Identifying vulnerable areas of aquifer under future climate change (case study: Hamadan aquifer,West Iran)

Groundwater is the main source of water in arid and semi-arid regions, so it is very important to recognize vulnerable parts of aquifer under future climate change conditions. In this research, 16 climate models were evaluated based on weighting approach. HADCM3 and CGCM2.3.2a models were selected for temperature and precipitation prediction, respectively. LARS-WG was used for downscaling AOGCMs outputs. Results show that temperature increase by 1.4 °C and precipitation changes between +10 and ?6 % under B1 and A2 emission scenario, respectively. Runoff volumes will decrease by ?39 % under A2 emission scenario whereas runoff volume will increase by +12 % under B1 emission scenario. Simulation of groundwater head variation by MODFLOW software indicates higher groundwater depletion rate under A2 scenario compared to B1 scenario. Groundwater model outputs indicate that the most vulnerable part of the aquifer is located in the southwest region. Large number of extraction wells and low aquifer transmissivity are the reasons for high vulnerability of the region.     

Comparison of logistic regressions and snowfall intensity–duration threshold as forecasting tools for direct-action snow avalanches in the Presidential Range,New Hampshire,USA

Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.     

Impacts of climate change on the hydro-climatology of the upper Ishikari river basin,Japan

Evidence for climate change impacts on the hydro-climatology of Japan is plentiful. The objective of the present study was to evaluate the impacts of possible future climate change scenarios on the hydro-climatology of the upper Ishikari River basin, Hokkaido, Japan. The Soil and Water Assessment Tool was set up, calibrated, and validated for the hydrological modeling of the study area. The Statistical DownScaling Model version 4.2 was used to downscale the large-scale Hadley Centre Climate Model 3 Global Circulation Model A2 and B2 scenarios data into finer scale resolution. After model calibration and testing of the downscaling procedure, the SDSM-downscaled climate outputs were used as an input to run the calibrated SWAT model for the three future periods: 2030s (2020–2039), 2060s (2050–2069), and 2090s (2080–2099). The period 1981–2000 was taken as the baseline period against which comparison was made. Results showed that the average annual maximum temperature might increase by 1.80 and 2.01, 3.41 and 3.12, and 5.69 and 3.76 °C, the average annual minimum temperature might increase by 1.41 and 1.49, 2.60 and 2.34, and 4.20 and 2.93 °C, and the average annual precipitation might decrease by 5.78 and 8.08, 10.18 and 12.89, and 17.92 and 11.23% in 2030s, 2060s, and 2090s for A2a and B2a emission scenarios, respectively. The annual mean streamflow may increase for the all three future periods except the 2090s under the A2a scenario. Among them, the largest increase is possibly observed in the 2030s for A2a scenario, up to approximately 7.56%. Uncertainties were found within the GCM, the downscaling method, and the hydrological model itself, which were probably enlarged because only one single GCM (HaDCM3) was used in this study.     

Assessing the effects of the climate change on land cover changes in different time periods

The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.     

Improvement of multiple linear regression method for statistical downscaling of monthly precipitation

This article aims at proposing an improved statistical model for statistical downscaling of monthly precipitation using multiple linear regression (MLR). The proposed model, namely Monthly Statistical DownScaling Model (MSDSM), has been developed based on the general structure of Statistical DownScaling Model (SDSM). In order to improve the performance of the model, some statistical modifications have been incorporated including bias correction using variance correction factor (VCF) to improve the computed variance pattern. We illustrate the effectiveness of the proposed model through its application to 288 rain gauge stations scattered in different climatic zones of Iran. Comparison between the results of SDSM and the proposed MSDSM has indicated superiority of the proposed model in reproducing long-term mean and variance of monthly precipitation. We found that the weakness of MLR method in estimating variance has been considerably improved by applying VCF. We showed that the proposed model provides a promising alternative for statistical downscaling of precipitation at monthly time scale. An investigation of the effects of climate change in different climatic zones of Iran by the use of Representative Concentration Pathways (RCPs) has shown that the most significant change is an increase in precipitation in fall and that the largest share of this increase belongs to arid climate.     

Impacts of climate change on stream flow and water quality in a drinking water source area,Northern China

Understanding the impacts of climate change on water quality and stream flow is important for management of water resources and environment. Miyun Reservoir is the only surface drinking water source in Beijing, which is currently experiencing a serious water shortage. Therefore, it is vital to identify the impacts of climate change on water quality and quantity of the Miyun Reservoir watershed. Based on long-time-series data of meteorological observation, future climate change scenarios for this study area were predicted using global climate models (GCMs), the statistical downscaling model (SDSM), and the National Climate Centre/Gothenburg University—Weather Generator (NWG). Future trends of nonpoint source pollution load were estimated and the response of nonpoint pollution to climate change was determined using the Soil and Water Assessment Tool (SWAT) model. Results showed that the simulation results of SWAT model were reasonable in this study area. The comparative analysis of precipitation and air temperature simulated using the SDSM and NWG separately showed that both tools have similar results, but the former had a larger variability of simulation results than the latter. With respect to simulation variance, the NWG has certain advantages in the numerical simulation of precipitation, but the SDSM is superior in simulating precipitation and air temperature changes. The changes in future precipitation and air temperature under different climate scenarios occur basically in the same way, that is, an overall increase is estimated. Particularly, future precipitation will increase significantly as predicted. Due to the influence of climate change, discharge, total nitrogen (TN) and total phosphorus (TP) loads from the study area will increase over the next 30 years by model evaluation. Compared to average value of 1961?~?1990, discharge will experience the highest increase (15%), whereas TN and TP loads will experience a smaller increase with a greater range of annual fluctuations of 2021 ~ 2050.     

Thermal comfort and forecast of energy consumption in Northwest Iran

Assessing the climatic characteristics and identifying the climatic parameters of a specific region can play a major role in human welfare. Thermal comfort conditions are among the most significant factors of climatic variables in the northwestern regions of Iran due to the considerable spatial and temporal variations and are vital for environmental, energy and economic management. It is therefore necessary to advance our knowledge of the climatic conditions in order to provide an appropriate tool for managing climatic extremes. This requires charting of the range of clusters of the thermal comfort conditions in this region. In this study, the general atmosphere circulation model HADCM3 and the A1 scenario, downscaled by the LARS-WG model, were employed to simulate the climatic conditions in Iran during the period 2011–2040. The data obtained were compared with sampled data from six Iranian climatic stations for the 30-year period (1961–1990). In order to tabulate this comparison, six clusters per climatic station were defined based on intrinsic similarity of data. Results show an increase in the annual average temperature of these six stations by 1.69 °C for the predicted years, projected from the base years 1961–1990. This factor has resulted in an increment of the annual average thermal comfort temperature inside buildings by a magnitude of 0.52 °C in future decades. When the thermal requirements of the studied region were evaluated based on the real temperature difference and the degree of thermal comfort, it becomes clear that apart from cluster 1, the energy required to reach thermal comfort inside buildings will increase in the future. As a result of this temperature increase, an increase of the energy required to reach the thermal comfort is expected. This new methodology is an interesting tool and needs to be seriously considered by engineers and architects in designing buildings of the future.     

Potential Salinity and Temperature Futures for the Chesapeake Bay Using a Statistical Downscaling Spatial Disaggregation Framework

Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO₂ emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.     

Potential impacts of climate change on groundwater levels on the Kerdi-Shirazi plain,Iran

It is important to predict how groundwater levels in an aquifer will respond to various climate change scenarios to effectively plan for how groundwater resources will be used in the future. Due to the overuse of groundwater resources and the multi-year drought in the Kerdi-Shirazi plain in Iran, some land subsidence and a drop in groundwater levels has taken place, and without active management, further degradation of the groundwater resource is possible under predicted future climate change scenarios in the country. To determine the potential impacts of climate change on groundwater levels in the region, the groundwater model GMS was coupled with the atmospheric circulation model HADCM3 using scenarios A1B, A2 and B1 for the period 2016–2030. The results of the climate modelling suggest that the Kerdi-Shirazi plain will experience an increase in minimum temperature and maximum temperature of, respectively, between 0.03 and 0.47, and 0.32–0.45 °C for this time period. The results of the groundwater modelling suggest that water levels on the Kerdi-Shirazi plain will continue to decline over the forecast period with decreases of 34.51, 36.57 and 33.58 m being predicted, respectively, for climate scenarios A1B, A2 and B1. Consequently, groundwater resources in the Kerdi-Shirazi plain will urgently need active management to minimize the effects of ongoing water level decline and to prevent saltwater intrusion and desertification in the region.     

Minimum, maximum, and mean annual temperatures, relative humidity, and precipitation trends in arid and semi-arid regions of Iran

One of the most important scientific concerns of the last few decades is climate change, which is the result of a great many factors like global warming. Although a number of studies have been dedicated to understand the phenomenon of climate change, more attention is required to understand the potential effects of global warming on the ecosystems as well as on human life. The present study was designed to survey the trends of minimum, maximum and mean temperatures, relative humidity, and the time series of annual precipitation and 10-year moving average low-pass filter in the 13 synoptic weather stations of Iran’s arid and semi-arid regions during the last 55 years by using τ Kendall test. The analyses indicate a significantly increasing trend for the minimum and mean temperatures while a decreasing trend for the mean relative humidity in the arid and semi-arid regions, especially during the last few years up to the year 2000. Any clear increasing or decreasing trend was not found for the maximum temperature, while the precipitation did not show any increasing/decreasing trend for most of the surveyed stations. Further studies, with long-term programming, are recommended to be carried out to evaluate the climate change and its effects on such regions.     

未来气候情景下天山西部山区融雪径流变化研究

基于三种不同模式的CMIP5气象数据,采用互信息法挑选预报因子结合RBF神经网络模型,预测不同排放情景(RCP2.6、RCP4.5、RCP8.5)下未来气候变化下天山西部山区融雪径流的变化情况。对三种模式下不同排放情景预测出的未来径流量进行分析发现:(1)未来径流量在2020～2030年将持续上升,在2060年趋于稳定;未来径流量在非汛期有大幅度的增加而在汛期径流量减少;(2)通过灰色相关性分析找到未来不同模式不同情景下影响径流的主要相关因子,对各相关因子未来变化情况进行分析,发现径流在非汛期有大幅度的增加而在汛期径流量减少的主要原因是:非汛期的降水增加而蒸发减少或增加幅度不大;汛期降水减少而蒸发随气温升高导致汛期的径流量减少。     

Episodic recharge and climate change in the Murray-Darling Basin, Australia

In semi-arid areas, episodic recharge can form a significant part of overall recharge, dependant upon infrequent rainfall events. With climate change projections suggesting changes in future rainfall magnitude and intensity, groundwater recharge in semi-arid areas is likely to be affected disproportionately by climate change. This study sought to investigate projected changes in episodic recharge in arid areas of the Murray-Darling Basin, Australia, using three global warming scenarios from 15 different global climate models (GCMs) for a 2030 climate. Two metrics were used to investigate episodic recharge: at the annual scale the coefficient of variation was used, and at the daily scale the proportion of recharge in the highest 1% of daily recharge. The metrics were proportional to each other but were inconclusive as to whether episodic recharge was to increase or decrease in this environment; this is not a surprising result considering the spread in recharge projections from the 45 scenarios. The results showed that the change in the low probability of exceedance rainfall events was a better predictor of the change in total recharge than the change in total rainfall, which has implications for the selection of GCMs used in impact studies and the way GCM results are downscaled.     

基于Budyko假设预测长江流域未来径流量变化

基于Budyko水热耦合平衡假设,推导了年径流变化的计算公式,分析了长江流域多年平均潜在蒸发量、降水量、干旱指数和敏感性参数的空间变化规律。选用BCC-CSM1-1全球气候模式和RCP4.5排放情景,把未来气候要素预估值与LS-SVM统计降尺度方法相耦合,预测长江流域未来的气温、降水和径流变化情况。采用乌江和汉江流域的长期径流观测资料,分析验证了基于Budyko公式计算年径流变化的可靠性。结果表明:降水量变化是影响径流量变化的主导因素;长江各子流域未来径流相对变化增减不一,最大变幅10%左右;在未来2020s(2010—2039年)、2050s(2040—2069年)和2080s(2070—2099年)3个时期内,长江南北两岸流域的径流将出现"南减北增"现象,北岸径流变化增幅逐渐升高,南岸径流变化减幅逐渐降低。     

Runoff Change in Taizihe River Basin Under Future Climate Change Based on HBV Model

Taking the Taizihe River Basin located in Liaoning Province as a study area, we applied HBV hydrological model to simulate the hydrological process of this river basin with the support of observed daily precipitation, mean temperature, hydrological data in Xiaolinzi hydrologic station, and global digital elevation model data from SRTM3, land utilization types, etc. According to the simulation results of daily runoff, the possible impact of future climate change on runoff was analyzed through forcing HBV model by RegCM4.4 dynamic downscaled climatic data. The results show that HBV model performed generally well for daily simulation of the Taizihe River Basin with Nash Sutcliffe coefficient and deterministic coefficient being all over 0.60 in the calibration period and validation period, and the response of flooding to precipitation were simulated better. This indicates the HBV model can be successfully applied to the Taizihe River Basin. Mean temperature will increase obviously with persistent rising trend by RegCM4.4 model in 2021-2070 under RCP4.5 scenario. Annual precipitation and runoff depth are expected to reduce a bit. Compared with the baseline period (1986-2005), annual runoff depth will increase by 9.79%. At the same time, the runoff depth will increase significantly in summer and autumn. The variation of runoff quantile indicates that both peak extreme runoff and dry extreme runoff will increase to different degrees than that in the baseline period. In the future, the Taizihe River Basin will be likely to experience extreme flooding.     

基于HBV模型的太子河流域径流变化情景预估

以太子河流域为研究区域,采用HBV水文模型对流域的水文过程进行模拟,并选取RegCM4.4区域气候模式输出的平均气温和降水数据来驱动HBV水文模型,模拟逐日径流过程,分析RCP4.5排放情景下未来太子河流域径流的演变。结果表明,HBV水文模型在太子河流域模拟效果较好,率定期与验证期Nash效率系数与确定性系数均在0.60以上,模型基本模拟出了洪水对降水的响应过程。RCP4.5情景下,2021 2070年太子河流域年平均气温呈持续升温趋势,流域降水和年径流深度呈微弱减少趋势。相较于基准期,年径流深度将增多9.79%,夏季和秋季径流深度上升明显。径流分位数的变化表明,峰值极端径流和枯水极端径流均较基准期有不同程度的增多,未来太子河流域发生极端洪涝的可能性较高。     

Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China

Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5.     

Assessment of climate change impact on the stream-flow in a typical debris flow watershed of Jianzhuangcuan catchment in Shaanxi Province, China

The study on the stream-flow change associated with future climate change scenarios has a practical significance for local socio-economic development and eco-environmental protection. A study on the Jianzhuangcuan catchments was carried out to quantify the expected impact of climate change on the stream-flow using a multi-model ensemble approach. Climate change scenarios were developed by ensemble four Global Climate Models, which showed good performance for Jianzhuangcuan catchment. Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, was used to investigate the impacts on stream-flow under climate change scenarios. The model was calibrated and validated using daily stream-flow records. The calibration and validation results showed that the SWAT model was able to simulate the daily stream-flow well, with Nash–Sutcliffe efficiency >0.83 for Yaoping Long station, for calibration and validation at daily and monthly scales. Their difference in simulating the stream-flow under future climate scenarios was also investigated. The results indicate a 0.6–0.9 °C increase in annual temperature and changes of 12.6–18.9 mm in seasonal precipitation corresponded to a change in stream-flow of about 0.62–3.67 for 2020 and 2030 scenarios. The impact of the climate change increased in both scenarios.     

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km². The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds.     

Impact of climate change on rainfed rice and options for adaptation in the lower Mekong Basin

We assessed the potential impact of climate change on the yield of rainfed rice in the lower Mekong Basin and evaluated some adaptation options, using a crop growth simulation model. Future climate projections are based on IPCC SRES A2 and B2 scenarios as simulated by ECHAM4 global climate model downscaled for the Mekong Basin using the PRECIS system. We divided the basin into 14 agro-climatic zones and selected a sub-catchment within each zone for the model and assessed the impact for the period of 2010–2030 and 2030–2050. In general, the results suggest that yield of rainfed rice may increase significantly in the upper part of the basin in Laos and Thailand and may decrease in the lower part of the basin in Cambodia and Vietnam. The increase is higher during 2030–2050 compared to the period of 2010–2030 for A2 scenario. For B2 scenario, yield increase is higher during 2010–2030. The impact is mainly due to the change in rainfall and CO₂ concentration in the atmosphere. We have tested widely used adaptation options such as changing planting date, supplementary irrigation, and reduction in fertility stress and found that negative impact on yield can be offset and net increase in yield can be achieved.