Changes In Drought Conditions Research Articles

Synergistic changes in precipitation and soil water use efficiency and their driving mechanisms of terrestrial ecosystems in China

Soil water use efficiency (SWUE) and precipitation use efficiency (PUE) are paramount indicators in examining the coupling mechanism of carbon and water cycles in terrestrial vegetation ecosystems. However, the mechanisms influencing variations in PUE and SWUE warrant further investigation due to existing research gaps. The gross primary productivity (GPP) and net primary productivity (NPP) in China were simulated using the TL-LUE model and improved CASA model, respectively. PUE and SWUE were then calculated in conjunction with precipitation and soil water data. A further investigation was conducted on the joint evolutionary patterns of PUE and SWUE. The impact of factors such as climate change, vegetation variation, carbon dioxide emission, and human activities on PUE and SWUE was separately identified using partial derivatives and elasticity coefficients. The results suggest an insignificant downward in PUE and a significant upward trend in SWUE in China from the period of 2000–2018. Notably, drought mitigation in the arid zone led to a steeper decrease in PUE compared to humid, sub-humid and semi-arid zones. Drought mitigation resulted in a decrease of SWUE in arid zones, yet an increase in humid, sub-humid and semi-arid zones. The correlations between vegetation change and precipitation and PUE or SWUE, respectively, were predominantly influenced by changes in drought conditions. NPP and precipitation collectively dominated the alterations in PUE, contributing 41.55% and −58.45%, respectively. However, GPP exerted a more substantial influence on SWUE compared to precipitation, with notable contributions of 93.93% and −6.07%, respectively. The contributions to PUE from climate variation, human activities, vegetation change and carbon dioxide were −57%, 30%, 12% and −1%, respectively. Simultaneously, the contributions to SWUE from these same factors appeared as 20%, 63%, 16% and 1%, respectively. This study intends to augment the comprehension of the influence exerted by global climate change and human activities on the correlation between carbon and water in terrestrial vegetation ecosystems.

Calculation of the SPI, SPEI, and GRDI Indices for Historical Climatic Data from Doñana National Park: Forecasting Climatic Series (2030–2059) Using Two Climatic Scenarios RCP 4.5 and RCP 8.5 by IPCC

In this study, we utilized three different indices to assess drought conditions in the Doñana National Park (DNP) located in southern Spain. These indices included the Standardized Precipitation Index (SPI), which is based on precipitation statistics, the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates both precipitation and air temperature data, and the Groundwater Recharge Drought Index (GRDI), a newly developed index specifically designed to evaluate groundwater drought. The analysis covered the time period from 1985 to 2015, and future projections were made for the years 2030–2060 under different climate scenarios (RCP 4.5 and RCP 8.5). Our findings revealed a significant decrease in total precipitation of approximately 13–14% compared to historical records (1985–2015). Moreover, severely to extremely wet periods exhibited a reduction ranging from 25% to 38%. A key contribution of this study is the application of the GRDI index, which allowed us to assess groundwater recharge rates. We observed a decline in the simulated mean recharge rates during the 21st century when compared to the historical period spanning from 1950 to 2009. This decline can be attributed to increased evapotranspiration. The results of this research provide valuable insights for the Spanish water resources administration. The observed reductions in precipitation and groundwater recharge rates emphasize the need for appropriate mitigation measures. The findings will aid the administration in formulating an integrated water resources management strategy in the Doñana National Park and its surrounding basin. By understanding the projected changes in drought conditions, the administration can make informed decisions to ensure sustainable water resource management in the region.

Use of a MODIS Satellite-Based Aridity Index to Monitor Drought Conditions in the Pearl River Basin from 2001 to 2021

In recent decades, global climate change has made natural hazards increasingly prevalent. Droughts, as a common natural hazard, have been a hot study topic for years. Most studies conducted drought monitoring in arid and semi-arid regions. In humid and sub-humid regions, due to climate change, seasonal droughts and seasonal water shortages were often observed too, but have not been well studied. This study, using a MODIS satellite-based aridity index (SbAI), investigated spatiotemporal changes in drought conditions in the subtropical Pearl River Basin. The study results indicated that the inter-annual SbAI exhibited a significant decreasing trend, illustrating a wetter trend observed in the basin in the past two decades. The decreasing trend in the SbAI was statistically significant in the dry season, but not in the monsoon season. The drought conditions displayed an insignificant expansion in the monsoon season, but exhibited statistically significant shrinking in the dry season. The Pearl River Basin has become wetter over past two decades, probably due to the results of natural impacts and human activities. The areas with increased drought conditions are more likely impacted by human activities such as water withdrawal for irrigation and industrial uses, and fast urbanization and increased impervious surfaces and resultant reduction in water storage capacity. This study provided a valuable reference for drought assessment across the Pearl River Basin.

Trivariate Analysis of Changes in Drought Characteristics in the CMIP6 Multimodel Ensemble at Global Warming Levels of 1.5°, 2°, and 3°C

Abstract Drought is a major natural hazard with far-reaching social, economic, and environmental impacts whose characteristics are highly interdependent across different spatial and temporal scales. Traditional global warming impact assessments on drought at the global scale have, however, taken into account only one drought characteristic at a time, likely leading to an underestimation of the overall impact. Here, we perform a trivariate analysis of changes in drought conditions at 1.5°, 2°, and 3°C global warming levels using 25 CMIP6 GCMs. Drought properties are characterized by the Standardized Soil Moisture Index (SSI). The future joint return periods of droughts historically associated with 10-, 20-, and 30-yr return periods are computed under the warming levels using copula functions considering drought duration, peak, and severity. Our comparative assessments of global warming impact on drought properties between univariate and trivariate analyses corroborate the substantial underestimation of the impact by the univariate analysis. The trivariate analysis shows that around 63%–91% of the global land will be subject to more recurrent droughts, while the percentage of the land reduces to 41%–56% for the univariate analysis. The difference between the univariate and trivariate analyses enlarges with global warming levels and the extremity of drought events. Based on the trivariate analysis, a 30-yr drought would become at least threefold more recurrent in 11%, 15%, and 20% of the global land at 1.5°, 2°, and 3°C warming levels, respectively, but the univariate analysis could not reach such large increases in drought conditions.

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor.

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Change in drought conditions and its impacts on vegetation growth over the Tibetan Plateau

Understanding climate change as well as its impacts on vegetation growth over the Tibetan Plateau has important implication for ecosystems. This study investigated changes in drought conditions and their impacts on vegetation growth over the Tibetan Plateau. The results reveal that the precipitation in growing season (May–September) shows a significant increase over most parts of the central and eastern Tibetan Plateau during 1961–2019. Consequently, drought conditions have generally relieved except in the south and northeast where precipitation has decreased. Combining analyses of gridded-dataset-derived drought indices with vegetation indices during 1982–2015, vegetation improvement in most regions of the Plateau is mainly due to lessened drought conditions. Noticeably, vegetation degradation is also found in part of southern Tibetan Plateau mainly resulting from drought enhancement. This study is expected to provide scientific basis for understanding of change in drought condition and its impacts on vegetation over different regions of TP under global warming background.

A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018

Abstract. The monthly standardized precipitation evapotranspiration index (SPEI) can be used to monitor and assess drought characteristics with 1-month or longer drought duration. Based on data from 1961 to 2018 at 427 meteorological stations across mainland China, we developed a daily SPEI dataset to overcome the shortcoming of the coarse temporal scale of monthly SPEI. Our dataset not only can be used to identify the start and end dates of drought events, but also can be used to investigate the meteorological, agricultural, hydrological, and socioeconomic droughts with a different timescales. In the present study, the SPEI data with 3-month (about 90 d) timescale were taken as a demonstration example to analyze spatial distribution and temporal changes in drought conditions for mainland China. The SPEI data with a 3-month (about 90 d) timescale showed no obvious intensifying trends in terms of severity, duration, and frequency of drought events from 1961 to 2018. Our drought dataset serves as a unique resource with daily resolution to a variety of research communities including meteorology, geography, and natural hazard studies. The daily SPEI dataset developed is free, open, and publicly available from this study. The dataset with daily SPEI is publicly available via the figshare portal (Wang et al., 2020c), with https://doi.org/10.6084/m9.figshare.12568280.Highlights. A multi-scale daily SPEI dataset was developed across mainland China from 1961 to 2018. The daily SPEI dataset can be used to identify the start and end days of the drought event. The developed daily SPEI dataset in this study is free, open, and publicly available.

Drought Characterization Using Drought Indices and El Niño Effects

Droughts are extreme meteorological and hydrological events having severe impacts on the natural environment and socioeconomic conditions of the affected region. Drought characterization is vital in planning, design, and management of water resources systems. Drought indices such as standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) are generally used as a tool for monitoring changes in drought conditions. The variability of monsoon droughts over India is significantly influenced by the tropical sea surface temperature anomalies. A major portion of the drought variability is influenced by the El Nino–Southern Oscillation (ENSO). Drought characterization for Indo Gangetic alluvial plain demonstrates the efficacy of the present work. The values of SPI and SPEI are found in all the possible ranges representing mild, moderate, and severe drought events in the study area. Most drought events are observed in Raebareli District, as both SPI and SPEI indices showed 27 and 23 severe events. Also, correlation is found between observed drought periods, ENSO episodes, and drought indices.

Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia

Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.

Evaluating a generic drought index as a predictive tool for aflatoxin contamination of corn: From plot to regional level

Corn (Zea mays L.) kernel infection by Aspergillus flavus and subsequent aflatoxin accumulation in grain can have a deleterious effect on both humans and animals that consume contaminated grain. Predicting the aflatoxin risk is challenging due to complex interactions of biotic and abiotic stress factors that govern and exacerbate the phenomenon. The goal of this study was to determine whether a drought index could be used to predict the risk for pre-harvest aflatoxin contamination in corn. Risk assessment was approached at: 1) field (plot) level with data obtained from an in-field controlled experiment (Mississippi study), and 2) state level, where corn fields were sampled at a county level (Georgia study). The data used for this study consisted of historical records on aflatoxin contamination collected over thirteen growing seasons from 2000 to 2011, 2013, and 2014 at Mississippi State, Mississippi (1), and from random corn fields in 53 counties across Georgia between 1977 and 2004 (2). A controlled experiment was conducted at Mississippi with two soil types (a Leeper silty clay loam and a Myatt loam), and three commercial hybrids characterized by different susceptibility levels to aflatoxin contamination. The Agricultural Reference Index for Drought (ARID), a generic drought index for calculating drought on daily basis was evaluated as an aflatoxin risk prediction tool. Mid-silk day was selected to split each growing season into two time periods, which were further divided into positive and negative weeks representing weeks after and before mid-silk, respectively. Weekly ARID factors were calculated for all periods to evaluate the in-season alterations in aflatoxin risk. In both studies, multiple logistic regression models were used to predict aflatoxin risk as a function of the weekly ARID values. In Mississippi, risk level changes were additionally tested according to soil type and corn hybrid aflatoxin susceptibility. The United States Food and Drug Administration restricts corn grain consumption by humans and young animals if the contamination level is above 20 μg/kg; thus, this threshold (20 μg/kg) was selected to develop a binary dependent variable for the logistic model from the raw aflatoxin data. The results revealed that ARID might be used as a predictive tool to assess aflatoxin risk, soil type and hybrid susceptibility to aflatoxin contamination were statistically significant independent factors, and there are critical week windows during the growing season when changes in drought conditions affect the likelihood for aflatoxin contamination. These findings can be used to minimize risk by adapting site-specific management strategies such as triggering irrigation during critical risk weeks, selecting the most appropriate hybrid for a given site/location based on soil type, and determining optimum harvest date.

SMAP Soil Moisture Change as an Indicator of Drought Conditions

Soil moisture is considered a key variable in drought analysis. The soil moisture dynamics given by the change in soil moisture between two time periods can provide information on the intensification or improvement of drought conditions. The aim of this work is to analyze how the soil moisture dynamics respond to changes in drought conditions over multiple time intervals. The change in soil moisture estimated from the Soil Moisture Active Passive (SMAP) satellite observations was compared with the United States Drought Monitor (USDM) and the Standardized Precipitation Index (SPI) over the contiguous United States (CONUS). The results indicated that the soil moisture change over 13-week and 26-week intervals is able to capture the changes in drought intensity levels in the USDM, and the change over a four-week interval correlated well with the one-month SPI values. This suggested that a short-term negative soil moisture change may indicate a lack of precipitation, whereas a persistent long-term negative soil moisture change may indicate severe drought conditions. The results further indicate that the inclusion of soil moisture change will add more value to the existing drought-monitoring products.

Global Changes in Drought Conditions Under Different Levels of Warming

AbstractHigher evaporative demands and more frequent and persistent dry spells associated with rising temperatures suggest that drought conditions could worsen in many regions of the world. In this study, we assess how drought conditions may develop across the globe for 1.5, 2, and 3°C warming compared to preindustrial temperatures. Results show that two thirds of global population will experience a progressive increase in drought conditions with warming. For drying areas, drought durations are projected to rise at rapidly increasing rates with warming, averaged globally from 2.0 month/°C below 1.5°C to 4.2 month/°C when approaching 3°C. Drought magnitudes could double for 30% of global landmass under stringent mitigation. If contemporary warming rates continue, water supply‐demand deficits could become fivefold in size for most of Africa, Australia, southern Europe, southern and central states of the United States, Central America, the Caribbean, north‐west China, and parts of Southern America. In approximately 20% of the global land surface, drought magnitude will halve with warming of 1.5°C and higher levels, mainly most land areas north of latitude 55°N, but also parts of South America and Eastern and South‐eastern Asia. A progressive and significant increase in frequency of droughts is projected with warming in the Mediterranean basin, most of Africa, West and Southern Asia, Central America, and Oceania, where droughts are projected to happen 5 to 10 times more frequent even under ambitious mitigation targets and current 100‐year events could occur every two to five years under 3°C of warming.

Changes in Drought Conditions in Poland over the Past 60 Years Evaluated by the Standardized Precipitation-Evapotranspiration Index

This paper investigates the variability of drought conditions in Poland in the years 1956–2015 with the use of the Standardized Precipitation-Evapotranspiration Index (SPEI). The study provides a new insight into the phenomenon of the past expansion of the drought-affected area as well as evidence of drying trends in a spatiotemporal context. 3-month, 6-month, and 12-month SPEI were considered, representing drought conditions relevant to agriculture and hydrology. The analysis demonstrates that the spatial extent of droughts shows a broad variability. The annual mean of the percentage of the area under drought has witnessed an increase for all three SPEI timescales. This also pertains to the mean area affected by drought over the growing season (April–September). A decreasing trend in the SPEI values indicates an increase in the severity of droughts over the 60-year period in question in an area extending from the south-west to the central part of Poland.

Drought variability and change across the Iberian Peninsula

Drought variability and change was assessed across the Iberian Peninsula over more than 100 years expanding through the twentieth century and the first decade of the twenty-first century. Daily temperature and precipitation data from 24 Iberian time series were quality controlled and homogenized to create the Monthly Iberian Temperature and Precipitation Series (MITPS) for the period 1906–2010. The Standardized Precipitation Index (SPI), driven only by precipitation, and the Standardized Precipitation Evapotranspiration Index (SPEI), based on the difference between the precipitation and the reference evapotranspiration (ET0), were computed at annual and seasonal scale to describe the evolution of droughts across time. The results confirmed that a clear temperature increase has occurred over the entire Iberian Peninsula at the annual and seasonal scale, but no significant changes in precipitation accumulated amounts were found. Similar drought variability was provided by the SPI and SPEI, although the SPEI showed greater drought severity and larger surface area affected by drought than SPI from 1980s to 2010 due to the increase in atmospheric evaporative demand caused by increased temperatures. Moreover, a clear drying trend was found by the SPEI for most of the Iberian Peninsula at annual scale and also for spring and summer, although the SPI did not experience significant changes in drought conditions. From the drying trend identified for most of the Iberian Peninsula along the twentieth century, an increase in drought conditions can also be expected for this region in the twenty-first century according to future climate change projections and scenarios.

A comparative performance analysis of three standardized climatic drought indices in the Chi River basin, Thailand

Drought indices are generally used as a tool for monitoring changes in drought conditions. This paper evaluated the performance of three climatic drought indices to characterize drought trends in the Chi River basin in Northeast Thailand. Initially, the drought assessment was conducted using the Standardized Precipitation Index (SPI), a precipitation-based index, and the Standardized Precipitation Evapotranspiration Index (SPEI), an index taking into account the difference between precipitation and potential evapotranspiration (PET). Then, this study simply applied an index called the Standardized Precipitation Actual Evapotranspiration Index (SPAEI), similar to the commonly used SPEI, with the difference being in the use of actual evapotranspiration (AET) instead of PET. Time series of the three indices were compared with observed droughts. The results indicated that various indicators of different indices can have diverse effects on drought conditions. The simple SPI, considering only precipitation, can be used to identify characteristics of droughts with certain restrictions. Being multivariate indices, the SPEI and the SPAEI were able to clearly detect the temporal variability of droughts to a greater extent than the SPI index. Moreover, the different results derived from using P-AET instead of P-PET made a substantial difference to temporal drought severity. Thus, climatic water demand had important aspects in determining the drought conditions for this area.

Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.

Multi-GCM projections of future drought and climate variability indicators for the Mediterranean region

Future climate conditions for the Mediterranean region based on an ensemble of 16 Global Climate Models are expressed and mapped using three approaches, giving special attention to the intermodel uncertainty. (1) The scenarios of mean seasonal temperature and precipitation agree with the projections published previously by other authors. The results show an increase in temperature in all seasons and for all parts of the Mediterranean with good intermodel agreement. Precipitation is projected to decrease in all parts and all seasons (most significantly in summer) except for the northernmost parts in winter. The intermodel agreement for the precipitation changes is lower than for temperature. (2) Changes in drought conditions are represented using the Palmer Drought Severity Index and its intermediate Z-index product. The results indicate a significant decrease in soil moisture in all seasons, with the most significant decrease occurring in summer. The displayed changes exhibit high intermodel agreement. (3) The climate change scenarios are defined in terms of the changes in parameters of the stochastic daily weather generator calibrated with the modeled daily data; the emphasis is put on the parameters, which affect the diurnal and interdiurnal variability in weather series. These scenarios indicate a trend toward more extreme weather in the Mediterranean. Temperature maxima will increase not only because of an overall rise in temperature means, but partly (in some areas) because of increases in temperature variability and daily temperature range. Increased mean daily precipitation sums on wet days occurring in some seasons, and some parts of the Mediterranean may imply higher daily precipitation extremes, and decreased probability of wet day occurrence will imply longer drought spells all across the Mediterranean.

Assessing the changes in drought conditions during summer in the Republic of Moldova based on RegCM simulations

We assess the changes in drought conditions during summer in the Republic of Moldova based on the Standardized Precipitation Index (SPI) calculated from monthly precipitation data simulated by the regional climatic model RegCM3. The RegCM simulations were conducted at a horizontal resolution of 10 km in the framework of EU-FP6 project -CECILIA. The domain was centered over Romania at 46°N, 25°E and included the Republic of Moldova.