Quantitative Assessment of Cropland Exposure to Agricultural Drought in the Greater Mekong Subregion

Abstract

Accurate and reliable information on the spatiotemporal characteristics of agricultural drought is important in understanding complicated drought processes and their potential impacts. We proposed an integrated approach for detecting agricultural droughts and their cropland exposure using remote sensing data over the Greater Mekong Subregion (GMS) collected from 2001 to 2020. The soil moisture (SM) dataset (0.05°) was first reconstructed based on an ESACCI SM dataset using a random forest (RF) model. Subsequently, the standardized soil moisture index (SSMI) was used to identify the agricultural droughts by a three-dimensional (latitude-longitude-time) identification method. In addition, the cropland’s exposure to agricultural droughts was evaluated. Results showed that: (1) the reconstructed SM data achieved spatial continuity and improved spatial resolution. The verified consequences showed that the reconstructed SM data agreed well with the in situ SM data. Additionally, the SSMI based on reconstructed SM had good correlations with the standardized precipitation evapotranspiration index (SPEI) calculated from station observations. (2) Twenty agricultural drought events lasting at least 3 months were identified over the GMS region. The averaged durations, areas, and severity were 7 months, 9 × 105 km2, and 45.6 × 105 month·km2, respectively. The four worst drought events ranked by severity were the 2019–2020 event, the 2015–2016 event, the 2009–2010 event, and the 2004–2005 event. (3) Based on the 20 identified agricultural drought events, cropland exposure was high in Myanmar, Thailand, and Cambodia. On average, the cropland exposure over the GMS was 1.71 × 105 km2, which accounts for 34% of the total cropland. Notably, the four severest drought events swept over 80% of the total cropland area. This study enriched our understanding of the development process of agricultural droughts from a space-time perspective, which was pivotal for assessing drought impacts and managing agricultural water resources.

Teleconnections of Atmospheric Circulations to Meteorological Drought in the Lancang-Mekong River Basin

“…Vietnam's water use in the dry season has already exceeded its capacity. Considering that the dry season is the peak period for agricultural water use in the Mekong River Basin [25,32], and our research has found that the main areas with high water demand for fisheries and agriculture in the basin in recent years have shown a significant trend of drought, these areas are more likely to be affected and threatened when a meteorological drought occurs in the basin. Therefore, it is recommended that all regions in the basin properly manage the seasonal allocation of water resources to ensure the agricultural production and water resource security of each community and improve their ability to cope with Meteorological drought [50,51].…”

“…Figure 1 illustrates the geographic location, water system distribution, elevation, and land use types. The rainy season (May to October) in the LMRB is dominated by the southwest monsoon from the ocean, resulting in a humid and rainy climate [32]. Conversely, during the dry season (November to April of the subsequent year), the basin is influenced by the northeast monsoon from the continent, leading to dry and less rainy conditions [7].…”

“…However, due to climate and environmental changes, meteorological droughts have become increasingly frequent in the region, affecting both the rainy and dry seasons and exacerbating the overall impact of drought [7]. The SPEI is suitable for monitoring the characteristics of meteorological droughts under global warming [36], due to the advantageous feature of SPEI encompassing multiple time scales [32]. Therefore, this paper calculated the SPEI on different time scales of 1 month, 6 months, and 12 months for 38 years, respectively.…”

A Big Data Grided Organization and Management Method for Cropland Quality Evaluation

“…Raster data are based on image element arrays to represent geographic information [27], and the specific processing steps are: clipping the data in the range of administrative divisions, resampling the data based on the grid scale, defining projections, and converting coordinates based on the multilevel grid system. In addition, geospatial data are interpolated using interpolation techniques such as inverse distance weighted interpolation [28] to fill in missing values to ensure data integrity. Compared with geospatial data, the data structure of text data is relatively simple, and the specific content of data processing is data cleaning and data standardization.…”