Drought Time-scales Research Articles

Solar-Induced Chlorophyll Fluorescence-Based GPP Estimation and Analysis of Influencing Factors for Xinjiang Vegetation

With climate change and the intensification of human activity, drought event frequency has increased, affecting the Gross Primary Production (GPP) of terrestrial ecosystems. Accurate estimation of the GPP and in-depth exploration of its response mechanisms to drought are essential for understanding ecosystem stability and developing strategies for climate change adaptation. Combining remote sensing technology and machine learning is currently the mainstream method for estimating the GPP in terrestrial ecosystems, which can eliminate the uncertainty of model parameters and errors in input data. This study employed extreme gradient boosting, random forest (RF), and light use efficiency models. Additionally, we integrated solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation, and the leaf area index (LAI) to construct various GPP estimation models. The standardised precipitation evapotranspiration index (SPEI) was utilised at various timescales to analyse the relationship between the GPP and SPEI during dry years. Moreover, the potential pathways and coefficients of environmental factors that influence GPP were explored using structural equation modelling. Our key findings include the following: (1) the model combining the SIF and RF algorithms exhibits higher accuracy and applicability in estimating vegetation GPP in the arid zone of Xinjiang, with an overall accuracy (MODIS R2) of 0.775; (2) the vegetation in Xinjiang had different response characteristics to different timescales of drought, in which the optimal timescale for GPP to respond to drought was 9 months, with a mean correlation coefficient of 0.244 between grass land GPP and SPEI09, indicating high sensitivity; (3) using structural equation modelling, we found that temperature and precipitation can affect GPP both directly and indirectly through LAI. This study provides a reliable tool for estimating the GPP in Xinjiang, and its methodology and conclusions are important references for similar environments. In addition, this study bridges the research gap in drought response to GPP at different timescales, and the potential influence mechanism of natural factors on GPP provides a scientific basis for early warning of drought and ecosystem management. Further validation using a longer time series is required to confirm the robustness of the model.

Analysis of the spatial and temporal evolution of drought in Henan based on a nonlinear composite drought index

It is of practical significance to provide a reference for drought mitigation and water resource enhancement planning in the Henan Province region and to analyze the spatial and temporal evolution characteristics of meteorological agricultural drought in the region. In this study, a multivariate (precipitation, potential evapotranspiration, soil moisture and normalized vegetation index) composite drought index CCDI was constructed based on the D Vine copula model, and the correlations between the CCDI and the standardized precipitation evapotranspiration index (SPEI) and between the CCDI and the standardized soil moisture index (SSI) were analyzed. The ability of the CCDI to evaluate meteorological agricultural drought was assessed, and typical drought events were selected for validation. The spatial and temporal evolution of drought in the Henan region from 1982 to 2022 was characterized using linear trends, M–K mutability tests, and drought centers of gravity, and future trends were analyzed using the Hurst index. The results show that (1) the CCDI correlates well with the SPEI and SSI (P < 0.01) and is able to characterize both meteorological and agricultural drought conditions in a better integrated way. (2) The fluctuation frequencies of the CCDI were obviously different at different time scales, indicating the different sensitivities of drought time scales to precipitation, potential evapotranspiration, soil moisture and the NDVI. All four seasons showed increasing trends at different rates. (3) During the study period of 1982–2022, the frequency of drought in Henan Province was mainly in the range of 21.95%-41.46%, and the intensity of drought occurrence was mainly dominated by light to moderate drought. (4) Based on the migration characteristics of the center of gravity of drought determined by ArcGIS, the center of gravity of drought during the study period is mostly concentrated in several cities in the central part of Henan, and the migration trajectory is mostly in the north‒south north–south direction, which is in line with the characteristics of the distribution of the data expressed by the standard deviation ellipse. (5) To analyze future drought trends in Henan, the mean values of the Hurst index in spring, summer, autumn and winter were 0.578, 0.766, 0.62 and 0.596, respectively, all of which showed persistent trends, while spring and winter Most areas may still have a dry trend in the future, the future dry trend in summer is mainly concentrated in the area around Henan, and the wet trend is distributed in the central-eastern part of the country and individual areas in the north and south. In the fall, most of the areas are wet trend, and individual areas have arid trend.The comprehensive drought index constructed in this paper can provide a new reference for drought prevention and drought relief in Henan Province.

Identification of seasonal drought and its impacts on cropping season in Bangladesh

PurposeThis study aims to identify seasonal drought using standardized precipitation index (SPI). The following specific objectives are to generate result and identify seasonal drought and determine different scale of seasonal drought and its impacts on cropping season.Design/methodology/approachSeasonal SPI was calculated using long-term rainfall data for three seasons. The SPI was calculated using the formula and it is effective for the determinants. This study showed the functional relationship between drought duration, frequency and drought time scale using the SPI. SPI=X−X¯σ.FindingsSeasonal drought occurs more frequently in Bangladesh that affects crops and the agricultural economy every year. More severe drought was recorded during the Kharif-1 and Kharif-2 seasons and most crops were affected in these two seasons. No severe or moderate drought was recorded during the Rabi season. The results showed that monsoon crops were severely affected severely by extreme and severe droughts during the Kharif-2 season. Eventually, the people remain jobless during the monsoon, and they experience food shortages like monga. Several obstacles were recorded during the season, including delayed preparation of land, sowing, transplanting and other farming activities because of monsoon droughts. This study revealed that very frequently, mild dryness occurs in winter, but crop loss is minimal. The scale and occurrence of extreme droughts are more frequent during monsoons and reduce crop yields, affecting livelihoods in the study area. Seasonal drought affects cropping patterns as well as reduce crop yields.Originality/valueThe outcome of this study derived from the secondary data and field data.

A comparative study of various drought indices at different timescales and over different record lengths in the arid area of northwest China.

Although many drought indices have been developed to monitor drought conditions, their applicability differs across climatic regions, which results in different characteristics of drought index assessments at different timescales and over different record lengths. Therefore, the applicability of precipitation-based and precipitation-evapotranspiration/temperature-based drought indices was examined in this study using the Generalized Extreme Value Index (GEVI), Homogeneity Index (HI) of precipitation and temperature, K index (K), precipitation anomaly percentage (Pa), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and China-Z Index (CZI) over different record lengths and at a timescale range of 1-24months. In addition, the results that were obtained using these indices were compared with the Self-Calibrating Palmer Drought Severity Index (scPDSI). The stability, accuracy, and consistency of the different drought indices were evaluated using precipitation and evapotranspiration/temperature data (1-24months) collected from different climatic regions in the arid area of northwest China in the 1961-2017 period. The results indicated that the Pa, CZI, K, and SPEI were more stable; the SPI, SPEI, and HI were more accurate; and the GEVI, SPI, HI, and SPEI were more consistent with the scPDSI. In addition, the results indicated that it is more appropriate to select a long record length (> 35 years) to monitor drought when sufficient data are available. However, defining constant drought classes may not be appropriate for all drought timescales. In fact, precipitation and evapotranspiration data from different timescales had different optimal distribution functions. The drought indices also demonstrated that they were applicable to a temperate arid area in the study region. In addition, the HI and SPEI better captured the precipitation and evapotranspiration/temperature characteristics, while the CZI, K, and Pa overestimated or underestimated the frequency of different drought classes at different timescales to some extent in the study region. The results of this study suggest that greater priority should be given to the precipitation-evapotranspiration-based indices. In addition, it is suggested to change the drought index class thresholds in future related studies on drought event recognition at different timescales to ensure more accurate drought monitoring.

The impact of drought time scales and characteristics on gross primary productivity in China from 2001 to 2020

ABSTRACT Drought events have occurred frequently in the past and have had profound effects on Gross Primary Productivity (GPP). However, the role of different drought event characteristics in the response of GPP to drought is not well understood. To address this issue, we investigated the spatiotemporal response patterns of Chinese terrestrial ecosystem GPP to different time scales of meteorological droughts, and explored the difference of GPP response to drought events under various drought characteristics. Our research revealed that: Different time scales of meteorological droughts significantly impacted the GPP of 17.3%–25.6% area of China. Among them, the impact range of 1-month and 12-month meteorological drought was the smallest and largest respectively. GPP along the 400 mm isohyet line was more susceptible to drought. Second, grasslands and shrubs are more susceptible to drought than forested areas. Among the characteristics of drought events, drought severity had the greatest impact on GPP changes, while drought frequency had the least impact. An increase in drought frequency intensified the impact of 1-month meteorological drought on GPP. Increased drought duration and severity weakened the impact of 3-month meteorological drought on GPP and intensified the impact of 1-month and 12-month meteorological droughts on GPP. Our study reveals the impact of different time scales drought and drought characteristics on plant productivity, and provides more possibilities for understanding the effect of drought on plant productivity.

Declining coupling between vegetation and drought over the past three decades

AbstractDroughts have been implicated as the main driver behind recent vegetation die‐off and are projected to drive greater mortality under future climate change. Understanding the coupling relationship between vegetation and drought has been of great global interest. Currently, the coupling relationship between vegetation and drought is mainly evaluated by correlation coefficients or regression slopes. However, the optimal drought timescale of vegetation response to drought, as a key indicator reflecting vegetation sensitivity to drought, has largely been ignored. Here, we apply the optimal drought timescale identification method to examine the change in coupling between vegetation and drought over the past three decades (1982–2015) with long‐term satellite‐derived Normalized Difference Vegetation Index and Standardized Precipitation‐Evapotranspiration Index data. We find substantial increasing response of vegetation to drought timescales globally, and the correlation coefficient between vegetation and drought under optimal drought timescale overall declines between 1982 and 2015. This decrease in vegetation–drought coupling is mainly observed in regions with water deficit, although its initial correlation is relatively high. However, vegetation in water‐surplus regions, with low coupling in earlier stages, is prone to show an increasing trend. The observed changes may be driven by the increasing trend of atmospheric CO2. Our findings highlight more pressing drought risk in water‐surplus regions than in water‐deficit regions, which advances our understanding of the long‐term vegetation–drought relationship and provides essential insights for mapping future vegetation sensitivity to drought under changing climate conditions.

Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought

Net primary productivity (NPP) is an important indicator used to evaluate the carbon sequestration capacity of forest ecosystems. Subtropical forest ecosystems play an indispensable role in maintaining the global carbon balance, while frequently occurring drought events in recent years have seriously damaged their productivity. However, the spatiotemporal patterns of NPP, as well as its response to drought, remain uncertain. In this study, the multiscale drought characteristics in subtropical China during 1981–2015 were analyzed based on the standardized precipitation evapotranspiration index. Then, simulated and analyzed the spatiotemporal NPP of subtropical forests by the boreal ecosystem productivity simulator model. Finally, the response of NPP to drought was analyzed based on multiple statistical indices. The results show that most regions in subtropical China experienced mild and moderate drought during 1981–2015. In particular, the extent of drought severity has shown a noticeable increasing trend after 2000. The forest NPP ranged from 622.64 to 1323.82 gC·m−2·a−1, with an overall increase rate of 16.15 gC·m−2·a−1; in particular, the contribution of the western forest NPP became increasingly important. Drought stress has limited the growth of subtropical forest NPP in China, with summer and wet season time scales of drought having the greatest impact on forest NPP anomalies, followed by autumn time scales. The limitation is mostly because the drought duration continually increased, leading to differences in the impact of drought on forest NPP before and after 2000, with declines of 59.55 % and 82.45 %, respectively, mainly concentrated in southwestern regions, such as Yunnan, Guangxi, and Sichuan provinces. This study quantitatively analyzed the impact of drought on subtropical forest NPP, and provides scientific basis for subtropical forest response and adaptation to climate change.

Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Grassland is essential for maintaining the stability and functionality of terrestrial ecosystems. Although previous research has explored how grassland responds to drought, the drought sensitivity of grassland (DSG) across climate zones and aridity gradients remains uncertain. In this study, we conducted a comprehensive investigation spanning 1982 to 2015 in Northwest China. To assess the time-cumulative effect (TCE) and the time-lag effect (TLE) of drought on grassland, we employed Spearman rank correlation analysis, utilizing long-term datasets of the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). This analysis allowed us to quantify the DSG in the region and further examine its variations across climate zones and aridity gradient. Our results revealed that 81.2 % and 99.7 % of the grassland in Northwest China was influenced by the TCE and TLE of drought, respectively, with 38.2 % and 60.9 % of these effects being statistically significant (p < 0.05). The mean accumulated and lagged timescales of drought on grassland were 7.89 and 9.41 months, respectively. Remarkably, the highest DSG was observed in the semi-arid zone (0.58), followed by the arid (0.54), sub-humid (0.51), and humid (0.44) zones. Furthermore, we identified significant nonlinear variation patterns of DSG along the aridity gradient, characterized by several discernible trend breaks. These findings contribute to our understanding of the impacts of drought on vegetation, particularly in ecologically fragile regions.

Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI)

Abstract Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation. Significance Statement This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.

Drought characteristics in Mediterranean under future climate change

The present work aims to address the physical properties of different drought types under near-future climates in the Mediterranean. To do so, we use a multi-model mean of the bias-adjusted and downscaled product of five Earth System Models participating in the Coupled Model Intercomparison Project—phase6 (CMIP6), provided by Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), under four shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) for the period 2021–2060, to estimate the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 6-, and 12-month time scales, and address the meteorological, agricultural, and hydrological drought, respectively. Additionally, SPEI is calculated from the bias-adjusted CMIP6 historical simulations and the reanalysis ‘WFDE5’ for 1980–2014 as a historical and reference period. The comparison of the CMIP6 with WFDE5 reveals a consistently increasing tendency for drought occurrences in the Mediterranean, particularly for agricultural and hydrological drought time scales. Nonetheless, an overestimation in historical trend magnitude is shown by the CMIP6 with respect to WFDE5. The projection results depict drought frequencies ranging between 12 and 25% of the studied period 2021–2060, varying with regions and climate scenarios. The tendency to increase the drought frequency is more pronounced in the southern than northern Mediterranean countries. Drought severity is remarkable in the aggregated time scales; consequently, more pressure is foreseen in the food and water sectors. Drought seasonality reveals a higher tendency for drought occurrences in summer (autumn) months for the meteorological (agricultural) droughts. The driving factor(s) for drought occurrence strongly depends on regional climate characteristics.

Crop response pattern to several drought timescales and its possible determinants: A global-scale analysis during the last decades

Crop response characteristics to different timescales of precipitation deficit may represent crop system resilience to drought characteristics. In this study, we assess the crop yield response of major crops to meteorological drought estimated by a standardized precipitation index with multiple timescales (1-12 months) during 1981-2016 all over the globe. We estimate that about one- to two-thirds of global harvested areas of maize, rice, soybean, and wheat, were significantly affected by various drought timescales. Soybean and wheat might respond to more prolonged droughts, while rice and maize responded to short-medium drought time scales. Using multiple machine learning models, we reveal that set of determinants could explain most variations of crop response to drought timescale with average accuracies between 45.7% - 56.0% (across models and crop types). Moreover, this study suggests that crops in warmer and higher water availability (precipitation minus potential evapotranspiration) might respond significantly to more short-term drought. The other factors (i.e., socioeconomic, fertilizer, soil, topography, production, irrigation) shows a complex and weaker effect on defining crop vulnerability to the various drought characteristics. This study attempts to fill the gaps in understanding global crop resistance to different drought characteristics. The future challenge in understanding the multifaceted effect of physical and socioeconomic factors on global crop vulnerability to drought may remain and should be addressed in further studies.

A Study of Drought and Flood Cycles in Xinyang, China, Using the Wavelet Transform and M-K Test

Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021.

Do Derived Drought Indices Better Characterize Future Drought Change?

AbstractCurrent methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments.

Response Time of Vegetation to Drought in Weihe River Basin, China

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.

Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).

Drought Legacy in Sub‐Seasonal Vegetation State and Sensitivity to Climate Over the Northern Hemisphere

AbstractDroughts affect ecosystems at multiple time scales, but their sub‐seasonal legacy effects on vegetation activity remain unclear. Combining the satellite‐based enhanced vegetation index MODIS EVI with a novel location‐specific definition of the growing season, we quantify drought impacts on sub‐seasonal vegetation activity and the subsequent recovery in the Northern Hemisphere. Drought legacy effects are quantified as changes in post‐drought greenness and sensitivity to climate. We find that greenness losses under severe drought are partially compensated by a ∼+5% greening within 2–6 growing‐season months following the droughts, both in woody and herbaceous vegetation but at different timings. In addition, post‐drought sensitivity of herbaceous vegetation to hydrological conditions increases noticeably at high latitudes compared with the local normal conditions, regardless of the choice of drought time scales. In general, the legacy effects on sensitivity are larger in herbaceous vegetation than in woody vegetation.

Comprehensive Quantification of the Responses of Ecosystem Production and Respiration to Drought Time Scale, Intensity and Timing in Humid Environments: A FLUXNET Synthesis

AbstractDrought is one of the most important natural hazards impacting ecosystem carbon cycles. However, it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. First, drought impacts can have different time dimensions such as simultaneous, cumulative, and lagged impacts on ecosystem carbon balance. Second, drought is not only a multiscale (e.g., temporal and spatial) but also a multidimensional (e.g., intensity, time‐scale, and timing) phenomenon, and ecosystem production and respiration may respond to each drought dimension differently. In this study, we conducted a comprehensive drought impact assessment on ecosystem productivity and respiration in humid regions by including different drought dimensions using global FLUXNET observations. Short‐term drought (e.g., 1‐month drought) generally did not induce a decrease in plant productivity even under high severity drought. However, ecosystem production and respiration significantly decreased as drought intensity increased for droughts longer than 1 month in duration. Drought timing was important, and ecosystem productivity was most vulnerable when drought occurred during or shortly after the peak vegetation growth. We found that lagged drought impacts more significantly affected ecosystem carbon uptake than simultaneous drought, and that ecosystem respiration was less sensitive to drought time scale than ecosystem production. Overall, our results indicated that temporally‐standardized meteorological drought indices can be used to reflect plant productivity decline, but drought timing, antecedent, and cumulative drought conditions need to be considered together.

Investigating the response of leaf area index to droughts in southern African vegetation using observations and model simulations

Abstract. In many regions of the world, frequent and continual dry spells are exacerbating drought conditions, which have severe impacts on vegetation biomes. Vegetation in southern Africa is among the most affected by drought. Here, we assessed the spatiotemporal characteristics of meteorological drought in southern Africa using the standardized precipitation evapotranspiration index (SPEI) over a 30-year period (1982–2011). The severity and the effects of droughts on vegetation productiveness were examined at different drought timescales (1- to 24-month timescales). In this study, we characterized vegetation using the leaf area index (LAI) after evaluating its relationship with the normalized difference vegetation index (NDVI). Correlating the LAI with the SPEI, we found that the LAI responds strongly (r=0.6) to drought over the central and southeastern parts of the region, with weaker impacts (r&lt;0.4) over parts of Madagascar, Angola, and the western parts of South Africa. Furthermore, the latitudinal distribution of LAI responses to drought indicates a similar temporal pattern but different magnitudes across timescales. The results of the study also showed that the seasonal response across different southern African biomes varies in magnitude and occurs mostly at shorter to intermediate timescales. The semi-desert biome strongly correlates (r=0.95) to drought as characterized by the SPEI at a 6-month timescale in the MAM (March–May; summer) season, while the tropical forest biome shows the weakest response (r=0.35) at a 6-month timescale in the DJF (December–February; hot and rainy) season. In addition, we found that the spatial pattern of change of LAI and SPEI are mostly similar during extremely dry and wet years, with the highest anomaly observed in the dry year of 1991, and we found different temporal variability in global and regional responses across different biomes. We also examined how well an ensemble of state-of-the-art dynamic global vegetation models (DGVMs) simulate the LAI and its response to drought. The spatial and seasonal response of the LAI to drought is mostly overestimated in the DGVM multimodel ensemble compared to the response calculated for the observation-based data. The correlation coefficient values for the multimodel ensemble are as high as 0.76 (annual) over South Africa and 0.98 in the MAM season over the temperate grassland biome. Furthermore, the DGVM model ensemble shows positive biases (3 months or longer) in the simulation of spatial distribution of drought timescales and overestimates the seasonal distribution timescales. The results of this study highlight the areas to target for further development of DGVMs and can be used to improve the models' capability in simulating the drought–vegetation relationship.

A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data

Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the standardized precipitation index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981–2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23%, 8%, 30%, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5–12 months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.

Assessing the impacts of drought on net primary productivity of global land biomes in different climate zones

Drought is the most widespread event under climate change and is projected to lead to high uncertainties in quantifying the terrestrial carbon exchange. However, considering the complexity of drought quantification and the differences in the physiological responses to drought among different vegetation types, the potential mechanisms of vegetation in response to drought across different climate regions and their responses to drought at varying time scales remain unclear globally. Here, we analyzed the response of vegetation activity to drought at different time scales and determined the time scale dominating the drought-induced vegetation variations, then explored its potential driving factors (temperature, precipitation, potential evapotranspiration, and water balance) under different climatic conditions and vegetation types from 2000 to 2015 using the Standardized Precipitation Evapotranspiration Index (SPEI) and Net Primary Productivity (NPP). Our results indicated that the NPP-SPEI correlation was stronger under arid and semi-arid conditions compared to that in humid and sub-humid zones globally. Vegetation mainly responded to the drought from short to medium time scales and the shortest response time scale was observed for deciduous needle-leaf forests and closed shrublands, which mainly located in semi-arid regions of southeastern Australia, central Africa, and southern Russia. Moreover, grassland and cropland ecosystems were found to be also highly vulnerable to drought across the globe. Analysis of the effects of various meteorological factors on NPP-SPEI correlation and drought timescales showed that water balance was the most predominant factor affecting the response of vegetation to drought. In particular, the increase in water balance significantly reduced the response intensity of vegetation to drought and prolonged the response timescales. Analysis of the effects of various meteorological factors on NPP-SPEI correlation and drought time-scales showed that water balance was the most predominant factor affecting the response of vegetation to drought. In particular, the increase in water balance significantly reduces the response intensity of vegetation to drought and prolongs the response time-scales. Therefore, the identification of the dominant timescales at which drought mostly impact land biomes is projected to assist assessing the stability of terrestrial ecosystem and thus support drought mitigation to reduce land degradation.