Numerical modeling and prediction of future response of permafrost to different climate change scenarios on the Qinghai–Tibet Plateau

Projecting the future distribution of permafrost under different climate change scenarios is essential, especially for the Qinghai–Tibet Plateau (QTP). The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The simulation results show the following: (1) from now until 2070, the permafrost will experience different degrees of significant degradation under the four RCP scenarios. This will affect 25.68%, 40.54%, 45.95%, and 62.84% of the current permafrost area, respectively. (2) The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs. (1) In RCP2.6, the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070. (2) In RCP4.5, the rate of permafrost loss during the period 2030–2050 (about 12.73%) is higher than between 2050 and 2070 (about 8.33%). (3) In RCP6.0, the permafrost loss rate for the period 2030–2050 (about 16.52%) is similar to that for 2050–2070 (about 16.67%). (4) In RCP8.5, there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070: the rate is only about 3.70% for the first period but about 29.49% during the second.     

Assessment of surface and subsurface waterlogging,water level fluctuations,and lithological variations for evaluating groundwater resources in Ganga Plains

Abstract

In the present study, the multi-temporal satellite images of IRS P6 LISS III were used to map waterlogging dynamics over different seasons. An area of 594.36 km² (6.75%) and 4.17 km² (0.04%) was affected by surface waterlogging during pre and postmonsoon season, respectively. The average annual groundwater level fluctuations were calculated using 18 years (1990–2007) pre and postmonsoon groundwater level data to identify the areas which are under groundwater induced waterlogging conditions. The soil map clearly indicates that salinity and sodicity exhibit the highest severity and occur in areas with shallow groundwater levels. The hydrogeomorphical units mapped using IRS P6 LISS III satellite images are flood plain, alluvial plain, paleochannels, and oxbow lakes. The study revealed that 44.65% areas have very good to excellent groundwater resources. The litholog data clearly indicate an alternating sequence of clay and sand in which deep aquifers made up of coarse sand would be best suited for adequate water supply and good groundwater quality. The integrated study utilizing digital spatial data pertaining to waterlogging, soil salinity, water level fluctuation, and lithological variation proved that planning of any surface and subsurface water resources development activity should be taken up after assessments of said parameters.     

NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.     

Development of groundwater favourability map using GIS-based driven data mining models: an approach for effective groundwater resource management

The development of groundwater favourability map is an effective tool for the sustainability management of groundwater resources in typical agricultural regions, such as southern Perak Province, Malaysia. Assessing the potentiality and pollution vulnerability of groundwater is a fundamental phase of favourability mapping. A geographic information system (GIS)-based Boolean operator of a spatial analyst module was applied to combine a groundwater potentiality map (GPM) model and a groundwater vulnerability to pollution index (GVPI) map, thereby establishing the favourable zones for drinking water exploration in the investigated area. The area GPM model was evaluated by applying a GIS-based Dempster–Shafer–evidential belief function model. In the evaluation, six geoelectrically determined groundwater potential conditioning factors (i.e. overburden resistivity, overburden thickness, aquifer resistivity, aquifer thickness, aquifer transmissivity and hydraulic conductivity) were synthesized by employing the probability-based algorithms of the model. The generated thematic maps of the seven hydrogeological parameters of the DRASTIC model were considered as pollution potential conditioning factors and were analysed with the developed ordered weighted average–DRASTIC index model algorithms to construct the GVPI map. Approximately 88.8 and 85.71% prediction accuracies for the Groundwater Potentiality and GVPI maps were established using the reacting operating characteristic curve method and water quality status–vulnerability zone relationship scheme, respectively. Finally, the area groundwater favourability map (GFM) model was produced by applying a GIS-based Boolean operator on the Groundwater Potentiality and GVPI maps. The GFM model reveals three distinct zones: ‘not suitable’, ‘less suitable’ and ‘very suitable’ zones. The area analysis of the GFM model indicates that more than 50% of the study area is covered by the ‘very suitable’ zones. Results produce a suitability map that can be used by local authorities for the exploitation and management of drinking water in the area. The study findings can also be applied as a tool to help increase public awareness of groundwater issues in developing countries.     

Land subsidence under different land use in the eastern Beijing plain,China 2005-2013 revealed by InSAR timeseries analysis

Land subsidence has been occurring in Beijing since the 1970s. Five major land subsidence areas have been formed: Dongbalizhuang–Dajiaoting, Laiguangying, Changping Shahe–Ba Xianzhuang, Daxing Yufa–Lixian, and Shunyi–Ping Gezhuang. In this paper, we studied on land subsidence in Dongbalizhuang–Dajiaoting and Laiguangying using small baseline subset interferometry and interferometric point target methods of 47 ENVISAT ASAR and 29 RADARSAT-2 data. The results showed that the degree of land subsidence in these areas varied significantly. The mean land subsidence rate ranged from 143.43 to 8.2 mm/a and from 132.11 to 7.3 mm/a during 2005–2010 and 2011–2013, respectively. We correlated the observed settlement with the land use (agricultural, residential, and industrial). Displacement in the agricultural areas was greater than that in the other areas from 2005 to 2013. Moreover, we compared the observed deformation and the groundwater level in phreatic and confined aquifers. There was a strong correlation between ground subsidence and the groundwater level and the ground settlement increased with a decrease in the groundwater level and the maximum correlation coefficient can reach 0.525. Furthermore, subsidence appeared to be associated with compressible deposits, suggesting that for 90–210-m thick compressible deposits, ground settlement is more likely to occur as the thickness of the compressible layer increases.     

基于AHP和GIS的扬泰地区浅层地下水脆弱性评价

地下水脆弱性评价是保护地下水资源的重要手段,本文根据扬泰地区浅层地下水的水文地质条件,对DRASTIC模型进行了修改。选取地下水位埋深、大气降水入渗净补给量、含水层介质、含水层厚度、地形坡度、包气带介质影响、含水层富水性7个因子作为评价因子,利用层次分析法确定各因子的权重值,建立了符合扬泰地区浅层地下水脆弱性评价模型;利用ArcGIS10．0软件的空间叠加分析功能,编制了扬泰地区浅层地下水脆弱性评价等级图,评价结果与该地区实际水文地质条件基本吻合,对扬泰地区的地下水资源保护和区域规划具有一定的参考价值。     

利用GRACE时变重力场反演黑河流域水储量变化

黑河流域水储量变化对该区域的生态环境和经济建设等具有重要影响。本文利用2002年8月至2011年6月GRACE（Gravity Recovery and Climate Experiment）时变重力场模型GRGS-EIGEN-GL04,采用去相关滤波P3M6与300km高斯滤波相结合的滤波方法反演了黑河流域陆地水储量变化,扣除GLDAS（Global Land Data Assimilation System）水文模型计算的土壤水和冰雪变化,给出了黑河流域地下水储量的时空变化,并利用张掖地区23口地下水测井数据对地下水反演结果进行了初步验证。研究结果表明：（1）黑河流域陆地水储量整体上呈现减少趋势,与该流域气候变化和CPC水文模型的计算结果具有较好的一致性,其减少速率为2.3cm/a等效水高;（2）黑河流域地下水储量呈现长期减少趋势,其减少速率为2.5cm/a等效水高,上、中游区域地下水储量减少速率相当,下游区域地下水储量减少速率明显小于中上游区域。     

Biodiversity Conservation in the REDD

Background

Forests occur across diverse biomes, each of which shows a specific composition of plant communities associated with the particular climate regimes. Predicted future climate change will have impacts on the vulnerability and productivity of forests; in some regions higher temperatures will extend the growing season and thus improve forest productivity, while changed annual precipitation patterns may show disadvantageous effects in areas, where water availability is restricted. While adaptation of forests to predicted future climate scenarios has been intensively studied, less attention was paid to mitigation strategies such as the introduction of tree species well adapted to changing environmental conditions.

Results

We simulated the development of managed forest ecosystems in Germany for the time period between 2000 and 2100 under different forest management regimes and climate change scenarios. The management regimes reflect different rotation periods, harvesting intensities and species selection for reforestations. The climate change scenarios were taken from the IPCC's Special Report on Emission Scenarios (SRES). We used the scenarios A1B (rapid and successful economic development) and B1 (high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development). Our results indicate that the effects of different climate change scenarios on the future productivity and species composition of German forests are minor compared to the effects of forest management.

Conclusions

The inherent natural adaptive capacity of forest ecosystems to changing environmental conditions is limited by the long life time of trees. Planting of adapted species and forest management will reduce the impact of predicted future climate change on forests.     

Modeling projected changes of mangrove biomass in different climatic scenarios in the Sunda Banda Seascapes

ABSTRACT

Mangroves are critical in the ecological, economic and social development of coastal rural and urban communities. However, they are under threat by climate change and anthropogenic activities. The Sunda Banda Seascape (SBS), Indonesia, is among the world’s richest regions of mangrove biomass and biodiversity. To inform current and future management strategies, it is critical to provide estimates of how mangroves will respond to climate change in this region. Therefore, this paper utilized spatial analysis with model-based climatic indicators (temperature and precipitation) and mangrove distribution maps to estimate a benchmark for the mangrove biomass of the SBS in six scenarios, namely the Last Inter-glacial Period, the current scenario (1950–2000) and all four projected Representative Concentration Pathways in 2070 due to climate change. Despite mangroves gaining more biomass with climate change (the increase in CO₂ concentration), this paper highlighted the great proportion of below-ground biomass in mangrove forests. It also showed that the changes in spatial distribution of mangrove biomass became more variable in the context of climate change. As mangroves have been proposed as an essential component of climate change strategies, this study can serve as a baseline for future studies and resource management strategies.     

Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques

Tropical cyclones and their devastating impacts are of great concern to coastal communities globally. An appropriate approach integrating climate change scenarios at local scales is essential for producing detailed risk models to support cyclone mitigation measures. This study developed a simple cyclone risk-modelling approach under present and future climate change scenarios using geospatial techniques at local scales, and tested using a case study in Sarankhola Upazila from coastal Bangladesh. Linear storm-surge models were developed up to 100-year return periods. A local sea level rise scenario of 0.34?m for the year 2050 was integrated with surge models to assess the climate change impact. The resultant storm-surge models were used in the risk-modelling procedures. The developed risk models successfully identified the spatial extent and levels of risk that match with actual extent and levels within an acceptable limit of deviation. The result showed that cyclone risk areas increased with the increase of return period. The study also revealed that climate change scenario intensified the cyclone risk area by 5–10% in every return period. The findings indicate this approach has the potential to model cyclone risk in other similar coastal environments for developing mitigation plans and strategies.     

Spatiotemporal variations in heat-related health risk in three Midwestern US cities between 1990 and 2010

Mortality from extreme heat is a leading cause of weather-related fatality, which is expected to increase in frequency with future climate scenarios. This study examines the spatiotemporal variations in heat-related health risk in three Midwestern cities in the USA between the years 1990 to 2010; cities include Chicago, Illinois, Indianapolis, IN and Dayton, OH. In order to examine these variations, we utilize the recently developed extreme heat vulnerability index (EHVI) that uses a principal components solution to vulnerability. The EHVI incorporates data from the US Decadal Census and remotely sensed variables to determine heat-related vulnerability at an intra-urban level (census block group). The results demonstrate significant spatiotemporal variations in heat health risk within the cities involved.     

ArcGIS地统计分析在地下水脆弱性评价中的应用

地下水是水资源的重要组成部份,地下水污染危害人的健康,影响人们的生产和生活,查明某一地区地下水容易受污染的可能性即地下水脆弱性,能为管理决策部门提供合理开发地下水资源,防治地下水污染的科学规划和管理依据。在脆弱性评价工作中,应用当前国际上最先进的地理信息系统平台ArcGIS,并结合地统计分析原理,完成地下水系统脆弱性编...     

GIS技术在地下水系统脆弱性编图示范中的应用

主要论述应用GIS完成地下水系统脆弱性编图的技术方法,查明某一示范区地下水固有脆弱性,并进行地下水环境保护功能分区,以保护地下水资源免受污染或是减少到最低限度,为城市规划和建设服务。     

Trends and Change-Point in Satellite Derived Phenology Parameters in Major Wheat Growing Regions of North India During the Last Three Decades

The virtual certainty of the anticipated climate change will continue to raise many questions about its aggregated impact of environmental changes on our regional food security in imminent future. Crop responses to these changes are certain, but its exact characteristics are hardly understood at regional scale due to complex overlapping effects of climate change and anthropogenic manipulation of agro-ecosystem. This study derived phenology of wheat in north India from satellite data and analyzed trends of phenology parameters over last three decades. The most striking change-point period in phenology trends were also derived. The phenology was derived from two sources: (1) STAR-Global vegetation Health Products-NDVI, and (2) GIMMS-NDVI. The results revealed significant earliness in start of growing season (SOS) in Punjab and Haryana while delay was found in Uttar Pradesh (UP). End of the wheat season almost always occurred early, to even those place where SOS was delayed. Length of growing season increased in most of Punjab and northern Haryana whereas its decrease dominated in UP. The early sowing practice of the farmers of the Punjab and Haryana may be one of the adaptation strategies to manage the terminal heat stress in reproductive stage of the crop in the region. The change-point occurred in late 1990s (1998–2000) in Punjab and Haryana, while in eastern UP it was in early 1990s (1990–1995). Despite the difference in temporal aggregation and spatial resolution, both the datasets yielded similar trends, confirming both the robustness of the results and applicability of the datasets over the region. The results demands further research for proper attribution of the effects into its causes and may help devising crop adaption practices to climatic stresses.     

Bayesian Hierarchical ANOVA of Regional Climate-Change Projections from NARCCAP Phase II

We consider current (1971–2000) and future (2041–2070) average seasonal surface temperature fields from two regional climate models (RCMs) driven by the same atmosphere–ocean general circulation model (GCM) in the North American Regional Climate Change Assessment Program (NARCCAP) Phase II experiment. We analyze the difference between future and current temperature fields for each RCM and include the factor of season, the factor of RCM, and their interaction in a two-way ANOVA model. Noticing that classical ANOVA approaches do not account for spatial dependence, we assume that the main effects and interactions are spatial processes that follow the Spatial Random Effects (SRE) model. This enables us to model the spatial variability through fixed spatial basis functions, and the computations associated with an ANOVA of high-resolution RCM outputs can be carried out without having to resort to approximations. We call the resulting model a spatial two-way ANOVA model. We implement it in a Bayesian framework, and we investigate the variability of climate-change projections over seasons, RCMs, and their interactions. We find that projected temperatures in North America are credibly higher, that the associated warming effects differ in spatial areas and in seasons, and that they are of much larger magnitude than the variability between RCMs.     

Lithology-controlled subsidence and seasonal aquifer response in the Bandung basin,Indonesia, observed by synthetic aperture radar interferometry

Land subsidence in the Bandung basin, West Java, Indonesia, is characterized based on differential interferometric synthetic aperture radar (DInSAR) and interferometric point target analysis (IPTA). We generated interferograms from 21 ascending SAR images over the period 1 January 2007 to 3 March 2011. The estimated subsidence history shows that subsidence continuously increased reaching a cumulative 45 cm during this period, and the linear subsidence rate reached ∼12 cm/yr. This significant subsidence occurred in the industrial and densely populated residential regions of the Bandung basin where large amounts of groundwater are consumed. However, in several areas the subsidence patterns do not correlate with the distribution of groundwater production wells and mapped aquifer degradation. We conclude that groundwater production controls subsidence, but lithology is a counteracting factor for subsidence in the Bandung basin. Moreover, seasonal trends of nonlinear surface deformations are highly related with the variation of rainfall. They indicate that there is elastic expansion (rebound) of aquifer system response to seasonal-natural recharge during rainy season.     

结合季节调整和NAR神经网络的流域地下水储量预测

利用重力场恢复与气候实验卫星反演的陆地水储量和全球陆地数据同化系统（global land data assimilation system,GLDAS）水文模型,从流域降雨分布信息出发,结合季节调整技术和非线性自回归（non-linear autoregressive,NAR）神经网络对流域地下水储量变化进行预测,并与未经过季节调整的NAR神经网络、自回归（autoregressive, AR）模型以及季节性自回归差分移动平均（seasonal autoregressive integrated moving average, SARIMA）模型进行对比分析。以长江流域、勒拿河流域、鄂毕河流域以及叶尼塞河流域为例,结果表明,经过季节调整后的流域降雨和地下水分别服从独立分布和一阶自回归模型,为NAR神经网络时延数的确定提供了新的途径。经过季节调整后的NAR神经网络的预测结果在4个流域的模型表现优于传统的AR模型和SARIMA模型,均方根误差在1 cm以内,相关系数超过0.96。结合季节调整和NAR神经网络提高了流域地下水储量预测精度,减少了训练参数,加快了神经网络的收敛速度。     

Groundwater resources development in hard rock terrain - an approach using remote sensing and GIS techniques

To demonstrate the capabilities of remote sensing and Geographic Information System (GIS) techniques for groundwater resources development in hard rock terrains, specifically for the demarcation of suitable sites for artificial recharge of groundwater aquifers, a study was carried out in the Kallar Basin, which is located in parts of the Salem and Tiruchirapalli districts, Tamil Nadu, India. Thematic maps defining lithology, lineaments, landforms, landuse, drainage density, thickness of weathered zone, thickness of fractured zone, hydrological soils, and well yield were prepared from data collected by the Indian Remote Sensing Satellite (IRS) -1C and by conventional methods. All the thematic layers were integrated using a GIS-based model developed specifically for this purpose, enabling a map showing artificial recharge zones to be generated. The exact type of artificial recharge structure, eg, check dam, nallabund, gully plugging and percolation pond, suitable for replenishing groundwater was identified by superposing a drainage network map over an artificial recharge zones map. The GIS-based demarcation of artificial zones developed in the study was based on logical conditions and reasoning, so that the same techniques (with appropriate modifications) could be adopted elsewhere, especially in hard rock terrain, where the occurrence of groundwater is restricted and subject to greater complexity.     

1975年—2018年白洋淀湿地变化分析

白洋淀湿地生态功能重要、战略地位特殊,研究其时空变化规律具有重要意义。本文基于1975年—2018年间10期Landsat卫星遥感影像,辅以2017年—2018年高分二号（GF-2）卫星遥感影像,在野外考察湿地类型及其覆被特征基础上,人机交互解译获取各期土地利用/覆被分类图,从面积变化、类型转化、景观格局变化方面分析了近43年白洋淀湿地变化时空特征,讨论了影响分析结果的不确定性因素以及湿地变化成因。结果表明：1975年—2018年白洋淀湿地面积总体呈减少趋势,净变化-68.20 km²（-24.83%）。其中,1975年—1990年湿地面积波动性小、基本稳定,1990年—2011年湿地面积持续性减少,2011年—2018年湿地面积呈现增加趋势。湿地与非湿地类型相互转换区域主要分布于淀区南部、西部、北部的水体—水生植物—耕地—建设用地过渡区域。近43年白洋淀湿地景观趋于破碎、复杂和异质。遥感影像选取月份、年份,以及土地利用/覆被分类体系、分类方法,是影响分析结果的主要不确定性因素。气候、水文等自然因素变化,叠加工农业及城镇生活用水、上游水利工程蓄水、地下水开采等人为因素变化,是白洋淀湿地面积减少、趋于干化的成因。     

Multi-source Classification Using Artificial Neural Network in a Rugged Terrain

Abstract

This study advocates the use of GIS and remote sensing technologies to establish urban evolution maps and assess the impact of urbanization on agricultural areas over the last three decades. The target area is the city of Béni‐Mellal, located in central Morocco. The methodology adopted makes use of panchromatic SPOT images to survey the urban areas during the 1980s and 1990s. Available topographic maps provided the information for the 1970s. Maps and statistics of land use and urban growth for Béni Mellal were established after manually classifying images on a per-polygon basis and digitizing topographic maps using GIS capabilities. The results show an increase in dense urban area by 980.7 ha from the 1970s to the 1990s. This increase occurred at the expense of forests (24.7 ha), plantations (752.3 ha), rangeland (113.4 ha), non‐irrigated land (69.7 ha), and irrigated land (20.6 ha). During this period, scattered urban areas, predominantly suburbs, increased by 755.9 ha to the detriment of forests (14.9 ha), plantations (109.8 ha), rangeland (138.9 ha), non‐irrigated land(400.5 ha), and irrigated land (91.9 ha). These cartographic and statistic results are efficient decision‐making tools for protecting agricultural land and planning urban and suburban areas.