Single Drought Research Articles

Distributed Composite Drought Index Based on Principal Component Analysis and Temporal Dependence Assessment

A variety of drought indices were developed to monitor different types of drought, a significant natural hazard with multidimensional impacts. However, no single drought index can capture all dimensions of drought, necessitating a composite drought index (CDI) that integrates a range of indicators. This study proposes a CDI using principal component analysis (PCA) and a temporal dependence assessment (TDA) applied to time series of drought indices in a spatially distributed approach at the basin level. The indices considered include the Simplified Standardized Precipitation Index (SSPI), Simplified Standardized Precipitation-Evapotranspiration Index (SSPEI), soil moisture (SM), Normalized Difference Vegetation Index (NDVI), and streamflow (SF) from two climatically distinct small-sized basins in Portugal. Lag correlation analyses revealed a high contemporaneous correlation between SSPI and SSPEI (r > 0.8) and weaker but significant lagged correlations with SF (r > 0.5) and SM (r > 0.4). NDVI showed lagged and negligible correlations with the other indices. PCA was iteratively applied to the lag correlation-removed matrix of drought indices for all grid points, repeating the procedure for several SSPI/SSPEI time scales. The first principal component (PC1), capturing the majority of the matrix’s variability, was extracted and represented as the CDI for each grid point. Alternatively, the CDI was computed by combining the first and second PCs, using their variances as contribution weights. As PC1 shows its highest loadings on SSPI and SSPEI, with median loading values above 0.52 in all grid points, the proposed CDI demonstrated the highest agreement with SSPI and SSPEI across all grid cells, followed by SM, SF, and NDVI. Comparing the CDI’s performance with an independent indicator such as PDSI, which is not involved in the CDI’s construction, validated the CDI’s ability to comprehensively monitor drought in the studied basins with different hydroclimatological characteristics. Further validation is suggested by including other drought indicators/variables such as crop yield, soil moisture from different layers, and/or groundwater levels.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Drought Quantification in Africa Using Remote Sensing, Gaussian Kernel, and Machine Learning

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Identifying the spatiotemporal patterns of drought-flood alternation based on IMERG product in the humid subtropical Poyang Lake basin, China

Study RegionA humid subtropical basin, Poyang Lake Basin, China. Study focusDrought-flood alternation (DFA) is sub-seasonal precipitation anomaly, which has more serious impact on agricultural production, ecology and environment than a single drought or flood event. The study evaluated the suitability of high-resolution satellite-based precipitation product for identifying the DFA event and quantified the spatiotemporal patterns of the frequencies and intensities of DFA at various time scales. New hydrological insights for the regionThe Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) product could effectively identify the DFA events at 1-month scale, with high consistency with the results based on traditional rain-gauge data, however, its performance was not very satisfactory at 3-month and 6-month scales. Drought-to-Flood (DTF) events usually occurred from March to June, while Flood-to-Drought (FTD) events mainly occurred during from July to September. Frequencies and intensities of both DTF and FTD exhibited significant spatial heterogeneity. The lake area was mainly affected by DTF events, and the Ganjiang sub-catchment was mainly affected by FTD events. Especially, the southern part of the basin was a high-risk area affected by DFA, with higher frequencies of both severe DTF and FTD events. Furthermore, although the total area affected by DFA events showed a long-term decreasing trend from 2000 to 2019, the area affected by severe DTF and FTD events was gradually expanding in Poyang Lake basin.

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and Its Evaluation

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

Responses of Physiological Traits and Soil Properties in Pinus thunbergia and Euonymus japonicus Saplings under Drought and Cadmium (Cd) Stress

Pinus thunbergii and Euonymus japonicus are two species commonly found in arid and semi-arid areas; however, their responses in terms of physiological traits and soil properties under drought and cadmium (Cd) stress are not clear. In this study, we carried out single and combined stress treatments consisting of drought and Cd on saplings of P. thunbergii and E. japonicus and investigated the responses in terms of the physiological traits and soil properties of both species. For both species, under single Cd stress, Cd was observed in both the xylem and phloem, while the root Cd2+ flow rate fluctuated at different levels of Cd stress. Under both single and combined stress, as the stress level increased, the abscisic acid (ABA) content of the leaves and roots increased significantly, while the indole-3-acetic acid (IAA) content of the leaves and roots decreased significantly. Moreover, the non-structural carbohydrate (NSC) content of the leaves, stems, and roots, as well as the leaf chlorophyll content, decreased significantly. Under drought stress, the xylem water potential and hydraulic conductivity significantly decreased, which was exacerbated by Cd stress; this led to a more significant decrease in water potential and hydraulic conductivity under the combined stresses. Meanwhile, no significant changes in the conduit lumen diameter and double-wall thickness were observed, except for the double cell wall thickness of the P. thunbergii tracheid, which increased. In addition, both the single stresses and the combined stress of drought and Cd induced significant changes in the soil properties of the two species, i.e., the ammonium nitrogen, nitrate nitrogen, and effective phosphorus of the soil increased significantly, and the increase in content was more significant under combined stress. The diversity of the soil microbial community of P. thunbergii saplings significantly increased, while no change was found in its microbial community abundance under the single stresses and combined stress; however, the diversity and abundance of the soil microbial community in E. japonicus saplings showed the opposite pattern, which indicates that the effect of Cd on soil microorganisms is more significant than the effect of drought. The activity of sucrase and catalase in P. thunbergii soil fluctuated under the single stress and combined stress when compared, and the activity of sucrase in the soil of the E. japonicus species decreased. However, its catalase activity increased significantly under the single drought and Cd stress and combined stress when compared. We found that the combined stresses exacerbated the effects of the single stress in both species. Our study provides more detailed information on the responses in terms of the physiological traits and soil properties of the two species under single and combined stress consisting of drought and Cd.

Exploring the coordinated hydraulic plasticity across organs in soybean plants exposed to drought cycles

The coordination of hydraulic function among different organs and their plasticity under stressful conditions are crucial for understanding drought tolerance in crops but remains poorly understood. Here, we evaluate the hydraulic plasticity in soybean organs exposed to one or two droughts and correlated this plasticity with recovery after rewatering. Repeated drought events resulted in different levels of dehydration in soybean leaves since plants exposed to two droughts (D2 plants) maintained higher water potential than plants exposed to a single drought (D1 plants). The difference in leaf water potential reflected the different drought acclimation strategies in D1 and D2 plants. D1 plants increased root hydraulic conductance, a change that can occur rapidly due to molecular modifications. The first drought also acted as an environmental cue, triggering changes that continued to develop over time (anatomical changes) and which increased water transport in the stem of D2 plants, reducing the drought impact in these organs and maintaining the water transport to the leaves. Leaves of D1 and D2 plants, in turn, reduced vulnerability to embolism, avoiding losses in rehydration capacity and helping to maintain the hydraulic functions essential for the rapid recovery of photosynthesis after rewatering.

Divergent effects of single and combined stress of drought and salinity on the physiological traits and soil properties of Platycladus orientalis saplings.

Drought and salinity are two abiotic stresses that affect plant productivity. We exposed 2-year-old Platycladus orientalis saplings to single and combined stress of drought and salinity. Subsequently, the responses of physiological traits and soil properties were investigated. Biochemical traits such as leaf and root phytohormone content significantly increased under most stress conditions. Single drought stress resulted in significantly decreased nonstructural carbohydrate (NSC) content in stems and roots, while single salt stress and combined stress resulted in diverse response of NSC content. Xylem water potential of P. orientalis decreased significantly under both single drought and single salt stress, as well as the combined stress. Under the combined stress of drought and severe salt, xylem hydraulic conductivity significantly decreased while NSC content was unaffected, demonstrating that the risk of xylem hydraulic failure may be greater than carbon starvation. The tracheid lumen diameter and the tracheid double wall thickness of root and stem xylem was hardly affected by any stress, except for the stem tracheid lumen diameter, which was significantly increased under the combined stress. Soil ammonium nitrogen, nitrate nitrogen and available potassium content was only significantly affected by single salt stress, while soil available phosphorus content was not affected by any stress. Single drought stress had a stronger effect on the alpha diversity of rhizobacteria communities, and single salt stress had a stronger effect on soil nutrient availability, while combined stress showed relatively limited effect on these soil properties. Regarding physiological traits, responses of P. orientalis saplings under single and combined stress of drought and salt were diverse, and effects of combined stress could not be directly extrapolated from any single stress. Compared to single stress, the effect of combined stress on phytohormone content and hydraulic traits was negative to P. orientalis saplings, while the combined stress offset the negative effects of single drought stress on NSC content. Our study provided more comprehensive information on the response of the physiological traits and soil properties of P. orientalis saplings under single and combined stress of drought and salt, which would be helpful to understand the adapting mechanism of woody plants to abiotic stress.

Responses of radial growth of Larix principis-rupprechtii at different densities to drought events

In recent years, a surge in drought occurrences has dramatically impacted tree growth worldwide. We examined the ecological resilience of Larix principis-rupprechtii plantations with varying densities (1950, 2355, and 2595 trees·hm-2) at the Saihanba Mechanical Forest Farm, by extracting the increment cores using the standard dendrochronological method to measure individual-tree basal area increments (BAI) as part of our assessment of ecological resilience, including resistance (Rt), recovery (Rc), and resilience (Rs). The results showed that drought events occurred in 2006-2010, 2015, and 2018. The Rt for L. principis-rupprechtii plantations varied from 0.76 to 2.01 across three drought events, indicating generally high resistance, except for the plantation with 2355 trees·hm-2 during the second dry year (Rt=0.69). The Rt for the plantation with 2595 trees·hm-2 significantly decreased across all drought events, while no significant change was observed in the plantations with 1950 and 2355 trees·hm-2. The Rc showed no differences in response to a single drought event across plantation densities, with a significant upward trend for all the densities with each occurrence of drought event. There was no significant difference in the resilience of different densities of L. principis-rupprechtii to the first drought event, whereas the plantation with 2595 trees·hm-2 exhibited significantly lower Rs during the second and third drought events compared with 1950 and 2355 trees·hm-2, respectively. During the 2015 drought event, plantation with 2595 trees·hm-2 experienced a significant growth decline (radial growth change rate was -26.5%), while no such decline was observed in the plantations with 1950 and 2355 trees·hm-2. Overall, the plantation with 2595 trees·hm-2 demonstrated the lowest resilience to drought events.

On the timescale of drought indices for monitoring streamflow drought considering catchment hydrological regimes

Abstract. There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

Optimal Carbon Storage During Drought.

Allocation of non-structural carbohydrates (NSC) to storage allows plants to maintain a carbon pool in anticipation of future stress. However, to do so, plants must forego use of the carbon for growth, creating a trade-off between storage and growth. It is possible that plants actively regulate the storage pool to maximise fitness in a stress-prone environment. Here, we attempt to identify the patterns of growth and storage that would result during drought stress under the hypothesis that plants actively regulate carbon storage. We use optimal control theory to calculate the optimal allocation to storage and utilisation of stored carbon over a single drought stress period. We examine two fitness objectives representing alternative life strategies: prioritisation of growth (MaxM) and prioritisation of storage (MaxS), as well as strategies in between these extremes. We find that optimal carbon storage consists of three discrete phases: 'growth', 'storage without growth', and the 'stress' phase where there is no carbon source. This trajectory can be defined by the time point when the plant switches from growth to storage. Growth-prioritising plants switch later and fully deplete their stored carbon over the stress period, while storage-prioritising plants either do not grow or switch early in the drought period. The switch time almost always occurs before soil water is depleted, meaning that growth stops before photosynthesis. We conclude that the common observation of increasing carbon storage during drought could be interpreted as an active process that optimises plant performance during stress.

Growth, Morphological Alterations, and Enhanced Photosynthetic Performance Promote Tolerance of Distylium chinense to Alternate Drought–Flooding Stresses

Disentangling the underlying processes of plant adaptations to multiple abiotic stressors is crucial regarding promissory species for the restoration of riparian ecosystems prone to suffering extreme flood and drought events in the context of global climate change and human activities. Distylium chinense is a dominant evergreen shrub, distributed in the riparian areas of the Yangtze River in China. Here, one field study and five controlled experiments (Control, CK; single drought, D; single flooding, FF; from drought to recovery to full flooding, D-R-FF; from full flooding to recovery to drought, FF-R-D) were conducted. More hypertrophied lenticels, adventitious roots, and the increased stem-base hypertrophy of D. chinense were observed under the D-R-FF condition compared with FF and FF-R-D. Interestingly, the increase of the net photosynthetic rate (Pn) coincidentally occurred with the increase of heme degradation by heme oxygenase (r = 0.608, p = 0.003). Pn of D. chinense in D-R-FF was about twice as much as that in FF-R-D. The enhanced photosynthetic performance was functionally coupled with the adequate water supply to promote the tolerance of D. chinense to alternate drought–flooding condition compared with no any flooding condition. The accumulation of soluble sugar was highest under D, followed by FF-R-D, FF and D-R-FF, which showed that soluble sugar accumulation over the drought period could trigger the recovery growth of flooded plants in later flooding. These data provided the first insights into the tolerance mechanisms by a suite of morphological alterations and physiological adaptations, especially in the enhanced photosynthetic performance of D. chinense under alternating drought and flooding stresses. So, D. chinense could be considered as a prominent shrub species in the restoration practices of wetlands, riparian areas, and other flood-prone forests.

A novel composite drought index combining precipitation, temperature and evapotranspiration used for drought monitoring in the Huang-Huai-Hai Plain

Single drought index such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI) and so on, has a limited capability to indicate drought state. It has been proved that the composite drought index proposed by integrating multiple single drought indices can indicate and predict drought state more efficiently and accurately. This paper presents the Precipitation Temperature Evapotranspiration Drought Index (PTEDI) combining the PCI, TCI and ETCI. The three-dimensional Euclidean PCI-TCI-ETCI space is built, and the PTEDI is defined as the magnitude of the vector in the PCI-TCI-ETCI space. To verify the rationality and effectiveness of the PTEDI as a desired drought index in the Huang-Huai-Hai Plain, it is compared with the PCI, TCI, ETCI and PCACDI (Principal Component Analysis Combined Drought Index), and is evaluated with the Drought Affected/Disaster Area (DAA/DDA) and Net Primary Productivity (NPP). The results indicate that the PTEDI outperforms the PCI, TCI, ETCI and PCACDI, and is highly correlated with the Standardized Precipitation Index (SPI-1) (R = 0.61), Soil Moisture Condition Index (SMCI) (R= 0.56), and NPP (R = 0.56). The time series of the PTEDI and four indices (SPI-1, SMCI, Crop Water Stress Index (CWSI) and Temperature Vegetation Drought Index (TVDI)) exhibits good spatial consistency. The PTEDI has a negative correlation with the DAA/DDA (R < −0.6). Furthermore, to illustrate the applicability of the PTEDI, the drought spatiotemporal variation is analyzed by taking it as a drought index. At the monthly scale, moderate drought occurs most frequently in March and July. At the annual scale, the PTEDI fluctuates between 0.34 and 0.60 without obvious periodicity, and the study area is mainly characterized by mild drought. Moreover, the drought evolution is analyzed by using the timing analysis methods. In conclusion, the PTEDI can be applied to monitor drought effectively.

Drought analysis in southern region of Tamil Nadu using meteorological and remote sensing indices

Drought is a natural phenomenon caused due to inadequate rainfall over a region as compared to the expected amount, which when sustained over an extended period of time, eventually leads to shortage of water to sustain various human activities. One-month SPI showed that the southern zone is highly prone to moderate drought conditions. The seasonal analysis of SPI showed that the region faced more drought instances during the South West Monsoon compared with North East Monsoon season. Thoothukudi, Dindigul, Pudukkottai and Virudhunagar showed the high occurrences of drought at seasonal and annual scale. The weekly MAI calculated indicated a risk in the rainfed cropping season. Tirunelveli and Tenkasi showed highly vulnerable to moderate drought. NDVI during the NEM 2016, 2017 and 2018 showed that more than 80 per cent of the total area in the southern districts was under drought stress. NDVI analysis showed that Thoothukudi, Ramanathapuram, Pudukkottai, Sivagangai and Virudhunagar districts are highly vulnerable to drought. NDWI analysis during the NEM 2016, 2017 and 2018 showed high drought stresses with more than 90 per cent of the area showing drought stress during these three years. NDVI and NDWI analysis showed that the Southern Zone of Tamil Nadu was most vulnerable to Moderate and Severe droughts. The comparison of NDVI and NDWI and 3-, 6-, 9- and 12-month SPI showed that the three indices are fairly accurate with each other and hence are useful in the analysis of drought. However, just a single drought index cannot clearly define accurately the spatial and temporal extent of drought. Thus, a combination of meteorological and remote sensing indices gave a detailed idea about the spatio-temporal extent of drought.

Growth and drought reaction of European hornbeam, European white elm, field maple and wild service tree

Key messageConsidering their drought tolerance and growth characteristics, rare native tree species are well-suited admixed species for the development of climate-stable forests in Central Europe.In our study, we assessed the growth and drought reaction of the four rare native tree species European hornbeam (Carpinus betulus L.), European white elm (Ulmus laevis Pall.), field maple (Acer campestre L.), and wild service tree (Sorbus torminalis (L.) Crantz). Based on tree-ring data, we (I) evaluated their species-specific growth characteristics and variability and examined the influencing site and tree characteristics on annual growth. (II) We quantified their reaction to single drought events, also depending on site and tree variables. (III) We compared our results to oak (Quercus robur L., Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.). As they are well-known Central European tree species, there is a broad knowledge about their growth and drought response across wide geographical ranges available. Bringing the results of European beech and oak in relation with the rare native species, it allows to categorise their growth and drought reaction and to contextualise their performance. Our results show, that besides European white elm, the rare species showed an overall lower annual growth with a higher variability than European beech and oak. However, especially field maple and wild service tree were better adapted to drought than European beech and partially even recovered better than oak. Combining the aspects of growth stability and drought tolerance, we conclude that rare native tree species are well suited as admixed species in future forest stands. European hornbeam is a suitable match for European beech on wetter sites, while field maple and wild service tree are a sensible complement for the climate stable oak on drier sites.

Will China's Yellow River basin suffer more serious combined dry and wet abrupt alternation in the future?

At present, many studies have investigated the evolution characteristics, frequency estimation techniques, and prediction of droughts and floods, but a comprehensive understanding of the spatiotemporal evolution law and joint risk of these two composite extreme events is still lacking. The synergistic effect of dry and wet abrupt alternation (DWAA) events has more significant consequences than a single drought and flood event, and these events have a considerable impact on agriculture, ecology, and economy. Furthermore, owing to the intensification of global warming and climate change, the potential synchronisation between DWAA events and extreme precipitation in the future requires special attention. Therefore, it is necessary to establish a reliable framework to analyse the evolution characteristics and risks of composite extreme events (DWAA). In this study, a cascade modelling chain comprising climate model downscaling, non-uniform bias-correction technique, and model integration was developed to study the spatiotemporal evolution characteristics and combined risk impact of DWAA events. The proposed methodology was applied to the Yellow River Basin (YRB) in the historical period (1960–2014, Hist) and the future period (2021–2060, FUT1; and 2061–2100, FUT2) under the Shared Socioeconomic Pathway (SSP) 3–7.0 and 5–8.5 scenarios. The results show that: (1) The constructed cascade modelling chain has high simulation accuracy for precipitation, and can be used to analyse future DWAA events. (2) Considering the precipitation characteristics under SSP3-7.0 and SSP5-8.5 scenarios, the precipitation increases slightly in the near future FUT1 (about 5–10%), and increases significantly in the far future FUT2 (about 10–70%). (3) Following either scenario, the frequency of DWAA events is expected to decrease in the future compared to that in the Hist, but the average intensity is expected to increase compared to that in the Hist. (4) The joint risk of the impact area and intensity of future DWAA events of FUT2 is greater than that of FUT1.

Recent Tree Mortality Dampens Semi‐Arid Forest Die‐Off During Subsequent Drought

AbstractClimate change is expected to increase drought intensity and frequency, which are commonly predicted will threaten the survival of forests. Most forest die‐off projections assume that recent tree mortality will not alter die‐off severity during subsequent droughts. We tested this assumption by comparing die‐off in semi‐arid conifer forest stands in California that were exposed to a single drought in 2012–2015 (“2nd Drought Only”) with forest stands that experienced drought in both 1999–2002 and 2012–2015 (“Both Droughts”). We quantified die‐off severity as a reduction in the satellite observed Normalized Difference Moisture Index, and cumulative moisture deficit as negative 4‐year Precipitation minus Evapotranspiration (4‐year Pr‐ET overdraft). Here we show that recent tree morality reduces die‐off severity in semi‐arid conifer forests exposed to subsequent drought. Stands in the 2nd Drought Only sample experienced severe die‐off associated with extreme 4‐year Pr‐ET overdraft in 2012–2015. Stands in the Both Droughts sample experienced severe die‐off and 4‐year Pr‐ET overdraft in 1999–2002, but comparatively little 2012–2015 die‐off despite continued 4‐year Pr‐ET overdraft. We interpret this as a dampening effect, where prior tree mortality reduces forest die‐off severity during subsequent drought exposure. As forests continue to experience disturbances linked to climate change, dampening effects will impose a transient, and perhaps long‐term, constraint on the impact of repeated drought.

A New Bayesian Network-Based Generalized Weighting Scheme for the Amalgamation of Multiple Drought Indices

Drought is one of the most multifaceted hydrologic phenomena, affecting several factors such as soil moisture, surface runoff, and significant water shortages. Therefore, monitoring and assessing drought occurrences based on a single drought index are inadequate. The current study develops a multiscalar weighted amalgamated drought index (MWADI) to amalgamate multiple drought indices. The MWADI is mainly based on the normalized average dependence posterior probabilities (ADPPs). These ADPPs are obtained from Bayesian networks (BNs)-based Markov Chain Monte Carlo (MCMC) simulations. Results have shown that the MWADI correlates more with the standardized precipitation index (SPI) and the standardized precipitation temperature index (SPTI). As proposed, the MWADI synthesizes drought characteristics of different multiscalar drought indices to reduce the uncertainty of individual drought indices and provide a comprehensive drought assessment.

Effects of recurrent summer droughts on arbuscular mycorrhizal and total fungal communities in experimental grasslands differing in plant diversity and community composition

IntroductionBiodiversity loss and climate change have been determined as major global drivers affecting ecosystems and their functioning. In this context, drought was shown to have negative effects on ecosystems by disrupting ecological processes, which could be buffered in more biodiverse systems. Many studies, however, focus on effects on aboveground communities of single drought events, while dynamics of soil-borne communities are still widely unclear, despite their important roles in ecosystem functioning.MethodsTo elucidate the effect of recurrent summer drought periods on fungal communities in a long-term grassland biodiversity experiment, roof shelters were installed on grassland plots ranging in plant species richness from 1 to 16 species and plant functional group richness (1-4 groups) and composition. After 9 years of summer droughts, bulk soil was sampled and used for Illumina sequencing of the ITS2 and SSU genes to characterize the total fungal and arbuscular mycorrhizal fungal (AMF) communities, respectively.ResultsWe found shifts of AMF and total fungi community structures caused by recurrent drought and plant species richness, but no buffering of drought effects by plant diversity. Alpha-diversity (VT or ASV richness) of both AMF and total fungi increased with plant species richness but was not significantly affected by drought. Even though drought overall had minimal long-lasting effects, we found Diversispora and Paraglomus among the AMF and Penicillium among total fungal communities to be more abundant after the drought treatment. AMF communities were affected by the presence of individual plant functional groups, reacting stronger to presence of legumes under drought, while total fungal interaction with plant communities were similar under drought as control. AMF α-diversity differed between plant functional groups in control conditions but was independent of plant community composition under drought. In contrast, total fungi α-diversity was increased by presence of herbs and legumes only under drought.DiscussionFrom our results, we conclude that recurring moderate summer droughts do not strongly affect soil fungal communities. All shifts can be explained by indirect effects through the plant community and its top-down effect on soils altered by drought. Further, AMF are not less affected than total fungal communities, but rather respond differently by interacting more strongly with legumes in response to drought. Consequently, not plant species richness, but plant functional composition, dominates in shaping fungal communities under recurrent droughts.