Drought Patterns Research Articles

Geo-spatial analysis of drought in The Gambia using multiple models

Climate change has made The Gambia vulnerable to drought hazard. Variability and negative trends in rainfall quantity and mid-season dry spells mainly attributed to the impacts of climate change. The inadequacy in hydrometeorological information puts the agricultural sector at a high risk which employs over 70% of the population. The aim of this study was to establish the intensity and spatiotemporal pattern of drought in The Gambia from 2000 to 2020 using multiple drought indices. Rainfall data, satellite images, and government policy documents were analyzed to determine the state of drought in The Gambia. Rainfall data, using Standardized Precipitation Index (SPI) and Precipitation Anomaly Percentage (PAP) were calculated and interpolated, and satellite images were processed using Vegetation Condition Index (VCI) to determine drought intensity and spatial distribution. The findings revealed that drought exists in The Gambia at moderate levels of SPI values (− 1.00 to − 1.49), (35% of PAP), and VCI of no drought intensity of more than 35%. The most drought prone areas in The Gambia are North Bank Region and Eastern parts of country in both north and south of The Gambia River banks. Recommendations of adaptation practice both on-farm and off-farm such as damming and economic diversification was drawn from other parts of the world, to reduce the negative effects of drought hazard in The Gambia.

Harmonic oscillator seasonal trend (HOST) model for hydrological drought pattern identification and analysis

Droughts, due to their complex nature, are difficult to predict; however, research is showing that there are often clearly defined patterns to their occurrence. The temporal aspects are usually more apparent when comparing regions with defined climatic characteristics, such as those with recognized wet or dry seasons, but recognizing these patterns can help in defining and understanding the overall processes influencing drought occurrence. Especially in the context of climate change, it is crucial to recognize temporal patterns and quantify them to help diagnose current and future event probabilities. The objective of this work is to introduce a new tool for assessing temporal patterns of streamflow drought, based on the harmonic oscillator theorem called the harmonic oscillator seasonal trend (HOST) model. The testing dataset consists of monthly minimal flows and drought occurrence information calculated based on daily flows from the National Water Model (NWM) retrospective v.2.1 dataset for the period 1979–2020. The model framework allows for trend and seasonality signal decomposition as well as the extraction of the first five harmonics by means of a Fast Fourier Transform (FFT)-based machine learning algorithm. The model framework enables the extraction and evaluation of five different harmonic models based on short and long-range components as well as linear regression sloped harmonics. The results show that the best-fit models are based on two superimposed sinusoidal functions with an accuracy of around 70–90 % for streamflow drought occurrence, with different spatial parameters reflecting varied environmental conditions. The models were able to capture two patterns: (1) annual precipitation variation and (2) longer-term inertia, the latter of which is likely driven by large-scale circulation with a roughly-eight year period.

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis.

Anthropogenic Forcing Decreased Concurrent Soil Drought and Atmospheric Aridity in the Historical Period 1850–2013

AbstractAs awareness of the importance of anthropogenic climate change has increased, attention is being focused on “compound extremes,” such as co‐occurring soil drought (low amounts of soil moisture, SM) and atmospheric aridity (high vapor‐pressure deficits, VPD), because of the disproportionate impacts of such extreme conditions on natural and societal systems. Few advances, however, have been made in isolating the net effect of anthropogenic forcing on the occurrence of such compound extremes. Nine Earth System models (ESMs) under natural‐only and fully‐forced simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) indicated that the presence of anthropogenic forcing in the historical period 1850–2013 weakened the coupling between SM and VPD and thus decreased the frequency of compound drought and aridity globally. The anthropogenically induced decrease in the strength of land–atmospheric feedbacks, which offset the drying trend effects of global warming by countering the expected natural correlation between SM and VPD, appears to have been responsible for the relative rarity of concurrent drought and aridity in the historical period. We also modeled independently the anthropogenic forcings of aerosols and greenhouse gases (GHGs) to further highlight that the widespread weakening of land–atmospheric feedbacks may have been associated primarily with the cooling effects induced by increases in anthropogenic emissions of aerosols, because the increase in intensity and frequency of compound drought and aridity that might have been expected from rising GHG concentrations was widely counter‐balanced until very recently by an aerosol‐driven cooling, particularly for the middle and high latitudes of the Northern Hemisphere. This finding indicates that the trade‐off between these two main anthropogenic forcings may determine future patterns of concurrent drought and aridity in a changing climate. If global emissions of anthropogenic aerosols decrease in the future, as expected, our results imply a renewed strengthening of land–atmospheric feedbacks, and thus an intensification of concurrent drought and aridity.

Regional analysis of the 2015–16 Lower Mekong River basin drought using NASA satellite observations

Study regionLower Mekong River Basin (LMRB) Study focusSatellite remote sensing products are widely used for monitoring droughts. Using NASA satellite sensors of precipitation (Global Measurement Mission, GPM), soil moisture (Soil Moisture Active and Passive, SMAP), and terrestrial water storage (Gravity Recovery and Climate Experiment, GRACE), this study evaluates the historical drought in the LMRB during 2015–16. SMAP soil moisture was validated against in-situ soil moisture, and GPM precipitation and SMAP soil moisture were cross-validated with streamflow observations. The spatiotemporal dynamics of soil moisture were also examined in different ranges of catchment areas. In performing the analysis, we used lagged correlations between hydrological variables and the indices of the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI). New hydrological insights for the regionsSpatio-temporal patterns of drought in 2015–16 were examined from the entire basin to small watersheds. A mismatch occurs when using GRACE data to study droughts in small watersheds (many of the small watersheds would be a fraction of the few 100 km2 spatial resolutions of GRACE pixel). In smaller watersheds, hydrological drought (SSI) was closely defined with SMAP soil moisture downscaled to 1 km rather than the meteorological drought index (SPI). By leveraging satellite-based observations across a range of spatial scales, this study highlights the utility of Earth observations in informing water resources and land management decisions at the regional scale.

Hazard Assessment of Drought Using PNPI and GIS in Fars Province, Iran

Drought is among the natural disasters that seriously impact the environment and human life. This study aims to explore the spatial pattern of drought using the percent of normal precipitation index (PNPI) in Fars Province, located in the Southern part of Iran. To this end, a drought risk model based on data from 42 stations in Fars province from 1990 to 2019 was evaluated. The model includes three criteria of maximum drought intensity in the period, drought trend, and a maximum number of consecutive dry years. The final drought risk map was obtained with an arithmetic mean of three indicators of intensity, continuity, and trend. The final hazard map and the 3-criteria map were interpolated by the inverse distance weighting (IDW) method and were classified into five risk classes: none, mild, moderate, severe, and very severe. The final vulnerability map shows that moderate hazard areas (5% of the region), which are observed in the Sothern parts of the region, are less widespread than areas under severe hazard (83% of the region), which are observed in almost all parts of the region. According to the final vulnerability map, about 94% of the area of Fars province is under severe and very severe conditions. Overall, this study, regarding its simplicity and considering different dimensions of drought, may be utilised as a basic framework to evaluate drought hazards for other locations worldwide. In this respect, it is necessary to study the multiple sights of this phenomenon for land use planning, resource management, and prevention of water and food crises. Therefore, this model can help users and administrations with executive initiatives.

Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China

Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the Jinghe River Basin in the Loess Plateau as the research object. The standardized precipitation index (SPI) and standardized runoff index (SRI) series were used to represent meteorological drought and hydrological drought with monthly runoff generated by the SWAT model. In addition, the evolution laws of the JRB in the future based on Copula functions are discussed. The results showed that: (1) the meteorological drought and hydrological drought of the JRB displayed complex periodic change trends of drought and flood succession, and the evolution laws of meteorological drought and hydrological drought under different spatiotemporal scales and different scenario differ significantly. (2) In terms of the spatial range, the JRB meteorological and hydrological drought duration and severity gradually increased along with the increase in the time scale. (3) Based on the joint distribution model of the Copula function, the future meteorological drought situation in the JRB will be alleviated when compared with the historical period on the seasonal scale, but the hydrological drought situation is more serious. The findings can help policy-makers explore the correlation between meteorological drought and hydrological drought in the background of future climate change, as well as the early warning of hydrological drought.

Responses of agricultural drought to meteorological drought under different climatic zones and vegetation types

Meteorological drought (MD) is strongly causal linked to agricultural drought (AD), therefore, MD can be used as an appropriate early warning of AD, enhancing human mitigation of AD. However, drought responses of AD to MD have not been well understood. Here we used the nonstationary standardized precipitation evapotranspiration index (NSPEI) and the standardized soil moisture index (SSI) to analyze the evolution characteristics of MD and AD. The drought response time (DRT) and response patterns of drought in different climatic zones with different underlying surface conditions (vegetation type, irrigation) were calculated. A high frequency of MD and AD in southeastern China and the Tibetan Plateau was detected. Besides, AD in southeastern China and MD in central and southern China showed a significant increasing trend. Meanwhile, MD and AD are in the same decreasing trend over the Tibetan Plateau. The highest probability of compound droughts can be found in northern China and regions with a semi-humid warm temperate climate. Drought response sensitivity is higher with shorter agriculture drought response time (DRT) in southeastern China and eastern parts of the Tibetan Plateau than in other regions of China. Besides, DRT is short in summer and autumn, and long in winter and spring. Moreover, when compared to DRT in irrigated agricultural areas, DRT in rainfed agricultural areas is longer and more spatially variable. DRT in double- and triple-crop irrigated areas is remarkably shorter than in single-crop irrigated areas, and DRT with higher crop intensity is shorter.

Characteristics of drought, low temperature, and concurrent events of maize in Songliao Plain

AbstractMaize is susceptible to drought and low temperatures. In recent years, drought, low temperatures, and their composite events have often occurred during the growth period of maize, causing a huge impact. In this study, maize in the Songliao Plain was used as the research object. Based on the crop water surplus deficit index (CWSDI) and heat index (F(T)), combined with the Theil–Sen median and Mann–Kendall tests (Sen–MK) and empirical orthogonal function (EOF), the temporal trends and spatial patterns of drought and low temperatures in different growth periods of maize were obtained. Copula modelling was used to analyse the joint probability distribution and return period of drought and low temperatures. The results showed that the temporal variation trend of the CWSDI was significantly different at different growth periods of maize, and there were different trends and spatial modal distributions. However, the temporal variation trend of F(T) was similar, showing a downward trend over the entire region. There was no obvious asymmetry or skew‐dependent structure in the distribution of concurrent events of drought and low temperature; however, the joint probability contours of different growth periods were biased to the upper left. The recurrence period of drought and low‐temperature concurrent events was within 2 years, and the recurrence period was basically between 2 and 5 years, with a reduction in drought or low temperatures.

Understanding wheat growth and the seasonal climatic characteristics of major drought patterns occurring in cold dryland environments from Iran

Drought is one of the major constraints affecting wheat productivity globally. Understanding of the nature of drought stress (i.e. drought pattern, DP) including its severity, timing and duration is required to deal with the negative impacts of drought on crop production. Drought patterns can be influenced by numerous factors including climatic, soil, and crop growth characteristics. Accordingly, a simulation modelling approach was applied in the current study to capture the characteristics of DPs and their long-term impact on wheat grain yield in dryland cropping systems of north-west Iran. Results showed that light drought patterns (DP1 and 2) were characterized by higher grain yield (3 t ha−1), biomass (10.6 t ha−1), LAI at flowering (2.1), cumulative rainfall from floral initiation to maturity stages (122.6), and cumulative transpiration (158.6 mm), as compared to severe drought patterns (DP3 and DP4). This is mainly because of a lower mean and minimum water supply/demand ratio (sdr) that occurs in severe DPs (0.8 and 0.06, respectively) compared to light DPs (0.92 and 0.22, respectively). Results also indicated that the occurrence of DP4 (53.5%) was dominant across all the dryland study regions while the lowest frequency was simulated for DP3 (3.5%). As drought is frequently happening in the dryland environments through various patterns (i.e. DPs), farmers should understand the patterns of drought that occur in their own regions, and the seasonal climatic characteristics of these drought pattern. Based on the results, it is recommended that (1) farmers sow dryland wheat genotypes that are highly tolerant to drought during their grain filling period for DP4; (2) sow genotypes that have high drought tolerant during vegetative stages for DP3, and (3) use late-maturing genotypes that absorb radiation for a longer period and thus increase photosynthesis and grain yield to take advantage of the optimal environmental conditions under both DP1 and DP2.

Assessing Spatial Variability and Trends of Droughts in Eastern Algeria Using SPI, RDI, PDSI, and MedPDSI—A Novel Drought Index Using the FAO56 Evapotranspiration Method

Drought is one of the most severe natural disasters worldwide, but with a particular emphasis in sub-humid and semi-arid climates. Several indices have been created to appropriately identify drought’s characteristics and variability. The main objectives of this study consisted of analyzing the behavior of different indices applied in northeast Algeria and comparing them across a long-term data set (1961–2014). The SPI and RDI at 9-month time scales were compared to the PDSI and MedPDSI based on 123 rainfall stations and gridded PET data interpolated to all the locations. A principal component analysis (PCA) in S-mode with varimax rotation (RPC) was applied to the monthly values of all indices to analyze the spatiotemporal patterns of droughts. Two principal components were retained, which identified two sub-regions with coherent differences related to their distance from the Mediterranean Sea and the UNEP aridity index. Trends in the RPC scores were assessed using the modified Mann–Kendall (MMK) test and Sen’s slope estimator, which showed a fundamental difference between the two sub-regions. The RPC of all drought indices showed trends of decreases in the frequency and severity of droughts in the northern sub-region, and trends of increases in the frequency and severity of droughts in the southern region, where the climate is mostly semi-arid and arid. Only a few cases were statistically significant, mostly when using the PDSI and MedPDSI for the southern sub-region. The spatial patterns of moderate, severe, and extreme drought occurrences were similar for the SPI and RDI pair of indices based on the probability of rainfall anomalies, and for the Sc-PDSI and MedPDSI pair based on water balance anomalies. The interpretation of the spatial variability of droughts, mainly of the extreme ones, was supported by an analysis of semi-variograms. The novel index MedPDSI compared well with the other indices and showed advantages of performing the soil water balance following the FAO56 dual Kc method with the actual olive evapotranspiration instead of PET, and of better explaining the spatial variability of extreme droughts; in addition, the trends detected were significant for both the northern and southern sub-regions.

A 21st‐Century perspective on snow drought in the Upper Colorado River Basin

AbstractIn the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term “snow drought” has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin‐wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—“warm,” “dry,” and “warm & dry”—that minimize within‐group variance. Over the period 1916–2018, we identify 14 warm years ( = 160 mm; = 0.24), 24 dry years ( = 117 mm; = 0.35), and 21 warm & dry years ( = 94 mm; = 0.23). An elevation‐based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas “dry” scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management.

Modeling adaptation of sorghum in Ethiopia with APSIM—opportunities with G×E×M

Sorghum is an important food and feed crop in the dry lowland areas of Ethiopia. Farmers grow both early-sown long-duration landraces and late-sown short-duration improved varieties. Because timing and intensity of drought stress can vary in space and time, an understanding of major traits (G), environments (E), management (M), and their interactions (G×E×M) is needed to optimize grain and forage yield given the limited available resources. Crop simulation modeling can provide insights into these complex G×E×M interactions and be used to identify possible avenues for adaptation to prevalent drought patterns in Ethiopia. In a previous study predictive phenology models were developed for a range of Ethiopian germplasm. In this study, the aims were to (1) further parameterize and validate the APSIM-sorghum model for crop growth and yield of Ethiopian germplasm, and (2) quantify by simulation the productivity-risk trade-offs associated with early vs late sowing strategies in the dry lowlands of Ethiopia. Field experiments involving Ethiopian germplasm with contrasting phenology and height were conducted under well-watered (Melkassa) and water-limited (Miesso) conditions and crop development, growth and yield measured. Soil characterization and weather records at the experimental sites, combined with model parameterization, enabled testing of the APSIM-sorghum model, which showed good correspondence between simulated and observed data. The simulated productivity for the Ethiopian dry lowlands environments showed trade-offs between biomass and grain yield for early and late sowing strategies. The late sowing strategy tended to produce less biomass except in poor seasons, whereas it tended to produce greater grain yield except in very good seasons. This study exemplified the systems approach to identifying traits and management options needed to quantify the production-risk trade-offs associated with crop adaptation in the Ethiopian dry lowlands and further exemplifies the general robustness of the sorghum model in APSIM for this task.

Spatiotemporal Characteristics and Hazard Assessments of Maize (Zea mays L.) Drought and Waterlogging: A Case Study in Songliao Plain of China

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale.

Spatiotemporal Characteristics of Meteorological and Agricultural Droughts in China: Change Patterns and Causes

Drought is complex and is also one of the main disasters affecting China. Exploring the response of agricultural drought and meteorological drought to climatic factors helps us to understand the causes of drought. In this paper, we evaluated the temporal and spatial characteristics of soil drought and meteorological drought (SMD) and explored their responses to climatic factors and latent heat fluxes (LHF), and then explained their variation from the perspective of atmospheric circulation. The following results were obtained. (1) Meteorological drought has gradually increased in the Liaohe River Basin, on the North China Plain, and on the Loess Plateau while average soil moisture has been maintained at only approximately 25%. The impacts of drought are very serious in these regions. (2) LHF response to short time-scale (3-month scale) drought performance is high in the dry season, and the regions with high correlation coefficients are spatially distributed and concentrated in the monsoon climate zone. The regions with high correlation coefficients between drought and LHFs on long time scales (12-month scale) are concentrated in the coastal basin of southeast China. (3) Short- and long-term SMDs showed highly responsive and significant relationships with PDO, showing variations in the southeast coastal basin, the Pearl River basin, the northwest inland basin and the eastern part of the Heilongjiang basin, with a maximum correlation coefficient of 0.21 (p < 0.01). The short-term SMD in the northwestern inland region was significantly negatively correlated with AMO (correlation coefficient of −0.19, p < 0.01). the Nino3.4 index is significantly positively correlated with the SMD in the southeast coastal region of China, with a maximum correlation coefficient of 0.23 (p < 0.01). The decrease in convective precipitation led to a stronger association between soil and meteorological drought and climatic factors. This study helps to reveal the changing patterns of SMDs and can also be used globally to identify the local development patterns of drought under climate change.

Comparative study on appropriate drought and flood index selection in a tropical farming island in China

The traditional drought and flood analysis method had not fully considered the proportion analysis of different drought and flood grades in the historical years of each rainfall station. This made results unconvincing and made it difficult to deeply understand the characteristics and applicability of various methods. Based on the daily rainfall data of 88 stations in Hainan Island from 1970 to 2019, the China-Z index and the Standardized Precipitation Index (SPI) were used to compare and analyze the spatial and temporal distribution characteristics of droughts and floods from three different time scales (flood season, non-flood season and the whole year). The results showed that both SPI and China-Z index can well reflect the actual drought and flood situations in Hainan Island. The analysis of the proportions of different drought and flood grades in the historical years of each rainfall station and regional historical drought and flood statistics suggested that the China-Z index had a better indication effect than SPI on the extreme drought and flood grades. The alternation of drought and flood between different eras were obvious. Hainan Island generally presented an east-west reverse drought-flood variation trend, as well as a north-south reverse drought-flood variation trend. The drought and flood in the central mountainous area of Hainan Island had been relatively stable. The distribution pattern of drought and flood had a good spatial consistency in the three periods. On the whole, Hainan Island had shown a trend of flood in the east and drought in the west in the past 50 years.

Drought Propagation in Brazilian Biomes Revealed by Remote Sensing

Drought events have been reported in all Brazilian regions every year, evolving slowly over time and large areas, and largely impacting agriculture, hydropower production, and water supplies. In the last two decades, major drought events have occurred over the country, such as the 2010 and 2015 events in the Amazon, the 2012 event in the Pampa, and the 2014 event in the Cerrado biome. This research aimed to understand drought propagation and patterns over these biomes through joint analysis of hydrological, climatic, and vegetation indices based on remote sensing data. To understand the drought cascade propagation patterns, we assessed precipitation, evapotranspiration, soil moisture (at surface and sub-surface), terrestrial water storage, land surface temperature, enhanced vegetation index, and gross primary productivity. Similar drought patterns were observed in the 2015 Amazon and 2012 Pampa droughts, with meteorological and agricultural droughts followed by a hydrological drought, while the 2014 event in the Cerrado was more associated with a hydrological drought. Moreover, the 2015 Amazon drought showed a different pattern than that of 2010, with higher anomalies in precipitation and lower anomalies in evapotranspiration. Thus, drought propagation behaves differently in distinct Brazilian biomes. Our results highlight that terrestrial water storage anomalies were able to represent the hydrological drought patterns over the country. Our findings reveal important aspects of drought propagation using remote sensing in a heterogenous country largely affected by such events.

A dual-flow RootChip enables quantification of bi-directional calcium signaling in primary roots.

One sentence summary: Bi-directional-dual-flow-RootChip to track calcium signatures in Arabidopsis primary roots responding to osmotic stress. Plant growth and survival is fundamentally linked with the ability to detect and respond to abiotic and biotic factors. Cytosolic free calcium (Ca2+) is a key messenger in signal transduction pathways associated with a variety of stresses, including mechanical, osmotic stress and the plants' innate immune system. These stresses trigger an increase in cytosolic Ca2+ and thus initiate a signal transduction cascade, contributing to plant stress adaptation. Here we combine fluorescent G-CaMP3 Arabidopsis thaliana sensor lines to visualise Ca2+ signals in the primary root of 9-day old plants with an optimised dual-flow RootChip (dfRC). The enhanced polydimethylsiloxane (PDMS) bi-directional-dual-flow-RootChip (bi-dfRC) reported here adds two adjacent inlet channels at the base of the observation chamber, allowing independent or asymmetric chemical stimulation at either the root differentiation zone or tip. Observations confirm distinct early spatio-temporal patterns of salinity (sodium chloride, NaCl) and drought (polyethylene glycol, PEG)-induced Ca2+ signals throughout different cell types dependent on the first contact site. Furthermore, we show that the primary signal always dissociates away from initially stimulated cells. The observed early signaling events induced by NaCl and PEG are surprisingly complex and differ from long-term changes in cytosolic Ca2+ reported in roots. Bi-dfRC microfluidic devices will provide a novel approach to challenge plant roots with different conditions simultaneously, while observing bi-directionality of signals. Future applications include combining the bi-dfRC with H2O2 and redox sensor lines to test root systemic signaling responses to biotic and abiotic factors.

Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia

ABSTRACT This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data..

Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data

ABSTRACT Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p < 0.005). The analysis revealed an increase in drought risk in some provinces of the Central Highlands including Gia Lai, Kon Tum, and Dak Lak. It was also found that changes in LULC patterns could minimize (reforestation) or exacerbate (deforestation) the impacts of drought. Our study suggests that applying a multivariate drought index enables a better understanding of drought patterns at the local scale. This provides valuable information for the development of appropriate land and environmental management practices that can affect and mitigate climate change effects.