Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data

An integrated dryness index based on geographically weighted regression and satellite earth observations

Drought, characterized by the limited water availability in the atmosphere and hydrological systems, is one of the most destructive natural calamities. Defining droughts based on a single variable/index (e.g., precipitation, temperature, TCI, VCI) may not be sufficient for describing intricate conditions, impacts, and decision-making. Therefore, an integrated set of variables and indices is necessary to capture various aspects of intricate drought conditions. This paper has developed an Integrated Geographically Weighted Dryness Index (IGWDI) to model the drought. In this index, climatic parameters (CP) (i.e., precipitation, temperature, evapotranspiration) and remote-sensing-based drought indices (RSDI) (i.e., PCI, VCI, TCI, SMCI) were inputted into a GWR (Geographically Weighted Regression) model to predict the TVDI as independent variables in two distinct models, IGWDI-CP and IGWDI-RSDI, respectively. In this study, the proposed IGWDI is utilized to characterize the drought conditions in the Iranian plateau on a monthly scale from April to September over 20 years, including 2003-2022. According to adjusted R2 and AICc values, the findings revealed that IGWDI-CP is the best-fitting model for drought monitoring in all months. The IGWDI-CP model demonstrated that over the 20 years, from April to September, nearly 90 % of the examined study area experienced a range of drought severity levels. The warmest month, July, stood out, with approximately 71 % of the regions facing severe and extreme drought conditions. These adverse conditions were predominantly observed in scattered locations within Iran's middle and southern regions. Overlay, throughout all studied months, the southwestern regions of Iran emerged as the focal point for the most severe drought conditions. In most regions, an inverse relationship was discovered between TVDI and precipitation and evapotranspiration, while a positive correlation was observed between TVDI and temperature. This study employed the GWR model to combine several crucial climatic parameters and remote sensing-based indices to derive a novel index for monitoring a wider range of droughts. Consequently, these findings benefit decision-makers and authorities responsible for environmental sustainability, agriculture, and addressing the consequences of climate change.

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

A review of recent developments on drought characterization, propagation, and influential factors

Droughts have impacted human society throughout its history. However, the occurrence of severe drought events in the last century and the concerns on the potential effects of climate change have prompted remarkable advances in drought conceptualization and modeling in recent years. This review intends to present the state-of-the-art on drought characterization and propagation, as well as providing insights on how climate dynamics and anthropogenic activities might affect this phenomenon. For this purpose, we first address the distinct concepts of droughts and their relationships. Next, we present two frequently utilized methods based on the run theory for drought characterization and explain the development and recovery stages of droughts. Then, we discuss potential drivers for drought occurrence and propagation, with focus on meteorological factors, catchments' physical characteristics and human activities. Later, we describe how droughts can affect several parameters of water quality. This review also addressed flash droughts, encompassing their definitions, commonly used indices, and potential drivers. Finally, we briefly address the roles of climate change and long-term persistence on future drought scenarios. This review may be useful for researchers and stakeholders for attaining a broader understanding on drought dynamics and impacts.

Effects of drought and salt stress on seed germination and seedling growth of Elymus nutans

Drought and soil salinization are global environmental issues, and Elymus nutans play an important role in vegetation restoration in arid and saline environments due to their excellent stress resistance. In the process of vegetation restoration, the stage from germination to seedling growth of forage is crucial. This experiment studied the effects of PEG-6000 simulated drought stress and NaCl simulated salinization stress on the germination of E. nutans seeds, and explored the growth of forage seedlings from sowing to 28 days under drought and salinization stress conditions. The results showed that under the same environmental water potential, there were significant differences in responses of seed germination, seedling growth, organic carbon, total nitrogen and total phosphorus of above-ground and underground parts of E. nutans to drought stress and salinization stress. Using the membership function method to comprehensively evaluate the seed germination and seedling indicators of E. nutans, it was found that under the same environmental water potential, E. nutans was more severely affected by drought stress during both the seed germination and seedling growth stages. E. nutans showed better salt tolerance than drought resistance.

A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes.

Association of solid fuel use with a risk score capturing dementia risk among middle-aged and older adults: A prospective cohort study

OBJECTIVES

Whether household air pollution is associated with dementia risk remains unknown. This study examined the associations between solid fuel use for cooking and heating (the main source of household air pollution) and dementia risk.

METHODS

This analysis included data on 11,352 participants (aged 45+ years) from the 2011 wave of China Health and Retirement Longitudinal Study, with follow-up to 2018. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), which was subsequently standardized for analysis. Household fuel types of cooking and heating were categorized as solid (e.g., coal and crop residue) and clean (e.g., central heating and solar). Multivariable analyses were performed using generalized estimating equations. Moreover, we examined the joint associations of solid fuel use for cooking and heating with the BDRM score.

RESULTS

After adjusting for potential confounders, we found an independent and significant association of solid (vs. clean) fuel use for cooking and heating with a higher BDRM score (e.g., β = 0.17 for solid fuel for cooking; 95% confidence interval [CI]: 0.15-0.19). Participants who used solid (vs. clean) fuel for both cooking and heating had the highest BDRM score (β = 0.32; 95% CI: 0.29-0.36). Subgroup analysis suggested stronger associations in participants living in rural areas.

CONCLUSIONS

Solid fuel use for cooking and heating was independently associated with increased dementia risk in Chinese middle-aged and older adults, particularly among those living in rural areas. Our findings call for more efforts to facilitate universal access to clean energy for dementia prevention.

How does precipitation data influence the land surface data assimilation for drought monitoring?

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions. Here, we utilize our recently developed LSM data assimilation system for probabilistically monitoring drought over the Contiguous United States (CONUS). The Noah-MP LSM model is forced with two widely used precipitation data including IMERG (Integrated Multi-satellitE Retrievals for GPM) and NLDAS (North American Land Data Assimilation System). Soil moisture and evapotranspiration are known to have a strong relationship in the land-atmospheric interaction processes. Unlike other studies that attempted the individual assimilation of these variables, here we propose a multivariate data assimilation framework. Therefore, in both modeling scenarios, the data assimilation approach is used to integrate remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) evapotranspiration and SMAP (Soil Moisture Active Passive) soil moisture observations into the Noah-MP LSM. The results of this study indicate that the source of precipitation data has a significant impact on the performance of LSM data assimilation system for drought monitoring. The findings revealed that NLDAS and IMERG precipitation can result in a significant difference in identifying drought severity depending on the region and time of the year. Furthermore, our analysis indicates that regardless of the precipitation forcing data product used in the land surface data assimilation system, our modeling framework can effectively detect the drought impacts on crop yield. Additionally, we calculated the drought probability based on the ensemble of soil moisture percentiles and found that there exist temporal and spatial discrepancies in drought probability maps generated from the NLDAS and IMERG precipitation forcings.

Applicability of a CEEMD–ARIMA Combined Model for Drought Forecasting: A Case Study in the Ningxia Hui Autonomous Region

In the context of global warming, the increasing frequency of drought events has caused negative impacts on agricultural productivity and societal activities. However, the drought occurrences have not been well predicted by any single model, and precipitation may show nonstationary behavior. In this study, 60 years of monthly precipitation data from 1960 to 2019 for the Ningxia Hui Autonomous Region were analyzed. The standard precipitation index (SPI) was used to classify drought events. This study combined the strengths of autoregressive integrated moving average (ARIMA) and complementary ensemble empirical mode decomposition (CEEMD) to predict drought. First, based on the precipitation dataset, the SPI at timescales of 1, 3, 6, 9, 12, and 24 months was calculated. Then, each of these SPI time series was predicted using the ARIMA model and the CEEMD–ARIMA combined model. Finally, the models′ performance was compared using statistical metrics, namely, root-mean-square error (RMSE), mean absolute error (MAE), Kling–Gupta efficiency (KGE), Willmott index (WI), and Nash–Sutcliffe efficiency (NSE). The results show that the following: (1) Compared with the ARIMA forecast value, the prediction results of the CEEMD–ARIMA model were in good agreement with the SPI values, indicating that the combined model outperformed the single model. (2) Two different models obtained the lowest accuracy for the SPI1 prediction and the highest accuracy for the SPI24 prediction. (3) The CEEMD–ARIMA model achieved higher prediction accuracy than the ARIMA model at each time scale. The most precise model during the test phase was the CEEMD–ARIMA model at SPI24 at Xiji Station, with error measures of MAE = 0.076, RMSE = 0.100, NSE = 0.994, KGE = 0.993, and WI = 0.999. Such findings will be essential for government to make decisions.

Seasonal drought analysis of Akşehir Lake with temporal combined sentinel data between 2017 and 2021 spring and autumn

The threat of drought has been felt almost worldwide in recent years. It is critical to determine the causes of drought and how seasonal changes affect it. Additionally, it is necessary to determine the speed and impact area of drought, monitor drought areas, and attempt to find solutions against drought. With the developing satellite sensing systems, remote sensing methods are being used to investigate topics such as the increase and extent of drought, uncontrolled water consumption in agricultural activities, and the effects of unnatural pollutants on freshwater resources such as lakes and rivers. Using Synthetic Aperture Radar (SAR) satellite data to monitor changes in water bodies is a relatively new area of study in remote sensing. The spatial extent and seasonal change (spring and autumn) of droughts between 2017 and 2021 in Akşehir Lake were determined from Sentinel-1A SAR satellite data, and the Normalized Differential Water Index (NDWI) was calculated using Sentinel-2A optical satellite data and Standardized Precipitation Index (SPI) in this research. In addition, a different approach was applied to determine the change of wetland boundaries more accurately by converting the linear Sigma0 band to the decibel (dB) band and applying a non-linear 3 × 3 maximum filter to the dB band to Sentinel-1A data. Consequently, it has been established that Akşehir Lake, which used to have wetlands during the spring seasons but began to dry up in the autumn seasons, had completely dried up in both periods in 2021.

A new comprehensive approach for regional drought monitoring

The Standardized Precipitation Index (SPI) is a vital component of meteorological drought. Several researchers have been using SPI in their studies to develop new methodologies for drought assessment, monitoring, and forecasting. However, it is challenging for SPI to provide quick and comprehensive information about precipitation deficits and drought probability in a homogenous environment. This study proposes a Regional Intensive Continuous Drought Probability Monitoring System (RICDPMS) for obtaining quick and comprehensive information regarding the drought probability and the temporal evolution of the droughts at the regional level. The RICDPMS is based on Monte Carlo Feature Selection (MCFS), steady-state probabilities, and copulas functions. The MCFS is used for selecting more important stations for the analysis. The main purpose of employing MCFS in certain stations is to minimize the time and resources. The use of MCSF makes RICDPMS efficient for drought monitoring in the selected region. Further, the steady-state probabilities are used to calculate regional precipitation thresholds for selected drought intensities, and bivariate copulas are used for modeling complicated dependence structures as persisting between precipitation at varying time intervals. The RICDPMS is validated on the data collected from six meteorological locations (stations) of the northern area of Pakistan. It is observed that the RICDPMS can monitor the regional drought and provide a better quantitative way to analyze deficits with varying drought intensities in the region. Further, the RICDPMS may be used for drought monitoring and mitigation policies.

Regional frequency analysis of drought severity and duration in Karkheh River Basin, Iran using univariate L-moments method

Drought is one of the natural disasters that causes a great damage to human life and natural ecosystems. The main differences are in the gradual effect of drought over a relatively long period, impossibility of accurately determining time of the beginning and end of drought, and geographical extent of the associated effects. On the other hand, lack of a universally accepted definition of drought has added to the complexity of this phenomenon. In the last decade, due to increasing frequency of drought in Iran and reduction of water resources, its consequences have become apparent and have caused problems for planners and managers. So in this research, regional frequency analysis using L-moments methods was performed to investigate severity and duration of Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSI) and to study of meteorological, agricultural, and hydrological droughts in Karkheh River Basin in Iran. Using K-means clustering method, basin was divided into four homogeneous areas. Uncoordinated stations in each cluster were removed. The best regional distribution function was selected for each homogeneous region, and it was found that Pearson type (3) has the highest fit on the data set in the basin. Based on Hosking and Wallis heterogeneity test, Karkheh Basin with H₁ < 1 was identified as acceptable homogeneous in all clusters. The results showed that hydrological drought occurs with a very short time delay in Karkheh River Basin after the meteorological drought, and two indicators show meteorological and hydrological drought conditions well. Agricultural drought occurs after meteorological and hydrological drought, respectively, and its severity and duration are less than the other indicators. Meteorological, hydrological, and agricultural droughts do not occur at the same time in all of the years. In general, the SPI drought index shows the most severe droughts compared with the other three indices. By this way, in 5- to 20-year return period with severity of 3SPI and in 20- to 100-year return period with severity of 7SPI, region IV or the western and northwestern areas of the basin has been affected by severe meteorological drought. By using the regional standardized quantities, it is possible to estimate the probability of drought in any part of the catchment that does not have sufficient data for hydrological studies.

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.

Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.

Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China

Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.

Monitoring Meteorological Drought in Southern China Using Remote Sensing Data

Severe meteorological drought is generally considered to lead to crop damage and loss. In this study, we created a new standard value by averaging the values distributed in the middle 30–70% instead of the traditional mean value, and we proposed a new index calculation method named Normalized Indices (NI) for meteorological drought monitoring after normalized processing. The TRMM-derived precipitation data, GLDAS-derived soil moisture data, and MODIS-derived vegetation condition data from 2003 to 2019 were used, and we compared the NI with commonly used Condition Indices (CI) and Anomalies Percentage (AP). Taking the mid-to-lower reaches of the Yangtze River (MLRYR) as an example, the drought monitoring results for paddy rice and winter wheat showed that (1) NI can monitor well the relative changes in real precipitation/soil moisture/vegetation conditions in both arid and humid regions, while meteorological drought was overestimated with CI and AP, and (2) due to the monitoring results of NI, the well-known drought event that occurred in the MLRYR from August to October 2019 had a much less severe impact on vegetation than expected. In contrast, precipitation deficiency induced an increase in sunshine and adequate heat resources, which improved crop growth in 78.8% of the area. This study discusses some restrictions of CI and AP and suggests that the new NI index calculation provides better meteorological drought monitoring in the MLRYR, thus offering a new approach for future drought monitoring studies.

The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.