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The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change. [ABSTRACT FROM AUTHOR]

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the public. To achieve this goal, we will be utilizing advanced machine learning algorithms to analyze TDS levels and create geospatial maps for each year between the 1990s and 2010s. To ensure the accuracy of our data, we have gathered information from two trusted sources: the Texas Water Development Board (TWDB) and the Groundwater Database (GWDB). We have analyzed the TDS and other elemental analyses from TWDB–GWDB lab reports and compared them with the quality cutoff set by the World Health Organization (WHO). Our approach involves a thorough examination of the data to identify any emerging patterns. The machine learning algorithm has been successfully trained and tested, producing highly accurate results that effectively predict water quality. Our results have been validated through extensive testing, highlighting the potential of machine learning approaches in the fields of environmental research. Overall, our findings will contribute to the development of more effective policies and regulations in predicting groundwater quality and improving water resource management in Texas. Therefore, this research provides important information for groundwater protection and the development of plans for water resource use in the future. [ABSTRACT FROM AUTHOR]

This study investigates the response of water resources regarding the climate and land-cover changes in a humid subtropicalwatershed during the period 1970-2009. A 0.7°C increase in temperature and a 16.3% increase in precipitation were observed. Temperature had a lower increase trend, and precipitation showed definite increasing trend compared to previous studies. The main trend of land-cover change was conversion of vegetation and barren lands to developed and crop lands affected by human intervention, and forest and grass to bush/shrub which considered to be caused by natural climate system. Hydrologic responses to climate and land-cover changes resulted in increases of surface run-off (15.0%), soil water content (2.7%), evapotranspiration (20.1%) and a decrease in groundwater discharge (9.2%). We found that surface run-off is relatively stable with precipitation, whereas groundwater discharge and soil water content are sensitive to changes in land cover, especially land cover brought about by human intervention. [ABSTRACT FROM AUTHOR]

Blue ice extent and its geographical distribution during the three years from December 1999–January 2003 have been mapped together with rock exposure areas for the entire Lambert-Amery glacial basin at an unprecedented level of spatial detail using Landsat ETM+ images. Various geometric and shape attributes for each blue ice patch have been derived. The total area of blue ice is estimated to be 20 422 km2, accounting for 1.48% of the glacial basin. We also found that the image texture information is helpful for distinguishing different types of blue ice: rough blue ice, smooth blue ice, and level blue ice. Rough blue ice areas are mostly associated with glacial dynamics and located in relatively low elevation regions, smooth blue ice areas are often related to nunataks and mountains, steep slopes and wind blows, and level blue ice areas are formed by melt-induced lakes in the margin of the ice shelf and melt-induced ponds on the ice shelf in low elevations. The elevation and surface slope properties of different types of blue ice area are characterized through regional topographical analysis with ICESat laser altimetry data. The effect of mountain height on the blue ice extent is also examined with local topographic profile analysis based on the ASTER global digital elevation model. The wide variation of the ratio of blue ice area length to mountain height indicates that the factors controlling the formation and extent of blue ice are more complicated than we previously thought. [ABSTRACT FROM AUTHOR]