Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset

Investigation of the climate change effects on drought is required to develop management strategies for minimizing adverse social and economic impacts.Therefore,studying the future meteorological drought conditions at a local scale is vital.In this study,we assessed the efficiency of seven downscaled Global Climate Models(GCMs)provided by the NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP),and investigated the impacts of climate change on future meteorological drought using Standard Precipitation Index(SPI)in the Karoun River Basin(KRB)of southwestern Iran under two Representative Concentration Pathway(RCP)emission scenarios,i.e.,RCP4.5 and RCP8.5.The results demonstrated that SPI estimated based on the Meteorological Research Institute Coupled Global Climate Model version 3(MRI-CGCM3)is consistent with the one estimated by synoptic stations during the historical period(1990-2005).The root mean square error(RMSE)value is less than 0.75 in 77%of the synoptic stations.GCMs have high uncertainty in most synoptic stations except those located in the plain.Using the average of a few GCMs to improve performance and reduce uncertainty is suggested by the results.The results revealed that with the areas affected by wetness decreasing in the KRB,drought frequency in the North KRB is likely to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios.At the seasonal scale,the decreasing trend for SPI in spring,summer,and winter shows a drought tendency in this region.The climate-induced drought hazard can have vast consequences,especially in agriculture and rural livelihoods.Accordingly,an increasing trend in drought during the growing seasons under RCP scenarios is vital for water managers and farmers to adopt strategies to reduce the damages.The results of this study are of great value for formulating sustainable water resources management plans affected by climate change.

An Analysis of Historical and Future Temperature Fluctuations over China Based on CMIP5 Simulations

The trends and fluctuations of observed and CMIP5-simulated yearly mean surface air temperature over China were analyzed.In general,the historical simulations replicate the observed increase of temperature,but the multi-model ensemble （MME） mean does not accurately reproduce the drastic interannual fluctuations.The correlation coefficient of the MME mean with the observations over all runs and all models was 0.77,which was larger than the largest value （0.65） from any single model ensemble.The results showed that winter temperatures are increasing at a higher rate than summer temperatures,and that winter temperatures exhibit stronger interannual variations.It was also found that the models underestimate the differences between winter and summer rates.The ensemble empirical mode decomposition technique was used to obtain six intrinsic mode functions （IMFs） for the modeled temperature and observations.The periods of the first two IMFs of the MME mean were 3.2 and 7.2,which represented the cycle of 2-7-yr oscillations.The periods of the third and fourth IMFs were 14.7 and 35.2,which reflected a multi-decadal oscillation of climate change.The corresponding periods of the first four IMFs were 2.69,7.24,16.15 and 52.5 in the observed data.The models overestimate the period of low frequency oscillation of temperature,but underestimate the period of high frequency variation.The warming rates from different representative concentration pathways （RCPs） were calculated,and the results showed that the temperature will increase by approximately 0.9℃,2.4℃,3.2℃ and 6.1℃ in the next century under the RCP2.6,RCP4.5,RCP6.0 and RCP8.5 scenarios,respectively.

Scenario simulation of water retention services under land use/cover and climate changes: a case study of the Loess Plateau, China

Comprehensive assessments of ecosystem services in environments under the influences of human activities and climate change are critical for sustainable regional ecosystem management. Therefore,integrated interdisciplinary modelling has become a major focus of ecosystem service assessment. In this study, we established a model that integrates land use/cover change(LUCC), climate change, and water retention services to evaluate the spatial and temporal variations of water retention services in the Loess Plateau of China in the historical period(2000–2015) and in the future(2020–2050). An improved Markov-Cellular Automata(Markov-CA) model was used to simulate land use/land cover patterns, and ArcGIS 10.2 software was used to simulate and assess water retention services from 2000 to 2050 under six combined scenarios, including three land use/land cover scenarios(historical scenario(HS), ecological protection scenario(EPS), and urban expansion scenario(UES)) and two climate change scenarios(RCP4.5 and RCP8.5, where RCP is the representative concentration pathway). LUCCs in the historical period(2000–2015) and in the future(2020–2050) are dominated by transformations among agricultural land, urban land and grassland. Urban land under UES increased significantly by 0.63×10^(3) km^(2)/a, which was higher than the increase of urban land under HS and EPS. In the Loess Plateau, water yield decreased by 17.20×10^(6) mm and water retention increased by 0.09×10^(6) mm in the historical period(2000–2015),especially in the Interior drainage zone and its surrounding areas. In the future(2020–2050), the pixel means of water yield is higher under RCP4.5 scenario(96.63 mm) than under RCP8.5 scenario(95.46mm), and the pixel means of water retention is higher under RCP4.5 scenario(1.95 mm) than under RCP8.5 scenario(1.38 mm). RCP4.5-EPS shows the highest total water retention capacity on the plateau scale among the six combined scenarios, with the value of 1.27×10^(6) mm. Ecological restoration projects in the Loess Plateau have enhanced soil and water retention. However, more attention needs to be paid not only to the simultaneous increase in water retention services and evapotranspiration but also to the type and layout of restored vegetation. Furthermore, urbanization needs to be controlled to prevent uncontrollable LUCCs and climate change. Our findings provide reference data for the regional water and land resources management and the sustainable development of socio-ecological systems in the Loess Plateau under LUCC and climate change scenarios.

Projection of the Zhujiang(Pearl) River Delta's potential submerged area due to sea level rise during the 21st century based on CMIP5 simulations

Projections of potential submerged area due to sea level rise are helpful for improving understanding of the influence of ongoing global warming on coastal areas. The Ensemble Empirical Mode Decomposition method is used to adaptively decompose the sea level time series in order to extract the secular trend component. Then the linear relationship between the global mean sea level （GMSL） change and the Zhujiang （Pearl） River Delta （PRD） sea level change is calculated： an increase of 1.0 m in the GMSL corresponds to a 1.3 m （uncertainty interval from 1.25 to 1.46 m） increase in the PRD. Based on this relationship and the GMSL rise projected by the Coupled Model Intercomparison Project Phase 5 under three greenhouse gas emission scenarios （representative concentration pathways, or RCPs, from low to high emission scenarios RCP2.6, RCP4.5, and RCP8.5）, the PRD sea level is calculated and projected for the period 2006-2100. By around the year 2050, the PRD sea level will rise 0.29 （0.21 to 0.40） m under RCP2.6, 0.31 （0.22 to 0.42） m under RCP4.5, and 0.34 （0.25 to 0.46） m under RCP8.5, respectively. By 2100, it will rise 0.59 （0.36 to 0.88） m, 0.71 （0.47 to 1.02） m, and 1.0 （0.68 to 1.41） m, respectively. In addition, considering the extreme value of relative sea level due to land subsidence （i.e., 0.20 m） and that obtained from intermonthly variability （i.e., 0.33 m）, the PRD sea level will rise 1.94 m by the year 2100 under the RCP8.5 scenario with the upper uncertainty level （i.e., 1.41 m）. Accordingly, the potential submerged area is 8.57x103 km2 for the PRD, about 1.3 times its present area.

Characteristics and future projections of summer extreme precipitation in Sichuan Province, China

Based on the precipitation data observed by stations and data simulated by 23 CMIP5 models,the features and future changes of summer(Jun-JulAug)extreme precipitation events in Sichuan Province of China were analysed.We found that the total precipitation(RSum),extreme precipitation threshold(Threshold90),extreme precipitation(TR90),extreme precipitation percentage(TR90 pct)and extreme precipitation intensity(TR90 str)decreased from the southeast to the northwest in Sichuan Province,reflecting the differences between eastern Sichuan(ESC,basins)and western Sichuan(WSC,mountains).Compared with the observations,most of the CMIP5 models showed that there were wet biases in WSC and an unclear bias pattern in ESC for the RSum,Threshold90,TR90,and TR90 str.However,the extreme precipitation days(ND90)and TR90 pct values simulated by the models were generally overestimated and underestimated,respectively.Compared with the historical period,most models showed obvious increases in the TR90 and TR90 pct in the 21 century,while the characteristics of Rsum,ND90,and TR90 str were inconspicuous.Compared with the mid-21 st century,the extreme precipitation in the late-21 st century exhibited a certain degree of increase.Even during the same period,the results of RCP8.5 were higher than those of RCP4.5,especially for the ND90,TR90,and TR90 pct.

Effect of future climate change on the water footprint of major crops in southern Tajikistan

Danghara,a major food production area in southern Tajikistan,is currently suffering from the impact of rapid climate change and intensive human activities.Assessing the future impact of climate change on crop water requirements(CWRs)for the current growing period and defining the optimal sowing date to reduce future crop water demand are essential for local/regional water and food planning.Therefore,this study attempted to analyze possible future climate change effects on the water requirements of major crops using the statistical downscaling method in the Danghara District to simulate the future temperature and precipitation for two future periods(2021-2050 and 2051-2080),under three representative concentration pathways(RCP2.6,RCP4.5,and RCP8.5)according to the CanESM2 global climate model.The water footprint(WFP)of major crops was calculated as a measure of their CWRs.The increased projection of precipitation and temperature probably caused an increase in the main crop’s WFP for the current growing period,which was mainly due to the green water(GW)component in the long term and a decrease in the blue water(BW)component during the second future period,except for cotton,where all components were predicted to remain stable.Under three scenarios for the two future potato and winter wheat decreased from 5.7%to 4.8%and 3.4%to 2.2%,respectively.Although the WFP of cotton demonstrated a stable increase,according to the optimal sowing date,adecrease in irrigation demand or Bw was expected.The results of our study might be useful fordeveloping a new strategy related to irrigation systems and could help to find a balance betweenwater and food for environmental water demands and human use.

Examining the Impact of Bias Correction on the Prediction Skill of Regional Climate Projections

Rainfall is crucial for many applications e.g. agriculture, health, water resources, energy among many others. However, quantitative rainfall estimation is normally a challenge especially in areas with sparse rain gauge network. This has introduced uncertainties in rainfall projections by climate models. This study evaluates the performance of three representative concentration pathways, RCP i.e. 4.5, 6.0 and 8.5 over Uganda using the Weather Research and Forecasting (WRF) model. It evaluates the model output using observed daily rain gauge data over the period 2006-2018 using Pearson correlation;relative root mean square error;relative mean error and skill scores (accuracy). It also evaluates the potential improvement in the performance of the WRF model with respective RCPs by applying bias correction. The bias correction is carried out using the quantile mapping method. A poor correlation with observed rainfall is generally found (-0.4 to +0.4);error magnitudes in the ranges of 1 to 3.5 times the long-term mean are observed. The RCPs presented different performances over different areas suggesting that no one RCP is universally valid. Application of bias correction did not produce realistic improvement in performance. Largely, the RCPs underestimated rainfall over the study area suggesting that the projected rainfall cases under these RCPs could be seriously underestimated. However, the study found RCP8.5 with slightly better performance and is thus recommended. Due to the general weak performance of the RCPs, the study recommends re-evaluating the assumptions under the RCPs for different regions or attempt to improve them using data assimilation.

Assessment of climate damage in China based on integrated assessment framework

Developing a localized and consistent model framework for climate loss and damage assessment is crucial for the policy-making of climate change mitigation and adaptation.This study introduces a comprehensive,multidisciplinary Integrated Assessment Model(IAM)framework for evaluating climate damage in China,utilizing BCC-SESM climate model and FUND sectoral climate damage model under the SSP2-RCPs scenario.Employing a bottom-up approach,the research estimates climate damage across eight major sectors,recalibrates sectoral climate damage functions and parameters for China,and elucidates distinctions among direct climate loss,market climate loss,and aggregate climate loss.The findings reveal that the total climate damage function for China follows a quadratic pattern in response to temperature rise.By 2050,the estimated climate damage is projected to be 5.4%,5.7%,and 8.2%of GDP under RCP2.6,RCP4.5,and RCP8.5,respectively.Additionally,both direct and market climate losses are projected to remain below 2%of GDP by 2050,while the aggregate climate loss could reach as high as 8.2%,which is predominantly attributed to non-market sectors.From a sectoral perspective,under the RCP8.5 scenario,human health damage constitutes the largest share(61.9%)of the total climate loss by 2050,followed by sea-level rise damage(18.6%).This study sheds lights on the adaptation policy that should attach importance to the non-market sectors,particularly focusing on human health and sea-level rise.

Mapping the Global-Scale Maize Drought Risk Under Climate Change Based on the GEPIC-Vulnerability-Risk Model

Drought is projected to become more frequent and increasingly severe under climate change in many agriculturally important areas.However,few studies have assessed and mapped the future global crop drought risk—defined as the occurrence probability and likelihood of yield losses from drought—at high resolution.With support of the GEPIC-Vulnerability-Risk model,we propose an analytical framework to quantify and map the future global-scale maize drought risk at a 0.5°resolution.In this framework,the model can be calibrated and validated using datasets from in situ observations(for example,yield statistics,losses caused by drought)and the literature.Water stress and drought risk under climate change can then be simulated.To evaluate the applicability of the framework,a global-scale assessment of maize drought risk under 1.5℃warming was conducted.At 1.5℃warming,the maize drought risk is projected to be regionally variable(high in the midlatitudes and low in the tropics and subtropics),with only a minor negative(-0.93%)impact on global maize yield.The results are consistent with previous studies of drought impacts on maize yield of major agricultural countries around the world.Therefore,the framework can act as a practical tool for global-scale,future-oriented crop drought risk assessment,and the results provide theoretical support for adaptive planning strategies for drought.

Assessment of drought risk for winter wheat on the Huanghuaihai Plain under climate change using an EPIC model-based approach

Climate change-induced drought poses a serious negative impact on global crop production and food security.The Huang Huai Hai(HHH)Plain,one of the most important grain production areas in China,is heavenly stricken by drought.Motivated by formulating drought risk prevention strategies that adapt to climate change on the HHH Plain,therefore,the present study aims to quantitatively evaluate the winter wheat drought risk under multiple climate scenarios using the Environmental Policy Impact Climate(EPIC)model.Based on the well-validated EPIC model,the drought hazard intensity(dHI),physical vulnerability(pV),and drought risk(dR)of the HHH Plain from 2010 to 2099 are assessed.Temporally,the dR showed an increasing trend in the long term,the high dR areas increased by 0.63%and 1.18%under the RCP4.5 and RCP8.5 scenarios,respectively.Spatially,dR showed a pattern of high in the south and low in the north whether under RCP4.5 or RCP8.5 scenario.Comparatively,the dR was 0.211 under the RCP4.5 scenario which was slightly higher than that under the RCP8.5 scenario,i.e.0.207.The Huanghuai Plain agricultural subregion will be a high dHI-pV-dR region.The temperature increase might be the main factor affecting the wheat drought risk.

A multi-model assessment of climate change damage in China and the world

Quantifying climate damage is essential to informing rational climate policies,but only a few studies have systematically compared the climate damage estimates made by different models,especially for China.In this study,we used three widely applied integrated assessment models-FUND,RICE,and PAGE-to estimate the damage under coupled shared socioeconomic pathways and representative concentration pathways(RCPs).Results show that the costs of climate damage constitute approximately 1.5%and 0.7%of China's GDP and global GDP per 1℃ temperature rise on average,respectively.Mitigation can reduce climate risk by lowering the average estimate and worst-case effects of climate damage.Compared with business-as-usual emissions(RCP8.5),the 2℃ target will reduce the average estimate of climate damage for China and the world by 93%and 87%,respectively,and by 80%and 84%,respectively,in the worst-case situation.Sectorial analysis of climate damage highlights the inconsistency of sector scope and significant parameter uncertainties in damage modules,requiring further improvement to integrate subfield research advances,particularly for damage related to rising sea levels and cooling energy demand.

Modeling vulnerability of protected areas to invasion by Chromolaena odorata under current and future climates

Invasive plant species and climate change are among the biggest threats to the ecological integrity of many ecosystems,including those of protected areas.Effective management of invasive plants requires information regarding their spatialdistributions Using maximum entropy,wemodeled habitat suitability for an invasive plant species Chromolaena odorata under current and future climatic conditions(HadGEM2-ESand MIROC5)in protected areas of four West African countries(Benin,Cote d'voire,Ghana,and Togo).Under current climatic conditions,approximately 73%of total land area within the protected areas was suitable for colonization by C.odorata.Under future climate projections,the total area of suitable habitats for this invasive plant was projected to decrease by 7-9%(HadGEM2-ES)and 12-14%(MIROC5).Country-specific patterns suggest that major protected areas in Cote d'Ivoire and Ghana will be more vulnerable to invasion by C.odorata than those in Benin and Togo under both current and future dimatic scenarios.To maintain normal ecosystem functioning and provisioning of ecosystem services within the protected areas studied here,locations that have been identified as most vulnerable to invasion by C.odorata should be accorded proportionately higher priority when formulating appropriate management strategies.