Proportional Variation of Potential Groundwater Recharge as a Result of Climate Change and Land-Use: A Study Case in Mexico

Artículo en revista indexadaThis work proposes a methodology whereby the selection of hydrologic and land-use cover change (LUCC) models allows an assessment of the proportional variation in potential groundwater recharge (PGR) due to both land-use cover change (LUCC) and some climate change scenarios for 2050. The simulation of PGR was made through a distributed model, based on empirical methods and the forecasting of LUCC stemming from a supervised classification with remote sensing techniques, both inside a Geographic Information System. Once the supervised classification was made, a Markov-based model was developed to predict LUCC to 2050. The method was applied in Acapulco, an important tourism center for Mexico. From 1986 to 2017, the urban area increased 5%, and by 2050 was predicted to cover 16%. In this period, a loss of 7 million m3 of PGR was assumed to be caused by the estimated LUCC. From 2017 to 2050, this loss is expected to increase between 73 and 273 million m3 depending on the considered climate change scenario, which is the equivalent amount necessary for satisfying the water needs of 6 million inhabitants. Therefore, modeling the variation in groundwater recharge can be an important tool for identifying water vulnerability, through both climate and land-use change.CONACyT Centro de Ciencias de Desarrollo Regional (CCDR

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

Assessment of Land Cover Changes in the Hinterland of Barranquilla (Colombia) Using Landsat Imagery and Logistic Regression

Barranquilla is known as a dynamically growing city in the Colombian Caribbean. Urbanisation induces land use and land cover (LULC) changes in the city and its hinterland affecting the region’s climate and biodiversity. This paper aims to identify the trends of land use and land cover changes in the hinterland of Barranquilla corresponding to 13 municipalities in the north of the Department Atlántico. Landsat TM/ETM/OLI imagery from 1985 to 2017 was used to map and analyse the spatio-temporal development of land use and land cover changes. During the investigation period, the settlement areas grew by approximately 50% (from 103.3 to 153.6 km2), while areas with woody vegetation cover experienced dynamic changes and increased in size since 2001. Peri-urban and rural areas were characterized by highly dynamic changes, particularly regarding clearing and recovery of vegetated areas. Regression analyses were performed to identify the impact factors of detected vegetation cover changes. Computed logistic regression models included 20 independent variables, such as relief, climate, soil, proximity characteristics and socio-economic data. The results of this study may act as a basis to enable researchers and decision-makers to focus on the most important signals of systematic landscape transformations and on the conservation of ecosystems and the services they provide

Back to the people:The role of community-based responses in shaping landscape trajectories in Oaxaca, Mexico

Land use change results from top-down drivers, such as policies, trade, and migration. Land use change may also result from community-based responses. In Mexico, rural communities govern most of the country's forests. This study aimed to assess how socio-economic and biophysical factors affected the landscape trajectories of rural communities in southern Mexico. It also aimed at evaluating the role of communities in landscape change. Land use change of 63 rural communities was analyzed for the years 1987 and 2017. Four land uses were distinguished: forest, shrubland, agriculture, and bare soil. Five groups of communities were identified according to their socio-economic and biophysical factors. Two groups located in areas with high slopes and elevated marginalization index values showed deforestation patterns. Two other groups, consisting of more than half of the municipalities assessed, showed reforestation trends. The final group did not reveal major changes in land use. Two municipalities with reforestation trends were selected for an in-depth analysis of how community-based responses impacted natural resource management and conservation. Through local assemblies, the population voted for regulations that increased the forest area and reduced the bare soil. There was no evidence that these regulations affected croplands. These results show how a combination of socio-economic and biophysical factors can affect landscape change, but it also shows the often overlooked role of communities as a relevant bottom-up driver of change.</p

Spatio-temporal land use/land cover changes analysis and monitoring in the Valencia Municipality, Spain

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIssues of land use/land cover changes and the direct or indirect relationships of these changes have drawn much attention in recent years. In the Mediterranean Spain, observed environmental changes influenced with dramatic urban growth and their likely changes can have extensive unforeseen ramification. Thus, the objectives of this research were to map and determine the nature, extent and rate of changes and to analyze the spatio-temporal land use/land cover change patterns and fragmentation that has occurred in Valencia Municipality. Multi-temporal Landsat MSS1976, TM1992 and ETM2001 images were acquired. Digital orthophotos, IKONOS images and existing Corine land cover maps were used as reference. More than 130 training samples were selected for classification of the Landsat images using supervised method parallelepiped-maximum likelihood algorithm in ERDAS Imagine 9.1, and land cover maps were generated and change detection analysis was performed.(...

The importance of the traditional milpa in food security and nutritional self-sufficiency in the highlands of Oaxaca, Mexico

Around 30% of global food is produced by smallholder farmers, yet they constitute the most food-insecure group. In Mexico, food self-sufficiency is declining. Rural policies in the country have stimulated the production of cash crops to the detriment of the traditional intercropping system, the milpa. Such a decline may have negative consequences for the food security of subsistence farmers. This study aimed to assess changes in nutritional self-sufficiency over the last 30 years and the role of milpa systems in food security for two communities in the highlands of Oaxaca, Mexico. The study used satellite images, censuses, and field data to estimate food production. Three cropping systems, monoculture of maize, monoculture of common bean, and the milpa were compared in terms of nutrients and vitamins produced. Furthermore, a household typology was developed for each community to contrast nutritional self-sufficiency levels between the different household types. Results showed that the milpa produced more volume of food per area compared to the other systems. The milpa also produced all the nutrients and vitamins (except for B12) required to feed at least 2 persons ha-1. Monocultures of maize lacked vitamins A, B9, B12, and C, and the common bean lacked vitamins A, B12, and C. While farmers recognized the importance of the milpa, they preferred monocultures due to the reduced labor demands of this system. Households that obtained most of their income from off-farm activities had the lowest nutritional self-sufficiency. Enhancing nutritional self-sufficiency through crop diversification has the potential to not only improve the nutrition of subsistence farmers, but also to enhance ecosystem service provision, promote biodiversity conservation and restoration, and improve resilience to climate change.</p

Climate Justice in the City: Mapping Heat-Related Risk for Climate Change Mitigation of the Urban and Peri-Urban Area of Padua (Italy)

The mitigation of urban heat islands (UHIs) is crucial for promoting the sustainable development of urban areas. Geographic information systems (GISs) together with satellite-derived data are powerful tools for investigating the spatiotemporal distribution of UHIs. Depending on the availability of data and the geographic scale of the analysis, different methodologies can be adopted. Here, we show a complete open source GIS-based methodology based on satellite-driven data for investigating and mapping the impact of the UHI on the heat-related elderly risk (HERI) in the Functional Urban Area of Padua. Thermal anomalies in the territory were mapped by modelling satellite data from Sentinel-3. After a socio-demographic analysis, the HERI was mapped according to five levels of risk. The highest vulnerability levels were localised within the urban area and in three municipalities near Padua, which represent about 20% of the entire territory investigated. In these municipalities, a percentage of elderly people over 20%, a thermal anomaly over 2.4 °C, and a HERI over 0.65 were found. Based on these outputs, it is possible to define nature-based solutions for reducing the UHI phenomenon and promote a sustainable development of cities. Stakeholders can use the results of these investigations to define climate and environmental policies

Mapping and monitoring of agricultural drought across different land uses and land cover in the North-Eastern KwaZulu Natal

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of Witwatersrand in fulfilment of the academic requirements for the degree of Master of Science in Environmental Sciences June 2018. Johannesburg, South Africa.Drought is complex and one of the least understood natural hazards in Southern Africa. Timely information about the extent, the intensity, duration and impacts of the agricultural drought is essential for adaptation and management. In this study, the research aims, are made to monitor and map agricultural drought across different land uses and land cover in north-eastern KwaZulu-Natal as it was declared a disaster area in 2016 (AgriSA, 2016). Droughts occurred throughout South Africa during the summer season of 2014 to 2015 and 2015 to 2016. In this study the adopted methodology was through the use of remote sensing and Geographic Information System (GIS) techniques. Remote sensing and GIS was used to map and monitor the agricultural drought in the study area. To understand the impacts of the drought across different agricultural land use and other land cover types, the land uses and land cover was classified using Landsat earth observation data and maximum likelihood algorithm in the study area, and multi-temporal Normalized Difference Vegetation Index (NDVI) (1997-2017) with a twenty year interval used to map and monitor the agricultural drought and the meteorological (rainfall) in order to validate the NDVIs. Agricultural drought was then determined from investigating changes between 2015 and 2017 which were years that experienced severe conditions. The rainfall data was interpolated using Inverse Distance Weighted (IDW) interpolation to understand the mean rainfall from the weather stations services. Thereafter, Standardized Precipitation Index (SPI) values were determined from the rainfall data in order to understand the severity of the droughts in certain parts of the study area from the weather station data. The meteorological analysis was cross compared with agricultural drought. The mean NDVI and mean rainfall interpolated shows that their relationship is inversely proportional, because where rainfall is low; NDVI is high for the years 2015 to 2017. The land use and land cover in the study is largely dominated by bush, cultivated cane crop, grassland and plantations. Looking at the overall classification in the year 2015, it is clear that bush land use and land cover was largely dominated in the study area, with other land use and land cover classes which were also part of the year 2015. During the year 2016 the other classes of land use and land cover where also dominating the study area for example grasslands and plantations. In the year 2017 we see cultivated cane crop start to emerge in the study area but land use and land cover is largely dominated by bush land use and land cover. The overall accuracy of the study was 74.2%. Keywords: Agricultural drought, Land use/land cover, Remote sensing, Landsat 8 OLI/TIRS, Normalized Difference Vegetation Index, Standardized Precipitation Index, Accuracy Assessment.LG201

Determinants of Deforestation in Nepal\u27s Central Development Region

The process of deforestation in the Central Development Region (CDR) of Nepal is diverse in space and time, with rapid deforestation still occurring in areas outside the national parks and wildlife reserves. This paper identifies the spatial driving forces (SDFs) of deforestation in CDR for 1975-2000 using satellite data of 1975 (MSS), 1990 (TM), and 2000 (ETM+) along with socio-demographic and socioeconomic variables. Radiometrically calibrated satellite images are individually classified into seven distinct classes and merged together to cover the entire CDR. Classification accuracies are also assessed. Areas of land use and cover within the areas of each Village Development Committees (VDCs) and municipalities are calculated from the classified images by overlaying vector files of 1,250 VDCs. A transition matrix is generated for 1975-1990 using classified images of 1975 and 1990 and then this product is used to further develop another transition matrix for 1990 - 2000 with the classified ETM+ 2000 images as the final stage. The VDCs vector layer of land use and cover areas is overlaid on the transition matrices to calculate deforestation areas by VDCs for 1975-1990 and 1990-2000. A digital elevation model (DEM) compiled from 35 ASTER scenes taken on different dates is used to examine areas at different elevation levels: 30- 1,199 m, 1,200 — 2,399 m, 2,400- 4,999 m, and \u3e5,000 m. Only the first three elevation levels are used in the analysis because area \u3e 5,000 m is under permanent snow cover where human related forestry activities are almost negligible. Biophysical and socioeconomic information collected from various sources is then brought into a geographic information systems (GIS) platform for statistical analyses. Six linear regression models are estimated using SAS; in effect, two models for each elevation range representing 1975-1990 and 1990- 2000 periods of change to identify SDF influences on deforestation. These regression analyses reveal that deforestation in the CDR is related to multiple factors, such as farming population, genders of various ages, migration, elevation, road, distance from road to forest, meandering and erosion of river, and most importantly the conversion of forestland into farmland.\u2