The fast‐changing climate reality of Arequipa, Peru

Abstract

Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid-changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann-Kendall test, at a significance level of 0.10, was used to determine trends and the Theil-Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December-March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0 C between the decades of 1988-1997 and 2008-2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking.

Trend Analysis of Climatic Parameters Using Geographical Information Systems: A Case Study of Sivas Province (1982-2021)

“…It is robust against outliers and single data errors, making it a reliable tool for trend analysis. The method assumes that the trend line is a linear function in the time series data and requires a time series of equally spaced data (Shahid, 2010;Atif et al, 2018;Panda and Sahu, 2019;Uzun et al, 2021;Moraes et al, 2023). To estimate the true slope of the trend, SS method compares all possible pairs of data points and calculates the median of the slopes.…”

“…To estimate the true slope of the trend, SS method compares all possible pairs of data points and calculates the median of the slopes. This approach makes it less sensitive to extreme values and provides an unbiased estimation of the trend magnitude (Ito, 2021;Moraes et al, 2023). The SS method has been applied in various studies to analyze trends in different variables.…”