Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015
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land Article Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015 Jinxiu Liu 1, Weihao Shen 1 and Yaqian He 2,* 1 School of Information Engineering, China University of Geosciences, Beijing 100083, China; [email protected] (J.L.); [email protected] (W.S.) 2 Department of Geography, University of Central Arkansas, Conway, AR 72035, USA * Correspondence: [email protected]; Tel.: +1-304-777-3037 Abstract: India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) ◦C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC. Citation: Liu, J.; Shen, W.; He, Y. Effects of Cropland Expansion on Keywords: cropland expansion; climate extremes; western Indian; land cover and land use change; Temperature Extremes in Western GLASS-GLC; maxima of daily maximum temperature India from 1982 to 2015. Land 2021, 10, 489. https://doi.org/10.3390/ land10050489 1. Introduction Academic Editor: Nir Krakauer Climate extremes, such as heat waves, droughts, and flooding, are among the most Received: 24 March 2021 ecologically and economically significant hazards that affect millions of people globally [1]. Accepted: 1 May 2021 India, located in South Asia (Figure1), is one of the most populous countries and has experi- Published: 5 May 2021 enced intensified climate extremes over the last four decades [2–4]. Between 1950 and 2015, central India has experienced a three-fold rise in extreme precipitation events [2], affecting Publisher’s Note: MDPI stays neutral the food and water security of the country’s more than one billion people, especially the with regard to jurisdictional claims in poor [5]. In 2010, India faced a severe heat wave, with temperatures reaching as high as ◦ published maps and institutional affil- 46.8 C[6], resulting in the deaths of 269 people [7]. Such deadly consequences emphasize iations. the importance of improving our understanding of the causes of climate extremes in India. Among the factors driving climate extremes [8], greenhouse gas (GHG) emission- induced global warming is a first-order contributor [9,10]. Climate change can affect climate extremes through both dynamic (linked to the atmospheric circulation) and thermodynamic Copyright: © 2021 by the authors. (linked to atmospheric water vapor content) processes [10]. However, climate change Licensee MDPI, Basel, Switzerland. alone cannot completely explain the dynamics of climate extremes [11]. Another key This article is an open access article factor influencing climate extremes is land cover and land use (LCLU) [12]. To date, distributed under the terms and approximately 41% of the Earth’s surface has been changed, with natural vegetation such conditions of the Creative Commons as forest and grassland replaced by other land cover and land use types such as cropland Attribution (CC BY) license (https:// and built-up areas [13]. Such extensive LCLUC is likely to influence both weather and creativecommons.org/licenses/by/ climate, as the land surface sets the prescribed conditions for the overlying atmosphere 4.0/). Land 2021, 10, 489. https://doi.org/10.3390/land10050489 https://www.mdpi.com/journal/land Land 2021, 10, 489 2 of 17 through biogeophysical processes that affect soil moisture, energy balance, boundary layer development, and biogeochemical processes that alter carbon emission [14–18]. Considerable evidence indicates that LCLUC has affected regional climate in In- dia [19–21]. More than 25% of the warming from 2001 to 2010 in the Eastern state of Odisha, India, was associated with urbanization [22]. This urbanization-induced warm- ing also shows in other cities in India. From 2008 to 2016, the mean surface temperature in Surat and Bharuch has increased at a rate of 2.42 ◦C/decade and 2.13 ◦C/decade, respectively [23]. In contrast, Nayak and Mandal (2019) suggested that the conversion of shrubs to agricultural areas increased transpiration in India, contributing to a cooling by ~0.02 ◦C/decade during 1981 to 2006 [24]. The LCLUC also affects Indian rainfall patterns. Irrigation-induced evaporative cooling during the pre-monsoon season has caused decreased July surface temperature and a reduced land–sea thermal contrast, which has resulted in a weak summer monsoon in India [25]. A similar conclusion was revealed by a modeling study showing that deforestation in India resulted in decreasing evapotranspiration and subsequently decreased the recycled component of precipitation, leading to a weakening of the India summer monsoon [26]. While the impacts of LCLUC on mean climate in India have been widely explored, LCLUC is rarely studied as a causing factor for climate extremes [12]. However, re- cently, several modeling studies have claimed that LCLUC could influence climate ex- tremes [14,27,28]. Sy and Quesada (2020) used five Earth system models to identify LCLUC impacts on 20 extreme weather indices over the 21st century. They found that in Southern Asia, LCLUC can lessen projections of very wet days (annual total precipitation from days >95th percentile) by 38%, and enhance projection of the annual maxima of daily maxi- mum temperature by 1.11% [29]. Nevertheless, such model-based studies have produced inconsistent results. For instance, Christidis et al. (2013) found that daily temperature extremes are less severe with deforestation [30], while Strack et al. (2008) claimed the enhanced severity of June extremes [31]. This spread of model response in extreme climates is presumably due to the fact that the models differ in terms of their dynamical cores, numerical schemes, parameterizations, and simulation periods [16,32]. In addition, most modeling studies employed extreme sensitivity experiments by completely replacing one land cover and land use type with another type (e.g., replacing forests with bare ground for deforestation experiments). Such extreme experiments are likely to be unrealistic, as regional vegetation changes are usually gradual, and rarely involve total change to an entire landscape [33]. Therefore, observational studies are critical to confirm the model results and quantify the real-world LCLUC impacts on climate extremes. We address some of these aforementioned limitations by using novel 34-year land cover and land use data derived from satellite data to empirically quantify cropland expansion effects on temperature extremes in India. As one of the first attempts, this study aims to address the following questions: (1) What are the spatiotemporal patterns of croplands in India during the last three decades? (2) How does cropland expansion in India affect temperature extremes? and (3) What are the physical mechanisms underlying the influences? Land 2021, 10, 489 3 of 17 Figure 1. Study area of India with the land cover and land use information in the year 2015 from the Global Land Surface Satellite global land cover (GLASS-GLC) dataset [34] (The map is made using ArcGIS Pro 2.8 with geographic coordinate system of WGS 1984, angular unit of Degree, prime meridian of Greenwich, datum of D_WGS_1984, spheroid of WGS 1984, semimajor axis of 6378137.0, semiminor axis of 6356752.3, and inverse flattening of 298.3). 2. Materials and Methods 2.1. Materials The land cover and land use data used in this study is the Global Land Surface Satellite Global Land Cover (GLASS-GLC) dataset downloaded from https://doi.pangaea.de/10.1 594/PANGAEA.913496 (accessed on 23 March 2021) [34]. This newly released annual-scale LCLU data from 1982 to 2015 was generated using a random forest classifier based on multisource remotely sensed datasets, including a normalized difference vegetation index (NDVI), leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) from GLASS climate data records (CDRs), and elevation data from Global Multi- resolution Terrain Elevation Data 2010 (GMTED2010) [34]. Based on a comparison with more than 2000 reference samples, the overall accuracy of the GLASS-GLC dataset was estimated at ~82.81%, which is higher than that of the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (~73.9%) and the European Space Agency Climate Change Initiative (ESA-CCI) land cover data (~80.38%) [34]. The spatial resolution of GLASS-GLC data is 5 km with seven classes: cropland, forest, grassland, shrubland, tundra, barren land, and snow/ice. We use the “raster” package in R [35] to extract the GLASS-GLC for India (Figure1). To be consistent with the meteorological data described below, we aggregate the 34-year LCLU maps from 5 km to 0.25◦ over 1982 to 2015 using a fractional method following He et al. [33], to generate maps of the fraction of the area in India covered by cropland for each year. The temperature data is obtained from European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) (https://cds.climate.copernicus.eu/ cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form (accessed on 23 March 2021)) [36].