Climate change characteristics of Amur River

Unusually severe weather is occurring more frequently due to global climate change. Heat waves, rainstorms, snowstorms, and droughts are becoming increasingly common all over the world, threatening human lives and property. Both temperature and precipitation are representative variables usually used to directly reflect and forecast the influences of climate change. In this study, daily data (from 1953 to 1995) and monthly data (from 1950 to 2010) of temperature and precipitation in five regions of the Amur River were examined. The significance of changes in temperature and precipitation was tested using the Mann-Kendall test method. The amplitudes were computed using the linear least-squares regression model, and the extreme temperature and precipitation were analyzed using hydrological statistical methods. The results show the following: the mean annual temperature increased significantly from 1950 to 2010 in the five regions, mainly due to the warming in spring and winter; the annual precipitation changed significantly from 1950 to 2010 only in the lower mainstream of the Amur River; the frequency of extremely low temperature events decreased from 1953 to 1995 in the mainstream of the Amur River; the frequency of high temperature events increased from 1953 to 1995 in the mainstream of the Amur River; and the frequency of extreme precipitation events did not change significantly from 1953 to 1995 in the mainstream of the Amur River. This study provides a valuable theoretical basis for settling disputes between China and Russia on sustainable development and utilization of water resources of the Amur River.     

Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee,India

Intensification and frequency of hydrologic events are attributed to climate change and are expected to increase in coming future. Intensity-Duration-Frequency (IDF) curves quantify the extreme precipitation and are used extensively to assess the return periods of rainfall events. It is expected that climate change will modify the occurrence of extreme rainfall events. Thus a need of updating IDF curves arises under the climate change scenario. This paper aims at updating the IDF curves for a typical Indian town using an ensemble of five General Circulation Models (GCMs) for all the Representative Concentration Pathways (RCP) scenarios. Sub-daily maximum intensities (15-, 30-, 45-, 60-, 120-, and 180 min) were obtained from the observed records. Equidistance quantile method was used to study the relationships between the historical and projected GCM data, and the historical GCM and observed sub-daily data. This relationship was used to obtain projected sub-daily intensities. The IDF curves were developed using observed and projected data. Analysis of the curves indicated increase in precipitation intensities for all the RCP scenarios. It was also found that intensities of all return periods increases with intensifying RCP scenarios. The variation in the intensities across the GCMs was attributed to the driving forces considered in a particular GCM.     

Evaluating Future Joint Probability of Precipitation Extremes with a Copula-Based Assessing Approach in Climate Change

Changes in climate extremes may cause the variation of occurrence and intensity of floods and droughts. To investigate the future changes in joint probability behaviors of precipitation extremes for water resources management, an approach including three stages for analyzing the spatial variation of joint return periods of precipitation extremes is proposed in this paper. In the first stage, a weather generator model (WGM) was conducted with general circulation models (GCMs) under representative concentration pathway (RCP) scenarios to generate daily rainfall time series during 2021–2040 (S) and 2081–2100 (L) based on the statistics of the observed rainfall data. Four extreme precipitation indices are defined to represent extreme precipitation events. In the second stage, copula methods are adopted to establish the joint distribution of the precipitation extreme indices. The watershed-scale assessment of flood and drought applied in Shih-Men reservoir in northern Taiwan is conducted to demonstrate the possible change of joint return period. In the third stage, the change rates of joint return periods for bivariate extreme indices are demonstrated to present the occurrence possibility of floods or droughts in the future. The results indicate that floods and droughts might occur more frequently in the upstream region of the reservoir during the twenty-first century. The reservoir operations would be more important for water supply and flood mitigation. In conclusion, the possible changes of future joint probability of the precipitation extremes should be paid attention to for water resources management and draft plans to confront potential challenges in the future.     

1961-2005年黄河流域极端气候事件变化趋势

利用黄河流域58个气象站点1961-2005年的逐日平均气温、最高气温、最低气温和逐日降水量数据,采用百分比阈值法定义极端气温和极端强降水事件,计算气象综合干旱指数(CI),并分析了黄河流域极端气候事件的变化趋势及其空间格局.结果表明:黄河流域极端低温和极端高温天数分别呈减少和增加趋势,平均速率分别为-3.8d/10a和1.7d/10a;年极端强降水总量的变化趋势存在着明显的区域差异,河源区增加最显著,而中游的黄土高原中、东部减少最显著;干旱天数呈减少趋势,河源区减幅最大,河套-宁夏平原以及鄂尔多斯高原西北部的减幅最小.     

深圳市1953-2012年极端气候事件变化分析

全球气候变化导致极端气候事件频发,选择描述极端气候事件的相关指标,利用Mann-Kendall趋势检验法等对深圳市1953-2012年极端气候指标进行分析。结果表明:总体上深圳市降水频次与极端降水量均呈现起伏不定的变化规律,四季极端降水量主要集中在夏季,全年和四季降水量均没有明显的变化趋势。深圳市极端气温总体呈现较为显著稳定的变化状况;极端高温事件不断增加,极端低温事件不断减少,且趋势显著,同一季节的气温差异性较大,且差异性呈现出比较显著的加大趋势。     

Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.     

乌江中上游地区极端降水特征及未来预估

选取乌江中上游地区1961-2019年的逐日降水数据,采用趋势分析、EOF、小波分析等方法,研究极端降水在时空上的变化特征,同时为了使研究具有整体性,利用CMIP6中5个GCMs下的3种情景数据(SSP126、SSP245、SSP585),在降尺度处理后预估未来(2020-2100年)极端降水的变化。结果表明：1961-2019年整个流域地区极端降水事件虽然有增有减,但无显著性变化;在变化周期上,信号强烈的周期主要在23～30 a的时间尺度上,且贯穿整个时序;5个极端降水指数的第1模态表明其在空间变化上具有一致性,第2模态则有差异;未来极端降水事件整体上随SSPs情景的升高而愈发显著,且多以正趋势为主。研究结果可为乌江流域地区水安全管控、规划建设、防灾减灾等提供参考。     

区域气候模式RegCM3 初始和侧边界条件的敏感性分析

不同初始和侧边界条件的选取对区域气候模式的模拟结果有着直接影响。本文分别采用NNRP1和ERA40两套应用广泛的全球再分析资料作为区域气候模式RegCM3的初始场和侧边值,采用20km水平分辨率和An thes-Kuo积云对流参数化方案对海河流域1998年夏季(6—8月)降雨和温度进行了模拟。结果表明:两套资料驱动下模式都能够模拟出海河流域降雨和温度的大尺度空间分布特征。从降雨中心、高(低)温中心位置和分布范围上,使用ERA40资料要好于NNRP1资料;从不同时间(日、月、季)尺度站点降雨和温度模拟与观测值上,ERA40资料也好于NNRP1资料,但两套资料驱动下对降水极值过程模拟效果均不理想。就本次模拟而言,ERA40再分析资料的可信度更高,更适合海河流域气候变化的数值模拟研究。     

多模式下泾河上游流域未来降水变化预估

利用站点实测资料、GCMs 月数据对 GCMs 进行秩评分评估排序, 从 21 种 GCMs 模式优选出的 6 种 GCM模式的日数据、6 种 GCM 集成的气候模式、站点实测资料和 NCEP 再分析资料构建统计降尺度模型 SDSM, 预估泾河上游流域的未来降水变化。结果表明: 构建的降尺度模型对降水模拟较为可靠, 率定期各模式决定系数 R2 为 0.228~ 0.324, 标准误差为 0.354~ 0.450, 率定期和验证期模拟月均降水与实测值年内分布相近。在降尺度性能评价中集成模式表现最好。在 RCP 4.5 情景下, 泾河上游流域未来降水大多数模式和集成模式呈增加趋势, 到 2030 年泾河上游流域降水量将增加 4.8% , 且当地的春季雨量会增加, 夏季雨量会减少。     

A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling

Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.

     

赣南地区近58年来极端气候变化趋势分析

极端天气事件对人类和自然环境的影响巨大,为评估其变化趋势,采用Mann-Kendall趋势检验和线性倾向估计法分析了赣南地区1956—2013年基于气温和极端降水的8个指标的变化趋势。结果表明:①年降雨总量没有显著变化,季节性变化差异较大;②气温在年尺度和季节尺度变化一致,几乎全部站点年最高气温和年最低气温都有显著上升趋势,且秋季上升幅度最大,其最低气温最大上升幅度为每10 a上升0.39 ℃;除春季外,日温差均为减小趋势;③极端降水事件有增加趋势,年尺度上最大日降雨量和最大3日降雨量均有显著增加趋势,冬季增加趋势更加显著。本研究结果显示赣南地区气温变化趋势与全球变暖保持一致,极端降水事件也在加剧。     

金沙江流域极端强降水分布拟合

极端降水的统计分布研究是当前国内外极端水文事件研究的前沿方向之一。根据金沙江流域45个气象站的长系列日降水资料,采用年极大值抽样方法建立了不同统计时段(最大1 d、最大3 d、最大7 d等)的降水极值系列,通过比选11种常见的概率分布函数确定了金沙江流域极端强降水的最优分布。对基于站点与面暴雨的2套极端强降水序列的频率分析表明,韦克比分布是拟合金沙江流域极端强降水发生概率的最优分布形式。     

Evaluating the Utility of IPCC AR4 GCMs for Hydrological Application in South Korea

Understanding the uncertainty of climate models in space and time is necessary to help water resources managers and hydrologists in the selection of appropriate model for a specific application. In this paper, we use three separate methods to evaluate and compare the utility of 14 climate models for seven basins with area range of 2,656–26,355 km² on the South Korean Peninsula. On the one hand, the method of probabilistic uncertainty analysis is used to evaluate the capability of the studied General Circulation Models (GCMs) in recognizing the extreme events. On the other hand, we use two statistical tests (correlation coefficient and root mean square error) to examine the capability of the GCMs in simulating quantitatively each event. The results show that, for the first method, the performance of climate model varies depending on the number of climate model nodes used for a specific application of given basin, especially for monthly time scale. In addition, we find that, there are several GCMs showing good results for the probabilistic uncertainty test but poor results for the statistical test and conversely. Therefore, climate models should be evaluated for specific applications and specific regions. The results indicated quite clearly that, it is not easy to select an optimal climate model which can satisfy both applications using precipitation and temperature projections. However, the results of this study suggest that, there are several GCMs which are more useful than the others for general hydrological application in South Korean peninsula.     

The effect of climate change on urban drainage: an evaluation based on regional climate model simulation.

That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 x 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at timescales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.     

Impact of Spatial Data Availability on Climate Change Prediction in the Weyib River Basin in Ethiopia

Impact of spatial data availability on the temperature and precipitation prediction characteristics of Weyib River basin in Ethiopia has been investigated using CMIP5-CanESM2 model for the RCP8.5, RCP4.5 and RCP2.6 scenarios. The objective of the present study is to characterize how future temperatures and precipitation prediction under CMIP5-CanESM2 model output varies against diverse averaged arbitrary spatial weather stations found in the basin. The statistical downscaling model tested and verified using the observed daily data for twelve, six and three averaged arbitrary spatial weather stations as well as for a single weather station was used to predict the future climate scenarios. The results revealed that the mean annual daily maximum and minimum temperature and precipitation for twelve, six and three arbitrary spatial stations have revealed an increasing trend in the upcoming periods until the end of the century. In single station analysis, the trend itself has changed from increasing trend to decreasing trend in case of maximum and minimum temperature. In case of precipitation, no visible trend has been observed in case of single station analysis. Therefore, the variation in amount and distribution of precipitation and temperature among the four averaged spatial stations in the same study area might affect the water resources and agriculture of the basin and also instead of using a single weather station to predict future climate variables for a particular study basin, it is more reliable using averages of numerous spatial weather stations data.     

Uncertainty Modeling of Statistical Downscaling to Assess Climate Change Impacts on Temperature and Precipitation

Consideration of different Statistical Downscaling (SD) models and multi-sources global climate models’ (GCMs) data can provide a better range of uncertainty for climatic and statistical indices. In this study, results of two SD models, ASD (Automated Statistical Downscaling) and SDSM (Statistical Downscaling Model), were used for uncertainty analysis of temperature and precipitation prediction under climate change impacts for two meteorological stations in Iran. Uncertainty analysis was performed based on application of two GCMs and climate scenarios (A2, A1B, A2a and B2a) for 2011–2040, 2041–2070 and 2071–2100 future time slices. A new technique based on fuzzy logic was proposed and only used to describe uncertainties associated with downscaling methods in temperature and precipitation predictions. In this technique, different membership functions were defined to fuzzify results. Based on these functions width, precipitation had higher uncertainty in comparison with the temperature which could be attributed to the complexity of temporal and local distribution of rainfall. Moreover, little width of membership functions for temperatures in both stations indicated less uncertainty in cold months, whereas the results showed more uncertainty for summer. The results of this study highlight the significance of incorporating uncertainty associated with two downscaling approaches and outputs of GCMs (CGCM3 and HadCM3) under emission scenarios A2, A1B, A2a and B2a in hydrologic modeling and future predictions.     

气候变化下黄河流域未来水资源趋势分析

开展流域水资源变化趋势研究是水资源规划和开发利用的基础工作。基于RCPs（Representative Concentration Pathways）排放情景下7个全球气候模式的气候情景资料,分析了黄河流域未来气温及降水的变化趋势;采用RCCC-WBM模型动态模拟了黄河流域未来水资源情势。结果表明:黄河流域在未来30年（2021—2050年）气温将持续显著升高（线性升率为0.24～0.35 ℃/（10 a））;与基准期（1961—1990年）相比,流域降水总体可能增多,但对降水变化预估的不确定性较大;受气候变化影响,黄河流域未来水资源量较基准期的可能会略微偏少,流域水资源供需矛盾可能进一步加剧;不确定性及其带来的评估风险是目前及未来气候变化影响及水资源评估中需要加强研究的重要内容。     

Assessment of Extreme Precipitation in Future through Time-Invariant and Time-Varying Downscaling Approaches

Skill of a time-varying downscaling approach, namely Time-Varying Downscaling Model (TVDM), against time-invariant Statistical Downscaling Model (SDSM) approach for the assessment of precipitation extremes in the future is explored. The downscaled precipitation is also compared with a Regional Climate Model (RCM) product obtained from Coordinated Regional Climate Downscaling Experiment (CORDEX). The potential of downscaling the extreme events is assessed considering Bhadra basin in India as the study area through different models (SDSM, TVDM and RCM) during historical period (calibration: 1951–2005, testing: 2006–2012). Next, the changes in precipitation extremes during future period (2006–2035) have been assessed with respect to the observed baseline period (1971–2000), for different Representative Concentration Pathway (RCP) scenarios. All the models indicate an increasing trend in the precipitation, for the monsoon months and maximum increase is noticed using RCP8.5. The annual precipitation during the future period (RCP8.5) is likely to increase by 7.6% (TVDM) and 4.2% (SDSM) in the study basin. An increase in magnitude and number of extreme events during the future period is also noticed. Such events are expected to be doubled in number in the first quarter of the year (January–March). Moreover, the time-invariant relationship (in SDSM) between causal-target variables is needed to be switched with time-varying (TVDM). This study proves that the time-varying property in TVDM is more beneficial since its performance is better than SDSM and RCM outputs in identifying the extreme events during model calibration and testing periods. Thus, the TVDM is a better tool for assessing the extreme events.

     

近50年辽宁省极端气候事件的趋势变化及空间特征

为明确辽宁省近50年极端气候事件的趋势变化特征以及时空分布情况,本文以辽宁省1964—2015年逐日平均气温、最低气温、最高气温以及降水数据为基础,选取省域范围内的23个气象站点,依据世界气象组织(WMO)确定的"气候变化检测和指标",选取适用于研究区的8个极端气温指数和4个降水指数,采用线性趋势法、Mann-Kendall突变检验法及反距离加权插值法,分析近50年来辽宁省极端气温和降水事件的时空演变特征。结果表明:时间尺度上,表征极端高温事件的极端最高温、夏日日数、暖昼、暖夜日数逐渐增多且在未来一段时间具有持续上升趋势;极端低温事件指数如霜冻日数、冷昼数、冷夜数均表现为下降趋势变化;极端降水指数除普通日降水强度较为平稳外均呈上升趋势,辽宁省极端降水事件增加。空间尺度上,极端最高温、最低温、霜冻日数、夏日日数呈由北向南递减的规律,冷夜、冷昼、暖夜、暖昼数呈不规律分布。与降水量相关的极端降水指数呈南高北低,东高西低的格局。     

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.