Spatial-temporal variation and impacts of drought in Xinjiang (Northwest China) during 1961-2015

An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018

The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (>27%) and a high missing ratio (>33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD > 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.

Teleconnections of Large-Scale Climate Patterns to Regional Drought in Mid-Latitudes: A Case Study in Xinjiang, China

Drought is one of the most important environmental disasters. Assessment of the effects of oceanic atmospheric oscillations upon regional drought behavior has valuable implications for water resources management, especially for arid regions. This study aims to explore the climate drivers of drought conditions in Xinjiang, an arid region in mid-latitude Asia. Standardized Precipitation Evapotranspiration Index (SPEI) was adopted to describe drought variation over Xinjiang during the period of 1951–2020. Teleconnection effects of El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) on Xinjiang drought variability were analyzed based on cross-correlation and stepwise regression methods. Partial correlation analysis was applied to discuss the responding mechanism of drought behavior to teleconnection signals from the perspective of regional climate factors. Findings from this study indicate that synchronous ENSO featured by Nino3.4 index has a significant positive correlation with Xinjiang dry/wet variation. El Niño may favor to wetness in Xinjiang, while La Niña may exacerbate drought effect in the region. ENSO mainly acts on the short-term drought variability in Xinjiang region. The synchronous PDO makes a leading contribution on drought variation at 12-month time scale among the four signals. The significant positive correlation between PDO and drought variation suggests that positive-phase (negative-phase) PDO may contribute to wetting (drying) epochs in Xinjiang region. AMO indicates a significant negative correlation with Xinjiang drought on both synchronous and asynchronous modes. Positive (negative) phase AMO may favor to dry (wet) effects in Xinjiang. AMO appears a predominant teleconnection effect on long-term drought variability, and fluctuates a persistent anti-phase mode with Xinjiang dry/wet variability since the mid-1980s. AO mainly acts on short-term drought fluctuations, indicating a significant negative correlation with drought behavior within a 12-month moving time window. Positive (negative) phase AO may contribute to dry (wet) epochs over Xinjiang. ENSO and PDO affect short-term dry/wet variation mainly through the teleconnection effect on precipitation variability. AMO mainly influences Xinjiang drought evolution by acting on regional temperature variation. The influence of internal atmospheric variability on regional climate behavior has a delayed effect, and drought variability is affected by precursor pattern of teleconnection likewise.

Diverse response of vegetation growth to multi-time-scale drought under different soil textures in China's pastoral areas

The pastoral areas of China are mainly located in ecologically fragile regions, where its ecosystems are highly sensitive to drought trends. Although numerous studies have been carried out on the response of vegetation to droughts, it is not entirely clear whether soil properties can influence this relationship. Using the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI), covering the period 1982 to 2015, we carefully analyzed drought impacts on vegetation in China's pastoral areas, to determine the effects of vegetation communities and soil types on vegetation response to multi-time-scale drought. Significantly positive correlations between NDVI and SPEI were observed in most regions, properly indicating that vegetation was largely influenced by drought, particularly the pastures in Inner Mongolia. Generally, forest was sensitive to longer time-scales of droughts, while grassland and cropland showed a close relationship with shorter or median drought time-scales. However, noticeable differences were found on the Tibetan Plateau, mainly because drought was not the main factor affecting vegetation growth in the region. The NDVI-SPEI correlations and the corresponding SPEI time-scales of each soil texture differed considerably, even in areas of the same land cover type, revealing that soil properties, here mainly refer to soil texture (classified by fractions of each separate soil, i.e., sand, silt, and clay), can assuredly affect the resistance and resilience of vegetation to drought stress. The underlying mechanism is the difference in particle size and permeability which can alter the storage and position of available soil water, thus affecting water absorption by the root system. Our results highlight the considerable importance of properly integrating edaphic factors when exploring the impact of likely climate change on ecosystems.

Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century

Drought is among the costliest natural disasters on both ecosystems and agroeconomics in China. However, most previous studies have used coarse resolution data or simply stopped short of investigating drought projection and its impact on crop yield. Motivated by the newly released higher-resolution climate projection dataset and the crucial need to assess the impact of climate change on agricultural production, the overarching goal of this study was to systematically and comprehensively predict future droughts at unprecedented resolutions over China as a whole. rather than region-specific projections, and then to further investigate its impact on crop yield by innovatively using a soil water deficit drought index. Methodologically, the drought projections were quantified from very high resolution climate data and further predicted impacts on crop yield over China using the standardized precipitation–evapotranspiration index (SPEI) at a relatively high (25 km) spatial resolution from NASA’s Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The results showed that (1) overall, China is projected to experience a significant decrease in SPEI (−0.15/decade under RCP (representative concentration pathway) 4.5; −0.14/decade under RCP8.5). Seasonally, the decreasing rate of SPEI is projected to be largest in winter (−0.2/decade and −0.31/decade) and the least in summer (−0.08/decade and −0.10/decade) under respective RCPs. (2) Regionally, winter/spring will get drier, especially at high latitudes/altitudes (North China and Tibetan plateau), and summer/autumn will get wetter in southern China. (3) Both the frequency and duration for medium and severe drought are projected to decrease, while extreme drought, particularly in high latitudes/altitudes, is projected to increase. (4) The percentage of the potential crop production affected by drought would increase to 36% (47%) by 2100 under RCP4.5 (RCP8.5). Especially, the ratio impacted by extreme drought is projected to increase over time and with much worse magnitude under RCP8.5; thus, adaptive crop policies are expected to address such a risk.

Drought and Desertification in Iran

Iran has different climatic and geographical zones (mountainous and desert areas), mostly arid and semi-arid, which are suffering from land degradation. Desertification as a land degradation process in Iran is created by natural and anthropogenic driving forces. Meteorological drought is a major natural driving force of desertification and occurs due to the extended periods of low precipitation. Scarcity of water, as well as the excessive use of water resources, mainly for agriculture, creates negative water balances and changes in plant cover, and accelerates desertification. Despite various political measures having been taken in the past, desertification is still a serious environmental problem in many regions in Iran. In this study, drought and aridity indices derived from long-term temperature and precipitation data were used in order to show long-term drought occurrence in different climatic zones in Iran. The results indicated the occurrence of severe and extremely severe meteorological droughts in recent decades in the areas studied. Moreover, the De Martonne Aridity Index (IDM) and precipitation variability index (PVI) showed an ongoing negative trend on the basis of long-term data and the conducted regression analysis. Rapid population growth, soil salinization, and poor water resource management are also considered as the main anthropogenic drivers. The percentage of the rural population in Iran is decreasing and the urban area is growing fast. Since the 1970s, the usage of groundwater in Iran has increased around fourfold and the average annual decrease in the groundwater table has been around 0.51 m. The results of the study provide a better ex-post and ex-ante understanding of the occurrence of droughts as key driving forces of the desertification in Iran. Additionally, they can enable policymakers to prepare proper regional-based strategic planning in the future. Desertification cannot be stopped or managed completely, but could be mitigated by the adoption of some proposed sustainable land management strategies.

Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China

BACKGROUND

Drought is a major natural disaster that causes severe social and economic losses. The prediction of regional droughts may provide important information for drought preparedness and farm irrigation. The existing drought prediction models are mainly based on a single weather station. Efforts need to be taken to develop a new multistation-based prediction model.

OBJECTIVES

This study optimizes the predictor selection process and develops a new model to predict droughts using past drought index, meteorological measures and climate signals from 32 stations during 1961 to 2016 in Shaanxi province, China.

METHODS

We applied and compared two methods, including a cross-correlation function and a distributed lag nonlinear model (DLNM), in selecting the optimal predictors and specifying their lag time. Then, we built a DLNM, an artificial neural network model and an XGBoost model and compared their validations for predicting the Standardized Precipitation Evapotranspiration Index (SPEI) 1-6 months in advance.

RESULTS

The DLNM was better than the cross-correlation function in predictor selection and lag effect determination. The XGBoost model more accurately predicted SPEI with a lead time of 1-6 months than the DLNM and the artificial neural network, with cross-validation R² values of 0.68-0.82, 0.72-0.89, 0.81-0.92, and 0.84-0.95 at 3-, 6-, 9- and 12-month time scales, respectively. Moreover, the XGBoost model had the highest prediction accuracy for overall droughts (89%-97%) and for three specific drought categories (i.e., moderate, severe, and extreme) (76%-94%).

CONCLUSION

This study offers a new modeling strategy for drought predictions based on multistation data. The incorporation of nonlinear and lag effects of predictors into the XGBoost method can significantly improve prediction accuracy of SPEI and drought.