Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

Sri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

Murray-Darling Basin Plan: Case Study in Market-Based Approach to Water Sharing in Australia

The Murray-Darling Basin (MDB) is an area in southeastern Australia that has the largest and most regulated river system in the country. Historically, it has been an area of conflict over water resources, with efforts to bring the different states together to negotiate water sharing since the early 1900s. In the 20th century, the focus of water policy was predominantly on water supply infrastructure: building large-scale dam storages, weirs, and other irrigation region infrastructure. However, increasing problems with both water quality and quantity from the 1970s onwards—such as acid sulphate soils, salinity, declines in vegetation health, and species loss—meant that more attention was turned to water demand management options. These included establishing formal water markets, trade liberalization, and water extraction caps. The National Water Initiative (2004) and the Water Act (2007) laid the groundwork in unbundling water and land ownership and created the Murray-Darling Basin Authority (MDBA). The MDBA was tasked with developing the MDB Plan (Basin Plan 2012) to readjust the balance between consumptive water use and the environment. The Basin Plan when implemented in 2012 aimed to return up to one third of consumptive water extraction to environmental use, making it one of the biggest reallocations of water to the environment in the world. It has predominantly used market-based approaches to do so. However, conflict over water sharing has remained a dominant feature of MDB water reform. Self-interest among states and irrigation interests have impacted environmental water recovery methods, resource expenditure, and allocation—subsequently weakening both the Basin Plan and water policy in general. Given current policy developments, there is real danger of targets not being met, and environmental sustainability being continually compromised. The ongoing issues of drought, climate change, and readdressing First Nations access to—and ownership of—water have emphasized distributional issues in water sharing. It is clear also that the Basin Plan has been wrongly blamed and misattributed for ongoing rural community declines, with current amendments and reductions in water reallocation targets a result of this. What is clear is that the Basin Plan is currently not the fully sustainable solution for water sharing that it set out to be. It will need to continually evolve, along with various institutions to support water governance and rural community economic development in general, to address existing overallocation and future climate challenges. The challenges of equity, rural community development, and distributional fairness lie firmly in the sphere of strong governance, high-quality data, and first-best economic and scientific policies.

A Recent and Systematic Review on Water Extraction from the Atmosphere for Arid Zones

Water is essential for food security, industrial output, ecological sustainability, and a country’s socioeconomic progress. Water scarcity and environmental concerns have increased globally in recent years as a result of the ever-increasing population, rapid industrialization and urbanization, and poor water resource management. Even though there are sufficient water resources, their uneven circulation leads to shortages and the requirement for portable fresh water. More than two billion people live in water-stressed areas. Hence, the present study covers all of the research based on water extraction from atmospheric air, including theoretical and practical (different experimental methods) research. A comparison between different results is made. The calculated efficiency of the systems used to extract water from atmospheric air by simulating the governing equations is discussed. The effects of different limitations, which affect and enhance the collectors’ efficiency, are studied. This research article will be very useful to society and will support further research on the extraction of water in arid zones.

Study on Monitoring Water Area in Irrigation Area by Local Space Self-correlation Index

Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.