DOI: 10.5281/zenodo.7406342
title: An innovative scheme to confront the trade-off between water conservation, drought reduction, and heat alleviation for urban sustainability: a case study in Phoenix, Arizona

description: <em><strong>The manuscript for this dataset is still under review in AGU Advances and can be accessed here: link. Please cite the literature when using the datasets.</strong></em> <strong>Citation format: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An innovative scheme to confront the tradeoff between water conservation, drought reduction, and heat alleviation for urban sustainability: a case study in Phoenix, Arizona. </strong> This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions. The dataset summarization Category Dataset Resolution Source/method Time Environmental database Summer daytime LST 70m ECOSTRESS 2019 Environmental database Summer nighttime LST 70m ECOSTRESS 2019 Environmental database Summer ETa 70m ECOSTRESS 2019 Environmental database Summer ESI 70m ECOSTRESS 2019 Environmental change database Trends of summer LST changes 30m Landsat-based Statistical Mono-Window algorithm 2000-2020 Environmental change database Trends of summer ETa changes 30m Landsat-based Simplified Surface Energy Balance 2000-2020 The results of new green space configurations The spatial distributions of new green space -- Spatial optimization -- note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is : VV_new_perNN_LSTWW where: VV = New vegetation for tree or grass NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %) WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65.
description: The datasets include (1) ECOSTRESS0-based land surface temperature in summer daytime and nighttime, evaporative stress index in summer, and total evapotranspiration in summer; (2) Landsat-based land surface temperature and evapotranspiration Sen's slope; (3)the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% in the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.