies in Europe as well as globally. In this study, we investigated the long‐term (25 years; 132 sites) and the short‐term (10 years; 475 sites) river temperature trends, patterns and underlying drivers within the period 1985–2010 in seven river basins of Germany. The majority of the sites underwent significant river warming during 1985–2010 (mean warming trend: 0.03 °C year?1, SE = 0.003), with a faster warming observed during individual decades (1985–1995 and 2000–2010) within this period. Seasonal analyses showed that, while rivers warmed in all seasons, the fastest warming had occurred during summer. Among all the considered hydro‐climatological variables, air temperature change, which is a response to climate forcing, was the main driver of river temperature change because it had the strongest correlation with river temperature, irrespective of the period. Hydrological variables, such as average flow and baseflow, had a considerable influence on river temperature variability rather than on the overall trend direction. However, decreasing flow probably assisted in a faster river temperature increase in summer and in rivers in NE basins (such as the Elbe basin). The North Atlantic Oscillation Index had a greater significant influence on the winter river temperature variability than on the overall variability. Landscape and basin variables, such as altitude, ecoregion and catchment area, induced spatially variable river temperature trends via affecting the thermal sensitivity of rivers, with the rivers in large catchments and in lowland areas being most sensitive. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

7.
This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

8.
An Erratum has been published for this article in Hydrological Processes 16(5) 2002, 1130–1131. Humid tropical regions are often characterized by extreme variability of fluvial processes. The Rio Terraba drains the largest river basin, covering 4767 km2, in Costa Rica. Mean annual rainfall is 3139±419sd mm and mean annual discharge is 2168±492sd mm (1971–88). Loss of forest cover, high rainfall erosivity and geomorphologic instability all have led to considerable degradation of soil and water resources at local to basin scales. Parametric and non‐parametric statistical methods were used to estimate sediment yields. In the Terraba basin, sediment yields per unit area increase from the headwaters to the basin mouth, and the trend is generally robust towards choice of methods (parametric and LOESS) used. This is in contrast to a general view that deposition typically exceeds sediment delivery with increase in basin size. The specific sediment yield increases from 112±11·4sd t km?2 year?1 (at 317·9 km2 on a major headwater tributary) to 404±141·7sd t km?2 year?1 (at 4766·7 km2) at the basin mouth (1971–92). The analyses of relationships between sediment yields and basin parameters for the Terraba sub‐basins and for a total of 29 basins all over Costa Rica indicate a strong land use effect related to intensive agriculture besides hydro‐climatology. The best explanation for the observed pattern in the Terraba basin is a combined spatial pattern of land use and rainfall erosivity. These were integrated in a soil erosion index that is related to the observed patterns of sediment yield. Estimated sediment delivery ratios increase with basin area. Intensive agriculture in lower‐lying alluvial fans exposed to highly erosive rainfall contributes a large part of the sediment load. The higher elevation regions, although steep in slope, largely remain under forest, pasture, or tree‐crops. High rainfall erosivity (>7400 MJ mm ha?1 h?1 year ?1) is associated with land uses that provide inadequate soil protection. It is also associated with steep, unstable slopes near the basin mouth. Improvements in land use and soil management in the lower‐lying regions exposed to highly erosive rainfall are recommended, and are especially important to basins in which sediment delivery ratio increases downstream with increasing basin area. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

9.
Laurie Boithias  Yves Auda  Stéphane Audry  Jean-Pierre Bricquet  Alounsavath Chanhphengxay  Vincent Chaplot  Anneke de Rouw  Thierry Henry des Tureaux  Sylvain Huon  Jean-Louis Janeau  Keooudone Latsachack  Yann Le Troquer  Guillaume Lestrelin  Jean-Luc Maeght  Pierre Marchand  Pierre Moreau  Andrew Noble  Anne Pando-Bahuon  Kongkeo Phachomphon  Khambai Phanthavong  Alain Pierret  Olivier Ribolzi  Jean Riotte  Henri Robain  Emma Rochelle-Newall  Saysongkham Sayavong  Oloth Sengtaheuanghoung  Norbert Silvera  Nivong Sipaseuth  Bounsamay Soulileuth  Xaysatith Souliyavongsa  Phapvilay Sounyaphong  Sengkeo Tasaketh  Chanthamousone Thammahacksa  Jean-Pierre Thiebaux  Christian Valentin  Olga Vigiak  Marion Viguier  Khampaseuth Xayyathip 《水文研究》2021,35(5):e14126
Mountain regions of the humid tropics are characterized by steep slopes and heavy rains. These regions are thus prone to both high surface runoff and soil erosion. In Southeast Asia, uplands are also subject to rapid land-use change, predominantly as a result of increased population pressure and market forces. Since 1998, the Houay Pano site, located in northern Lao PDR (19.85°N 102.17°E) within the Mekong basin, aims at assessing the long-term impact of the conversion of traditional slash-and-burn cultivation systems to commercial perennial monocultures such as teak tree plantations, on the catchment hydrological response and sediment yield. The instrumented site monitors hydro-meteorological and soil loss parameters at both microplot (1 m2) and small catchment (0.6 km2) scales. The monitored catchment is part of the network of critical zone observatories named Multiscale TROPIcal CatchmentS (M-TROPICS). The data shared by M-TROPICS in Houay Pano are (1) rainfall, (2) air temperature, air relative humidity, wind speed, and global radiation, (3) catchment land use, (4) stream water level, suspended particulate matter, bed particulate matter and stones, (5) soil surface features, and (6) soil surface runoff and soil detachment. The dataset has already been used to interpret suspended particulate matter and bed particulate matter sources and dynamics, to assess the impact of land-use change on catchment hydrology, soil erosion, and sediment yields, to understand bacteria fate and weed seed transport across the catchment, and to build catchment-scale models focused on hydrology and water quality issues. The dataset may be further used to, for example, assess the role of headwater catchments in large tropical river basin hydrology, support the interpretation of new variables measured in the catchment (e.g., contaminants other than faecal bacteria), and assess the relative impacts of both climate and land-use change on the catchment.  相似文献   

10.
Abstract

One of the scale problems in hydrology is to relate nonlinearity in basin response to size and other factors. On the Sputka basin (103.4 km2), three groups of unit hydrographs were identified, each group having a common shape parameter, N, of the Nash model and each, therefore, representing one dimensionless response. The existence of the three dimensionless responses can be explained in the first place by there being different spatial rainfall patterns for the events from which they were derived. The time parameter, K, within the individual groups depends primarily on the initial flow and on the skewness of the rainfall time pattern. However, when the conditions of rainfall uniformity and of a minimum depth are strictly met, and the initial flow is in a certain range, the basin behaves in a linear fashion.  相似文献   

11.
General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.  相似文献   

12.
The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models.  相似文献   

13.
《Journal of Hydrology》2006,316(1-4):213-232
The Magdalena River, a major fluvial system draining most of the Colombian Andes, has the highest sediment yield of any medium-sized or large river in South America. We examined sediment yield and its response to control variables in the Magdalena drainage basin based on a multi-year dataset of sediment loads from 32 tributary catchments. Various morphometric, hydrologic, and climatic variables were estimated in order to understand and predict the variation in sediment yield. Sediment yield varies from 128 to 2200 t km−2 yr−1 for catchments ranging from 320 to 59,600 km2. The mean sediment yield for 32 sub-basins within the Magdalena basin is ∼690 t km−2 yr−1. Mean annual runoff is the dominant control and explains 51% of the observed variance in sediment yield. A multiple regression model, including two control variables, runoff and maximum water discharge, explains 58% of the variance. This model is efficient (ME=0.89) and is a valuable tool for predicting total sediment yield from tributary catchments in the Magdalena basin. Multiple correlations for those basins corresponding to the upper Magdalena, middle basin, Eastern Cordillera, and catchment areas greater than 2000 km2, explain 75, 77, 89, and 78% of the variance in sediment yield, respectively. Although more variance is explained when dataset are grouped into categories, the models are less efficient (ME<0.72). Within the spatially distributed models, six catchment variables predict sediment yield, including runoff, precipitation, precipitation peakedness, mean elevation, mean water discharge, and relief. These estimators are related to the relative importance of climate and weathering, hillslope erosion, and fluvial transport processes. Time series analysis indicates that significant increases in sediment load have occurred over 68% of the catchment area, while 31% have experienced a decreasing trend in sediment load and thus yield. Land use analysis and increasing sediment load trends indicate that erosion within the catchment has increased over the last 10–20 years.  相似文献   

14.
石梁河水库是江苏省最大的人工湖泊.开展水库沉积速率研究,对认识水库环境变化具有重要意义.本文对石梁河水库沉积物岩芯粒度组成特征进行了分析,表明沉积物以黏土质粉砂和粉砂质黏土为主,从底部向上呈明显的变细趋势,并记录了1970年的强降雨事件.采用137Cs同位素测年分析,得到1963年和1986年两个时标.通过石梁河水库的沉积年代序列,推算出平均沉积速率:1963 1970年为10.85 cm/a,1970 1986年为3.81 cm/a,1986 2005年为1.32 cm/a.对比粒度沉积特征和流域降水记录,粗颗粒物质和降雨量的变化趋势基本相同,沉积物粒度组成特点直接反映了沉积时的降水、水动力搬运强度等信息.在河川来水来沙、坝前水位变化和地形等条件下,石梁河水库表现为三角洲淤积,且沉积速率逐渐变缓,符合水库淤积的一般规律.此外,石梁河水库上游建设的众多大型水库,拦截了大量泥沙,也使得沉积速率呈现减小趋势.  相似文献   

15.
This study was designed to develop a physically based hydrological model to describe the hydrological processes within forested mountainous river basins. The model describes the relationships between hydrological fluxes and catchment characteristics that are influenced by topography and land cover. Hydrological processes representative of temperate basins in steep terrain that are incorporated in the model include intercepted rainfall, evaporation, transpiration, infiltration into macropores, partitioning between preferential flow and soil matrix flow, percolation, capillary rise, surface flow (saturation‐excess and return flow), subsurface flow (preferential subsurface flow and baseflow) and spatial water‐table dynamics. The soil–vegetation–atmosphere transfer scheme used was the single‐layer Penman–Monteith model, although a two‐layer model was also provided. The catchment characteristics include topography (elevation, topographic indices), slope and contributing area, where a digital elevation model provided flow direction on the steepest gradient flow path. The hydrological fluxes and catchment characteristics are modelled based on the variable source‐area concept, which defines the dynamics of the watershed response. Flow generated on land for each sub‐basin is routed to the river channel by a kinematic wave model. In the river channel, the combined flows from sub‐basins are routed by the Muskingum–Cunge model to the river outlet; these comprise inputs to the river downstream. The model was applied to the Hikimi river basin in Japan. Spatial decadal values of the normalized difference vegetation index and leaf area index were used for the yearly simulations. Results were satisfactory, as indicated by model efficiency criteria, and analysis showed that the rainfall input is not representative of the orographic lifting induced rainfall in the mountainous Hikimi river basin. Also, a simple representation of the effects of preferential flow within the soil matrix flow has a slight significance for soil moisture status, but is insignificant for river flow estimations. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

16.
Rainfall, peak discharges, and suspended sediment transport were surveyed for 280 events in three small (0.8 to 10 km2) catchments in a hilly area derived from Neogene marls, silts, and sands. Under similar hydrological input conditions, stream flow behaviour and sediment delivery differed considerably from one catchment to another, depending on topography, lithology, land use, and especially sediment availability. Analytical treatment of data showed a good fit between sediment yield and peak flow discharge. Less good, although still significant, was the correlation between sediment concentration and discharge values for different flow stages. Rainfall peak/basin lag time and rainfall/discharge showed poor or no correlation, mainly due to strong variations in rainfall distribution. Sediment concentration in the catchments varied enormously according to season, from zero up to 334 g 1?1; sediment yield was 160-900 tonnes km?2 yr?1 in the two major catchments, and over 5200 tonnes km?2 yr?1 in the headwater catchment, stressing the importance of small tributaries not only in inducing floods in downstream channels, but also in sediment supply.  相似文献   

17.
采用水沙模型对流域水沙过程进行计算是目前分析和研究黄土地区水土流失、水沙锐减等问题的有效途径由于降雨的时段均化和缺测、漏测、误测等问题,导致水沙模型的重要输入项和动力因子——降雨资料存在误差,进而影响水流和泥沙过程模拟精度因此,本研究将降雨动态系统响应曲线的误差修正方法与概念性水沙模拟模型相结合以提高水沙过程模拟精度此方法将水沙模型的水流模拟部分看作响应系统,通过修正水沙模型的重要输入项——面平均雨量,利用修正之后的面平均雨量系列,通过模型重新计算以提高模型对产汇流和产汇沙过程的模拟精度通过理想案例验证该方法可行性后,选择黄土地区曹坪流域进行检验,结果表明修正后的水流和泥沙过程模拟精度均有显著提高,平均提高幅度分别为17.56%和15.86%.  相似文献   

18.
ABSTRACT

This study analysed long-term rainfall data (1851–2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) Bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the Bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through Bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871–1890, 1891–1910 and 1951–1970. Linear regression-based analysis revealed 1881–1910 and 1971–2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones.  相似文献   

19.
Conventional statistical downscaling techniques for prediction of multi-site rainfall in a river basin fail to capture the correlation between multiple sites and thus are inadequate to model the variability of rainfall. The present study addresses this problem through representation of the pattern of multi-site rainfall using rainfall state in a river basin. A model based on K-means clustering technique coupled with a supervised data classification technique, namely Classification And Regression Tree (CART), is used for generation of rainfall states from large-scale atmospheric variables in a river basin. The K-means clustering is used to derive the daily rainfall state from the historical daily multi-site rainfall data. The optimum number of clusters in the observed rainfall data is obtained after application of various cluster validity measures to the clustered data. The CART model is then trained to establish relationship between the daily rainfall state of the river basin and the standardized, dimensionally-reduced National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis climatic data set. The relationship thus developed is applied to the General Circulation Model (GCM)-simulated, standardized, bias free large-scale climate variables for prediction of rainfall states in future. Comparisons of the number of days falling under different rainfall states for the observed period and the future give the change expected in the river basin due to global warming. The methodology is tested for the Mahanadi river basin in India.  相似文献   

20.
River discharges vary strongly through time and space, and quantifying this variability is fundamental to understanding and modelling river processes. The river basin is increasingly being used as the unit for natural resource planning and management; to facilitate this, basin‐scale models of material supply and transport are being developed. For many basin‐scale planning activities, detailed rainfall‐runoff modelling is neither necessary nor tractable, and models that capture spatial patterns of material supply and transport averaged over decades are sufficient. Nevertheless, the data to describe the spatial variability of river discharge across large basins for use in such models are often limited, and hence models to predict river discharge at the basin scale are required. We describe models for predicting mean annual flow and a non‐dimensional measure of daily flow variability for every river reach within a drainage network. The models use sparse river gauging data, modelled grid surfaces of mean annual rainfall and mean annual potential evapotranspiration, and a network accumulation algorithm. We demonstrate the parameterization and application of the models using data for the Murrumbidgee basin, in southeast Australia, and describe the use of these predictions in modelling sediment transport through the river network. The regionalizations described contain less uncertainty, and are more sensitive to observed spatial variations in runoff, than regionalizations based on catchment area and rainfall alone. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

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