虚拟站点在黑河流域降水模拟中的应用

降水作为气候系统的关键因素,也是影响区域植被生长以及生态变化规律的重要因子。本文针对黑河流域气象站点分布稀疏情况,采用信息熵及半变异函数理论构建该流域虚拟气象站点,并结合部分已有站点对整个流域降水进行插值模拟。信息熵可以计算每个站点降水值所包含的信息,通过联合熵以及条件熵来依次选取所含信息量多的站点,同时结合半变异函数模型来观察各站点之间的空间相关性,以此构建最优站点数据集。利用1991-2003年该流域15个气象台站的年平均降水量作为基础数据,考虑高程、坡度、坡向对降水的影响,对降水进行相关性分析,建立回归方程反演虚拟站点降水值。最后,采用协同克里金（Co-Kriging）与具有漂移的克里金（KED）方法对该流域进行插值,对比插值精度。结果表明,增加虚拟站点有效提高了降水插值精度,在该情况下使用KED方法插值结果与观测值最接近。     

近50年中国地表净辐射的时空变化特征分析

基于GIS空间分析技术与Mann-Kendall趋势分析方法,对中国陆地区域699个气象站点1961-2010年逐年、季平均地表净辐射进行时空变化特征分析,结果表明:(1)参数拟合后的FAO Penman修正式对模拟站点逐日地表净辐射的总体精度较高,均方根误差为27.9W.m-2,相关系数为0.85,平均相对误差为0.13;(2)全国近50年站点平均地表净辐射在年、季均呈现出较明显的下降过程,年均降幅为0.74W.m-2.10a-1,不同季节的下降幅度存在差异,夏季降幅最大;(3)逐站点分析显示全国大部分站点(59.8%)年均地表净辐射呈显著下降趋势(0.05),东部趋势变化比西部明显,夏季在地表净辐射年际变化中的贡献最大,华北、华中、华南地区的站点在春夏秋季均呈显著下降趋势。     

结合TRMM数据的区域降水高精度曲面建模研究

高精度曲面建模方法（High Accuracy Surface Modeling, HASM）,从理论上解决了传统方法在插值过程中峰值削平和边界震荡等问题。其模拟精度相对于经典插值方法有很大提高,已成功应用于人口密度、土壤属性,以及气候要素等领域的空间制图。然而,由于地面气象站点数量和分布的限制,使得HASM仅依靠站点数据难以得到高精度的空间降水估计数据,因此,本文以地貌与气候类型复杂多样的我国中西部地区2010年年降水量空间分布模拟为例,采用混合插值法进行HASM区域降水模拟。结果表明,TRMM作为背景场的HASM模拟的年降水量精度,在全局和局部明显优于IDW、Spline和Kriging等经典插值方法的结果,作为背景场的HASM模拟精度,MAE和RMSE分别为125.15 mm和155.80 mm,其他方法最好的模拟结果比其误差值分别高出53.6%和54.5%;其模拟误差在不同子区域都较小;各种方法在平原的精度都高于山区的精度。     

遥感及再分析降水产品在缺资料干旱内陆盆地的适用性评估

山区降水较集中,但降水测站多位于山谷或人口密集区,代表性差。遥感和再分析降水产品能提供时空分布连续的数据,不受地形条件限制。柴达木盆地中心属干旱荒漠区,水是制约该区开发的首要条件,其四周属高寒山区,降水相对较多,但降水监测十分薄弱。为获取该区相对精确的降水时空分布信息,本文评估了4套高分辨率降水产品（CMADS、TRMM、GPM和MSWEP）的适用性。首先基于地面站点数据评估它们在不同时空尺度上的精度,并分析它们在柴达木盆地的空间分布和年内分配特征。然后,以盆地东南隅的无测站山区香日德河流域为研究区,利用降水产品驱动SWAT模型来评估它们的分布式水文模拟适用性。结果表明：① MSWEP在年、月尺度上与站点降水的吻合程度最高（R ≥ 0.79,PBIAS = 0.5%）,其次是GPM和TRMM,CMADS精度最低（R ≥ 0.64,PBIAS = 5.8%）;② 从降水精度与站点高程的关系来看,降水产品在相对低海拔区容易高估站点降水,而在相对高海拔区常低估实际降水;③ 在香日德河流域,MSWEP（NSE = 0.64）在基准期（2009—2012年）的径流模拟表现明显好于其它降水产品（NSE = 0.36~0.59）,变化期（2013—2016年）表现最好的是CMADS（NSE = 0.75,其余产品NSE = 0.53~0.68）。本研究可为缺资料干旱山区获取精确的降水时空信息和后续水资源的科学管理与规划提供重要支撑。     

TRMM及GPM降水数据在高寒内陆河流域的准确性评估

降水数据的准确性和时空分辨率成为水文过程模拟的关键。卫星遥感降水资料的日益丰富为资料缺乏区的水文模拟带来了新的突破。本研究拟在资料缺乏、下垫面复杂,观测难、建模难的柴达木盆地高寒内陆河流域—巴音河中上游,基于近5年的TMPA 3B42、GPM IMERG V5及GPM IMERG V6逐日降水数据和气象站点观测数据建立SWAT模型,采用流域出口径流数据及不同的参数化方案分别率定4个模型,比较和探究不同数据在巴音河流域的适用性。结果表明：① 在月、年尺度上,实测降水数据及TMPA 3B42 V07对应的SWAT模型径流模拟效果较好,前者模拟精度较后者仅高6%~12%,且二者对于流域水量平衡的刻画均较准确。说明TMPA 3B42数据对应的径流模拟结果误差相对较小,可直接用于高寒内陆河流域水文模拟;② GPMIMERG V5数据对应的那什系数N_SE值为0.13（月）、-1.58（年）,误差百分数P_BIAS值为41.2%（月、年）,均方根误差与标准误差比率R_SR值为0.93（月）、1.61（年）,其径流模拟误差较大,模拟效果不可信,说明GPMIMERG V5数据集并不适用于巴音河流域水文模拟;③ GPMIMERG V6-F对应的月径流模拟结果明显优于GPMIMERG V5-F,前者模拟精度较后者提高4倍,但其模拟的年径流对应的N_SE值为-0.12,R_SR值为1.09。该研究可为资料缺乏的高寒内陆河流域生态水文过程模拟提供参考。     

基于气象站观测数据的京津冀城市扩展对气温变化的影响研究

城市扩展引起的区域增温效应一直都是城市热环境研究中的热门领域。本研究首先基于1980-2015年7期城市扩展遥感监测数据,通过熵值法构建了京津冀地区58个气象站周边2 km半径范围内城市扩展程度指数;然后使用四分位法对该指数进行分级,将站点划分为低度城市扩展（C1）、中度城市扩展（C2）和高度城市扩展站点（C3）;最后通过3类站点年和季节平均气温变化趋势对比分析,揭示了城市扩展对气温变化的影响程度及其贡献率。结果表明：① 1980-2015年京津冀地区几乎所有站点周边都有城市扩展现象,58个气象站周边城市扩展程度指数平均为0.377,C3类站点周边城市扩展程度指数为0.650;② 3类站点年和季节平均气温增温速率均表现为C1 < C2 < C3,C3类站点年平均气温的增温速率为0.536 ℃/10a;在季节平均气温上,C1、C2和C3站点春季的增温速率均最高,其中C3类站点的为0.637 ℃/10a,而夏季或秋季最低;③ 城市扩展对C3类站点年和季节平均气温增温影响和贡献率均高于C2类站点,对C3类站点年平均气温的增温影响和贡献率分别为0.342 ℃/10a和63.81%;在季节平均气温上,城市扩展对C2和C3类站点的增温影响均在冬季最高,分别为0.229 ℃/10a和 0.410 ℃/10a,而在春季或夏季最低;城市扩展对C2和C3类站点的增温贡献率均在秋季最高,分别为73.24%和82.96%,而在春季最低。     

闽浙赣地区GPM IMERG降水产品降尺度建模与比较分析

针对目前的技术手段下难以直接获得大范围高精度精细化降水空间分布的问题,本文以闽浙赣地区为研究范围,选用GPM IMERG降水产品,综合应用地面实测降水数据以及水汽与植被指数数据,基于地理加权回归（GWR）法构建了基于水汽因子的降尺度模型,同时基于最小二乘（OLS）法构建了基于水汽因子与植被指数的对比模型,将降水产品的分辨率从0.1°提升至1 km,最终获得2015年闽浙赣地区各月精细化降水空间分布,使用验证站点实测数据进行验证。结果表明：① 构建的 3个降尺度模型中,GWR模型与2种OLS模型相比,拟合优度分别提升了102.9%和93.9%,模型降尺度结果整体优于2种OLS模型,且月际差异小,稳定性更高;2种OLS模型中,采用了水汽因子的模型拟合效果有8个月份更优;② 融合多源数据的GWR降尺度模型获得的结果在研究区内是可靠的,与GPM降水产品相比,在提升空间分辨率的同时,平均相对误差与均方根误差月均分别下降了42%和32%,精度明显改善。     

多路径误差对BDS-3变形监测精度的影响

为定量分析BDS-3观测值多路径误差对变形监测精度的影响,选取包含7个站点的某水利工程2022年共128 d的BDS-3监测数据,对监测站点周围树木裁剪树枝前后BDS-3观测值多路径误差、变形监测精度及二者的相关性进行研究。结果表明：1)多路径误差与BDS-3变形监测精度间存在强相关性,与其平面和高程精度的相关系数分别大于0.93和0.81;2)监测站点周围树枝裁剪后,使用B1I和B3I观测值的平均多路径误差从0.676 m、0.426 m降低至0.329 m、0.230 m,N、E、U方向的平均监测精度分别达0.9 mm、0.8 mm、1.7 mm和1.1 mm、1.0 mm、2.2 mm,较裁剪树枝前分别提高63%、69%、58%和52%、61%、48%;3)改变周围观测环境削弱多路径误差的影响后,使用B1I观测值的精度优于使用B3I观测值的精度,因此在BDS-3短基线变形监测的应用中推荐使用B1I观测值。     

中国南部地区大气加权平均温度模型精化研究

针对中国南部地区地势西高东低、沿海与内陆存在差异等情况,分析中国南部地区T_m与地面温度、测站高度、季节变化以及纬度的关系,利用中国南部地区19个探空站2015~2017年的探空数据,在Bevis公式的基础上建立只考虑地面温度的线性模型（T_m-SC1模型）和与地面温度、高程、季节变化以及纬度有关的新T_m模型（T_m-SC2模型）。以2018年的探空数据为参考值,对T_m-SC1模型和T_m-SC2模型进行精度验证,并与广泛使用的Bevis公式和GPT3模型进行精度比较。结果表明,T_m-SC1模型的年均偏差和均方根误差（RMS）分别为0.76 K和2.57 K,相比Bevis模型和GPT3模型,其精度（RMS值）分别提高13.8%和2.2%;T_m-SC2模型的年均偏差和均方根误差（RMS）分别为-0.10 K和1.64 K,相比Bevis模型和GPT3模型其精度（RMS值）分别提高44.9%和37.6%。T_m-SC2模型用于GNSS水汽计算导致的理论RMS误差和相对误差分别为0.16 mm和0.43%。因此,T_m-SC2模型更适用于中国南部地区的GNSS水汽探测以及气象研究。     

两种海浪模式对一次台风浪过程的模拟分析

以CCMP（Cross—Calibrated,Multi—Platfoml）风场为驱动场,分别驱动目前国际先进的第3代海浪模式ww3（WAVEWATCH—III）、SWAN（Simulating WAves Nearshore）,对2010年9月发生在东中国海的台风“圆规”所致的台风浪进行数值模拟,就台风浪的特征进行分析,并对比分析两个海浪模式的模拟效果。结果表明：1）以CCMP风场分别驱动WW3、SWAN海浪模式,可以较好地模拟发生在东中国海的台风浪,风向与波向保持了大体一致,波高与风速的分布特征保持了很好的一致性;2）综合相关系数、偏差、均方根误差、平均绝对误差来看,两个模式模拟的有效波高（SWH—Significant Wdve Height）都具有较高精度,SWAN模拟的SWH略低于观测值,WW3模拟的SWH与观测值更为接近;3）台风浪可给琉球群岛海域带来5m左右的大浪,台风浪进入东海后,波高、风速都有一定程度的增加,当台风沿西北路径穿越朝鲜半岛时,受到半岛地形的巨大影响,风速和波高都明显降低。     

Disintegration of uncertainties associated with real-time multi-satellite precipitation products in diverse topographic and climatic area in Pakistan

Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km²)and overestimate in large systems(>2000 km²).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.     

Recent changes of Northern Indian Ocean summer rainfall based on CMIP5 multi-model

This study evaluates the simulation of summer rainfall changes in the Northern Indian Ocean (NIO) based on the fifth phase of Coupled Model Intercomparison Project (CMIP5). The historical runs of 20 CMIP5 coupled General Circulation Models (GCMs) are analyzed. The Multi-Model ensemble (MME) of the CMIP5 models well reproduces the general feature of NIO summer rainfall. For a short period 1979–2005, 14 out of 20 models show an increased trend in the mean rainfall and a similar spatial distribution to the Global Precipitation Climatology Project (GPCP) observations in MME. The increasing of the convergence in the equatorial IO results in the increase of rainfall significantly. The equatorial rainfall trend patterns seem modulated by the SST warming in the tropical Indian Ocean, which confirm the mechanism of ‘warmer-get-wetter’ theory. For a long period 1950–2005, the trend of monsoon rainfall over India shows a decrease over the most parts of the India except an increase over the south corn er of the Indian Peninsula, due to a weakened summer monsoon circulation. The pattern is well simulated in half of the CMIP5 models. The rainfall over the north India is different for a short period, in which rainfall increases in 1979–2005, implying possible decadal variation in the NIO summer climate.     

区域气候模式RegCM3对中国夏季降水的模拟

利用意大利国际理论物理中心（ICTP）最新发布的区域气候模式RegCM3检验我国包括青藏高原地区夏季降水的模拟能力。初始值及边界值取自美国国家环境预测中心（NCEP）和国家大气中心（NCAR）的全球再分析资料。模式积分时间为2005年5月1日到2005年8月31日,考虑到模式的“spin-up”时间,只对6月1日-8月31日的模式结果进行分析。模式水平分辨率取为60km,范围包括整个青藏高原在内的我国及周边地区（14°-55°N,70°-140°E）。结果表明：RegCM3具有模拟我国夏季降水主要分布特征的能力,尤其在观测站点稀少的青藏高原地区可提供局地降水分布的较可靠信息。模式较好地模拟了包括整个青藏高原在内的我国区域降水的月际尺度变化和空间分布等基本特征,但对我国东南地区的夏季降水模拟能力有待进一步提高。     

Warmer-Get-Wetter or Wet-Get-Wetter? A Criterion to Classify Oceanic Precipitation

In this study, the temporal and spatial variations of observed global oceanic precipitation during 1979–2010 are investigated. It is found that the global trend in precipitation during this period varies at a rate of 1.5%/K of surface warming while the rate is 6.6%/K during 2006–2010. The precipitation is highly correlated with Sea Surface Temperature(SST) in both the temporal and the spatial patterns since the strong 1997–98 El Nino event. Considering the distributions of precipitation and SST, seven oceanic regions are classified and presented using the observed Global Precipitation Climatology Project(GPCP) data and Extended Reconstructed Sea Surface Temperatures, version 3(ERSST.v3) data. Further examining the mechanisms of the classified oceanic precipitation regions is conducted using the Tropical Rainfall Measuring Mission(TRMM) satellite, GFDL-ESM-2G model precipitation and SST data and Hadley Center sea ice and SST version 1(Had ISST1) data. More than 85% of global oceanic precipitations are controlled by either one or both of the warmer-get-wetter mechanism and wet-get-wetter mechanism. It is estimated that a 0.5 SST signal-to-noise ratio, representing the trend of SST time series to the standard deviation, is a criterion to distinguish the mechanism of a region. When the SST ratio is larger than 0.5, the precipitation of this region is controlled by the warmer-get-wetter mechanism. SST, rather than the humidity, is the pivotal factor. On the other hand, when the SST ratio is less than 0.5, the precipitation is controlled by the wet-get-wetter mechanism. The SST variability is a significant factor contributing to the precipitation variation.     

基于LSTM神经网络的青藏高原月降水量预测

青藏高原的降水量预测不仅为该地区水资源合理规划利用提供依据,同时对中国及周边国家气候变化研究有着重要的意义。论文利用1990—2016年青藏高原降水量数据,采用长短期记忆神经网络（LSTM）对青藏高原月降水量进行预测,主要包括：① 使用青藏高原86个测站1990—2013年的月降水资料,预测各个测站2014—2016年的月降水量,并与传统的RNN、NAR、SSA和ARIMA预测模型相比,平均决定系数R²分别提高了0.07、0.15、0.13和0.36,均方根误差（RMSE）和平均绝对误差（MAE）表现更低;② 分析了降水量预测精度的空间分布特征,将各模型的R²在青藏高原地区内插值,分析R²的空间分布特征,发现所有模型降雨稀少的干旱地区和降雨多的湿润地区R²较低,在气候稳定、降水规律性明显的地区R²较高,且LSTM模型R²≥0.6的空间范围远大于传统模型;③ 分析了不同预测长度对各模型预测精度的影响,发现所有模型会随着预测长度增加而预测精度降低,但在不同的预测长度下LSTM预测的RMSE值都低于其他模型。     

Mapping daily temperature and precipitation in the Qilian Mountains of northwest China

Daily meteorological data are the critical inputs for distributed hydrological and ecological models. This study modified mountain microclimate simulation model （MTCLIM） with the data from 19 weather stations, and compared and validated two methods （the MTCLIM and the modified MTCLIM） in the Qilian Mountains of Northwest China to estimate daily temperature （i.e., maximum temperature, minimum temperature） and precipitation at six weather stations from i January 2000 to 31December 2009. The algorithm of temperature in modified MTCLIM was improved by constructing the daily linear regression relationship between temperature and elevation, aspect and location information. There are two steps to modify the MTCLIM to predict daily precipitation： firstly, the linear regression relationship was built between annual average precipitation and elevation, location, and vegetation index; secondly, the distance weight for measuring the contribution of each weather station on target point was improved by average wind direction during the rainy season. Several regression analysis and goodness-of-fit indices （i.e., Pearson＇s correlation coefficient, coefficient of determination, mean absolute error, root-mean-square error and modelingefficiency） were used to validate these estimated values. The result showed that the modified MTCLIM had a better performance than the MTCLIM. Therefore, the modified MTCLIM was used to map daily meteorological data in the study area from 2000 to 2009. These results were validated using weather stations with short time data and the predicted accuracy was acceptable. The meteorological data mapped could become inputs for distributed hydrological and ecological models applied in the Qilian Mountains.     

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets(gauge observations, inverse distance weighted(IDW) data, Tropical Rainfall Measuring Mission(TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations(CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool(SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency(NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.     

Projections of the impacts of climate change on the water deficit and on the precipitation erosive indexes in Mantaro River Basin,Peru

Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain erosive potential, and precipitation concentration in the Mantaro River Basin, in the Peruvian Andes, which is important for agriculture and energy production in Peru. We assumed the Intergovernmental Panel on Climate Change (IPCC) A1B greenhouse gas emission scenario and simulated the global climate change by the HadCM3 global climate model. Due to the steepness of the mountain slopes and the narrowness of the river valley, this study uses the downscaling of the global model simulations by the regional Eta model down to 20-km resolution. The downscaling projections show decrease in the monthly precipitation with respect to the baseline period, especially during the rainy season, between February and April, until the end of the 21st century. Meanwhile, a progressive increase in the monthly evaporation from the baseline period is projected. The Modified Fournier Index (MFI) shows a statistically significant downward trend in the Mantaro River Basin, which suggests a possible reduction in the rain erosive potential. The Precipitation Concentration Index (PCI) shows a statistically significant increasing trend, which indicates increasingly more irregular temporal distribution of precipitation towards the end of the century. The results of this study allow us to conclude that there should be a gradual increase in water deficit and precipitation concentration. Both changes can be negative for agriculture, power generation, and water supply in the Mantaro River Basin in Peru.     

气候变化情景下淮河上游流域氮排放预测研究

淮河流域是水体遭受营养盐污染较严重的地区,本研究选择淮河上游的淮滨流域（淮滨站以上,流域面积1.6万km²）为研究对象,首先构建了淮滨流域SWAT水文水质模型,然后利用2011—2017年淮滨站实测的月径流和月氨氮浓度对SWAT模型进行了校正与验证,最后基于全球气候模型（GCM）气象数据,预测了未来30年（2020—2029年、2030—2039年、2040—2049年）不同气候变化情境（RCP2.6、RCP4.5、RCP6.0、RCP8.5）下的径流、氨氮浓度和非点源总氮负荷。结果发现,径流在校正期和验证期的Nash-Suttcliffe系数均为0.79,氨氮在校正期和验证期的Nash-Suttcliffe系数均高于0.5,表明模型的适用性良好。研究发现本研究区施肥量与土地利用类型是非点源氮负荷空间分异的主导因素。2020—2049年,不同气候变化情景下,本研究区的降水量和气温均为增长趋势。假如保持基准期（2011—2016年）污染排放强度,仅考虑气候变化影响,流域内非点源污染总氮负荷将比基准期最多增加31.8%,流域出水口淮滨站的年均氨氮浓度将最多减小42.6%。本研究可以为气候变化下淮滨流域的水文水质管理提供科学支撑。