Standardized Precipitation Evapotranspiration Index Research Articles

Spatiotemporal heterogeneity in meteorological and hydrological drought patterns and propagations influenced by climatic variability, LULC change, and human regulations

This study aims to quantify meteorological–hydrological drought propagations and examine the potential impacts by climatic variability, LULC change (LULC), and human regulations. An integrated observation-modeling framework quantifies drought propagation intervals and assesses mechanisms influencing hydrological droughts. Meteorological droughts are characterized using the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts are assessed through the Standardized Streamflow Index (SSI) across diverse climatic zones. Cross-correlation analysis between SPEI and SSI time series identifies the lag time associated with the highest correlation as the drought propagation interval. Mechanisms are investigated via a coupled empirical-process modeling framework incorporating the Soil and Water Assessment Tool (SWAT). Discrepancies between simulated and observed SSI time series help quantify the extent of human regulation impacts on hydrological drought characteristics and propagation. The Yellow River Basin (YRB), divided into six subzones based on climate characteristics, is selected as the case study. Key findings include: (1) Meteorological droughts were extremely severe across most YRB during the 1990s, while the 2000s showed some mitigation primarily due to precipitation increases. (2) Hydrological droughts and propagation times from meteorology to hydrology demonstrated substantial spatiotemporal variability. In general, summer propagation times were shorter than other seasons. (3) Propagation times were shorter in arid regions with cropland or built-up land cover versus grassland and woodland, while the reverse held for humid regions. (4) Human regulations prolonged propagation times, likely due to reservoir regulations designed to overcome water deficits. While the YRB is the focus of this paper, the methodologies and findings are applicable to other regions worldwide to enhance drought forecasting and water resource management. In various hydrological and climatic contexts worldwide.

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.

Stepwise clustering ensemble downscaling for future drought prediction under climate change: A case study of the Yangtze River Basin

Drought is a highly destructive natural disaster that can even lead to other complex and volatile extreme weather events in the Yangtze River Basin (YRB). Researching potential changes in future drought occurrences is crucial for developing effective climate change adaptation strategies under a warming climate. In this study, a stepwise clustered ensemble drought downscaling (SCEDD) model has been proposed, and two internationally recognized drought indices (the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI)) are used to evaluate drought events in YRB. The validation results show that the proposed model can be used to generate robust climate projections (daily precipitation) based on simulation. On this basis, SPI and SPEI are calculated from historical and future forecasts (Tmean and precipitation). The study suggests that predicted precipitation will increase dramatically in the future (2075–––2099) at all stations under the SSP245 and SSP585 scenarios. Drought events under SPI increased by 1.5% and decreased by 1.2% during 2075–––2099 under SSP245 and SSP585, respectively. In contrast, drought events in SPEI increased by 16.5% and 33.7%, respectively. In addition, drought events under SPI present an increase in spring, summer, and fall, with a decrease in winter under both SSPs. For comparison, drought events under SPEI will increase substantially in all seasons. In particular, drought predictions made using SPEI are more accurate and trustworthy because SPEI takes temperature into account. The results obtained can provide decision-makers in the water resources management sector with a reliable forecast of drought and minimize drought-related losses.

Evolution of drought characteristics and propagation from meteorological to agricultural drought under the influences of climate change and human activities.

Understanding the propagation of agricultural droughts (AD) is important to comprehensively assess drought events and develop early warning systems. The present study aims to assess the impacts of climate change and human activities on drought characteristics and propagation from meteorological drought (MD) to AD in the Yellow River Basin (YRB) over the 1950-2021 period using the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI). In total, the YRB was classified into three groups of catchments for spring wheat and four groups of catchments for winter wheat based on different human influence degrees (HId). In addition, the entire study period was divided into periods with natural (NP), low (LP), and high (HP) impacts of human activities, corresponding to 1950-1971, 1972-1995, and 1996-2021, respectively. The results demonstrated the significance and credibility of the application of the natural and human-impacted catchment comparison method for drought characteristics and propagation from meteorological to agricultural drought in the YRB. Winter wheat showed a more pronounced drying trend than spring wheat under both MD and AD. The results showed meteorological drought intensity (MDI) and agricultural drought intensity (ADI) intensified for spring and winter wheat in NP, with correspondingly a short propagation time, followed by those in the LP and HP in catchments minimally impacted by human activities. On the other hand, increases in the MDI and ADI, as well as in their times, for both spring and winter wheat were observed from the LP to the HP in all catchments. The MDI, ADI, and their propagation times for winter wheat generally showed greater fluctuations than those for spring wheat. Human activities increasingly prolonged the drought propagation time. In contrast, climate change insignificantly shortened the drought propagation time.

Multiscale Spatiotemporal Dynamics of Drought within the Yellow River Basin (YRB): An Examination of Regional Variability and Trends

Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management and fostering ecological conservation alongside high-quality development. This study utilizes meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI), for a detailed spatiotemporal analysis of drought conditions. It examines the effectiveness of these indices in the basin’s drought monitoring, offering a comprehensive insight into the area’s drought spatiotemporal dynamics. The findings demonstrate the following: (1) SPEI values exhibit distinct fluctuation patterns at varying temporal scales, with more pronounced fluctuations at shorter scales. Drought years identified via the 12-month SPEI time scale include 1965, 1966, 1969, 1972, 1986, 1997, 1999, 2001, and 2006. (2) A modified Mann–Kendall (MMK) trend test analysis of the scPDSI time series reveals a worrying trend of intensifying drought conditions within the basin. (3) Correlation analysis between SPEI and scPDSI across different time scales yields correlation coefficients of 0.35, 0.54, 0.69, 0.76, and 0.62, highlighting the most substantial correlation at an annual scale. Spatial correlation analysis conducted between SPEI and scPDSI across various scales reveals that, within diverse temporal ranges, the correlation peaks at a 12-month time scale, with subsequent prominence observed at 6 and 24 months. This observed pattern accentuates the applicability of scPDSI in the monitoring of medium- to long-term drought phenomena.

Drought Dynamics in the Nile River Basin: Meteorological, Agricultural, and Groundwater Drought Propagation

The Nile River Basin (NRB) has experienced a notable rise in drought episodes in recent decades. The propagation of meteorological, agricultural, and groundwater drought dynamics in the NRB was investigated in this study. The following drought indices examined the correlation and propagation among meteorological, agricultural, and groundwater droughts. These are the standardized precipitation evapotranspiration index (SPEI), soil moisture index, Gravity Recovery and Climate Experiment, and GRACE Follow-On (GRACE/GRACE-FO)-derived groundwater drought index (GGDI). These droughts were comprehensively evaluated in the NRB from 2003 to 2022. The cross-wavelet transform approach highlighted the links between droughts. The following are the key findings: (1) In the NRB, the cross-wavelet energy spectrum of wavelet coherence can indicate the internal connection between meteorological versus (vs.) agricultural and agricultural versus (vs.) groundwater drought. The time scale with the most significant correlation coefficient is the drought propagation time. (2) The El Niño–Southern Oscillation (ENSO) correlated with agricultural and groundwater drought much more than the Indian Ocean Dipole (IOD), demonstrating that ENSO has an important impact on drought advancement. (3) The R2 values were 0.68 for GGDI vs. standardized soil moisture index (SSI), 0.71 for Blue Nile Region (BNR) GGDI vs. SSI, and 0.55 for SSI vs. Standardized Precipitation Evapotranspiration Index (SPEI). Similarly, in the Lake Victoria Region (LVR), GGDI vs. SSI was 0.51 and SSI vs. SPEI was 0.55, but in the Bahr-el-Ghazal Region (BER), GGDI vs. SSI was 0.61 and SSI vs. SPEI was 0.27 during the whole research period with varied lag durations ranging from 1 to 6 months. Thus, the propagation of drought (i.e., meteorological, agricultural, and groundwater drought) dynamics has the potential to reshape our understanding of drought evolution, which could lead to early drought forecasting across the NRB and similar climatic regions.

Temporal and Spatial Evolution of Meteorological Drought in Inner Mongolia Inland River Basin and Its Driving Factors

In order to analyze the temporal and spatial evolution of meteorological drought and explore its driving factors, the inland river basin of Inner Mongolia (IMIRB) was taken as a typical research area, the Standardized Precipitation Evapotranspiration Index (SPEI) of various scales was calculated, and the spatio-temporal trend change characteristics of meteorological drought were analyzed combined with the modified Mann–Kendall trend test (MMK). The typical meteorological drought events were analyzed by using the three-dimensional identification method, and the spatio-temporal evolution characteristics and dynamic evolution law of meteorological drought were analyzed comprehensively and accurately. The driving effects of Pacific Decadal Oscillation (PDO), North Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and sunspot on meteorological drought were investigated by using the cross wavelet method. The results are as follows: (1) with the increase of SPEI time scale, the frequency of meteorological drought decreased, but the duration and intensity of drought increased; (2) the trend was greatest in spring, with the largest number of areas showing a significant downward trend in SPEI, the strongest persistence in intensity, and significant aridification characteristics; (3) summer meteorological droughts had the largest area of high intensity drought but the smallest area of high frequency areas, and winter droughts had the smallest area of high intensity drought but the largest percentage of high frequency areas; (4) the meteorological drought event that occurred from April 2017 to December 2017 was the most serious, and reached its maximum value in June 2017, which mainly experienced five processes: occurrence—intensification—attenuation—re-intensification—extinction; (5) atmospheric circulation factor, sunspot, and meteorological drought of IMIRB were correlated, and ENSO had the greatest effect on drought. This study provides effective theoretical support for IMIRB drought prevention and disaster reduction.

The Applicability of the Drought Index and Analysis of Spatiotemporal Evolution Mechanisms of Drought in the Poyang Lake Basin

Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin (PLB) is of great practical significance in the face of severe drought situations. This article explores the applicability of four drought indices (including the precipitation anomaly index (PJP), standardized precipitation index (SPI), China Z-index (CPZI), and standardized precipitation evapotranspiration index (SPEI)) based on historical facts. A systematic study was conducted on the spatiotemporal evolution patterns of meteorological drought in the PLB based on the optimal drought index. The results show that SPI is more suitable for the description of drought characteristics in the PLB. Meteorological droughts occur frequently in the summer and autumn in the PLB, with the frequency of mild drought being 17.29% and 16.88%, respectively. The impact range of severe drought or worse reached 22.19% and 28.33% of the entire basin, respectively. The probability of drought occurrence in the PLB shows an increasing trend in spring, while in most areas, it shows a decreasing trend in other seasons, with only a slight increase in the upper reaches of the Ganjiang River (UGR). One of the important factors influencing drought in the PLB is atmospheric circulation. The abnormal variation of the Western Pacific Subtropical High was one of the key factors contributing to the severe drought in the PLB in 2022. This study is based on a long-term series of meteorological data and selects the drought index for the PLB. It describes the spatiotemporal distribution characteristics and evolution patterns of drought and investigates the developmental path and influencing factors of drought in typical years. This study provides a reliable scientific basis for similar watershed water resource management.

Spatiotemporal analysis of drought severity using SPI and SPEI: case study of semi-arid Borana area, southern Ethiopia

Drought is a complex natural hazard that can cause damage to socio-economic and biophysical systems in any climatic region. The main objective of this paper was to analyze the spatiotemporal drought in the semi-arid Borana area of southern Ethiopia from 1981 to 2018. The climate data was obtained from the Ethiopian Meteorological Institute. Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at 3- and 12-month timescales were adopted to characterize drought during these periods and wet seasons in time and space. The results revealed that moderate to extreme drought conditions at various stations frequently hit months stretching from March to June and September to November. The years 1984, 1985, 1992, 1999, 2000, 2001 and 2011 were known to be the driest years, among others. A total of 484, 563, 573 and 620 combined moderate, severe and extreme drought events were observed across all stations using SPI-3, SPEI-3, SPI-12 and SPEI-12, respectively. Internal variability among the stations was noticed regarding the frequency and magnitude of drought. Ganna (March to May), the season that was supposed to be the main rainy season in the region, was by far drier than Hagayya (September to November), the short rainy period. In general, this study found a high frequency and intense magnitude of drought that varies across the stations. Therefore, any localized adaptation and mitigation strategies intended to tackle or minimize drought-associated impacts should take into account this local variability.

Compound drought and hot events assessment in Australia using copula functions

The occurrence of compound drought and hot events has been shown to cause stronger socio-economic, environmental and health impacts than the isolated events. Moreover, the frequency of these compound events has increased unevenly throughout the world and is expected to keep increasing in several regions. In this work, an assessment of compound drought and hot events in the summer months in Australia was made, using copula functions. Drought and hot conditions were identified by the Standardized Precipitation Evapotranspiration Index (SPEI) and the indices Number of Hot Days (NHD) and Number of Hot Nights (NHN) for the summer months, respectively. We analysed drought conditions in the current and the previous 1 to 3 months and the periods 1950–1978 and 1979–2020. The results show that the conditional probability of the occurrence of hot events given drought conditions is very high for the concurrent month in most of the study area, reaching 0.9 in some cases. Considering previous drought conditions, the higher probabilities are obtained in the southeastern region in December and in the north in February but, in most of the study area, these values are higher than for the case of non-drought conditions, pointing to an effect of previous drought conditions on hot events of up to 3 months. Moreover, an increased frequency of compound drought and hot events from the first to the second period was identified in more than half of the study area for lags of 1 and 2 months. We show that, although the conditional probabilities are mostly higher when computed with NHD, NHN is also affected by drought conditions, and should also be considered in this analysis, since nights can have a relieving contribution when impacts in health and wildfires are being analysed.

The Quantitative Inhibition Effects of Meteorological Drought on Sugarcane Growth Using the Decision Support System for Agrotechnology Transfer-CANEGRO Model in Lai-bin, China

Sugarcane is the most important cash crop for producing sugar and the most promising high-yield bioenergy crop in China. Lai-bin is a major sugarcane growing and karst area in the Guangxi Province of China. However, frequent droughts over the years have caused huge losses on sugarcane production in this region. Therefore, the daily quantitative response mechanisms of sugarcane growth to multiple meteorological drought scenarios were discovered in Lai-bin using the DSSAT−CANEGRO model. The daily Standardized Precipitation Evapotranspiration Index (SPEI) was developed to detect the possible scenarios of meteorological drought events over the sugarcane growth periods. The results indicate that, in general, the inhibitory effect on sugarcane growth is enhanced by the increase in the intensity and duration of meteorological drought, which involved cane yield (CY), stem height (SH), leaf area index (LAI), and evapotranspiration (ET). Additionally, a light drought at the seedling stage and a light, moderate, and severe drought at the maturity stage give a promotion effect on sugarcane growth, but its overall CY increase rate is less than 5%. The stem elongation stage is the most sensitive period of sugarcane growth to all scenarios of meteorological drought, and the CY reduction rates reached 7.12%, 16.48%, 18.80%, and 29.05%, when the plants suffered from light, moderate, severe, and extreme meteorological droughts, respectively. Alternate drought–flood scenarios presented a facilitating or an inhibiting effect on sugarcane growth during different periods, which cannot be ignored. In conclusion, the quantitative function relationships between meteorological drought and sugarcane growth are revealed by this study.

A Novel Fusion-Based Methodology for Drought Forecasting

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.

Can Growth Increase of Small Trees after Drought Compensate for Large Trees’ Growth Loss?

Large trees dominate stand-level biomass but their growth suffers more from droughts, while sheltering small trees during droughts. Under a warmer and drier climate, whether the growth decline of large trees could be compensated by prompted small trees’ growth remains unknown. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) series, drought characteristics were determined, and two drought events were selected. We reconstructed historical diameters at breast height (DBH) and the aboveground biomass of Larix gmelinii through tree ring data allometric equations. To clarify the difference in the responses of tree size to drought, we calculated resistance, recovery, and resilience in each diameter class. We used a growth dominance coefficient (GDC) to exhibit the contributions of different-sized individuals to stand growth and demonstrated the growth dynamics of both the individual and stand level. The results proved that large trees were more vulnerable to local droughts, the resilience of larch had a negative relationship with the DBH (p < 0.05), and small trees could recover to even exceed their pre-drought growth level. Most plots had a negative GDC and small trees contributed more to stand growth compared with their size, but their AGB growth was far less than that of large trees, which made it difficult to compensate for stand growth decline. Our results indicate that tree resilience has a negative relationship with their pre-drought sizes, as large trees in the stand fail to regain their growth level before drought. Even with a larger relative contribution and higher resilience, small trees cannot cover deficits in large trees’ growth. Under more frequent droughts, the total aboveground biomass growth of larches would decline.

Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress

Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).

The first global multi-timescale daily SPEI dataset from 1982 to 2021

Global warming accelerates water cycle, causing more droughts globally that challenge monitoring and forecasting. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to assess drought characteristics and response time of natural and economic systems at various timescales. However, existing SPEI datasets have coarse spatial or temporal resolution or limited spatial extent, restricting their ability to accurately identify the start or end dates or the extent of drought at the global scale. To narrow these gaps, we developed a global daily SPEI dataset (SPEI-GD), with a 0.25° spatial resolution from 1982 to 2021 at multiple timescales (5, 30, 90, 180 and 360 days), based on the precipitation from European Center for Medium Weather Forecasting Reanalysis V5 (ERA5) dataset and the potential evapotranspiration from Singer’s dataset. Compared to widely used SPEIbase dataset, the SPEI-GD can improve the spatial-temporal resolution and the accuracy of SPEI in areas where meteorological sites are lacking. The SPEI-GD significantly correlates with site-based SPEI and soil moisture. Our dataset solidly supports sub-seasonal and daily-scale global and regional drought research.

Drought offsets the vegetation greenness-induced gross primary productivity from 1982 to 2018 in China

Recent studies have shown that climate stress may cause asynchronous changes between greenness and productivity, but the mechanism how greenness and productivity respond to drought remains unclear. The significant greening of vegetation in China has attracted worldwide attention; nevertheless, it is unclear whether large-scale greening will translate into productivity in the context of recent droughts. In this study, we used the vegetation index dataset to extract long-term trends in vegetation greenness and productivity from 1982 to 2018, and then chose the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) index to measure how vegetation greenness and productivity change in response to drought. The results show that significant vegetation greening did not cause a simultaneous increase in productivity during 1982–2018. Vegetation greenness and productivity trends were relatively consistent before 2000 and diverged after 2000, especially during the prolonged drought of 2000–2009. The opposite trend between greenness and productivity in humid ecosystems during drought periods mainly caused this trend difference. In humid ecosystems, short-term drought promotes vegetation greening, while long-term drought has no significant impact on greenness. However, the inhibitory effect of drought on productivity deepens with an increase in drought duration. Therefore, prolonged droughts offset vegetation greenness-induced gross primary productivity. We also found that prolonged drought causes a significant decrease in greenness but has little effect on productivity in arid ecosystems, possibly because vegetation productivity is more sensitive to soil water than atmospheric drought. Our results highlight the complexity of vegetation growth in the context of climate change. Furthermore, it is crucial to pay attention to the impact of climate stress on regional carbon sequestration functions, considering the significant carbon sink potential of forest areas in southern China.

Analysis of Forest Inventory Data Shows Disparity in Tree Mortality and Resistance in Texas National Forests

Abstract In 2011, the forestlands of Texas were affected by one of the most severe droughts in the state’s history, killing an estimated 65 million trees in the East Texas region. This study uses USDA Forest Service Forest Inventory and Analysis data and a standardized precipitation-evapotranspiration index to estimate major tree species responses to the 2011 drought in Texas’ four national forests. We used an analysis of variance and a linear mixed-effect model to evaluate factors contributing to tree mortality and growth. We found that tree mortality rates varied greatly during the drought period relative to predrought levels; higher stand age, higher stand density, and higher mean summer temperature contributed to tree mortality volume. Study Implications: Drought is a recurring disturbance agent and is likely to continue to increase in frequency and severity in the future. We provide an understanding of an exceptional drought as a disturbance agent and its impacts on tree mortality and resistance. The effects of the 2011 drought on national forests in Texas have not previously been evaluated. Texas’ national forests represent over 256,975 ha (635,000 ac) in the state’s eastern region and nearly 10% of all public land in Texas. Our results are useful to decision makers in developing strategies and protecting forestlands in the face of potential future droughts.

Different drought indices showed different variations and applicability to dendrochronological studies

In drought studies based on tree-ring data, there is currently no unified drought index that can capture the drought signals contained in tree rings from different climates, habitats, and species. This makes it difficult to compare the results of numerous studies. This paper compared data charactering wet and dry variations in the Ulan Buh Desert in northwestern China from 1962 to 2017, as indicated by different drought indices. We selected the most commonly used drought indices in tree-ring research: precipitation (Pre), self-calibrating Palmer aridity index (scPDSI), and standardized precipitation evapotranspiration index (SPEI), based on observed meteorological data (-obs) and grid data from the Climatic Research Unit (-cru). The results showed that although the different drought indices were significantly correlated with each other, they showed different linear trends. Correlation analyses with the tree-ring width chronology in the study area showed that the applicability of drought indices based on observed data was better than that of drought indices based on CRU data. The correlation between SPEI-cru and chronology was much lower than that of SPEI-obs, scPDSI-obs, scPDSI-cru, Pre-obs and Pre-cru. The extreme drought years identified by SPEI-cru also differed from other indices. These results suggest that SPEI-cru is not applicable to dendrochronological studies in the Ulan Buh Desert, mainly due to the differences between the CRU data and the measured data, and the high sensitivity of SPEI to potential evapotranspiration. This case study illustrates that tree-ring based drought studies must be based on an assessment of the applicability of different drought indices.

Spatiotemporal characteristics of meteorological drought events in 34 major global river basins during 1901–2021

Meteorological drought is a crucial driver of various types of droughts; thus, identifying the spatiotemporal characteristics of meteorological drought at the basin scale has implications for ecological and water resource security. However, differences in drought characteristics between river basins have not been clearly elucidated. In this study, we identify and compare meteorological drought events in 34 major river basins worldwide using a three-dimensional drought-clustering algorithm based on the standardised precipitation evapotranspiration index on a 12-month scale from 1901 to 2021. Despite synchronous increases in precipitation and potential evapotranspiration (PET), with precipitation increasing by more than three times the PET, 47 % (16/34) of the basins showed a tendency towards drought in over half their basin areas. Drought events occurred frequently, with more than half identified as short-term droughts (lasting less than or equal to three months). Small basins had a larger drought impact area, with major drought events often originating from the basin boundaries and migrating towards the basin centre. Meteorological droughts were driven by changes in sea surface temperature (SST), especially the El Niño Southern Oscillation (ENSO) or other climate indices. Anomalies in SST and atmospheric circulation caused by ENSO events may have led to altered climate patterns in different basins, resulting in drought events. These results provide important insights into the characteristics and mechanisms of meteorological droughts in different river basins worldwide.

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes.