Drought Severity Index Research Articles

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.

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.

Climate change has driven multidecadal declines in lake levels in central Alberta, Canada

Timoney, KP. 2024. Climate change has driven multidecadal declines in lake levels in central Alberta, Canada. Lake Reserv Manage. 40:205–220. This study examined water level data from 5 lakes in central Alberta to determine the timing and rates of change and to identify the causes for those changes. Over the past century, the estimated average levels of Beaverhill and Cooking lakes declined ∼3 m. Highest lake levels were observed ca. 1902–1904. Lake levels varied widely over the decades, punctuated by periods of high or low water conditions embedded within the longer term declines. Over the past 50 yr, average rates of lake drawdown were as follows: Miquelon −6.8 cm/yr, Cooking −4.4 cm/yr, Beaverhill −4.4 cm/yr, Hastings −0.9 cm/yr, and Ministik −0.7 cm/yr. Over time, annual temperature, evaporation, and net evaporation increased while annual precipitation, runoff, and Palmer Drought Severity Index (PDSI) decreased. Annual precipitation was significantly correlated with the levels of all 5 lakes, and with annual runoff and PDSI. Net evaporation was significantly correlated with temperature, runoff, PDSI, and the levels of all lakes but Ministik. Annual runoff was significantly correlated with the levels of all 5 lakes and PDSI. Conversely, neither annual temperature nor evaporation was correlated significantly with the annual lake levels. Annual levels of all 5 lakes were significantly correlated with each other. The lake level declines are part of a larger pattern of declining levels observed in other lakes in western North America over many decades. In central Alberta, data from several sources indicate accelerated rates of drawdown since the late 1970s to early 1980s at some lakes and accelerated declines since the mid to late 1990s at other lakes.

Radial growth responses of Larix gmelinii to drought events in dry and wet areas of northern temperate forests

Drought stress caused by global climate warming affects tree growth in both dry and wet areas. However, the differences in tree growth responses to drought in dry and wet areas are poorly understood. Here, we collected 93 tree cores to analyze the differences in the radial growth responses of larch (Larix gmelinii) under climate change and tree growth resilience under drought events in the Altai Mountains (dry area) and Changbai Mountains (wet area). The results showed that larch growth in the Altai Mountains was significantly positively correlated with the self-calibrating Palmer Drought Severity Index (sc-PDSI) in all months and precipitation in the previous growth season and May, whereas it was significantly (p < 0.05) negatively correlated with temperature in May and the previous June to August. In the Changbai Mountains, larch growth was significantly positively correlated with May maximum and mean temperature, and significantly positively and negatively correlated with precipitation in April and May，respectively (p < 0.05). The mean resistance (recovery) of larch growth to drought in wet areas were significantly stronger (weaker) than that in dry areas (p < 0.05). Moreover, strengthening the drought frequency led to a significant (p < 0.05) decline in larch resistance in dry areas. Therefore, warming-induced increases in drought stress will aggravate negative impacts on the radial growth of larch forests in temperate dry areas but not in wet areas in the future.

A novel index for vegetation drought assessment based on plant water metabolism and balance under vegetation restoration on the Loess Plateau

Vegetation is vital to the ecosystem, contributing to the global carbon balance, but susceptible to the impacts of climate change. Monitoring vegetation drought remains challenging due to the lack of widely accepted drought indices. This study focused on vegetation, and simulated the vegetation suitable water demand and soil available water supply (calculated by Remote-sensing-based Water Balance Assessment Tool model). The standardized Vegetation Water deficit Index (SVWDI) was established by calculating the vegetation water deficit, which reflects the response of vegetation to drought. We examined the spatiotemporal evolution of vegetation drought on the Loess Plateau and evaluated the applicability of standardized vegetation water deficit index. Our findings revealed that the standardized vegetation water deficit index demonstrated an overall upward trend across different time scales from 1991 to 2020. Drought conditions were concentrated in the first 20 years of the study period, but vegetation drought on the Loess Plateau has been alleviated in the past decade. Moreover, as the time scale extended, the trend of SVWDI generally decreased, with approximately 49.50 % (1-month scale), 46.66 % (3-month scale), 47.08 % (12-month scale), and 32.16 % (24-month scale) of the grid areas experiencing increased SVWDI. The correlation between SVWDI and tree-ring width index (TRWI) performed well under all precipitation gradients, but the Palmer drought severity index (PDSI) was only highly correlated with TRWI in regions with low precipitation. In terms of the relationship with vegetation health, SVWDI demonstrated the highest correlation with the normalized difference vegetation index (NDVI) across different time scales, followed by PDSI and standardized precipitation evapotranspiration index (SPEI). This study provides insights into the evolution of vegetation drought in response to climate change. The findings can guide initiatives such as returning farmland to forest and grassland on the Loess Plateau to aid climate change adaptation strategies.

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

AbstractCrop yield prediction has gained major potential for global food production. Predicting crop yields based on specific parameters like soil, environment, crop, and water has been an interesting research topic in recent decades. To accurately predict crop yields, measuring the severities of natural calamities including water level is mainly required. However, the existing studies failed to predict crop yields accurately because of various issues like overfitting problems, difficulty in training, inability to handle large data, and reduced learning capability. Thus, the proposed study develops an efficient mechanism for accurately predicting crop yields by analyzing several natural calamities. Here, the input samples are initially pre‐processed to remove unwanted noises using data normalization and standardization. To enhance the performance of crop yield prediction, natural calamities are computed by using an Extreme Gradient Boosting (XGBoost) model based on parameters like the Palmer Drought Severity Index (PDSI), Severe Hail Index (SHI), and Storm Severity Index (SSI). Also, the hyperparameters of XGBoost model are tuned by utilizing Sheep Flock Optimization Algorithm (SFOA). Finally, the crop yield is predicted by proposing a new one‐dimensional convolutional gated recurrent unit neural network (1D‐CGRU). The proposed classifier predicts the crop yields with reduced error rates like mean square error (MSE) of 0.4363, root mean square error (RMSE) of 0.1904, normalized root mean squared error (NRMSE) of 0.00101, mean absolute error (MAE) of 0.2437, and R‐squared (R2) of .2756. Also, the significant findings of the proposed study positively indicate that this study can be applicable to real‐time agricultural practices and is highly suitable for water quality predictions. Also, it can assist farmers and farming businesses in predicting the yield of crops in a specific season when to harvest and crop a plant for attaining improved crop yields.

Aging Mongolian pine plantations face high risks of drought-induced growth decline: evidence from both individual tree and forest stand measurements

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas. An important species for afforestation in dry environments of northern China, Mongolian pine (Pinus sylvestris var. mongolica Litv.) has recently exhibited growth decline and dieback on many sites, particularly pronounced in old-growth plantations. However, changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood. In this study, tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young (9 − 13 years) and aging (35 − 52 years) plantations of Mongolian pine in a water-limited area of northern China. A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations. In addition, all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation. Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations, as reflected by their lower sapwood- and leaf-specific hydraulic conductivities. Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age. The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America.

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

Combined effects of mixing ratios and tree size: how do mixed forests respond to climate and drought events?

Although the relationship between biodiversity and ecosystem functionality (BEF) has been studied comprehensively, how the mixing ratio of tree species in mixed forests affects the response of trees to climate and drought remains an unexplored and rather unknown question. Hence, we established tree-ring chronologies for Pinus tabuliformis Carr. (P) and Quercus variabilis Blume. (Q) mixed forests with different mixing ratios. In the temperate region of China, we investigated three mixing ratios: 90% P and 10% Q (P9Q1), 60% P and 40% Q (P6Q4), and 20% P and 80% Q (P2Q8). We collected tree ring samples using three tree size categories: dominant, intermediate, and suppressed trees. We explored the climate sensitivity of these trees and their drought tolerance indices-resilience (Rs), resistance (Rt), and recovery (Rc) under two drought conditions: short-term drought (1993 drought) and long-term drought (1999-2015 drought). P6Q4 made P. tabuliformis more sensitive to the Palmer drought severity index (PDSI) from the previous year than the other two ratios. The effect of the mixing ratio on drought response was insignificant under short-term drought in both tree species. Rt, Rc, and Rs of P. tabuliformis decreased with an increasing Q. variabilis:P. tabuliformis ratio in long-term drought. Rt, Rc, and Rs of Q. variabilis were the highest in P6Q4. The sensitivity of trees to PDSI varied among classes and was influenced by the mixing ratio. Dominant trees were most sensitive to PDSI in P6Q4 and P2Q8, whereas intermediate and suppressed trees were more sensitive to PDSI in P9Q1. The impact of tree size on drought tolerance indices varied according to drought type and mixing ratio. These findings showed that the mixing ratio has a confounding effect on the drought sensitivity of temperate tree species. Differences in hydrological niches allow Q. variabilis to benefit from mixing with P. tabuliformis. Mixing with optimal proportion of P. tabuliformis maximizes the drought resilience of Q. variabilis. Additionally, weakly competitive species (P. tabuliformis) do not benefit from mixed forests during prolonged water deficits. This result complements previous arguments that species mixing reduces the biological vulnerability of individuals. This study emphasizes the importance of species selection based on the biological and physiological characteristics of tree species in the afforestation of mixed forests. It highlights the critical role of species mixing ratios in the resistance of mixed forest ecosystems to climate change, which may provide a reference for sustainable forest management.

Impact of Anthropogenic Warming on Future Unprecedented Droughts in California: Insights From Multiple Indices and Multi‐Model Projections

AbstractSo‐called hot droughts are seen as one of the very foreseeable extremes amid the faster‐than‐expected pace of global warming. In particular, the western part of North America has been pummeled by severe droughts due to a lack of precipitation as well as record‐breaking hot temperatures. This study assesses the joint return period of drought severity and duration using the COordinated Regional Climate Downscaling EXperiment‐COmmon Regional Experiment (CORDEX‐CORE) simulations over the California domain. Six dynamically downscaled simulations with 25 km resolution are analyzed for the historical (1956–2005) and future (2050–2099) periods, whose warming sensitivities differ based on three global climate models (GCMs) driving two regional climate models (RCMs). Our focus is on estimating the joint probabilities of the drought duration and severity constructed from multiple drought indices such as the Palmer Drought Severity Index (PDSI) (with original temperature (PDSI) and its detrended counterpart (PDSI_detrended)) and Standardized Precipitation Index (SPI). Under the RCP8.5 scenario, an unprecedented level of droughts in terms of both duration and severity is likely to emerge only when the drought is characterized by PDSI. A comparison of PDSI and PDSI_detrended explicitly reveals that the occurrence of severe and prolonged droughts is mainly attributed to the warming trend of temperature. Both PDSI_detrended and SPI, which do not incorporate the warming effect explicitly, barely differentiate the joint distributions from the historical and future simulations. Unlike the PDSI based on water budget, the SPI, based on solely accumulated precipitation, shows a loosely coupled joint structure between the severity and duration of droughts, with marked differences in their marginal distributions.

Enhancing spatial resolution of drought monitoring through a novel random forest-based GRACE drought index: a case study in Central Yunnan

The original coarse spatial resolution of Gravity Recovery and Climate Experiment (GRACE) data limits its application in small and medium-scale areas. The study proposes a novel random forest-based GRACE drought index (NRFGDI) to enhance the spatial resolution of drought monitoring, which is applied to central Yunnan. We first employed the random forest (RF) algorithm to improve the spatial resolution of GRACE-derived terrestrial water storage anomaly (TWSA) to 0.1°, then established the NRFGDI based on the downscaled TWSA. Subsequently, the NRFGDI was verified using traditional drought indexes, including the standardized precipitation evapotranspiration index (SPEI) and the self-calibrating Palmer drought severity index (sc-PDSI), along with the 2009/2010 Bulletin of Flood and Drought Disasters in China. The NRFGDI not only captures drought characteristics, but also offers finer spatial resolution. The study provides an effective means for medium to small-scale drought monitoring, thereby making valuable contributions to decision-making processes concerning droughts in central Yunnan.

Resilience and response: Unveiling the impacts of extreme droughts on forests through integrated dendrochronological and remote sensing analyses

Extreme droughts are anticipated to have detrimental impacts on forest ecosystems, especially in water-limited regions, due to the influence of climate change. However, considerable uncertainty remains regarding the patterns in species-specific responses to extreme droughts. Here, we conducted a study integrating dendrochronology and remote sensing methods to investigate the mosaic-distributed maple-oak (native) natural forests and poplar plantations (introduced) in the Horqin Sandy Land, Northeast China. We assessed the impacts of extreme droughts on tree performances by measuring interannual variations in radial growth and vegetation index. The results showed that precipitation and self-calibrated palmer drought severity index (scPDSI) are the major factors influencing tree-ring width index (RWI) and normalized difference vegetation index (NDVI). The severe droughts between 2000 and 2004 resulted in reduced RWI in the three studied tree species as well as led to NDVI reductions in both the maple-oak natural forests and the poplar plantations. The RWI reached the nadir during the 2000–2004 severe droughts and remained at low levels two years after the severe drought, creating a legacy effect. In contrast to the lack of significant correlation between RWI and scPDSI, NDVI exhibited a significant positive correlation with scPDSI indicating the greater sensitivity of canopy performance to droughts than radial growth. Furthermore, interspecific differences in RWI and NDVI responses were observed, with the fast-growing poplar species experiencing a more significant RWI decrease and more negative NDVI anomaly during severe droughts than native species, highlighting the species-specific trade-offs between drought resilience and growth rate. This study emphasizes the importance of combining tree-level radial growth with landscape-scale canopy remote sensing to understand forest resilience and response. Our study improves our understanding of forest responses to extreme drought and highlights species differences in climate responses, offering crucial insights for optimizing species selection in sustainable afforestation and forest management in water-limited regions under the influence of climate change.

Tree ring oxygen isotope in Asia

Tree-ring oxygen isotope (δ18OTR) is a powerful proxy for paleoclimatology, ecology and archaeology, δ18OTR studies in Asia over the past ∼50 years have made significant achievements in improving our understanding of Asian climatic and ecological systems. Here, we review these developments in δ18OTR research in Asia. Most study sites are located within East Asia, Southeast Asia, South Asia, and North Asia, and δ18OTR records from conifers, broadleaf trees, and shrubs have been produced. Progress has been made in the following areas: (1) Experimental techniques. The novel plate method for cellulose extraction has been developed, which reduces experimental time and greatly improves efficiency. (2) Development of δ18OTR chronologies. Ninety-six chronologies exceeding 200 years in length have been produced. A 4354-year δ18OTR chronology with an annual-resolution has been produced in Japan, and a 6700-year δ18OTR chronology with a 1–5 year resolution has been produced in the northeastern Tibetan Plateau, which is the longest in Asia. (3) Biological influences on δ18OTR. Variations of intra- and inter-tree and inter-species δ18OTR have been investigated, and the relationship between tree age and δ18OTR has been explored. (4) Climatic implications. Seasonal δ18OTR records primarily reflect the variability of precipitation δ18O, facilitating high-resolution paleoclimate reconstructions, while interannual δ18OTR records mainly reflect summer moisture variability. (5) Climate reconstructions. Quantitative estimates have been produced for past precipitation, relative humidity, PDSI (Palmer Drought Severity Index), runoff, and other parameters from δ18OTR records. Since 1850, δ18OTR chronologies in monsoon regions show upward trends indicative of drying, while these from westerlies regions exhibit downward shifts implying wetting. (6) Dating. Intra- and inter-annual δ18OTR records help determine the annual growth of trees without rings and facilitate the cross-dating of trees with rings. Thus, the dating of archaeological and buried wood is feasible via comparisons with δ18OTR master chronologies. Promising areas of development include climatic implications (e.g., Position-specific isotope analysis, seasonal δ18OTR records on longer timescales and larger spatial scales), climate reconstructions (syntheses of multiple sites and records, development of long chronologies, deep time paleoclimate reconstructions) and ecological applications (e.g., the response of trees to climatic extremes, and the combinations with multiple tree-ring multiple proxies such as width and density).

The evolution of “Hot” droughts in Southern California, USA from the 20th to the 21st century

Drought intensity and duration in southern California, USA during the 21st century has been exceptional, and the changing climate dynamics of drought in this arid and semiarid region have been linked to anthropogenic warming. We examine the frequency, intensity, and persistence of drought in southern California's two climatic divisions during 1900–2022 and use the monthly instrumental record of the Palmer Drought Severity Index (PDSI) to track how drought events have changed. We introduce an empirical definition of “hot drought” by combining mean summer PDSI values with standardized scores of mean summer minimum temperature. Droughts in southern California, USA have become increasingly more severe, frequent, and long-lived in the 21st century. Moderate drought conditions (PDSI < −2) were recorded during more than half of the months in the decade ending in 2020. Similarly, hot droughts, droughts associated with both water deficits and significantly above-normal temperatures, have become increasingly frequent in recent decades. By the 21st century, hot droughts were occurring in approximately half of the summers. Our findings support the idea that anthropogenic warming results in a changing drought climatology for arid and semiarid regions of southern California and that hot droughts will likely become the dominant drought type.

Drought limits vegetation carbon sequestration by affecting photosynthetic capacity of semi-arid ecosystems on the Loess Plateau

Drought is the driver for ecosystem production in semi-arid areas. However, the response mechanism of ecosystem productivity to drought remains largely unknown. In particular, it is still unclear whether drought limits the production via photosynthetic capacity or phenological process. Herein, we assess the effects of maximum seasonal photosynthesis, growing season length, and climate on the annual gross primary productivity (GPP) in vegetation areas of the Loess Plateau using multi-source remote sensing and climate data from 2001 to 2021. We found that maximum seasonal photosynthesis rather than growing season length dominates annual GPP, with above 90 % of the study area showing significant and positive correlation. GPP and maximum seasonal photosynthesis were positively correlated with self-calibrating Palmer Drought Severity Index (scPDSI), standardized precipitation and evapotranspiration index (SPEI) in >95 % of the study area. Structural equation model demonstrated that both drought indices contributed to the annual GPP by promoting the maximum seasonal photosynthesis. Total annual precipitation had a positive and significant effect on two drought indices, whereas the effects of temperature and radiation were not significant. Evidence from wood formation data also confirmed that low precipitation inhibited long-term carbon sequestration by decreasing the maximum growth rate in forests. Our findings suggest that drought limits ecosystem carbon sequestration by inhibiting vegetation photosynthetic capacity rather than phenology, providing a support for assessing the future dynamics of the terrestrial carbon cycle and guiding landscape management in semi-arid ecosystems.

Drought-heatwave compound events are stronger in drylands

Climate change is exacerbating the occurrence of compound droughts and heatwaves (CDHWs), which pose a serious threat to human health and socio-economic development. Using daily maximum temperature (Tmax) and monthly self-calibrating Palmer drought severity index (sc-PDSI) dataset, the evolution patterns of CDHWs and compound wet-heatwave events, the dominant drivers and the relative contributions of droughts and heatwaves in the drylands and humid areas from 1961 to 2020 were compared and analyzed. The results show that the CDHWs are stronger in drylands than in humid areas, the growth rate of CDHWs in drylands was almost twice that of the humid areas, the CDHWs are greater than the multi-year average intensity of compound wet-heatwave events by up to 2.4 times. Moreover, CDHWs has increased significantly from the past period (1961–1990) to the recent warm period (1991–2020), and the heatwave threshold has increased by about 5 °C. In most drylands, the contribution of heatwaves to CDHWs dominates, whereas in humid areas, the droughts contribution to CDHWs does. The compounding effects of droughts and heatwaves may exacerbate the severity of CDHWs regionally and are most pronounced in drylands, taking into account optimal lags. The study findings could provide scientific and technological support to actively address global climate change risks.

Improving the accuracy of reconstructed terrestrial water storage during the GRACE/GRACE-FO gap based on a new variable filter reconstruction model

ABSTRACT Terrestrial water storage (TWS) is a fundamental component of the global hydrological cycle. Understanding changes in TWS is essential for assessing the characteristics of drought and flood events in a region. However, the discontinuity between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) severely affects and limits their applicability in long-term hydrological studies. In this study, a new variable filter reconstruction model (NVFRM) is proposed to reconstruct the TWS anomaly (TWSA) by combining the Gamma test (GT) and Long short-term memory (LSTM) neural network. The research findings reveal that the TWSA reconstructed by the NVFRM is consistent with the GRACE-FO during the testing period (June 2018–December 2020). It shows that the Pearson correlation coefficient (PR), Nash-Sutcliffe efficiency coefficient (NSE), and root mean square error (RMSE) are 0.91, 0.81, and 4.90 cm, respectively, which demonstrates the reliability of the NVFRM. More importantly, four drought events are identified by the drought severity index (DSI) derived from the reconstructed TWSA, including August 2003–April 20 April 200420 April 200411–September 20 June 2011,20 June 201118–September 2018, and September 2019–December 2019, respectively. Flood events are monitored based on the flood potential index (FPI), which is calculated by using the reconstructed TWSA and precipitation. Flood events in the Poyang Lake Basin are characterized by a high FPI index of 0.58, 0.52 and 0.32 in summer (June–August), indicating a greater probability of flooding. The NVFRM model proposed in this study provides an effective measure for filling the missing TWSA in regional hydrological research, which is of great significance for monitoring extreme hydrological events and facilitating disaster prevention and mitigation.

Abstract 12832: Risk of Extreme Wet Climate Conditions and Racial/Ethnic-Specific Cardiovascular Mortality in Southwest United States

Introduction: Coastal regions are highly vulnerable to extreme climate conditions, which are known to have effects on adult cardiovascular disease mortality (CVDM). Racial/ethnic (R/E) minority populations (ie Non-Hispanic (NH) Black) are known to have a greater incidence of CVDM compared to their NH White counterparts. Consequently, there is a need to document associations between climate extremes and R/E-specific CVDM. Hypothesis: Extreme wet conditions associated with CVDM should vary by R/E, where strongest increased risk is expected for minority populations. Methods: Population-based study using sex, age (45-84 years), and R/E-specific (NH White, Black, Asian, &amp; Hispanic/Latino) CVD deaths [ICD-10 (I00-99)] from CDC for Southwest US (California, Arizona, New Mexico) from Jan 1999 to Dec 2020. Climate exposure categories (mild, moderate, extreme dry/wet) were based on Palmer Drought Severity Index (PDSI), which integrates temperature and precipitation. With population estimates, CVDM rates were associated with PSDI categories using negative binomial regression upon adjusting for diabetes, smoking prevalence, sex, trend, month, state, unemployment rate, and air pollution (PM 2.5 ). Results: From 1999-2020, 1,307,418 (67.6% NH White, 16.1% Hispanic, 7.4% NH Asian, 8.9% NH Black) CVD deaths were recorded. CVDM was associated with a significant increased risk during extreme wet events (compared to extreme drought) only among NH Asian (adjusted rate ratio [RR] 1.08; 95% CI 1.06-1.09) and NH White (RR 1.02; 95% CI 1.01-1.025) in Southwest US (Figure). Insignificant interactions were observed between extreme wet and PM2.5 on CVDM by R/E. Conclusions: Extreme wet climate conditions were associated with an increased risk of CVDM among NH Asian and White adults. Both climate extreme events and R/E should be considered as predisposing risk factors for CVDM. Further studies are needed to assess such CVDM risk factors for other susceptible areas in the US.

How Exceptional Was the 2015–2019 Central American Drought?

AbstractThe Central American Dry Corridor experienced five consecutive years of drought from 2015 to 2019. Here, we find that the severity of this drought was driven primarily by rainfall deficits in July–August. To determine if the magnitude of this event was outside the range of natural variability, we apply a statistical resampling method to observations that emulates internal climate variability. Our analyses show that droughts similar to the 2015–2019 event are possible, although extremely rare, even without anthropogenic influences. Persistent droughts in our ensemble are consistently linked to stronger easterly winds associated with the Caribbean Low‐Level Jet. We also examine the effects of temperature on soil moisture during this drought using the Palmer Drought Severity Index and show that anthropogenic warming increases the likelihood of severe deficits. Multi‐year droughts are likely to worsen by the end of the 21st century due to the compound effects of anthropogenic climate change.