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

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Spatiotemporal assessment of sustainable groundwater management using process-based and remote sensing indices: A novel approach

This study aims to develop a process-based method for evaluating groundwater sustainability and use the results in an archetypal analysis to fundamentally frame and understand sustainable development interactions in a river basin scale and sub-basin resolution. This method was applied in the Tashk-Bakhtegan-Maharloo (TBM) basin of Iran between 2003 and 2018; anthropogenic and natural factors were considered. With its 31 aquifers in 27 sub-basins, the TBM basin has repeatedly suffered severe droughts and water shortages over the past half a century, highlighting the importance of sustainable groundwater management. This study quantified anthropogenic and natural factors affecting groundwater dynamics to address sustainability and defined representative and relative indices, including climatological and drought conditions, vegetation cover, land cover, and population, to assess groundwater sustainability (GWS). Relative indices, prepared using measured data and remote sensing analysis, were chosen to explain groundwater-related situations, whereas representative indices, such as groundwater level and total dissolved solids, were used to explain the groundwater situation. GWS was spatially monitored using a couple-indicator trend-line slope comparison method to analyze process-based indices. Then, archetypal interaction patterns and their drivers in the groundwater system were investigated using results from process-based indices analyses results. The results showed that the TBM basin has moved towards unsustainable levels because of drought, increased irrigated croplands, unbalanced development of the sub-basins up- and downstream in the river's path, and over-exploitation of groundwater. These findings indicate that a deeper understanding of groundwater problems and stakeholder associations is required in order to adapt to the changing groundwater conditions.

Spatiotemporal evolution of agricultural drought and its attribution under different climate zones and vegetation types in the Yellow River Basin of China

Ecological protection and high-quality development of the Yellow River Basin (YRB) are major national strategies in China. Agricultural drought (AD) is one of the most important stress factors of the ecological security of the YRB. Currently, there is a lack of exploration of the spatiotemporal evolution of AD in the YRB under different climatic zones and vegetation types, and the mechanisms by the driving factors influence AD remain unclear. The Temperature Vegetation Dryness Index (TVDI) for the YRB in China during 2000-2020 was calculated using Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). We analyzed the spatiotemporal evolution of AD from the perspective of upstream of the YRB (UYRB), midstream of the YRB (MYRB), and downstream of the YRB (DYRB), as well as different climate zones and vegetation types. The driving factors were selected based on the Pearson correlation analysis, Geographical detector, and Mantel test. Structural equation modeling (SEM) was employed to quantify the direct and indirect effects of the driving factors on AD in the YRB. We found a slowing trend of AD in the YRB, mainly in the Loess Plateau, which is distributed in UYRB and MYRB, but an increasing trend for AD in DYRB. Temperature, which is the most direct influential factor, has exacerbated AD in UYRB and MYRB. However, surface solar radiation (SSR) has the greatest constraining effect on DYRB. AD increased in arid and desert zones, while a decreasing trend is observed for other climatic zones and vegetation types. In arid and semiarid zones, human activities and SSR were the largest indirect factors exacerbating AD. In humid and subhumid zones, the largest indirect factor exacerbating AD was potential evapotranspiration (PET). Temperature is the most direct factor exacerbating AD in cropland and forest, while PET is the largest indirect factor exacerbating AD in grassland. This study elucidates the driving factors and mechanisms of AD in the YRB to provide scientific decision support for mitigating regional drought and promoting regional sustainable development.

The negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics

There are complex bidirectional feedback relationships among different types of droughts (e.g., meteorological and agricultural droughts). As agricultural drought intensifies, meteorological drought response to agricultural drought may be changed from negative to positive feedback. Nevertheless, the negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics have remained unsolved. Herein, we proposed a new quantitative method to characterize the mutual feedback between meteorological drought and agricultural drought based on the vine copula function for the first time in this study. The negative-positive feedback transition threshold and the sensitivity of the feedback were quantified under certain drought conditions. In order to investigate the feedback relationship dynamics under a changing environment, the total study period was evenly divided into two stages: stage 1 (1982-1999) and stage 2 (2000-2018). Finally, the random forest method was used to explore the dominant factors on the transition threshold. Results indicate that: (1) the negative-positive feedback transition thresholds in August is generally lower than June and July in mainland China, the basin with large threshold is the Southwest River Basin; (2) the sensitivity of meteorological drought in response to agricultural drought was higher in positive feedback than in negative feedback; (3) the transition thresholds of stage 2 was mostly reduced, while the feedback sensitivity of positive feedback was mostly increased; and (4) compared with the single factor, the land-meteorological coupling strength (the correlation between precipitation and soil moisture) dominants the negative-positive feedback transition threshold. This study sheds new insights into droughts feedback.

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.

Diverse responses of gross primary production and leaf area index to drought on the Mongolian Plateau

Drought is a crucial factor regulating vegetation growth on the Mongolian Plateau (MP). Previous studies of drought effects on the MP have mainly concentrated on drought characterization, while the response of vegetation to drought remains unclear. To close this knowledge gap, we examined the response of MP vegetation to drought in terms of gross primary production (GPP) and leaf area index (LAI) from 1982 to 2018. Our findings show that intra-seasonally the frequency of drought occurrence in autumn had a greater impact on GPP (relative importance over 70 %), while the intensity of drought was more influential for LAI (relative importance approximately 60 %). Inter-seasonally, summer droughts had the most pronounced effect on vegetation (with median standardized anomalies of -0.72 for GPP and -0.4 for LAI, respectively). Additionally, we found that meteorological drought was more consistent with atmospheric aridity (high vapor pressure deficit) than soil drought (low soil moisture). This study advances knowledge of vegetation's susceptibility to climate extremes and improves the precision of predicting ecosystem response to climate change.

Wind farms dry surface soil in temporal and spatial variation

Wind energy is renewable and clean; however, the long-term operation of wind turbines can affect local climates. Soil moisture affects ecosystem balance, so determining the impact of wind farms on soil moisture is important. However, there has been little research on this, and only the impacts of wind farms on climate and vegetation have been considered. This study focuses on wind farms located in the grasslands of China. We analyzed changes in soil moisture in different wind directions and seasons and then judged the impacts of wind turbine operation on soil moisture. Our research shows that the operation of wind turbines will cause significant drying of soil, and this drought effect differs significantly according to season and wind direction. Our results show that 1) the soil moisture within wind farms decreases most significantly, with a decrease of 4.4 % observed; 2) in summer and autumn, the declines in soil moisture in the downwind direction are significantly greater than those in the upwind direction, with the opposite occurring in spring. (3) Wind farms aggravate the soil drying in grassland areas, which may have impacts on grassland ecosystems. Therefore, when building wind farms, we need to better understand their impacts on the environment.

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

The characteristics of the drought propagation from meteorological drought (MD) to agricultural drought (AD) differ in various climatic and underlying surface conditions. However, how these factors affect the process of drought propagation is still unclear. In this study, drought propagation and influencing factors were investigated in an arid region of Northeast Asia (ARNA) during 1982–2014. Based on run theory, the drought characteristics were detected using the standardized precipitation index (SPI) and standardized soil moisture index (SMI), respectively. The propagation time from MD to AD was investigated, and the influence factors were identified. Results demonstrated that five clusters (C1–C5) based on land cover distribution were further classified by the K-means cluster algorithm to discuss the spatial and seasonal propagation variation. MD and AD in ARNA became more severe during the study period in all five clusters. The propagation times from MD to AD in all five clusters were shorter (1–3 months) in summer and autumn and longer (5–12 months) in spring and winter. This result suggested that the impact of vegetation on the seasonal drought propagation time was more obvious than that of the spatial drought propagation time. Precipitation and vegetation were the major impactors of AD in spring, summer and autumn (p < 0.05). The impact of precipitation on AD was more noticeable in summer, while vegetation mainly influenced AD in spring and autumn. The research also found that drought propagation time had a negative relationship (p < 0.05) with precipitation, evapotranspiration, soil moisture and NDVI in this region, which indicated that a rapid hydrological cycle and vegetation can shorten the propagation time from MD to AD. This study can help researchers to understand the drought propagation process and the driving factors to enhance the efficiency of drought forecasting.

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.

Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis

The increase in drought frequency in recent years is considered as an important factor affecting vegetation diversity. Understanding the responses of vegetation dynamics to drought is helpful to reveal the behavioral mechanisms of terrestrial ecosystems and propose effective drought control measures. In this study, long time series of Normalized Difference Vegetation Index (NDVI) and Solar-induced chlorophyll fluorescence (SIF) were used to analyze the vegetation dynamics in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought was evaluated, and the corresponding differences among different vegetation types were revealed. Based on an improved partial wavelet coherence (PWC) analysis, the influences of teleconnection factors (i.e., large-scale climate patterns and solar activity) on the response relationship between meteorological drought and vegetation were quantitatively analyzed to determine the roles of factors. The results indicate that (a) vegetation in the PRB showed an increasing trend from 2001 to 2019, and the SIF increased more than that of NDVI; (b) the vegetation response time (VRT) based on NDVI (VRTN) was typically 4-6 months, while the VRT based on SIF (VRTS) was typically 2-4 months. The VRT was shortest in the woody savannas and longest in the evergreen broadleaf forests. (c) The relationship between the SIF and meteorological drought was more significant than that between the NDVI and meteorological drought. (d) There was a significant positive correlation between meteorological drought and vegetation in the period of 8-20 years. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and sunspots were important driving factors affecting the response relationship between drought and vegetation. Specifically, the PDO had the greatest impacts among these factors.

Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau

Soil moisture (SM) is a key parameter regulating the hydrothermal balance of global terrestrial ecosystems and plays an important role in local ecological environment, particularly in arid and semiarid areas. However, current studies have so far obtained insufficient knowledge of SM spatiotemporal variability and its primary control factors, which limits our understanding of the feedback effects of SM on surface vegetation and hydrothermal activity. Here, we chose the ecologically fragile Mongolian Plateau (MP) as the study area to quantitatively reveal the soil moisture spatiotemporal variability (SMSTV) and the influence of control factors (climate, vegetation, soil and groundwater) with the help of empirical orthogonal functions (EOFs) and geographical detector models. The results indicated that a significant trend of decreasing SM and one dominant spatial structure (EOF1) of SM was found in the MP from 1982 to 2019, which explained over 54% of the spatial variability in SM, and as the soil depth increased, the EOF1 interpretation capacity increased. In addition, EOF1 is high in the north and east and low in the south and west of the MP and that vegetation cover is also relatively greater in the high-value areas. Overall, groundwater has the greatest influence on SMSTV in the MP (q = 0.89); however, precipitation and potential evapotranspiration remain the main control factors for SMSTV for different ecological zones, while the influence of vegetation elements (NDVI and GPP) cannot be ignored, and soil textures (clay, sand, silt) have the least influence. Meanwhile, SMSTV is explained to a greater extent by the interaction of the factors rather than by a single factor. However, there are differences in the influence mechanisms of each factor on SMSTV. This study provides strong evidence that meteorological forcing is not the only factor that dominates SMSTV and that the dominant factors may vary considerably between ecological zones.

The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe

As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.