Drought Duration Research Articles

Multi-Scale Analysis of Agricultural Drought Propagation on the Iberian Peninsula Using Non-Parametric Indices

Understanding how drought propagates from meteorological to agricultural drought requires further research into the combined effects of soil moisture, evapotranspiration, and precipitation, especially through the analysis of long-term data. To this end, the present study examined a multi-year reanalysis dataset (ERA5-Land) that included numerous drought events across the Iberian Peninsula, with a specific emphasis on the 2005 episode. Through this analysis, the mechanisms underlying the transition from meteorological to agricultural drought and its features for the selected region were investigated. To identify drought episodes, various non-parametric standardized drought indices were utilized. For meteorological droughts, the Standardized Precipitation-Evapotranspiration Index (SPEI) was employed, while the Standardized Soil Moisture Index (SSI), Multivariate Standardized Drought Index (MSDI), and Standard Precipitation, Evapotranspiration and Soil Moisture Index (SPESMI) were utilized for agricultural droughts, while their ability to identify relative vegetation stress in areas affected by severe droughts was investigated using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Anomaly provided by the Copernicus European Drought Observatory (EDO). A statistical approach based on run theory was employed to analyze several characteristics of drought propagation, such as response time scale, propagation probability, and lag time at monthly, seasonal, and six-month time scales. The retrieved response time scale was fast, about 1–2 months, and the probability of occurrence increased with the severity of the originating meteorological drought. The duration of agricultural drought was shorter than that of meteorological drought, with a delayed onset but the same term. The results obtained by multi-variate indices showed a more rapid propagation process and a tendency to identify more severe events than uni-variate indices. In general terms, agricultural indices were found to be effective in assessing vegetation stress in the Iberian Peninsula. A newly developed combined agricultural drought index was found to balance the characteristics of the other adopted indices and may be useful for future studies.

Evolution of Drought Trends under Climate Change Scenarios in Karst Basin

Karst basins have a relatively low capacity for water retention, rendering them very vulnerable to drought hazards. However, karst geo-climatic features are highly spatially heterogeneous, making reliable drought assessment challenging. To account for geo-climatic heterogeneous features and to enhance the reliability of drought assessment, a framework methodology is proposed. Firstly, based on the history of climate (1963–2019) from the Global Climate Model (GCM) and station observations within the Chengbi River karst basin, a multi-station calibration-based automated statistical downscaling (ASD) model is developed, and the Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) are selected as performance metrics. After that, future climate (2023–2100) under three GCM scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are obtained by using the ASD model. Finally, the Standardized Precipitation Evapotranspiration Index (SPEI), calculated by future climate is applied to assess drought conditions. The results indicate that the multi-station calibration-based ASD model has good performance and thus can be used for climate data downscaling in karst areas. Precipitation mainly shows a significant upward trend under all scenarios with the maximum variation (128.22%), while the temperature shows a slow upward trend with the maximum variation (3.44%). The drought condition in the 2040s is still relatively severe. In the 2060s and 2080s, the basin is wetter compared with the historical period. The percentage of drought duration decreases in most areas from the 2040s to the 2080s, demonstrating that the future drought condition is alleviated. From the SSP1-2.6 scenario to the SSP5-8.5 scenario, the trend of drought may also increase.

Copula-based multivariate analysis of hydro-meteorological drought

Droughts have far-reaching detrimental impacts on the environment, society, and economy, ranging from regional to national levels. As the drought characteristics are interrelated, multivariate analysis of those is necessary to understand the actual drought situation in a region. However, such studies are limited. Hence, this study aimed to develop a framework to investigate the meteorological and hydrological droughts using a multivariate analysis of drought characteristics in the Pennar River basin (a semi-arid region) of India, dominated by agricultural activity. Long-term observations (1980–2015) of precipitation, temperature, and streamflow were used to calculate the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI) at a 3-month timescale. Based on these indices, drought duration, severity, and peak were abstracted using the Run Theory. The best-fit marginal distribution was determined for every drought characteristic to establish the bivariate joint probability distribution (the copula function). Based on the best-fit copula function, the joint probabilities and the joint return periods were estimated. Results revealed that, for meteorological drought, Frank Copula and Survival Clayton copula were the best-fit copula function for the joint risk of drought duration and severity and the combination of drought duration and peak as well as drought severity and peak, respectively. The joint return period of a drought event with characteristics above the 25th percentile of the same varies between 1 and 2 years, whereas it spans between 1 and 45 years for the 50th percentile. This analysis will provide vital information for water management and planning in a region to mitigate droughts.

Climate-Change-Driven Droughts and Tree Mortality: Assessing the Potential of UAV-Derived Early Warning Metrics

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner.

Three-dimensional-based global drought projection under global warming tendency

Global warming is anticipated to largely impact drought dynamics. Therefore, a comprehensive projection of global drought under various future warming levels is critical for water resources management. This study investigated the changes in the severity, intensity, area, and duration of droughts at 1.5 °C, 2 °C, 3 °C, and 4 °C of global warming above pre-industrial levels based on the latest simulations under climate change scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month time scale was adopted to characterize seasonal droughts. The results show that: 1) Global land is projected to experience a significant nonlinear increasing trend of drought severity, intensity, areal extent, and duration during the 21st century, and drought characteristics have the highest growth rate under the 3 °C warming scenario, followed by the 4 °C, 2 °C, and 1.5 °C warming scenarios. 2) Under the 1.5 °C and 2 °C warming levels, drought characteristics in most land areas change slightly compared to the baseline period of 1995–2014. 3) Hotspots defined as robust increases in drought severity appear in the 3 °C and 4 °C warming world, including northern South America, Europe, northern and southern Africa, western and central Asia, and central Australia, whereas eastern North America and the Russian Arctic exhibit substantial reduction in drought severity under different global warming levels. 4) Global warming is projected to change the timing of droughts, leading to more frequent summer droughts in Europe. Overall, the findings of this study provide a scientific basis for articulating future global drought adaption and highlight the need for mitigation measures to limit greenhouse gas emissions to achieve the 2 °C target of the Paris Agreement.

Spatial evaluation of climate change-induced drought characteristics in different climates based on De Martonne Aridity Index in Iran

In recent years, greenhouse gas emissions have caused extensive changes in the global climate. Climate change leads to extreme events, such as droughts. The present study investigates precipitation and temperature variations and the past and future drought characteristics in Iran through data from 40 synoptic stations and 33 general circulation models (GCMs) under RCP4.5 and RCP8.5 scenarios. As a first step, the country of Iran was classified into different climatic regions based on De Martonne aridity index. The GCMs were ranked using TOPSIS in four climatic regions and an ensemble of top ten GCMs was used in each region. Furthermore, the homogeneity of monthly precipitation was studied in the baseline and future periods. Meteorological drought was calculated through the standardized precipitation index (SPI), deriving drought severity, peak, and duration based on run theory. The results revealed that precipitation will reduce in future periods in the majority of Iran and temperature will reduce in the south and southeast and will increase in the northwest and north of Iran. Furthermore, the highest drought severity and peaks will occur in semi-arid and arid regions, while the longest drought duration will happen in the southeast and west of Iran. Overall, future droughts are found to have higher severity, duration, peaks, and standard deviation than the baseline period. Also, the results showed a reducing trend of the SPI values in northwestern regions, while the other stations indicated no significant trend.

Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning

An accurate gridded Standardized Precipitation Index (SPI) at high spatial resolution is important for meteorological drought monitoring and assessment. However, the spatial estimation of the SPI solely derived from gridded precipitation products or interpolation of meteorological station-based data is subject to low accuracy and large uncertainty. Here, we developed a Gaussian process regression-based method by blending multiple precipitation products (GPR-BMP) with information from meteorological stations and topographical characteristics to improve the resolution and accuracy of spatial SPI. GPR-BMP was applied over China in this study, using five widely used precipitation products and meteorological station data, as well as elevation, slope, longitude and latitude. The gridded multiscale (i.e., 1-month, 3-month, 6-month, 9-month and 12-month) SPI datasets over China for 1984–2020 (GPR-BMP-SPI) were generated. Results showed that GPR-BMI-SPIs have higher accuracy than the SPIs derived from single precipitation products at all time scales. Additionally, GPR-BMP-SPIs can relatively accurately identify historical drought events. By analysing the drought spatiotemporal variation over China during 1984–2020, it was found that the annual drought frequency, severity and duration were relatively high in southeast China during 1984–2020. The annual trends of drought frequency, severity and duration showed a significant decrease on the Qinghai-Tibet Plateau. Winter drought in 1984–2020 exhibited the most distinctive features between the east and the west. Additionally, the drought area percentage showed an overall decreasing trend over the whole region of China on both annual and seasonal scales. It was also found that drought frequency contributed more to drought severity than drought duration. The GPR-BMP-SPI datasets generated in this study can serve as fundamental data support for future studies. The proposed model can be applied to other regions globally for drought monitoring and mitigation.

Wildfire severity alters soil microbial exoenzyme production and fungal abundances in the southern Appalachian Mountains

Climate change has increased drought frequency and duration that are exacerbated by increased temperatures globally. This effect has, and will continue, to increase fire occurrence across many regions of North America. In the southern Appalachian Mountains, wildfires with high burn severity occurred in 2016 due to increased drought and human activity. To investigate the effects of burn severity on soil physicochemical properties, microbial extracellular enzyme production, and microbial abundances in a temperate region, surface soils (0–15 cm) were collected from two sites (the Great Smoky Mountains National Park in Tennessee and the Nantahala National Forest in North Carolina, USA) spanning lightly, moderately, and severely burned areas, accompanied by adjacent unburned locations that act as controls. The soil samples were collected at three time points between 2017 and 2019 (i.e., 0.5, 1, and 2.5 years post-fire) among burn severity plots. Total hydrolytic enzyme production varied over time, with severe burn plots having significantly lower enzyme production at 2.5 years post-fire. Individual enzymes varied among burn severities and across time post-fire. Light burn plots showed greater carbon-specific (β-glucosidase and β-xylosidase) and phosphorus-specific (acid phosphatase) enzyme activities at 0.5 years post-fire, but this effect was transient. At 2.5 years post-fire, the β-xylosidase and acid phosphatase activities were lower in severe or moderate burn plots relative to the controls. In contrast, the activity of nitrogen-specific enzyme leucyl aminopeptidase was the lowest in severe burn plots at 0.5 years post-fire, but was the lowest in light burn plots at 2.5 years post-fire. The fungi:bacteria ratio declined with burn severity, indicating that fungi are sensitive or less resilient to high burn severity during recovery. These results suggest that wildfires alter trajectories for soil microbial structure and function within a 2.5-year timeframe, which potentially has long-term impacts on biogeochemical cycling.

Future exposure of forest ecosystems to multi‐year drought in the United States

AbstractAs the future climate becomes hotter or drier, forests may be exposed to more frequent or severe droughts. To inform efforts to ensure resilient forests, it is critical to know which forests may be most exposed to future drought and where. Longer duration droughts lasting 2–3 years or more are especially important to quantify because forests are likely to experience impacts. We summarized exposure to 36‐month drought for forests across the conterminous United States using the Standardized Precipitation‐Evapotranspiration Index (SPEI) overlaid on forest inventory plot locations. Exposure was quantified under 10 scenarios that combined five modeled climates and two Representative Concentration Pathways (RCPs, 4.5 and 8.5) through 2070. Future projections indicate a tripling of the monthly spatial extent of forests exposed to severe or extreme drought—38% of forests were exposed on average by mid‐century as opposed to 11% during 1991–2020 (2041–2070). Increases in drought exposure were greatest under hotter (HadGEM2‐ES), drier (IPSL‐CM5A‐MR), and middle (NorESM1‐M) climate models, under either RCP. Projections agreed that forests in portions of the western United States, especially the southwestern United States, could face high levels of exposure. Forest types including pinyon/juniper, woodland hardwoods, and ponderosa pine were projected to be exposed to drought more than 50% of the time on average across all scenarios by mid‐century, when no forest type was exposed more than 25% of the time under any scenario during the recent period. Projections agreed less for the eastern United States, but in some scenarios, particularly under RCP 8.5, large portions of the East could be exposed to drought nearly as often as parts of the West. Moreover, a substantial portion of oak/hickory forests occur in eastern regions, where projections agree on increased drought exposure. This study provides novel insights about the changing conditions forests face in both the eastern and western United States. Our results can be combined with information about the sensitivities and adaptive capacities of forest ecosystems to prioritize drought adaptation efforts.

Effects of Water Supply Mode on Nitrogen Transformation and Ammonia Oxidation Microorganisms in a Tea Garden

Drought limits tea yield and can also negatively impact its quality. In this study, constant humidity and dry–wet alternating modes were compared to determine their impacts on soil nitrogen transformation and ammonia-oxidizing microorganisms. Drought was found to reduce the soil NH4+-N concentration under the constant humidity mode, and the NO3−-N concentration was highest in 60% water-holding capacity (WHC) soil. Soil NO3−-N content increased rapidly after rewatering, and increasing the frequency of dry–wet watering resulted in a higher accumulation of NO3−-N. In the constant humidity mode, drought reduced the abundance of ammonia-oxidizing archaea (AOA), whereas that of ammonite-oxidizing bacteria (AOB) increased. Increases in drought duration and the frequency of dry–wet watering inhibited the activity of AOA under the dry–wet alternating mode, whereas the relative activity of AOB increased after rehydration. The water supply mode did not change the community structure of AOA or AOB at the genus level but affected their relative abundance. In the constant humidity mode, the contribution rate of AOA to nitrification potential (PNR) was 42.75–49.72%, whereas that of AOB was 50.28–57.25%. In the dry–wet alternating mode, the contribution rate of AOA to PNR increased, and the contribution rate of AOB decreased. Taken together, these findings indicate that ammonia oxidation might be primarily driven by AOA and AOB in weakly acidic and neutral soil. This study reveals the effects of different water supply modes on soil nitrogen transformation and ammonia-oxidizing micro-organisms and provides a scientific basis for improving nitrogen use efficiency.

Analysis of the Variability Characteristics and Applicability of SPEI in Mainland China from 1985 to 2018

Drought is one of the most destructive natural disasters in China and can cause serious environmental and socio-economic impacts. Based on monthly precipitation and temperature data from 571 meteorological stations, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated on a 12-month scale (SPEI-12) from 1985 to 2018 in mainland China, and it was compared with the records of the Bulletin of Flood and Drought Disasters in China to verify its drought monitoring accuracy. Then, run theory was used to explore in depth the spatiotemporal distribution of drought characteristics and trends in various climatic sub-regions. The results showed that: (1) the comparison with the Bulletin of Flood and Drought Disasters in China indicated that the results of SPEI-12 monitoring drought had high accuracy and could identify drought events in mainland China. (2) About 70% of the regions in mainland China experienced droughts more than 50 times, and about 43% of the regions had drought durations of between 100 and 110 months. Nearly 11% of the regions had drought severities of more than 130, which were mainly located in the Northwest Desert (NWC), the southern part of Northeast China (NEC), and the western part of North China (NC). (3) In the past 34 years, the droughts in the Northwest Desert (NWC), the western part of Inner Mongolia (IM), and the Qinghai-Tibet Plateau (TP) showed an aridity trend, while the droughts in South China (SC) and eastern Central and South China (CSC) presented a mitigation tendency. (4) Specifically, the droughts in South China (SC) were more influenced by precipitation, while those in the Northwest Desert (NWC) and Northeast China (NEC) were influenced by temperature and potential evapotranspiration, and the evolution of drought in North China (NC) and the Qinghai-Tibet Plateau (TP) was mainly influenced by soil moisture. The study could provide scientific guidance and a reference for drought response and sustainable development in China.

Construction of a Time-Variant Integrated Drought Index Based on the GAMLSS Approach and Copula Function

Construction of an integrated drought index is a fundamental task to conducting drought disaster risk management and developing drought resistance planning strategies. Given the evident non-consistent features during the drought evolution process, firstly, the GAMLSS approach was utilized to construct multiple combination scenarios of time-variant parameters and corresponding probability distribution functions. Then, the time-variant comprehensive drought index integrating the variable characteristics of precipitation and soil moisture was established by means of the copula function. Finally, the reliability of the time-variant comprehensive drought index was verified through its application in frequency analysis and return period determination of drought hazard system in Huaibei Plain, China. The application results demonstrated that: (1) The variation of the time-variant integrated drought indicator presented strong consistency with both soil moisture and precipitation during historical years in Huaibei Plain. (2) The overall variation of the drought hazard system characterized by drought duration and severity presented a gradual mitigation trend from west to east and north to south in Huaibei Plain, which agrees with the geographic differences and water resources availability distribution features. (3) Drought recognition results, including the frequency of drought events and typical drought processes with extreme grades, are in agreement with the practical statistics and observed data series. On the whole, the proposed time-variant integrated drought indicator is capable of extracting complex variation characteristics within the drought hazard evolution process, and can be further applied in drought monitoring, recognition and assessment research fields.

Bivariate socioeconomic drought assessment based on a hybrid framework and impact of human activities

Socioeconomic drought generally occurs when water supply cannot satisfy water demand. This study proposed a hybrid framework for socioeconomic drought assessment, involving the construction of standardized water supply-demand reliability index (SWSDRI), truncation of drought events, and classification of bivariate drought levels. Using the run theory, socioeconomic drought events and their properties, including duration and severity, were characterized by the SWSDRI series. Then, bivariate socioeconomic drought levels, based on the Kendall frequency of drought duration and severity, were classified using the copula function. Impacts of human activities on socioeconomic drought, including mainly the regulation of reservoirs and water consumption were quantified in the Dongjiang River basin, located in the south of China. Results showed that the framework, based on SWSDRI, facilitated the investigation of socioeconomic drought properties. The joint distributions of drought duration and severity, based on naturalized streamflow and under the individual and total impacts of human activities, were fitted well by the Archimedean copulas. Both drought duration and severity clearly exhibited the slight, moderate, severe, and extreme droughts classified by bivariate drought levels. For naturalized streamflow, socioeconomic drought occurred approximately once a year in the Dongjiang River basin. Design values of the combination of drought duration and severity accounted for 3.4 months and 8.0 hundred million cubic meters of the boundary between slight and moderate droughts, 4.9 months and 13.6 hundred million cubic meters of the boundary between moderate and severe droughts, and 6.9 months and 20.0 hundred million cubic meters of the boundary between moderate and severe droughts, respectively. Under the impact of human activities, in particular the regulation of reservoirs, socioeconomic drought was remarkably alleviated with a decrease of 45.8% in quantity. If the impact of water consumption was considered, drought events in quantitive terms remained almost unchanged, but more extreme and severe droughts occurred, because some slight, moderate, and severe droughts were aggravated by one level. Among the main reservoirs, the impact of the Xinfengjiang reservoir in the period of its operation was remarkable with a decrease in drought events by 47.7%. Under the impact of the Fengshuba reservoirs, drought events changed little in quantitive terms, but more slight droughts occurred because some moderate, severe, and extreme droughts were alleviated.

The uneven impact of climate change on drought with elevation in the Canary Islands

Climate models project an increase in drought and aridity in many regions in response to greenhouse gas concentrations in the atmosphere. In areas with complex topography, such as the Canary Islands, elevation gradients may play an important role in future changes. Convection-permitting climate simulations driven by data from three global climate models included in the Coupled Model Intercomparison Project (CMIP5) have been performed for the Canary Islands. A significant increase in the duration and severity of drought is projected by the end of the twenty-first century (2070–2099), relative to the recent past (1980–2009), under intermediate and high emissions scenarios. In addition, the percentage of land affected by droughts, on average, would increase considerably, covering up to 96% in the higher elevations, in the business-as-usual scenario. These changes and the increase in aridity are more pronounced at higher altitudes due to a clear dependence of temperature rise as a function of elevation and a substantial decrease in precipitation.

Reconstructing a long-term water storage-based drought index in the Yangtze River Basin

Drought is a prolonged dry period in the natural climate cycle, and is one of the most costly weather events. The Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage anomalies (TWSA) have been widely used to assess drought severity. However, the relatively short cover period of GRACE and GRACE Follow-On limit our knowledge about the characterization and evolution of drought over decades time scale. This study proposes a standardized GRACE reconstructed TWSA index (SGRTI) to assess the drought severity based on a statistical reconstruction method calibrated by GRACE observations. Results show that the SGRTI correlates well with 6-month scale SPI and SPEI, with correlation coefficients reaching 0.79 and 0.81 in the YRB from 1981 to 2019. Soil moisture can capture drought condition like the SGRTI, while cannot further reflect deeper water storage depletion. The SGRTI is also comparable to the SRI and in-situ water level. As a case study for the Yangtze River Basin, its three sub-basins experience more frequent droughts, shorter drought duration, and lower severity drought, as identified by SGRTI during 1992–2019 relative to 1963–1991. The presented SGRTI in this study can provide a valuable supplement to the drought index before the GRACE era.

Identifying driving processes of drought recovery in the southern Andes natural catchments

Study regionThe natural river basins of Chile. Study focusDrought effects on terrestrial ecosystems produce hydroclimatic stress with variable extensions. Particularly, hydrological drought duration can provide a better understanding of recovery together with catchment characteristics and climatology. This study focuses on the impacts of the multi-year drought experienced in Chile for more than a decade.The recovery of relevant catchment variables to quantify the drought termination (DT) and drought termination duration (DTD) after the hydrological drought is presented. A composite analysis of natural catchments using the CAMELS-CL data set discharge (1988–2020), k-NDVI (2000–2020), and soil moisture (1991–2020) provides the average response of the recovery after severe droughts. New hydrological insights for the regionThis study demonstrates that local catchment properties can explain the recovery of studied variables after a hydrological drought.Explanatory variables from CAMELS-CL to derive the DT using random forest regression (RFR) were used with a strong correlation of 0.92, 0.84, and 0.89 for discharge, vegetation productivity, and soil moisture, respectively.The discharge patterns show longer recovery over environments dominated by shrublands with less precipitation and higher temperatures, in central Chile, while higher latitudes with higher vegetation cover, increasing precipitation, and lower temperatures present shorter recovery times. The vegetation productivity shows longer recovery over highly vegetated mountains in central Chile. The soil moisture recovery spatial distribution presented patterns that connect them with the discharge recovery. This work enables the identification of drought vulnerability, which is valuable for managing water resources and ecosystems and is helping to predict drought recovery periods in regions with a lack of observations.

Multivariate Drought Monitoring, Propagation, and Projection Using Bias‐Corrected General Circulation Models

AbstractUnderstanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias‐corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological‐hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought‐related consequences.

Perception and adaptation strategies of forest dwellers to climate variability in the tropical rainforest in eastern Cameroon: The case of the inhabitants of the Belabo-Diang Communal Forest

The design of appropriate adaptation strategies to the impacts of climate change requires a contextual study of local perceptions due to the non-homogeneity of climate in a given agro-ecological area. The research objective of the current study aims to examine the evolution of climate parameters from 1983 to 2019 linked to the perceptions of local populations and appropriate adaptation measures in the Belabo-Diang Communal Forest of Cameroon. The methodological approach includes collecting and analyze climate data from 1983 to 2019; and surveying existing local perceptions and adaptive strategies among 540 households using semi-structured questionnaires. A significant increase in temperature of about 1 °C over 36 years (1983–2019) and a non-significant decrease in precipitation (95.36 mm) over the same period were observed. Local perceptions related to climate change vary according to the sector of activity and are mainly associated with more heat in the dry season (90%), late onset of rains (84%), drought recurrence (82%), less rainfall during the year (80%), and increase in the duration of drought (80%). For 82–100% of households, according to the activity sector, no appropriate adaptation measures to climate change were applied depending on activities. The adaptation measures used by less than 0–20% of respondents, include mainly the abandonment or change of activity, and modification of the agricultural calendar. With the lack of appropriate and adequate adaptation measures by the riparian populations, this study appears necessary to inform policy-makers of the need to develop and implement more appropriate strategies to enable the riparian people living in forest area of Cameroon to better adapt to these effects of climate changes.

Evaluating the duration, severity, and peak of hydrological drought using copula

Evaluating the duration, severity, and peak of hydrological drought using copula

Assessment of Meteorological and Agricultural Drought Indices under Climate Change Scenarios in the South Saskatchewan River Basin, Canada

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI < 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.