Effect of Forest Thinning on Water Yield in a Sub-Humid Mediterranean Oak-Beech Mixed Forested Watershed

Forested watersheds provide fresh water with best quality and forestry practices play important role on the water production in the watersheds. Therefore, the objective of this study was to investigate the effect of 18 % thinning, which was under the 20 % threshold value reported in the literature on water yield in a forested watershed. Two experimental watersheds with similar ecological conditions in the Belgrad Forest of Istanbul were studied. Following a 6-year calibration period from December 2005 to October 2011, a simple linear regression equation was developed between the monthly streamflows of two watersheds with a significantly high correlation coefficient (r = 0.95). After 18 % of standing volume was removed from one watershed and the other was left untreated as a control, the streamflow was monitored for 2 years starting in January 2012 in both watersheds. The change in the monthly runoff was estimated as the difference between measured and values calculated with the linear regression equation. Average monthly streamflows were about 19, 15 and 10 mm in the control and 21, 20, and 11 mm in the treatment watersheds for calibration, first and second post-treatment periods, respectively. Paired watershed analysis showed that monthly streamflow did not significantly increase in either watershed for the first or the second year after the harvest. The results revealed that thinning intensity had to be greater to increase water yield significantly in this forest ecosystem and the threshold value for a streamflow increase was greater than 18 % for this region.     

Assessment of Future Climate Change Impacts on Water Resources of Upper Sind River Basin, India Using SWAT Model

A study has been conducted to assess future climate change impacts on water resources of the Upper Sind River Basin using Soil Water Assessment Tool. Sequential uncertainty fitting (SUFI-2) algorithm has been applied for model calibration and uncertainty analysis. Monthly observed stream flows matched well with simulated flows with respect to p-factor, d-factor, Correlation coefficient and Nash-Sutcliffe coefficients with values of 0.73, 0.42, 0.82, 0.80 during calibration (1992–2000) and 0.42, 0.36, 0.96, 0.93 during validation (2001–2005) respectively. PRECIS generated outputs under IPCC A1B Scenarios for Indian conditions corresponding to the baseline (1961–1990), midcentury (2021–2050) and endcentury (2071–2098); extracted by Indian Institute of Tropical Meteorology, Pune (India) have been used for the study. It has been found from the model results that the average annual streamflow could increase by 16.4 % for the midcentury and a significant increase of 93.5 % by the endcentury. The results also indicate that streamflow may rise drastically in monsoon season, but will decrease in non-monsoon season due to the projected future climate change.     

Temporal Variability and Potential Predictability of the Streamflow Regimes in the North‐Eastern Iberian Peninsula

This paper investigates the temporal variability and potential predictability of streamflow regimes in the north‐eastern Spain for the 1970–2010 period. Two different regimes are found, those characterized for having peak flows in the winter and those where this maximum appears in the spring. The main characteristic time scales of streamflows in each area are studied by singular spectral analysis (SSA). While winter streamflow regime only shows interannual variability (quasi‐oscillatory modes around 5.5 and 2.3 years), spring streamflow (2.6 and 6.6 years) also presents a decadal variability component. Based on this result, a modelling process is conducted using autoregressive moving average (ARMA) models, for interannual variability modelling, and stable teleconnections between global oceanic sea surface temperature (SST) anomalies and river flow, for decadal variability modelling. Finally, a one‐step‐ahead prediction experiment is computed to obtain forecasted streamflows. The results for winter streamflow regime modelling show a phase concordance between the raw and the forecasted streamflow time series of around 70% and a correlation around 0.7, for the validation period (2001–2010). For spring streamflow, additionally to the ARMA modelling for the interannual component, a model based on the SST has been established that involves some oceanic regions from previous seasons located, fundamentally, not only in the North Atlantic but also in the Indian Ocean. The combined model (SST + ARMA) significantly improves the prediction based on the ARMA model alone, showing a phase concordance and a correlation around 90% and 0.7 respectively. This modelling scheme provides predictability skills of the rivers from the Inland Catalan Basins at different time scales, representing an added value for water planning. Copyright © 2014 John Wiley & Sons, Ltd.     

Streamflow Response to Climate Change in the Brahmani River Basin,India

Climate change can significantly affect the water resources availability by resulting changes in hydrological cycle. Hydrologic models are usually used to predict the impacts of landuse and climate changes and to evaluate the management strategies. In this study, impacts of climate change on streamflow of the Brahmani River basin were assessed using Precipitation Runoff Modeling System (PRMS) run under the platform of Modular Modeling System (MMS). The plausible hypothetical scenarios of rainfall and temperature changes were used to assess the sensitivity of streamflow to changed climatic condition. The PRMS model was calibrated and validated for the study area. Model performance was evaluated by using joint plots of daily and monthly observed and simulated runoff hydrographs and different statistical indicators. Daily observed and simulated hydrographs showed a reasonable agreement for calibration as well as validation periods. The modeling efficiency (E) varied in the range of 0.69 to 0.93 and 0.85 to 0.95 for the calibration and validation periods, respectively. Simulation studies with temperature rise of 2 and 4°C indicated 6 and 11% decrease in annual streamflow, respectively. However, there is about 62% increase in annual streamflow under the combined effect of 4°C temperature rise and 30% rainfall increase (T4P30). The results of the scenario analysis showed that the basin is more sensitive to changes in rainfall as compared to changes in temperature.     

MACROINVERTEBRATE COMMUNITY CONDITION ASSOCIATED WITH THE SEVERITY OF STREAMFLOW ALTERATION

Natural streamflows play a critical role in stream ecosystems, yet quantitative relations between streamflow alteration and stream health have been elusive. One reason for this difficulty is that neither streamflow alteration nor ecological responses are measured relative to their natural expectations. We assessed macroinvertebrate community condition in 25 mountain streams representing a large gradient of streamflow alteration, which we quantified as the departure of observed flows from natural expectations. Observed flows were obtained from US Geological Survey streamgaging stations and discharge records from dams and diversion structures. During low‐flow conditions in September, samples of macroinvertebrate communities were collected at each site, in addition to measures of physical habitat, water chemistry and organic matter. In general, streamflows were artificially high during summer and artificially low throughout the rest of the year. Biological condition, as measured by richness of sensitive taxa (Ephemeroptera, Plecoptera and Trichoptera) and taxonomic completeness (O/E), was strongly and negatively related to the severity of depleted flows in winter. Analyses of macroinvertebrate traits suggest that taxa losses may have been caused by thermal modification associated with streamflow alteration. Our study yielded quantitative relations between the severity of streamflow alteration and the degree of biological impairment and suggests that water management that reduces streamflows during winter months is likely to have negative effects on downstream benthic communities in Utah mountain streams. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments (data are non-continuous and fragmental in some catchments), we used non-continuous calibration periods for more independent streamflow data for SIMHYD (simple hydrology) model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization (PSO) method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.     

Method of coupling 1-D unsaturated flow with 3-D saturated flow on large scale

A coupled unsaturated-saturated water flow numerical model was developed. The water flow in the unsaturated zone is considered the one-dimensional vertical flow, which changes in the horizontal direction according to the groundwater table and the atmospheric boundary conditions. The groundwater flow is treated as the three-dimensional water flow. The recharge flux to groundwater from soil water is considered the bottom flux for the numerical simulation in the unsaturated zone, and the upper flux for the groundwater simulation. It connects and unites the two separated water flow systems. The soil water equation is solved based on the assumed groundwater table and the subsequent predicted recharge flux. Then, the groundwater equation is solved with the predicted recharge flux as the upper boundary condition. Iteration continues until the discrepancy between the assumed and calculated groundwater nodal heads have a certain accuracy. Illustrative examples with different water flow scenarios regarding the Dirichlet boundary condition, the Neumann boundary condition, the atmospheric boundary condition, and the source or sink term were calculated by the coupled model. The results are compared with those of other models, including Hydrus-1D, SWMS-2D, and FEFLOW, which demonstrate that the coupled model is effective and accurate and can significantly reduce the computational time for the large number of nodes in saturated-unsaturated water flow simulation.     

Watershed Modeling to Assessing Impacts of Potential Climate Change on Water Supply Availability

Climate change could have impacts on hydrologic systems threatening, availability of water supply resources. In Illinois, regional water supply planning efforts are attempting to better understand potential impacts on low flow and surface water availability through analysis of hydrologic sensitivity to a range of climate scenarios. This paper explores the development, calibration and validation of Fox River watershed model using the soil and water assessment tool (SWAT) and the model’s application to assess impacts of potential climate change. The watershed model is calibrated and validated using daily flow records at three gauging stations. Automatic model calibration followed by manual refinement of parameter values was performed. Calibration results were generally good for monthly and annual time step but only satisfactory for daily simulations. Based on simulations of global climate models produced for IPCC fourth assessment report, climate scenarios were prepared by the Illinois State Water Survey for water supply planning initiatives in north-east and east-central Illinois. These scenarios showed ranges of temperature change between 0°C to +3.3°C and annual precipitation changes between −127 to +127 mm in the next 50 years, excluding the 5% extreme ends of those climate model simulations considered. Changes in climate were reflected using adjustments to the historical record, instead of using direct outputs from individual climate models. The watershed model was used to assess the impact of potential climate change. Application results indicate that annual precipitation change of 127 mm on average increases annual water yield and 7-day low flows by 28% and 19%, respectively. In contrast, a temperature change of +3.3°C results in average reductions of annual water yield by 13% and 7-day low flows by 10%. Seasonal effects were investigated through evaluation of changes in average monthly flows. Increasing precipitation resulted in significant changes in streamflows in late summer and fall months where as increasing temperature greatly affects winter flows due to snowmelt. The key implication is that climate change-induced variability of streamflows could have major impacts on water supply availability in the Fox River watershed and in particular, increased periods of drought could result in deficit of supplies during seasons of peak water use. It must be noted that this analysis does not examine the potential impacts of population growth and water use on water supply availability, which are also expected to have substantial influences in the region.     

Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence

Streamflow forecasting and predicting are significant concern for several applications of water resources and management including flood management, determination of river water potentials, environmental flow analysis, and agriculture and hydro-power generation. Forecasting and predicting of monthly streamflows are investigated by using three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree). Data from four different stations, Besiri and Malabadi located in Turkey, Hit and Baghdad located in Iraq, are used in the analysis. Cross validation method is employed in the applications. In the first stage of the study, the heuristic regression models are compared with each other and multiple linear regression (MLR) in forecasting one month ahead streamflow of each station, individually. In the second stage, the models are evaluated and compared in predicting streamflow of one station using data of nearby station. The research investigated also the influence of the periodicity component (month number of the year) as an external sub-set in modeling long-term streamflow. In both stages, the comparison results indicate that the LSSVR model generally performs superior to the MARS, M5-Tree and MLR models. In addition, it is seen that adding periodicity as input to the models significantly increase their accuracy in forecasting and predicting monthly streamflows in both stages of the study.     

Performance of a Monthly Streamflow Prediction Model for Ungauged Watersheds in Spain

Climate change, drought and the world??s growing population are increasing the demand for water which in turn requires improved water resources management. The sustainable management of a watershed requires a thorough knowledge of its water resources, including monthly streamflow. Spain is home to a large number of ungauged watersheds, the streamflows of which are often unknown. Chavez et al. (2007) reported a model for predicting monthly streamflow in ungauged watersheds that was validated for use in areas of tropical climate in Central America and a dry area in South America. This work reports an attempt to assess the performance of this model for eventual use with ungauged watersheds in Spain, using data for a number of watersheds for which gauging data were available. The proposed model took into account physical characteristics such us the soil infiltration rate, the slope of the terrain, plant fractional cover, the monthly moisture adequacy index, and the leaf area index. Comparisons of model-predicted monthly streamflows and those actually measured showed the Chavez et al. model unable to make reliable predictions for Spanish watersheds in its current form. A new approach has been developed considering only smaller watersheds in Spanish conditions, changing parameters in the original model. These parameters have been calibrated and validated, reaching adequate adjustment of results.     

Hydrologic Modelling Calibration for Operational Flood Forecasting

Runoff prediction in flood forecasting depends on the use of hydrological simulation models and on the input of accurate precipitation forecasts. Reliability of predictions thus obtained hinges on proper calibration of the model. Moreover, when the model is intended to be used systematically in operational forecasting of streamflows, the calibration process must take into account the variation of the model parameters over time, namely in response to changing weather and hydrological conditions in the basin. The goal of the study was to build a process to adjust, on a daily basis, the simulation model parameters to the current hydrological conditions of the river basin, in order for the model to be run operationally for prediction of the streamflow for the next 10-days period, and, thereby, to forecast the occurrence of flood events. Towards this end, hydrological simulations using the HEC-HMS model were performed, using a 3 h period time step. The present communication focuses on the hydrological model calibration and verification processes and on the evaluation of forecasts’ accuracy. The procedure was applied to a part of the largest (full) Portuguese river basin, the Mondego river basin, corresponding to the Aguieira dam section watershed, which comprises an area of 3070 km². Four wet periods, associated with the occurrence of flooding, were selected for the calibration and verification of the model, by adjustment of the model parameters. The results of the study aim to define the optimal calibration parameters values to model the observed streamflow for various hydro-meteorological states, thus enabling adequate prediction of flow in flooding situations and proper application of the model in operational flood forecasting.     

The hydroclimate niche: A tool for predicting and managing riparian plant community responses to streamflow seasonality

Habitat suitability is a consequence of interacting environmental factors. In riparian ecosystems, suitable plant habitat is influenced by interactions between stream hydrology and climate, hereafter referred to as “hydroclimate”. We tested the hypothesis that hydroclimate variables would improve the fit of ecological niche models for a suite of riparian species using occurrence data from the western United States. We focus on the climate conditions (temperature, precipitation and vapor pressure deficit) during the months of lowest and highest streamflow as integrative hydroclimate metrics of resource and stress levels. We found that the inclusion of hydroclimate variables improved model fit for all species in the western USA dataset. We then tested the utility of the improved habitat suitability models by projecting them onto a regulated segment of the Colorado River to assess potential impacts of streamflow seasonality on vegetation metrics of management concern. Species frequency derived from independent survey data in the Colorado River segment was significantly higher for species with predicted suitable habitat than for species without predicted suitable habitat. Under different simulated hydrographs for the Colorado River, overall species richness was predicted to be greatest with peak streamflows during summer, and native-to-non-native species ratios were predicted to be greatest with lowest streamflows in winter. Summer high flows were particularly associated with higher predicted habitat suitability for species that have increased in cover over recent decades (e.g., Pluchea sericea, Baccharis species). We conclude that hydroclimate covariates can be useful tools for predicting how riparian vegetation communities respond to changes in the seasonal timing of low and high streamflows.     

Simulating Future Groundwater Recharge in Coastal and Inland Catchments

Groundwater is a primary source of drinking water in the Mediterranean, however, climate variability in conjunction with mismanagement renders it vulnerable to depletion. Spatiotemporal studies of groundwater recharge are the basis to develop strategies against this phenomenon. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in one coastal and one inland hydrological basin in Greece. A double calibration/validation (CV) procedure using streamflow data and MODIS ET was conducted for the inland basin of Mouriki, whereas only ET values were used in the coastal basin of Anthemountas. Calibration and simulation recharge were accurate in both sites according to statistical indicators and previous studies. In Mouriki basin, mean recharge and runoff were estimated as 16% and 9%, respectively. In Anthemountas basin recharge to the shallow aquifer and surface runoff were estimated as 12% and 16%, respectively. According to the predicted RCP 4.5 and 8.5 scenarios, significant variations in groundwater recharge are predicted in the coastal zone for the period 2020–2040 with average annual recharges decreasing by 30% (RCP 4.5) and 25% (RCP 8.5). Variations in groundwater recharge in the inland catchment of Mouriki were insignificant for the simulated period. Anthemountas basin was characterized by higher runoff rates. Groundwater management in coastal aquifers should include detailed monitoring of hydrological parameters, reinforced groundwater recharge during winter and reduced groundwater abstraction during summer depending on the spatiotemporal distribution of groundwater recharge.

     

考虑水文模型参数时变的水库水位多预见期实时预报——以水布垭水库为例

精准可靠的坝前水位预测可支撑水库的调度决策,保障流域防洪安全。传统水库水位实时预报方法均基于降雨径流稳态假定,在变化环境下精度不高。为此,本研究提出了一种整合水文模型参数时变和实时校正的水库水位多预见期实时预报方法：识别变化环境下水文模型参数的时变过程,构建时变参数与自然、社会经济因子的函数关系;基于时变参数函数式构建预报调度集成模型,以预见期内多个时刻预报水位与观测水位的吻合程度为目标函数率定水文模型的参数;采用基于反向拟合算法的实时校正技术进行误差校正,输出不同预见期的水库水位预报。以水布垭水库流域为研究对象,结果表明,较传统水库水位预报方法,论文提出的方法在率定期和检验期的平均绝对值误差分别下降了0.050和0.040 m,可为防洪减灾提供高精度、长预见期的水库水位预测。     

基于改进降水输入模块的融雪径流模拟:以拉萨河为例

降水是自然界物质循环和水循环的重要组成部分,是高寒地区径流的重要来源,水文模型中降水数据的输入精度对提高高寒地区融雪径流模拟效果具有十分重要的作用。青藏高原地区气象站点较少,站点数据无法全面反映流域内降水时空分布的真实情况,传统的融雪径流模型在地形、风向和水汽等要素对降水垂直分布的影响考虑不够全面,制约了模型在山区融雪模拟以及预测中的应用,因此有必要对模型的降水输入项进行改进,以期提高半干旱高寒地区融雪径流模拟效果。本文基于改进的遥感卫星数据校正理论,开发了适用于半干旱高寒地区的降水输入模块,将其与度日因子模型进行耦合,利用高程分带将降水组合成半网格半站点的降水输入数据驱动模型,并在拉萨河流域进行试验研究。结果表明:降水输入模块能够显著提高降水卫星反演地面降水精度,改进后的融雪模型在率定期和验证期的NSE(Nash-Sutcliffe efficiency coefficient)分别为0.741和0.770,高于原融雪模型的模拟效果,表明改进后的模型能够在流域各个分区获得较为精确的降水数值,融雪径流模拟精度比原模型精度得到提高。总之,耦合降水输入模块的融雪模型可以有效提高降水输入精度,对缺资料半干旱高寒地区融雪模拟具有重要的参考价值。     

Identification of Streamflow Response to Climate Change and Human Activities in the Wei River Basin,China

In this study, the calibration and validation period with stable underlying surface conditions was determined by using a statistically significant change point of the annual streamflow in several catchments of the Wei River basin (WRB). The effects of climate changes and human activities on streamflow were estimated by using the sensitivity-based method and the dynamic water balance model, respectively. The contributions of climate effects and human activities effects on streamflow were also investigated. The results showed that almost all the catchments exhibited significant decreasing trend of streamflow in the early 1990s. The streamflow was more sensitive to changes in precipitation than changes in potential evapotranspiration (PET). Effects of climate due to changes in precipitation and PET are weak in Linjiacun, Weijiabao and Xianyang catchments, while it is strong in the catchments controlled by other hydrological stations, accounting for more than 40 % of streamflow reduction. Effects of human activities on streamflow in Linjiacun, Weijiabao, Xianyang and Zhangjiashan catchments accounted for more than 50 % of the streamflow reduction. The study provides scientific foundation to understand the causes of water resources scarcity and useful information for the planning and management of water resources in the ecological fragile arid area.     

Optimization of recharge and pumping rates by means of an inverse 3D model

The paper concerns the optimization of pumping and artificial recharge rates under hydraulic head constraints. This optimization is accomplished by means of an inverse three-dimensional (3D) model which is a combination of a numerical model, sensitivity analyses and nonlinear regression. The optimization criterion used is the minimization of the sum of the squares between the calculated and the desired water table maps. These maps were established for the end of each month of the period from December to August so that the annual average flux is outward from the modeling area. This outward flux will push back the salt water intrusion. The assumed natural recharge rates of the different months equal the mean values of the period 1958–1977. The aquifer parameters describing the water conducting and storing properties are also required as input data as well as the initial hydraulic heads. The optimized parameters are the artificial recharge rates of the months December, January, February and March and the pumping rates of the months April, May, June, July and August. For the first-mentioned months, the pumping rates are considered as known. During the last-mentioned period, no artificial recharge is possible. A unique solution is obtained. To check the optimized rates, a water balance is made. With this balance, it is shown that the treated phreatic aquifer can be used as a subterranean reservoir to store water that is abundantly present in the winter until the summer when water demand is large. It is also proved that the salt-water intrusion can be stopped by the temporal storage of the artificial recharged water.     

Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.     

参数率定过程中目标函数选择的不确定性对径流模拟的影响

在水文模型率定过程中,不同目标函数侧重于径流模拟的不同层面。为了探究目标函数选择的不确定性对模型参数和径流模拟的影响,将HyMod模型应用于黄河源区,选择纳什效率函数f_NS、总水量平衡误差函数f_t、低水量误差函数f_d和高水量误差函数f_g作为目标函数,采用遗传算法(GA)分别率定出不同目标函数下对应的最优参数值,并依次代入水文模型模拟水文过程;通过对比分析年尺度及年内各月实测与模拟径流过程、纳什效率系数NSE、决定系数R²和均方根误差RMSE评价指标来探究参数率定的目标函数不确定性对年尺度水资源演变过程的影响。结果表明:当HyMod模型应用于黄河源区水文模拟的率定期和验证期时,目标函数选择的不确定性对各评价指标的影响差异明显,如f_NS目标函数下NSE值最大,在f_g下次之,在f_d下最小,此外率定期模拟精度优于验证期;同样,目标函数不确定性对不同特征时期径流的影响差异显著,其中,f_NS和f_t目标函数下,非汛期分别高估和低估模拟流量。研究成果可为水文模型参数率定目标函数的选择提供理论参考。     

南水北调中线工程典型渠段一维水动力水质模拟与预测

为了研究南水北调中线工程复杂水工建筑物影响下的水流特性以及对污染物在渠道内输移、衰减过程的影响,保证输水过程中的水质安全,分析了突发性污染事故潜在污染源特征,构建了南水北调中线工程典型输水渠段的一维水动力水质模型,模拟倒虹吸、节制闸、涵洞、隧洞、渡槽、公路桥 6 种水工建筑物影响下输水渠道内的水流特性,采用七里庄断面实测水位、流量、水质( 总磷、氨 氮) 数据对模型进行了校核与证; 并运用该模型预测店北公路桥突发水污染事故时,3 种输水流量、 3 种负荷、3 种污染物泄露至渠道后污染物沿程分布规律。结果表明: 该模型能有效模拟复杂水工建筑物下的水动力和污染物输移特性,下车亭分水口处污染物浓度超标与否受输水流量和污染负荷共同影响。针对分水口处水质指标浓度变化,提出合理、有效分水和退水方式,为保障南水北调中线工程输水水质安全提供参考。