Meteorological drought in semi-arid regions: A case study of Iran

Drought occurs in almost all climate zones and is characterized by prolonged water deficiency due to unbalanced demand and supply of water, persistent insufficient precipitation, lack of moisture, and high evapotranspiration. Drought caused by insufficient precipitation is a temporary and recurring meteorological event. Precipitation in semi-arid regions is different from that in other regions, ranging from 50 to 750 mm. In general, the semi-arid regions in the west and north of Iran received more precipitation than those in the east and south. The Terrestrial Climate (TerraClimate) data, including monthly precipitation, minimum temperature, maximum temperature, potential evapotranspiration, and the Palmer Drought Severity Index (PDSI) developed by the University of Idaho, were used in this study. The PDSI data was directly obtained from the Google Earth Engine platform. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) on two different scales were calculated in time series and also both SPI and SPEI were shown in spatial distribution maps. The result showed that normal conditions were a common occurrence in the semi-arid regions of Iran over the majority of years from 2000 to 2020, according to a spatiotemporal study of the SPI at 3-month and 12-month time scales as well as the SPEI at 3-month and 12-month time scales. Moreover, the PDSI detected extreme dry years during 2000-2003 and in 2007, 2014, and 2018. In many semi-arid regions of Iran, the SPI at 3-month time scale is higher than the SPEI at 3-month time scale in 2000, 2008, 2014, 2015, and 2018. In general, this study concluded that the semi-arid regions underwent normal weather conditions from 2000 to 2020. In a way, moderate, severe, and extreme dry occurred with a lesser percentage, gradually decreasing. According to the PDSI, during 2000-2003 and 2007-2014, extreme dry struck practically all hot semi-arid regions of Iran. Several parts of the cold semi-arid regions, on the other hand, only experienced moderate to severe dry from 2000 to 2003, except for the eastern areas and wetter regions. The significance of this study is the determination of the spatiotemporal distribution of meteorological drought in semi-arid regions of Iran using strongly validated data from TerraClimate.     

甘肃历史时期干旱、饥荒和虫害相关性研究及应对策略建议

根据中国甘肃省近3000年干旱、饥荒、虫害的历史资料,系统研究了甘肃省干旱的历史发生规律及其产生的影响。结果显示:甘肃省历史时期旱灾频繁且有愈演愈烈的趋势,干旱与饥荒存在较为明显的正相关,干旱引发的虫害也会加重饥荒,同时,由干旱引发的一系列问题会导致社会秩序的不稳定,在古代就表现为朝代的更替及战争。作者建议,随着全球气候变暖,甘肃省的生计将受到更加严重的影响,气候变化导致的贫困问题会进一步加剧,社区应对气候变化的政策措施需要决策机构给予尽早关注。     

干旱气候变化对甘肃省干旱灾害的影响及防旱减灾技术的研究

西北地区干旱气候变化的主要特征,表现在20世纪90年代夏季降水量有明显减少趋势,干旱化趋势主要发生在西北地区东部,干旱连年发生,尤其是甘肃省河东地区.干旱气候的产生带来了严重的干旱灾害,甘肃省90年代的干旱发生最为频繁,干旱灾情最为严重,1995年是干旱最严重的一年,成灾率达45%,其次是2000年,成灾率为35%,伏秋旱连春末初夏旱是造成夏粮严重减产的旱灾类型.干旱气候变化引发干旱化趋势非常明显,应采取综合的技术措施减轻干旱危害,要加强干旱气候与干旱灾害的监测预测和改善生态环境、优化农业结构、提高水资源利用率以及大力开发空中水资源等防御对策.     

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network(GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data(including climate and topography data), and the performance of each network model was assessed using the mean estimation error(MEE), model efficiency factor(MEF), and correlation coefficient(r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future(in 2030 and 2080) under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm~2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production(0–100 kg/hm~2) will increase while the areas with moderate, moderately high and high levels of forage production(≥100 kg/hm~2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.     

APSIM-wheat模型在我国干旱半干旱地区的适应性评价——以内蒙古地区为例

利用内蒙古6个代表性站点的春小麦田间试验数据和同期逐日气象数据对APSIM-wheat模型在内蒙古地区的适应性进行研究,确定了10个春小麦品种的作物参数。结果表明:模拟春小麦的播种至出苗、开花和成熟各阶段天数与实测天数有较好的一致性,其均方根误差(RMSE)分别为0~1.7d,0.8~4.1d和1.0~2.8d;模拟的10个春小麦品种中,地上部分生物量模拟与实测的归一化均方根误差(NRMSE)为12%~24%,10个品种产量的模拟与实测的归一化均方根误差为2%~26%,作物生育期、地上部分生物量和产量的检验结果均在可接受的范围内。说明APSIM模型对内蒙古地区春小麦生育期、地上部分生物量和产量具有较好的模拟结果,验证后的APSIM模型在内蒙古地区有较好的适应性。以上结果为今后在内蒙古等干旱半干旱地区深入开展气候变化对作物生产的影响和适应研究奠定了良好的基础。     

半干旱区农户对干旱恢复力的定量分析——以甘肃省榆中县为例

选取我国干旱半干旱地区的一个主要的气候驱动因子—干旱作为系统的一个扰动,首先分析了对于干旱恢复力研究的现状,其次选择一种定量化的方法,选取甘肃省榆中县的三个典型乡镇作为研究对象,分别计算了三个乡镇的332户农户的干旱恢复力水平。结果显示,自然经济条件较好的中部川区的恢复力水平最高,南部山区的恢复力其次,最低的是北山的干旱区域。进一步对干旱恢复力指数(DRI)的结果与所选取的各变量进行皮尔森相关分析,得出DRI与农户的文化程度、劳动力数和有效灌溉面积三个变量的相关性最高,即这三个变量最能够影响农户的干旱恢复力。     

Influence of non-stationarity and auto-correlation of climatic records on spatio-temporal trend and seasonality analysis in a region with prevailing arid and semi-arid climate,Iran

Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes, especially in arid and semi-arid regions. In this study, various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables. The Mann-Kendall test was considered to identify the trend, while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series. Meanwhile, time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones, however, after eliminating the serial correlation factor, this increasing trend changes to an insignificant decreasing trend at a 95% confidence level. The seasonal mean air temperature trend suggested a significant increase in the majority of the stations. The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semi-arid climatic zones. Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones; furthermore, most of the stations follow a decreasing trend for seasonal precipitation. Furthermore, spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming. Stationarity analysis indicated that the stationarity of climatic series influences on their trend; so that, the series which have significant trends are not static. The findings of this investigation can help planners and policy-makers in various fields related to climatic issues, implementing better management and planning strategies to adapt to climate change and variability over Iran.     

基于可变模糊评价法的农业旱灾灾后恢复力评估研究——以河北省为例

在综合考虑河北自然条件、社会经济条件,并借鉴已有干旱灾害恢复力研究成果的基础上,依据数据的可取性和详尽程度,选取降水量、复种指数、单位面积粮食产量、人均粮食产量、有效灌溉率、劳动力比重、农民人均收入和农业产投比等八个指标,构建农业旱灾灾后恢复力评价指标体系。通过层次分析法确定各指标的权重,构建可变模糊评价模型,对河北省11个市干旱灾害灾后恢复力进行评估。结果表明,石家庄旱灾灾后恢复力最高,级别特征值为4．2329,其次是唐山市为4．0046,张家口市恢复力最低,级别特征值为1．8079,各地区旱灾灾后恢复力差异明显。而在影响恢复力高低的因素中,发现不同的因素对旱灾灾后恢复力的影响程度是不同的。降水量是影响农业干旱灾害灾后恢复力的最主要自然因素,有效灌溉率在农业干旱灾害灾后恢复力评价指标体系中占较大的权重,对灾后恢复力的影响较大,农民人均收入和人均粮食产量对灾后的救助及农民生活的恢复重建也具有较重要的调节作用。     

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model (DEM) and the Landsat Enhanced Thematic Mapper (ETM), respectively. These factors were contrasted for 334 soil samples (depth of 0-30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon (SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.     

Evaluation of CRU TS,GPCC, AgMERRA,and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province,Iran

In the past few decades, meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers. Based on the literature, meteorological datasets are not more accurate than synoptic stations, but their various advantages, such as spatial coverage, time coverage, accessibility, and free use, have made these techniques superior, and sometimes we can use them instead of synoptic stations. In this study, we used four meteorological datasets, including Climatic Research Unit gridded Time Series (CRU TS), Global Precipitation Climatology Centre (GPCC), Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), Agricultural Climate Forecast System Reanalysis (AgCFSR), to estimate climate variables, i.e., precipitation, maximum temperature, and minimum temperature, and crop variables, i.e., reference evapotranspiration, irrigation requirement, biomass, and yield of maize, in Qazvin Province of Iran during 1980-2009. At first, data were gathered from the four meteorological datasets and synoptic station in this province, and climate variables were calculated. Then, after using the AquaCrop model to calculate the crop variables, we compared the results of the synoptic station and meteorological datasets. All the four meteorological datasets showed strong performance for estimating climate variables. AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature. However, their normalized root mean square error was inferior to CRU for minimum temperature. Furthermore, they were all very efficient for estimating the biomass and yield of maize in this province. For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR. But for the estimation of biomass and yield, all the four meteorological datasets were reliable. To sum up, GPCC and AgCFSR were the two best datasets in this study. This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.     

Assessment of drought hazard,vulnerability and risk in Iran using GIS techniques

The drought has enormous adverse effects on agriculture, water resources and environment, and causes damages around the world. Drought risk assessment and prioritization of drought management can help decision makers and planners to manage the adverse effects of drought. This paper aims to determine the risk of drought in Iran. At the first stage, standardized precipitation index (SPI) was calculated for the period 1981-2016. Then the probability map of different drought classes or drought hazard probability map were prepared. After that the indicator-based vulnerability assessment method was used to determine the drought vulnerability index. Five indices including climate, topography, waterway density, land use and groundwater resources were chosen as the most critical factors of drought in Iran and followed by the analytical hierarchy process questionnaire, the weights of each index were obtained based on expert opinions. Fuzzy membership maps of each index and sub-index were prepared using ArcGIS software. The drought vulnerability map of Iran was plotted using these weights and maps of each indicator. Finally, the drought risk map of Iran was provided by multiplying drought hazard and vulnerability maps. According to the 43-completed questionnaires by experts, climate index has the highest vulnerability to drought. Climate does not have an important role in drought hazard index, but it is the most crucial factor to classified drought vulnerability index. The results showed that central, northeast, southeast and west parts of Iran are at high risks of drought. There are regions with different risks in Iran due to unusual weather and climatic conditions. We realized that the climate and the groundwater situation is almost the same in the central, east and south parts of Iran, because the land use plays a crucial role in the drought vulnerability and risk in these areas. The drought risk decreases from the center of Iran to the southwest and northwest.     

Mapping desertification potential using life cycle assessment method: a case study in Lorestan Province,Iran

In recent year, desertification has become one of the most important environmental hazards all over the world, especially in developing countries such as Iran. Understanding the factors impactingon desertification and identifying the regionswith high desertification potential are essential to control this phenomenon (i.e., desertification). The life cycle assessment (LCA) method is essential in assessing the desertification of ecosystems, especially for susceptible ecosystems with high degradationrisks. The aim of the present study was to evaluate the desertification potential of Lorestan Province, Iran, based on the LCA method. We selected aridity, fire and dust as three indicators of desertification and collected data from 2000 to 2015. We divided the study area into 6 types of ecoregionsaccording to the climate types (arid, semi-arid and dry sub-humid) and dominant species (Quercus brantii and Astragalusadscendens), and calculated the characteristic factor (CF) of eachindicator (aridity, fire and dust) by combining the indicator layers and ecoregion layer of the study area. In a given ecoregion, the sum of CF values of aridity, dust and fire indicators represents the life cycle inventory (LCI) desertification value (the higher the LCI value, the greater the desertification potential).Then, we obtained the desertification potential map by combining and overlapping the ecoregions and the normalized indicators based on the LCA method. Aridity and fire exhibit significant impacts on desertification in the study area compared with dust. In the study area, semi-arid ecoregion with Quercus brantiias the dominant species is the largest ecoregion, while arid ecoregion withQuercus brantiias the dominant species is the smallest ecoregion.Arid ecoregion withAstragalusadscendensas the dominant species (LCI desertificationvalue of 1.99) and dry sub-humid ecoregion withQuercus brantiias the dominant species (LCI desertification value of0.79)show the highest and lowest desertification potentials, respectively. Furthermore, arid ecoregion with Quercus brantii as the dominant species also has a higher LCI desertification value (1.89), showing a high desertification potential. These results suggest the necessity of proper management and appropriate utilization in these ecoregions. In general, assessing desertification potential using the LCA method on a local and regional scale can possibly provide a new methodology for identifying and protecting areas with high degradation risks.     

Emergy-based study on eco-economic system of arid and semi-arid region: a case of Gansu province, China

Taking Gansu province as a model case,this study provides an integrated analysis on the eco-economic system of arid and semi-arid region based on emergy synthesis theory. Through calculating the values of renewable emergy flow,non-renewable resources,imported emergy,exported emergy,waste emergy,and total emergy during the period of 1978-2007,the performance of Gansu eco-economic system was analyzed. The results indicated that the renewable emergy flow within the province basically remained steady state which was estimated at 2.99×1022 solar emjoules (sej) from 1978 to 2007. The imported emergy and exported emergy were estimated at 3.75×1017 sej and 2.99×1020 sej in 1978 and increased to 1.07×1022 sej and 1.44×1022 sej respectively in 2007. The nonrenewable emergy flow was estimated at 1.62×1022 sej and increased to 1.85×1023 sej,with annual growth rate of 8.7%,while the estimated total emergy was 4.58×1022 sej in 1978 and increased to 2.11×1023 sej in 2007,with annual growth rate of 5.41%. Our results indicate a deteriorate situation between economic development and environmental protection in the region. The rapid economic growth in the past thirty years was based on a great consumption of nonrenewable resource and caused continuous decrease in the capacity of sustainable development. The environmental loading ratio was 0.53 in 1978,increased to 6.06 in 2007,indicating a rapid degradation of the regional environment quality. We calculated that the actual population was 1.53 times the renewable resource population in 1978,increased to 7.06 times in 2007. During the period of 1978-2007,the emergy rose from 2.45×1015 sej/(capita·a) to 8.07×1015 sej/(capita·a). Our analysis revealed that the emergy density presented a trend of gradual increase,and then the emergy currency ratio in Gansu decreased from 7.08×1013 sej/Chinese Yuan to 7.82×1012 sej/Chinese Yuan.     

Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin,Xinjiang, China

Climate change may affect water resources by altering various processes in natural ecosystems. Dynamic and statistical downscaling methods are commonly used to assess the impacts of climate change on water resources. Objectively, both methods have their own advantages and disadvantages. In the present study, we assessed the impacts of climate change on water resources during the future periods (2020-2029 and 2040-2049) in the upper reaches of the Kaidu River Basin, Xinjiang, China, and discussed the uncertainties in the research processes by integrating dynamic and statistical downscaling methods (regional climate models (RCMs) and general circulation modes (GCMs)) and utilizing these outputs. The reference period for this study is 1990-1999. The climate change trend is represented by three bias-corrected RCMs (i.e., Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA), Regional Climate Model version 4 (RegCM4), and Seoul National University Meso-scale Model version 5 (SUN-MM5)) and an ensemble of GCMs on the basis of delta change method under two future scenarios (RCP4.5 and RCP8.5). We applied the hydrological SWAT (Soil and Water Assessment Tool) model which uses the RCMs/GCMs outputs as input to analyze the impacts of climate change on the stream flow and peak flow of the upper reaches of the Kaidu River Basin. The simulation of climate factors under future scenarios indicates that both temperature and precipitation in the study area will increase in the future compared with the reference period, with the largest increase of annual mean temperature and largest percentage increase of mean annual precipitation being of 2.4°C and 38.4%, respectively. Based on the results from bias correction of climate model outputs, we conclude that the accuracy of RCM (regional climate model) simulation is much better for temperature than for precipitation. The percentage increase in precipitation simulated by the three RCMs is generally higher than that simulated by the ensemble of GCMs. As for the changes in seasonal precipitation, RCMs exhibit a large percentage increase in seasonal precipitation in the wet season, while the ensemble of GCMs shows a large percentage increase in the dry season. Most of the hydrological simulations indicate that the total stream flow will decrease in the future due to the increase of evaporation, and the maximum percentage decrease can reach up to 22.3%. The possibility of peak flow increasing in the future is expected to higher than 99%. These results indicate that less water is likely to be available in the upper reaches of the Kaidu River Basin in the future, and that the temporal distribution of flow may become more concentrated.     

Climate change,water resources and sustainable development in the arid and semi-arid lands of Central Asia in the past 30 years

The countries of Central Asia are collectively known as the five "-stans": Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan and Kazakhstan. In recent times, the Central Asian region has been affected by the shrinkage of the Aral Sea, widespread desertification, soil salinization, biodiversity loss, frequent sand storms, and many other ecological disasters. This paper is a review article based upon the collection, identification and collation of previous studies of environmental changes and regional developments in Central Asia in the past 30 years. Most recent studies have reached a consensus that the temperature rise in Central Asia is occurring faster than the global average. This warming trend will not only result in a higher evaporation in the basin oases, but also to a significant retreat of glaciers in the mountainous areas. Water is the key to sustainable development in the arid and semi-arid regions in Central Asia. The uneven distribution, over consumption, and pollution of water resources in Central Asia have caused severe water supply problems, which have been affecting regional harmony and development for the past 30 years. The widespread and significant land use changes in the 1990 s could be used to improve our understanding of natural variability and human interaction in the region. There has been a positive trend of trans-border cooperation among the Central Asian countries in recent years. International attention has grown and research projects have been initiated to provide water and ecosystem protection in Central Asia. However, the agreements that have been reached might not be able to deliver practical action in time to prevent severe ecological disasters. Water management should be based on hydrographic borders and ministries should be able to make timely decisions without political intervention. Fully integrated management of water resources, land use and industrial development is essential in Central Asia. The ecological crisis should provide sufficient motivation to reach a consensus on unified water management throughout the region.     

Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index,Normalized Difference Water Index and Standardized Precipitation Index

Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdistan Region(IKR)is located in the north of Iraq,which has also suffered from extreme drought.In this study,the drought severity status in Sulaimaniyah Province,one of four provinces of the IKR,was investigated for the years from 1998 to 2017.Thus,Landsat time series dataset,including 40 images,were downloaded and used in this study.The Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index(NDWI)were utilized as spectral-based drought indices and the Standardized Precipitation Index(SPI)was employed as a meteorological-based drought index,to assess the drought severity and analyse the changes of vegetative cover and water bodies.The study area experienced precipitation deficiency and severe drought in 1999,2000,2008,2009,and 2012.Study findings also revealed a drop in the vegetative cover by 33.3%in the year 2000.Furthermore,the most significant shrinkage in water bodies was observed in the Lake Darbandikhan(LDK),which lost 40.5%of its total surface area in 2009.The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK(correlation coefficients of 0.92 and 0.72,respectively).The relationship between SPI and NDVI-based vegetation cover was positive but not significant.Low precipitation did not always correspond to vegetative drought;the delay of the effect of precipitation on NDVI was one year.     

Implications of future climate change on crop and irrigation water requirements in a semi-arid river basin using CMIP6 GCMs

Agriculture faces risks due to increasing stress from climate change, particularly in semi-arid regions. Lack of understanding of crop water requirement (CWR) and irrigation water requirement (IWR) in a changing climate may result in crop failure and socioeconomic problems that can become detrimental to agriculture-based economies in emerging nations worldwide. Previous research in CWR and IWR has largely focused on large river basins and scenarios from the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Coupled Model Intercomparison Project Phase 5 (CMIP5) to account for the impacts of climate change on crops. Smaller basins, however, are more susceptible to regional climate change, with more significant impacts on crops. This study estimates CWRs and IWRs for five crops (sugarcane, wheat, cotton, sorghum, and soybean) in the Pravara River Basin (area of 6537 km²) of India using outputs from the most recent Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) under Shared Socio-economic Pathway (SSP)245 and SSP585 scenarios. An increase in mean annual rainfall is projected under both scenarios in the 2050s and 2080s using ten selected CMIP6 GCMs. CWRs for all crops may decline in almost all of the CMIP6 GCMs in the 2050s and 2080s (with the exceptions of ACCESS-CM-2 and ACCESS-ESM-1.5) under SSP245 and SSP585 scenarios. The availability of increasing soil moisture in the root zone due to increasing rainfall and a decrease in the projected maximum temperature may be responsible for this decline in CWR. Similarly, except for soybean and cotton, the projected IWRs for all other three crops under SSP245 and SSP585 scenarios show a decrease or a small increase in the 2050s and 2080s in most CMIP6 GCMs. These findings are important for agricultural researchers and water resource managers to implement long-term crop planning techniques and to reduce the negative impacts of climate change and associated rainfall variability to avert crop failure and agricultural losses.     

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 0.63×10³ km²/a, which was higher than the increase of urban land under HS and EPS. In the Loess Plateau, water yield decreased by 17.20×10⁶ mm and water retention increased by 0.09×10⁶ 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 (95.46 mm), 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 1.27×10⁶ 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.