Impact of Climate Change on the Hydrological Regime and Water Resources in the Basin of Siatista

This paper examines an assessment of the impact of climate change on hydrological regimes and water resources in the basin of Siatista, a sub-basin of the Aliakmon river basin, located in Northern Greece. Initially all acquired hydrometeorological data of the study area, as well as the hydrometric data at the outlet of the basin, were analyzed and processed. A monthly conceptual water balance model was then calibrated using historical hydrometeorological data for determining changes in streamflow runoff under two different equilibrium scenarios (UKHI, CCC) referring to the years 2020, 2050 and 2100. It was found that by applying the two scenarios there will be a reduction of the mean winter runoff values, a serious reduction of summer runoff, an increase of maximum annual runoff and a decrease of minimum annual runoff values, an increase of potential and actual evapotranspiration, leading to a decrease of soil moisture, a reduction of snow accumulation and melting due to temperature increases, resulting in a decrease of spring runoff values and a shifting of the wet period towards December, resulting in severe prolongation of the dry period.     

气候变化影响下的赣江流域水资源变化趋势与幅度分析

利用全球气候模式输出结果,经统计降尺度模型降解后得到流域尺度的降水和气温要素,根据实测资料建立气温—蒸发回归关系以及新安江水文模型,使用耦合模拟和MK趋势分析评估未来气候变化情景下赣江流域水资源量的变化趋势和幅度。研究结果表明:未来不同排放情景下的年降水量、年蒸发量和年径流量等水文气候要素变化趋势以显著增加为主。未来年降水量、年蒸发量和年径流量的多年平均值相对基准期有较小幅度增加,最大增幅为年径流量的13.81%。降水、蒸发和径流的年内变化有明显的季节性特征,汛期径流增加、非汛期径流减少的不均匀情况加剧,在一定程度上可能增加赣江流域未来的防洪压力和枯水期供水压力。     

气候变化对汉江上游径流特征影响预估

为了预测水文站逐月径流,对该流域水资源变化进行评估,运用小波神经网络建立汉江上游流域气象因子与径流过程模拟预测模型,并依据未来气候变化增量情景,对石泉水文站以上流域径流变化响应过程进行不同时间尺度分析。由已知汉江上游流域的月降水量和月平均温度,经小波神经网络自动“学习”训练获得石泉水文站精度较高的逐月径流数据。模拟计算结果表明:在不同未来气候变化设定情景下,该区域径流变化过程较为明显,年平均径流量最大变化范围为-34.7% ~ 21.4%。在降雨量不变、气温升高的情况下,年平均径流的响应变化范围为-5.1% ~ -13.3%。温度升高引起冬季径流增加较为明显,春季及秋季径流则存在减小趋势,秋季明显减少,而降雨量变化对夏季径流的影响最显著。     

新安江流域气候变化及径流响应研究

针对新安江流域新安江水库控制区域,构建新安江月水文模型,利用1979-2005年实测水文资料对模型进行率定和验证,并以CMIP5大气环流模式输出驱动水文模型,生成2006-2099年该流域在RCP2.6、RCP4.5和RCP8.5情景下的逐月径流过程。在此基础上,研究气候变化背景下流域气温、降雨、蒸发和径流的变化趋势,并对其不确定性进行分析。结果表明:2006-2099年该流域年均气温与年蒸发深度均呈上升趋势,且对于辐射强度变化较敏感,呈显著正相关关系。流域年降雨量与径流深呈波动上升趋势,对于辐射强度变化敏感性并不显著。年径流深在丰水年和平水年相对基准期有所减少,而在枯水年和特枯水年则呈增加趋势。月径流深在秋、冬季呈上升趋势,在春、夏季则呈下降趋势。     

气候变化对北江流域径流影响的模拟研究

通过构建北江流域SWAT分布式水文模型,以北江流域13个雨量站10年逐月降水量及北江流域干流石角水文站同步逐月流量数据为输入条件进行水文模型参数率定,应用气候情景设置方法研究了北江流域在降水和气温等不同气候变化条件下径流量的变化规律。研究表明:气温变化1℃对年径流量及其年内分配的影响变化均在1%以内。降水量变化对年径流量影响十分显著,降水量变化10%对年径流量的影响变化可达到15%,而对径流年内分配的影响变化在1%以内,影响较小。随着气温下降和降水量的增加,枯季径流量占年内分配比例均有所上升,枯水期来水量提高,有利于流域城乡供水安全和生态用水安全。     

汾河流域径流减少因素的定量解析

分析汾河流域 1981—2015 年气候、植被和人类取用水的演变特征。结果表明,流域年降水和净辐射分别呈 不显著（p>0.05）增加和减少趋势,叶面积指数显著（p<0.05）增加,人为取用水量波动增加,实测年径流显著（p<0.05） 减少。基于分布式水文模型和因素去趋势方法定量解析降水、净辐射、温度、叶面积指数以及取用水对汾河年径 流减少的贡献。结果表明,取用水的增加是导致汾河径流减少的主要原因,其次为温度、叶面积指数和净辐射的 变化。在上述 4 个因素无趋势情景下,实测年平均径流将分别增加 27.4%、14.4%,13.7% 和 2.1%。降水增加缓解 了年径流的减少。在年降水无趋势的情景下,实测年平均径流将减少 5.3%。上述研究结果可为汾河流域的水资 源管理和规划提供参考。     

南水北调中线工程是保证天津经济发展的重要条件

天津是北方重要的交通枢纽，综合性工业基地，现代化国际港口城市。总面积１．１万平方公里，总人口９２０万人，人均水资源量４７３立方米，仅为全国的１８％，各河进入市境的水量逐年减少。过境入海水量５０年代为１４４．３亿立方米，至８０年代降至９．８亿立方米，汛后各河基本无水可取，中央决定再次引黄救急，并于１９８３年兴建引栾入津工程。引栾水后人均水资源量也仅５９７立方米，仅占全国平均的２２．７％。最新预测，本     

基于区域水平衡理论和SWAT模型的沁河流域水收支平衡演变分析

基于区域水平衡理论和SWAT模型,提出了分布式水收支平衡模型的构建思路及水收支平衡计算方法,对人类活动和未来气候变化情景下的沁河流域水收支平衡状况进行模拟,并通过皮尔逊相关系数和Mann-Kendall检验方法分析了水收支平衡的关键影响要素及其演变规律。结果表明:2010—2016年沁河流域蓄水总量呈下降趋势,降水量及出口径流量为沁河流域水收支平衡相对关键的影响要素；未来多气候情景下蓄水总量呈上升趋势,多年平均降水量及出口径流量两个关键影响要素的总体变化情况与辐射强迫水平呈正相关关系；典型气候情景SSP2-4.5下降水量及出口径流量在2037—2041年、2061—2063年可能存在突变。     

近60年气候与土地利用变化对金沙江径流的影响

气候变化与土地利用变化会对流域降水径流过程产生影响,如何定量区分气候变化和土地利用变化对径流过程的影响,一直是国际水文水资源领域研究的热点和前沿问题。为了定量研究金沙江流域土地利用与气候变化的径流响应,构建了金沙江流域SWAT模型,采用情景模拟的方法,对土地利用变化与气候变化对径流的影响展开了研究。研究结果表明:(1)过去57 a金沙江流域实测降水量和气温的上升趋势显著,而径流量的变化趋势不明显;(2)相较于金沙江流域气温的显著升高,各种土地利用变化很小;(3)由于过去30 a间土地利用变化很小,因此其对径流量的影响微小;(4)气候变化是影响金沙江流域径流的主要因素,然而,取用水、水库建设等人类活动对径流的影响也较为显著。     

气候变化对大海波水库来水及用水影响

为分析在未来气候变化情景下大海波水库来水及用水的变化情况,选取楚雄站蒸发(1953年-2001年)、降水(1953年-2009年)、气温等气象资料,以及大海波水库(1990年-2009年)来水量资料,采用线性回归法、Mann-Kendall及Spearman趋势检测法对楚雄站点的气象要素进行趋势检测,并计算了水库下游参考作物需水量。结果表明:大海波水库的来水量呈减少趋势,下游参考作物需水量呈下降趋势;在未来气候情景下,楚雄年平均气温将升高2.56℃,年降水变化率为5.9%,气温升高时,下游参考作物需水量增加,对大海波水库的供水带来一定的不利影响。     

RCP情景下都柳江上游气候变化及径流响应分析

建立都柳江上游三水源新安江月水文模型,根据1968-2004年历史降雨、蒸发及径流数据运用遗传算法来率定和优选敏感参数,使用降尺度GCM数据驱动模型得到不同RCP情景下的流域未来月平均径流,并通过线性拟合方法分析都柳江上游未来气象要素及模拟径流过程。结果表明:2020-2099年都柳江年平均降雨基本处于稳定状态,年平均气温和蒸发呈波动增长,局部短时段下降。流域径流流量总体低于历史平均值,年际间波动降低,年内分布不均,主要集中在5-10月份。     

Response to Late developers and the inequity of “equitable utilization” and the harm of “do no harm” by Kai Wegerich and Oliver Olsson,Water International, 35 (6), 707–717 (November 2010)

Abstract

A strategy is presented for predicting impacts of future climate change on water supply capabilities, which is based on using output from a general circulation model (GCM) developed by the Canadian Center for Climate Modeling and Analysis (CCCma) with a watershed hydrology model and a river/reservoir system management model. The GCM output was used to adjust input to a watershed hydrology model in order to predict the corresponding impacts on streamflows. Output from the watershed model was used to adjust naturalized streamflows in a river/reservoir system management model in order to determine the corresponding impacts on water supply reliabilities. The methodology was applied in an investigation of capabilities for supplying water to the City of Houston and other users in the San Jacinto River Basin of Texas. Historical versus 2040 to 2059 climate scenarios were compared. Study results indicate that long-term mean streamflows under 2040 to 2059 climate conditions were higher than under historical climate due to significant increases in floods and other high flows. However, flows were lower for the future climate scenario during periods of normal and low flows. Seasonal variations in flows were greater with the future climate scenario than the historical climate. Reservoir storage fluctuations increase under future climate. Due to relatively large storage capacities, reliabilities for water supply diversions were improved somewhat under future climate conditions.     

Quantifying the Urban Water Supply Impacts of Climate Change

The difference in timing between water supply and urban water demand necessitates water storage. Existing reservoirs were designed based upon hydrologic data from a given historical period, and, given recent evidence for climatic change, may be insufficient to meet demand under future climate change scenarios. The focus of this study is to present a generally applicable methodology to assess the ability of existing storage to meet urban water demand under present and projected future climatic scenarios, and to determine the effectiveness of storage capacity expansions. Uncertainties in climatic forcing and projected demand scenarios are considered explicitly by the models. The reservoir system in San Diego, California is used as a case study. We find that the climate change scenarios will be more costly to the city than scenarios using historical hydrologic parameters. The magnitude of the expected costs and the optimal investment policy are sensitive to projected population growth and the accuracy to which our model can predict spills.     

基于Copula函数的三峡库区万州段蓄水前后降雨量-径流量关系分析

三峡水库蓄水对其库区降雨量-径流量关系变化的影响程度对于库区水资源规划有着重要意义。选用万县水文站1977-2017共计41 a的降雨量和径流量实测值,确定了各蓄水阶段降雨量和径流量的分布情况,引入Copula函数模型计算各阶段的联合分布函数,定量分析水库调蓄对两者关系的影响,预测了2017年后水文情势。结果表明：采用Copula函数联合分布数学模型能较好计算三峡库区万州段不同阶段的降雨量-径流量关系。万州段从天然河道变为库区河道后,降雨量-径流量关系发生了较大变化。在天然河道阶段,降雨量和径流量均采用皮尔逊III型分布最为合理;三峡水库工程施工期和初步蓄水阶段,降雨量变为Gumbel分布,径流量变为对数正态分布;试验性蓄水阶段,降雨量变为对数正态分布,径流量恢复至皮尔逊III型分布。年降雨量和年径流量在施工期及初步蓄水阶段较天然河道阶段均有所减少,年降雨量变幅区间减小38.4%,年径流量变幅区间减小20.6%;试验性蓄水阶段的年降雨量增多,变幅区间增大24.5%,而年径流量减少,变幅区间减小57%。通过该数学模型预测三峡库区万州段今后年径流量不小于3 490×10⁸ m³（±5%）,最大不超过4 055×10⁸ m³（±5%）;年降雨量不小于1 048 mm（±5%）,最大值不超过1 842 mm（±5%）。该研究可为三峡库区万州段流域水资源开发利用与水文序列的重构工作等提供科学依据,也可为其他库区内河道的水文特性变化关系的研究提供参考。     

基于 CORDEX 数据集的碧流河流域径流预估

为了研究气候变化对碧流河流域径流的影响,基于CORDEX联合区域降尺度试验,采用区域气候模式对GCM所涉及的两个全球气候模式进行降尺度,获得三种情景下的气象数据集,并利用气象数据集驱动率定好的SWAT水文模型,预测未来气候变化条件下碧流河流域的径流过程,分别分析年、月平均径流相对于基准期的变化特征,以及各情景模式下设计丰水年(P=10%)、设计平水年(P=50%)、设计枯水年(P=90%)的设计年径流年内分配过程相对于基准期的变化。结果表明:气候变化对碧流河流域径流的影响主要体现在汛期;C1、C3情景在RCP4.5排放路径下汛期平均径流量相对于基准期增加17.65%、0.14%,多年平均径流量增加18.17%、5.58%;其余各情景下径流量皆呈减小趋势,其中汛期减幅(-7.22%～-0.75%);多年平均减幅(-10.25%～-10.58%)。设计年径流分配过程显示,未来时期设计丰水年,平水年的年径流最大值主要出现(5—10月),较基准期汛期(6—9月)而言范围较大,设计枯水年年内分配不均匀,碧流河水库在未来时期兴利防洪调度将面临一定的挑战。     

气候变化对嘉陵江流域水资源量的影响分析

 嘉陵江是长江的最大支流,流域面积约16万km2。针对2050,2100年不同的气候变化情景,选取较为不利的参数组合,根据降水、气温、湿度、风速、日照等气候要素的变化,建立潜在蒸发量模型计算流域的潜在蒸发量（ET0）,再根据流域内植被的蒸散发系数（Kc）,计算流域的面平均蒸散发量（ETc）。并利用流域面平均降水量减去径流深得到流域的实际蒸散发量,对计算的流域面平均蒸散发量进行验证。对不同的水平年利用降水的预测成果（气候变化情景不同具有不同的降水量预测成果）及计算流域的面平均蒸散发量,根据水量平衡模型分析计算气候变化对嘉陵江流域水量的影响。结果表明：不利条件下2050年年径流将减少23.0%～27.9%;2100将减少28.2%～35.2%;2050,2100年平均年径流分别相当于目前7年一遇和12年一遇的干旱年。由此说明,气候变化对流域内的水资源量影响十分显著。      

水库流域年均径流模型的灰色神经网络分析

水库流域年均径流量受诸多因素影响，而年均径流量对水库流域水资源经济开发利用作用显著。利用改进后的灰色模型对某水库流域实测数据的年均径流模型进行了相关影响因子的建模分析，将预测值与实测值作为神经网络的训练样本，对网络进行训练并分别进行识别，同时进行误差分析对比。结果表明，灰色神经网络能对水库流域年均径流模型进行预测分析，精度较高，所得结果与实际观测数据基本相符。     

Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations

This study has evaluated the effects of improved, hedging-integrated reservoir rule curves on the current and climate-change-perturbed future performances of the Pong reservoir, India. The Pong reservoir was formed by impounding the snow- and glacial-dominated Beas River in Himachal Pradesh. Simulated historic and climate-change runoff series by the HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used delta changes in temperature (from 0° to +2 °C) and rainfall (from ?10 to +10 % of annual rainfall). Reservoir simulations were then carried out, forced with the simulated runoff scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. The results show that the historic vulnerability reduced from 61 % (no hedging) to 20 % (with hedging), i.e., better than the 25 % vulnerability often assumed tolerable for most water consumers. Climate change perturbations in the rainfall produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 % without hedging; this was improved to 26 % with hedging. The fact that improved operational practices involving hedging can effectively eliminate the impacts of water shortage caused by climate change is a significant outcome of this study.     

土地利用变化对牤牛河流域径流的影响

土地利用变化对流域水资源配置及其循环过程有显著的影响,了解土地利用与径流的关系是流域水文学研究的重要内容。以牤牛河流域为研究区,基于SWAT分布式水文模型,利用流域DEM、土地利用、土壤、气象等数据,结合GIS和RS技术,并采用极端土地利用分析方法,对流域土地利用变化的径流响应进行定量研究与分析。结果表明:流域内1998年与2009年土地均以耕地、林地、未使用地为主,约占总面积的90%;相同的气象条件下,两期土地利用情景下年、月均径流量变化趋势一致,2009年土地利用情景下的年、月径流量较1998年略小,年均径流量减少605.5万m3;7,8月份月径流模拟减少量明显;土地植被覆盖度增大径流量减小,反之则增大;在极端土地利用情景下,径流量将发生剧烈变化。因此,在不考虑研究期间内气候变化等因素的条件下,土地利用变化是影响流域径流变化的主要因素。     

Scenario Assessment of Streamflow Simulation and its Transition Probability in Future Periods Under Climate Change

The effect of climate change on water resources is an important challenge. To analyze the negative effects of this phenomenon and recommend adaptive measures, it is necessary to assess streamflow simulation scenarios and streamflow transition probabilities in future periods. This paper employs the HadCM3 (Hadley Centre Coupled Model, version 3) model to generate climate change scenarios in future periods (2010–2039, 2040–2069, and 2070–2099) and under A2 emission scenarios. By introducing climatic variable time series in future periods to the IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) hydrological model, long-term streamflow simulation scenarios are produced. By fitting statistically different distributions on runoff produced by using goodness-of-fit tests, the most appropriate statistical distribution for each month is chosen and relevant statistical parameters are extracted and compared with statistical parameters of runoff in the base period. Results show that long-term annual runoff average in the three future periods compared to the period 2000–1971 will decrease 22, 11, and 65 %, respectively. ?Despite the reduction in total runoff volume in future periods compared to the baseline period, the decrease is related to medium and high flows. In low flows, total runoff volumes for future periods compared to the baseline period will increase 47, 41, and 14 %, respectively. To further assess the impact of annual average runoff on flows, it is necessary to examine the correlation of time series using streamflow transition probabilities. To compare the streamflow transition probability in each of the future periods with base period streamflow in each month, streamflow is discretized and performance criteria are used. Results show a low coefficient of correlation and high error indicators.