DOI: 10.5061/dryad.zpc866tkp
title: Urbanization's dual role in the exacerbation and mitigation of drought dynamics in China

description: Since the 1980s, China’s rapid urbanization has raised significant
 concerns about its potential impact on local drought conditions as it
 influences ecological, environmental, and meteorological systems, yet its
 specific effects on drought duration, severity, and intensity remain
 underexplored. This study examines the impact of urbanization on drought
 dynamics across mainland Chinese cities using seven decades (1951–2020) of
 historical weather station observations. Our analysis suggests that
 urbanization plays a dual role in both exacerbating and mitigating drought
 conditions. The findings reveal that 50–55% of urban weather observation
 stations exhibited exacerbation of drought (ED) dynamics, characterized by
 increases in drought duration and severity ranging from +15% to +40%.
 Conversely, 45–50% of them demonstrated drought mitigation (MD) effects,
 with reductions in these metrics ranging from -15% to -37%. These results
 highlight the complex and multifaceted ways in which urbanization
 influences drought, varying across different metrics and timescales,
 emphasizing the critical need for enhanced attention to urban drought
 preparedness, particularly as cities face ongoing urbanization, population
 growth, and the impacts of climate change. Integrating sustainable urban
 planning with effective water resource management strategies is essential
 to address the intricate challenges posed by urban-driven drought
 dynamics.
description: # Urbanization's dual role in the exacerbation and mitigation of
 drought dynamics in China Dataset DOI:
 [10.5061/dryad.zpc866tkp](10.5061/dryad.zpc866tkp) ## Description of the
 data and file structure The data were sourced from 700 benchmark ground
 observation stations or automatic stations covering most of the country.
 Precipitation data from 1951 to 2020 were obtained from the China
 Meteorological Data Sharing Service System
 (**[http://data.cma.cn/en](http://data.cma.cn/en)**) at a monthly
 timescale and were corrected for missing values using the Global
 Precipitation Climatology Centre (GPCC) dataset
 (**[http://gpcc.dwd.de/](http://gpcc.dwd.de/)**). Evapotranspiration data
 for the same period (1951–2020) were obtained from the global gridded
 dataset of monthly climate data developed by the Climatic Research Unit
 (CRU TS v4.06). Finally, the Standardized Precipitation Index (SPI) and
 the Standardized Precipitation Evapotranspiration Index (SPEI) were
 computed at multiple timescales (e.g., SPEI-3, -6, -9, and -12 months)
 using the aforementioned datasets. ### Files and variables 1. #### File:
 xy_locations.xlsx **Description:** latitudes and longitudes for all 700
 stations. **units**: "degrees"; **type**: float;
 **Cartesian_axis:** "X and Y"; **File format:** Excel sheets; 1.
 #### File: SPEI-SPI_files.zip **Description:** Historical Dataset of 700
 stations of the Standardized Precipitation Index (SPI) and Standardized
 Precipitation Evapotranspiration Index (SPEI) in China from 1951 to 2020,
 monthly data at 3, 6, 9, and 12 timescales. ##### Variables names:
 "SPEI, SPI"; **units**: "z values"; **type**: float;
 **time**: months; **not a number**: nan; **File format:** CSV sheets; ##
 Code/software
 **[Python-code-for-drought-characteristics-and SPEI-SPI trend](https://github.com/Karam-lab7/Python-code-for-drought-characteristics-/tree/main)** [https://github.com/Karam-lab7/Python-code-for-drought-characteristics-](https://github.com/Karam-lab7/Python-code-for-drought-characteristics-) ## Access information Other publicly accessible locations of the data: * Monthly SPI and SPEI data across multiple timescales, developed and used in this study, are accessible on Figshare ([https://doi.org/10.6084/m9.figshare.28429727.v2](https://doi.org/10.6084/m9.figshare.28429727.v2)) Original data for calculating both SPI and SPEI were derived from the following sources: * Monthly precipitation data (1951–2020) used in this study were sourced from the China Meteorological Data Sharing Service System ([http://data.cma.cn/en](http://data.cma.cn/en)). * Monthly precipitation data (1951–2020) used in this study were sourced the Global Precipitation Climatology Centre (GPCC) dataset ([http://gpcc.dwd.de/](http://gpcc.dwd.de/)) * Evapotranspiration data for the same period were obtained from the Climatic Research Unit’s gridded dataset (CRU TS v4.06) ([https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)).