Open AccessAugmenting the spatial resolution of climate-change temperature projections for city planners and local decision makers
Author(s) -
Diego Jijón,
Karl-Heinz Gaudry,
Jessica Constante,
César De la Cruz Valencia
Publication year - 2021
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/abf7f2
Subject(s) - downscaling , environmental science , emissivity , climate change , scale (ratio) , remote sensing , projection (relational algebra) , meteorology , climatology , computer science , geography , cartography , geology , algorithm , precipitation , oceanography , physics , optics
Before the 2010, studies in climate change (CC) projections embracing scales below 3° were difficult to find. This has changed dramatically over the past ten years, with literature addressing high resolution grids for climate studies, allowing a better understanding and forecasting of CC at finer scales. However, downscaling methods remain poorly explored in urban planning. Research shows that the main difficulties relate to mismatches between data needs and data availability, terminology, constraints of information technology and maps that inform spatial planning decision-making processes. Based on dynamic downscaled maps for RCP 4.5 and RCP 8.5 at 10 km resolution published by Ecuador’s Ministry of Environment and Water (MAAE), we develop a method for augmenting the resolution scale at 30 m. We use digital elevation models and Landsat 4/5/7/8 satellite imagery for land surface temperature (LST) and present a series of steps and equations before applying Stefan Bolzman’s law. We present the necessary equations between the filling-in of LST outliers, and their projection onto air temperature at 2 m height, taking surface emissivity estimates based on (Alves et al 2017 J. Hyperspectral Remote Sens. 7 91–100). We extrapolate the resulting air temperature in time with Fourier’s series, and for the purpose of coherence among scales, we upscale air temperature maps at 30 m to those at 10 km resolution. The resulting CC projection maps are validated with the temporal series of air temperature (max, min, mean) from the meteorological station in the Ecuadorian city of Portoviejo (Student’s t -test) for the period between 1981 and 2005, with Portoviejo city facing temperature increases of up to 2 °C under RCP 4.5 scenario in the period 2011–2040 vs 1981–2005. The final CC maps have an augmented resolution of 30 m, are compatible with those of MAAE, and offer a low-cost procedure for informing land-use and urban planners, as well as local development decision makers, of temperature anomalies due to climate change.