Vegetation Drought Research Articles

Space-Time Drought Dynamics and Soil Erosion in Puruliya District of West Bengal, India: A Conceptual Design

This paper integrates drought and soil erosion problems of Puruliya district of West Bengal. The space-time dynamics of vegetative drought is assessed based on the mean monthly time-series MODIS data for the period of 2000–2013. The drought recurrence ranges from 0 to 29 months, and the mean frequency was assessed to be 7.09 months for the entire period under deliberation. The potential soil disintegration of the district was estimated using the Revised Universal Soil Loss Equation in the geo-spatial environment. The locale has been arranged into six potential soil erosion classes ranging from low-to-severe risk depending upon the computed soil erosion amount. The mean soil erosion rate of the district was anticipated as 76.29 t ha−1 year−1. The erosion amount is high in gently sloping grounds covering mainly harvested agricultural fields and open fields. These areas contribute mostly the soil erosion in this district. It is found that in this area the slope length-steepness factor is the major erosion-controlling factor. About one-fourth of the total area comes under the threat of a very high-to-severe erosion zone. Also, it was observed that the drought condition of the district is strongly correlated with the soil moisture condition in the drought-affected months and the precipitation amount. The study likewise distinguished the conceivable interrelationship between the soil erosion problem and drought frequency for better monitoring and policy decision making. A considerable increasing trend is observed in mean soil erosion with increasing frequency of drought-affected months for the study period.

Spatio-Temporal Variation of Drought within the Vegetation Growing Season in North Hemisphere (1982–2015)

Drought disasters jeopardize the production of vegetation and are expected to exert impacts on human well-being in the context of global climate change. However, spatiotemporal variations in drought characteristics (including the drought duration, intensity, and frequency), specifically for vegetation areas within a growing season, remain largely unknown. Here, we first constructed a normalized difference vegetation index to estimate the length of the growing season for each pixel (8 km) by four widely used phenology estimation methods; second, we analyzed the temporal and spatial patterns of climate factors and drought characteristics (in terms of the Standardized Precipitation Evapotranspiration Index (SPEI)), within a growing season over vegetation areas of the northern hemisphere before and after the critical time point of 1998, which was marked by the onset of a global warming hiatus. Finally, we extracted the highly drought-vulnerable areas of vegetation by examining the sensitivity of the gross primary production to the SPEI to explore the underlying effects of drought variation on vegetation. The results revealed, first, that significant (p < 0.05) increases in precipitation, temperature, and the SPEI (a wetting trend) occurred from 1982 to 2015. The growing season temperature increased even more statistically significant after 1998 than before. Second, the duration and frequency of droughts changed abruptly and decreased considerably from 1998 to 2015; and this wetting trend was located mainly in high-latitude areas. Third, at the biome level, the wetting areas occurred mainly in the tundra, boreal forest or taiga, and temperate coniferous forest biomes, whereas the highly drought-vulnerable areas were mainly located in the desert and xeric shrubland (43.5%) biomes. Our results highlight the fact that although the drought events within a growing season decreased significantly in the northern hemisphere from 1998 to 2015, the very existence of a mismatch between a reduction in drought areas and an increase in highly drought-vulnerable areas makes the impact of drought on vegetation nonnegligible. This work provides valuable information for designing coping measures to reduce the vegetative drought risk in the Northern Hemisphere.

Drought Impacts on Vegetation in Southeastern Europe

We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation Evaporation Index (SPEI), at several time scales, and the Normalized Difference Vegetation Index (NDVI), as well as an assessment of the simultaneous occurrence of extremes in both indices. The analysis of the relationship between drought and vegetation was made for the growing season (from April to October of the entire period), and special attention was devoted to the severe drought event of 2000/2001, considered as the driest since 1961 for the study area. More than three quarters (77%) of the agricultural land exhibits a positive correlation between the two indices. The sensitivity of crop areas to drought is strong, as the impacts were detected from May to October, with a peak in July. On the other hand, forests were found to be less sensitive to drought, as the impacts were limited mostly to July and August. Moreover, vegetation of all land cover classes showed a dependence between the sign of the correlation and the elevation gradient. Roughly 60% (20%) of the study domain shows a concordance of anomalously low vegetation activity with dry conditions of at least 50% (80%) in August. By contrast, a lower value of concordance was observed over the Carpathian Mountains. During the severe drought event of 2000/2001, a decrease in vegetation activity was detected for most of the study area, showing a decrease lasting at least 4 months, between April and October, for more than two thirds (71%) of the study domain.

Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

Abstract Drought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China.

Assessing the value of electrical resistivity derived soil water content: Insights from a case study in the Critical Zone of the Chinese Loess Plateau

Acquiring data for the soil water content (θ) is important for assessing vegetation growth, drought, and climate change. However, it is a time-consuming and labor-intensive task that is especially challenging in deep soils. Therefore, we introduce the noninvasive technique of electrical resistivity tomography (ERT) for indirectly determining θ through the development of prediction models for loessial regions under different site conditions (i.e., soil texture, land use, soil depth, and dry/wet conditions). We obtained 2769 pairs of electrical resistivity (ρ) and θ datasets using ERT (53 sites) and a neutron probe (69 access tubes) on the Chinese Loess Plateau. We built linear and nonlinear models correlating ρ and θ and selected the best model according to the coefficient of determination (R2) and root-mean-square error (RMSE). The uncertainty and sensitivity of ERT-derived θ were further evaluated and acceptable results were obtained. The new models correlating ρ and θ under different site conditions are the first set of models based on field data from a loessial region, and their acceptable performance makes them applicable for measuring different soil parameters in the Loess Plateau and possibly other loessial regions around the world.

Container Design Affects Shoot and Root Growth of Vegetable Plant

The response of root growth in containers has been studied in recent decades. The objective was to evaluate the effect of four types of containers on root and shoot growth. The containers were two shapes, round and square, and in some containers, internal vertical walls (IVWs) were placed that increased the internal container surface area with two substrates: perlite and coir fiber. Seedlings of cucumber, pepper, and tomato were transplanted. Two experiments were performed: vegetative growth and drought stress by partial decapitation and a period without fertigation. After decapitation, preexisting and new leaf area, dry biomass or the leaves, and stem were measured. The results revealed that the type of container had no effect, nor were there significant differences between substrates. The containers with IVWs exhibited an increase in biomass and the root surface. The total contact surface with the substrate of the four container types was closely related to the recorded plant growth. Thus, IVWs not only decrease mechanical problems of roots by preventing spiralling but also favor the production of biomass in vegetable plants and substantially increase the root, enabling the plants to manage water deficit and potentially improve posttransplant stress.

Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages.

Has the Bosten Lake Basin been dry or wet during the climate transition in Northwest China in the past 30 years?

Research has shown that the climate in northwest China has turned to warm-wet in recent decades. Does this mean that the entire northwest of China has become wet in the last few decades? In the context of the climate transition in northwest China, this paper investigates whether the Bosten Lake Basin (BLB) located in southern Xinjiang has been dry or wet in the last 30 years. We used the Standardized Precipitation Evapotranspiration Index (SPEI) and Temperature Vegetation Drought Index (TVDI) to investigate these changes. The results show that although the dry-wet changes indicated by the SPEI and TVDI exhibited slight spatiotemporal differences, the entire BLB reflected a tendency to be dry. That is, the BLB has not become wet during the climate transition in northwest China, but is developing a trend toward aridification. The main conclusions of this study are as follows: (1) The SPEI indicated a drying trend in seasonal and interannual variations in plains; in mountains, except in spring and summer it has a drying trend, too. (2) The TVDI showed that although the drying trend in summer and winter was weaker than that in spring and autumn in the BLB, where some regions represented a significant tendency toward wetness owing to seasonal or annual changes, the entire study area has still become more dry.

Detecting vegetation drought dynamics in European Russia

Rainfall and temperature are the key factors responsible for vegetation condition, health and growth. This research work analysis spatiotemporal phenomena in between SPI, LST and VIs in summer session from 2000 to 2018 in European Russia. This study used MODIS, NDVI, LST and TRMM data. The negative SPI values represent increasing drought events with reducing rainfall and vice-versa. Numerical consequences specify that mean annual rainfall, VIs variate according to SPI values and they completely change in the year 2004, 2009 and 2015. VIs also indirectly related to LST as high LST values (high temperature) associated with low VIs values (low vegetation) and vice-versa, with correlation coefficients 0.90. Correlation analysis of VIs, SPI and LST indicate close relationship in between vegetation, rainfall and temperature and this relationship can be used for near real time vegetation drought dynamics monitoring through satellite data for short term to long term changes in vegetation growth.

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Temperature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and applied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature vegetation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry–wet boundaries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inverted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R = −0.45) than that for OTVDI (R = −0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

Performance of the pineapple sweet orange on different rootstocks

This study aimed at evaluating the agronomical performance of ‘Pineapple’ sweet orange grafted on ten rootstocks, in 2011-2017 harvests, so as to recommend the best combinations to be commercially explored in citrus growing regions in Bahia and Sergipe states, Brazil. An experiment was installed to test ten rootstock for 'pineapple' sweet orange: 'Santa Cruz' Rangpur lime, 'Red Rough' Lemon, 'Orlando' Tangelo, 'Sunki Tropical' Mandarin, 'Swingle' citrumelo, the citrandarins 'Indio' and 'Riverside' and the hybrids HTR-051, LVKxLCR-010 and TSKxCTTR-002. The trial was installed in 2008 in the municipality of Umbauba in Sergipe. The experimental design was complete randomized blocks with four replications and two plants per plot. Plant spacing was 6 x 4 m which corresponds to 416 plants per hectare and the orchard was rainfed and followed conventional management. The following agronomical parameters were evaluated: vegetative growth, drought tolerance, yield and physico-chemical quality of fruits as well as the abundance of phytophagous mites. Both hybrids LVK x LCR – 010 and TSKC x CTTR-002 and the ‘Santa Cruz’ rangpur lime bestowed higher tolerance to the dry period on the ‘Pineapple’ orange tree, by comparison with higher water deficit susceptibility conferred by the ‘Orlando’ tangelo and the ‘Swingle’ citrumelo. Rootstocks HTR-051, ‘Riverside’ citrandarin, ‘Swingle’ citrumelo and TSKC x CTTR-02 induced plants to remain small and, thus, showed aptitude for culture densification. Cumulative yield of the ‘Pineapple’ orange was higher on rootstocks ‘Red Rough’ lemon and ‘Santa Cruz’ Rangpur lime, the hybrid LVK x LCR-010 and ‘Sunki Tropical’. Yield efficiency was not influenced by the rootstocks. Physico-chemical quality of fruits of ‘Pineapple’ orange is affected by the rootstocks and meets the requirements of juice industries. Regarding plant resistance, the rootstocks did not influence the population density of mites P. oleivora, E. banksi and T. mexicanus on ‘Pineapple’ oranges. Results show that both rootstocks ‘Red Rough’ lemon and ‘Santa Cruz’ rangpur lime conferred high regularity to ‘Pineapple’ orange trees in citrus growing regions in Bahia and Sergipe states.

REMOTE SENSING RETRIEVAL OF SOIL MOISTURE IN GUANGXI BASED ON ATI AND TVDI MODELS

Abstract. In order to further to improve the monitoring accuracy of soil moisture spatial and temporal changes in Guangxi, this paper uses MODIS, Landsat8, ASTER GDEM data and measured relative soil moisture data as data sources. According to the design idea of complementary advantages, the EVI value of Landsat8 image is partitioned, and the relative soil moisture is inverted by ATI model method in the area of EVI≤0.33, and the relative soil moisture is inverted by TVDI model in the area of EVI>0.33. The apparent thermal inertia model (ATI) and the temperature vegetation drought index model (TVDI) was used to invert the relative soil moisture in Guangxi. The results show that the temporal variation of relative soil moisture in the study area in 2017 is the change cycle from rising to falling: the rising period is from January to July, and the falling period is from August to December, in which the relative soil moisture reaches the peak in July. The minimum value are December; the relative soil moisture in the north of Guangxi are generally higher than that in southern Guangxi. The correlation between the relative soil moisture value of the ATI model and the TVDI model partition retrieval and the measured relative soil moisture data is higher, and the relative soil moisture retrieval effects is better.

Spatial and temporal effects of drought on Chinese vegetation under different coverage levels.

Land surface vegetation dynamics are strongly affected by drought. Thus, understanding the responses of vegetation to drought can inform measures to increase biome stability. In this study, the normalized difference vegetation index (NDVI) and the Palmer drought severity index (PDSI) were utilized to investigate the relationship between vegetation activity and drought across different drought regions and ecological community types from 1982 to 2015. Our results showed that the highest correlation between monthly NDVI and PDSI at different timescales (1-36months) indicated the degree of drought impact on vegetation. There were diverse responses of vegetation to drought according to the drought features and climatic environment. The northern grassland, cropland, and desert ecosystems were strongly impacted by drought. These vegetation ecosystems had a low sensitivity to drought in southern China. Drought had the strongest impact on grassland in summer, which is the high frequency drought season. The most susceptible ecosystem types to drought were those with homogenous vegetation, especially under long-term drought conditions (such as the Inner Mongolia Plateau dominated by grassland). Under global warming, drought with high-temperature characteristics is expected to become more frequent and severe. Such drought could threaten the survival of plateau grassland, arid plain grassland, and rain-fed cropland, as high temperatures accelerate evaporation, leading to water deficit. However, moist forests showed little threat under normal drought. We suggest that future research should focus on vegetation activity in northern and southwestern China, where the vegetation shows the greatest sensitivity to drought.

Assessing the Response of Ecosystem Water Use Efficiency to Drought During and after Drought Events across Central Asia.

The frequency and intensity of drought are expected to increase worldwide in the future. However, it is still unclear how ecosystems respond to drought. Ecosystem water use efficiency (WUE) is an essential ecological index used to measure the global carbon–water cycles, and is defined as the carbon absorbed per unit of water lost by the ecosystem. In this study, we applied gross primary productivity (GPP), evapotranspiration (ET), land surface temperature (LST), and normalized difference vegetation index (NDVI) data to calculate the WUE and drought index (temperature vegetation dryness index (TVDI)), all of which were retrieved from moderate resolution imaging spectroradiometer (MODIS) data. We compared the mean WUE across different vegetation types, drought classifications, and countries. The temporal and spatial changes in WUE and drought were analyzed. The correlation between drought and WUE was calculated and compared across different vegetation types, and the differences in WUE between drought and post-drought periods were compared. The results showed that (1) ecosystems with a low (high) productivity had a high (low) WUE, and the mean ecosystem WUE of Central Asia showed vast differences across various drought levels, countries, and vegetation types. (2) The WUE in Central Asia exhibited an increasing trend from 2000 to 2014, and Central Asia experienced both drought (from 2000 to 2010) and post-drought (from 2011 to 2014) periods. (3) The WUE showed a negative correlation with drought during the drought period, and an obvious drought legacy effect was found, in which severe drought affected the ecosystem WUE over the following two years, while a positive correlation between WUE and drought was found in the post-drought period. (4) A significant increase in ecosystem WUE was found after drought, which revealed that arid ecosystems exhibit high resilience to drought stress. Our results can provide a specific reference for understanding how ecosystems will respond to climate change.

Responses of Water Use Efficiency to Drought in Southwest China

Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000–2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg−1H2O) > shrublands (2.00 gC kg−1H2O) > croplands (1.76 gC kg−1H2O) > grasslands (1.04 gC kg−1H2O). During the period 2000–2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management.

Multidimensional Response Evaluation of Remote-Sensing Vegetation Change to Drought Stress in the Three-River Headwaters, China

The impact of drought on ecosystems has become increasingly prominent. This study used the MODIS remote-sensing vegetation index and the standardized precipitation index to quantify vegetation coverage status and meteorological drought level. Using the copula method, the joint distribution of the drought index and related vegetation cover variables were simulated for the first time. The conditional distribution of vegetation biomass was deduced, and the multidimensional response between the vegetation biomass and drought was explored to understand the possible vegetation loss under different drought severity conditions. The three-river headwaters (TRH) region is the source place of the Yangtze River, Yellow River, and Lancang River, where the ecosystem is characterized by the innate vulnerability. Using this region as a case study, the results show that the spatiotemporal evolution of drought and vegetation in the TRH region has apparent regional heterogeneity. The vegetation cover in the eastern region is significantly better than that in the northwestern region, while the vegetation growth trend in the northwestern region is stronger than that in the southeastern region. It is feasible to build a vegetation-drought multidimensional response model based on the copula method. In 92.5% of the TRH region, vegetation cover was significantly affected by the severity of the drought. The impact on the growth of vegetation caused by persistent drought events was higher than that of short-term but high-intensity drought. The vegetation in the TRH area has a certain degree of drought resistance. This study provides outstanding theoretical support and reference for the protection of the TRH basin ecosystem.

Trends in Global Vegetative Drought From Long-Term Satellite Remote Sensing Data

In this article, the trends in global vegetative drought were investigated using MODIS- and AVHRR-based NDVI products. A set of selected methods were employed to perform trend analysis including trend test, trend location detection, and trend estimates. Accounting for the effect of the global geographical heterogeneity on trend analysis, the NDVI dataset was aggregated on designated divisions in view of latitude ranges and climate zones. From the results, it was concluded that: AVHRR has longer-term records that provide a critical historical perspective on vegetation activities necessary for global change research, and continuity and correctness is achievable from AVHRR VCI given the systematic offset between the NDVI values derived from the two sensors and the characteristics of the VCI algorithm. From a pixel level global trend analysis map, the proportion of pixels with rising trends is 54.7% in the world, 67.6% and 47.5% in the northern hemisphere and the southern hemisphere, respectively, which means there is an overall rising trends in the global VCI values, especially in the northern hemisphere. The North Temperate and the South Tropical have overall increases in the VCI values while all climate zones have overall increases in the VCI values. The piecewise trends basically adhere to the results of overall trend identification although there are some local variations. There are obvious rising trends during the latest years for all the climate zones. Dominant down trends were identified in A, B, Cw, Ds, and E while the piecewise trends for both Cs and Df are dominantly rising before 2000. An average of about four breakpoints were detected from both the climate zone- and latitude range-aggregated divisions Thus, the mean duration for a piecewise trend is 7-9 years.

Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties

For improved drought planning and response, there is an increasing need for highly predictive and stable drought prediction models. This paper presents the performance of both homogeneous and heterogeneous model ensembles in the satellite-based prediction of drought severity using artificial neural networks (ANN) and support vector regression (SVR). For each of the homogeneous and heterogeneous model ensembles, the study investigates the performance of three model ensembling approaches: (1) non-weighted linear averaging, (2) ranked weighted averaging, and (3) model stacking using artificial neural networks. Using the approach of “over-produce then select”, the study used 17 years of satellite data on 16 selected variables for predictive drought monitoring to build 244 individual ANN and SVR models from which 111 models were automatically selected for the building of the model ensembles. Model stacking is shown to realize models that are superior in performance in the prediction of future drought conditions as compared to the linear averaging and weighted averaging approaches. The best performance from the heterogeneous stacked model ensembles recorded an R2 of 0.94 in the prediction of future (1 month ahead) vegetation conditions on unseen test data (2016–2017) as compared to an R2 of 0.83 and R2 of 0.78 for ANN and SVR, respectively, in the traditional approach of selection of the best (champion) model. We conclude that despite the computational resource intensiveness of the model ensembling approach, the returns in terms of model performance for drought prediction are worth the investment, especially in the context of the continued exponential increase in computational power and the potential benefits of improved forecasting for vulnerable populations.

Quantitative assessment and driving force analysis of vegetation drought risk to climate change:Methodology and application in Northeast China

Given the significant increasing trend of droughts in Northeast China (NEC) under climate change, it is of particularly urgency to quantify the drought risk to vegetation in different ecosystems. Based on the theory of risk formation, we developed a quantitative framework for the assessment of ecosystem drought risk that integrates both drought hazard and vulnerability (sensitivity and adaptability). To quantitatively assess the drought risk to different vegetation types in NEC, we used long-term (1982–2015) normalized difference vegetation index (NDVI) data and standardized precipitation evapotranspiration index (SPEI) data. The effects of climate change and non-climatic factors (mainly human activities) on the NDVI dynamics and drought risk were analyzed using the partial least squares regression method to explore key drivers of risk formation. Our findings indicate that almost 45.4% of ecosystem vegetation in NEC is at a moderate or high risk of drought. Our findings indicate that vulnerability is key to NEC drought risk assessment, and adaptability plays an important role in regulating sensitivity. Land cover is the main driving factor for the spatial pattern of drought vulnerability in NEC, and forests have the lowest vulnerability. The impact of climate change on vegetation dynamics in dry years (R2dry = 0.35) is significantly higher than over the entire study period (R2all = 0.20). Cropland drought risk is dominated by human activities, whilst woodland and grassland drought risk is controlled by temperature and precipitation variability, respectively. The driving force analysis of vegetation dynamics and drought risk improves our understanding of the mechanisms behind the formation of risk patterns in NEC. Our study provides a basis for land use management and the formulation of drought adaptation options for vegetation types at different growth stages.

Overexpression of ABA Receptor PYL10 Gene Confers Drought and Cold Tolerance to Indica Rice.

Abscisic acid (ABA) plays versatile functions in regulating plant development and tolerance to various biotic and abiotic stresses. Towards elucidating the functions of one of the ABA receptors (ABARs) in rice, OsPYL10 was cloned from drought tolerant rice cv. Nagina 22 and was overexpressed under stress inducible RD29A promoter in a mega rice variety MTU1010 by using Agrobacterium mediated genetic transformation. Four single copy transgenic lines selected based on Southern blot analysis were used for physiological and molecular analysis. PYL10 receptor appears to regulate its ligand ABA accumulation as PYL10 overexpressing transgenics accumulated 2–3.3-fold higher levels of ABA than that of WT in flag leaf at anthesis under non-stress conditions. The enhanced accumulation of ABA was associated with enhanced expression of genes for ABA biosynthesis viz., ZEP1, NCED1, NCED2, NCED3, and NCED4 in transgenics than in WT plants. At seedling stage, PYL10 transgenics showed significantly higher survival rate under cold stress as compared with WT plants. qRT-PCR analysis showed that expression levels of cold responsive genes viz., DREB1F, MYB3R2, TPP1, COR410, DEHYDRIN, and LEA3 were significantly higher in PYL10 overexpressing transgenic lines as compared to WT plants under cold stress. PYL10 transgenic and WT plants grown in the same pot were subjected to -80 kPa drought stress and recovery treatments at vegetative and reproductive stages. At vegetative stage drought stress, three overexpressing lines showed significantly higher grain yield (40–58%) and at reproductive stage drought stress one of these overexpression lines showed two-fold higher grain yield than that of WT plants. Excised leaf water loss analysis showed that PYL10 transgenic lost about 20% less water than WT plants. At reproductive stage, OsPYL10 transgenic maintained higher RWC, membrane stability index, chlorophyll content, and accumulated lower amount of MDA and H2O2 as compared with WT plants. qRT-PCR analysis showed that expression levels of RAB16, Dehydrin, LEA3, and ABA45 were higher in PYL10 transgenics as compared with WT plants under drought stress. Thus, overall results showed that OsPYL10 overexpression has potential to improve both drought and cold stress tolerance of indica rice.