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Climate change has led to heightened variability in global rainfall patterns, resulting in greater unpredictability and inconsistency, and it has led to the origin of meteorological drought situation. This has amplified the frequency of droughts or drought-like conditions worldwide. India, being primarily agrarian, faces significant challenges due to drought, affecting various regions intermittently. Given the urgency of addressing recurring drought issues, it is crucial to determine specific ‘drought-prone’ areas through the analysis of historical and current meteorological data. It is still a challenge to quantitatively understand where and to what extent the impact of rainfall patterns could lead the drought. Whether any region likely comes under drought-prone area or not? Can we help policy makers to apply their knowledge effectively? It will help to undertake the long-term mitigation measures for drought assessment and management which encompasses early warning, monitoring, and relief toward the good health of the society. The present study is a further step in the same direction in which Akola district in Maharashtra, India has been assessed for drought-prone declaration using two drought measuring indices; seasonality index (SI) and aridity index (AI). For this, the measured meteorological data precipitation, and potential evapotranspiration, from 1952 to 2002 is made available from India Water Portal. HIGHLIGHTS In the present study, Akola district in Maharashtra, India has been assessed for drought-prone declaration using the two drought measuring indices; seasonality index (SI) and aridity index (AI).; For this, the measured meteorological data of precipitation, and potential evapotranspiration of 51 years was made available from India Water Portal.;

Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p

Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

Drylands are highly vulnerable to climate change and human activities. The drylands of China account for approximately 10.8% of global drylands, and China is the country most severely affected by aridity in Asia. Therefore, studying the spatial variation characteristics in soil multifunctionality (SMF) and investigating the driving factors are critical for elucidating and managing the functions of dryland ecosystems in China. Based on the environmental factors (mean annual precipitation (MAP), mean annual temperature (MAT), solar radiation (Srad), soil acidity (pH), enhanced vegetation index (EVI), and cation exchange capacity (CEC)) and aridity from the Dataset of soil properties for land surface modeling over China, we used non-linear regression, ordinary least square (OLS) regression, structural equation model (SEM), and other analytical methods to investigate the relationships of SMF with environmental factors across different aridity levels in China. SMF in different dryland regions varied significantly and showed a patchy distribution, with SMF index values ranging from −1.21 to 2.42. Regions with SMF index values from −0.20 to 0.51 accounting for 63.0% of dryland area in China. OLS regression results revealed that environmental factors like MAP, MAT, Srad, pH, EVI, and CEC were significantly related to SMF (P ​

Regular monitoring of drought events, watershed characterization, management, and development efforts is crucial for future disaster predictions and mitigation strategies. The drought susceptibility investigation has been carried out in the Ken River Basin of Bundelkhand region, one of the most drought‐prone areas in India. Proxies used in the present study are long‐term climatological data (rainfall, standardized precipitation index, and aridity index), satellite data (slope, drainage density [DD], distance to river, and normalized difference vegetation index), lithology, lineament density, and groundwater depth. By the analytical hierarchy process, weightage of each factor is assigned according to its importance. Study shows that nearly 48% of the area of the basin experiences moderate to severe drought conditions. In the last five decades, there have been 22 years of extreme drought, with the most extended period being 1972–1974 and the driest year being 2006–2007. Sensitivity analysis reveals that lithology, slope, and DD are the most significant parameters in the susceptibility analysis. Model validation through an artificial neural network demonstrates the model's high accuracy (0.9) and sensitivity with minor errors. An integrated study of drought susceptibility and morphometry is useful for identifying the drought risk hotspots in the basin. The investigation will be helpful in river basin management and disaster management strategies. [ABSTRACT FROM AUTHOR]

The Nebraska Sand Hills (NSH) is considered a major recharge zone for the High Plains Aquifer in the central United States. The uncontrolled expansion of the eastern redcedar (Juniperus Virginiana) under climate warming is posing threats to surface water and groundwater resources. The combined impact of land use and climate change on the water balance in the Upper Middle Loup River watershed (4,954 km2) in the NSH was evaluated by simulating different combinations of model scenarios using the Soil Water Assessment Tool (SWAT) model. A total of 222 climate models were ranked according to the aridity index and three models representing wet, median (most likely), and dry conditions were selected. Additionally, the impacts of carbon dioxide (CO2) emissions on root water uptake were simulated. Four plausible redcedar encroachment scenarios, namely 0.5% (no encroachment), 2.4, 4.6, and 11.9%, were considered in the numerical simulations. We, therefore, built: i) the historical scenario (2000–2019) with the current climate and redcedar cover leading to baseline results; ii) the most-likely future scenario (2020–2099) with projected climate (50th percentile of aridity index distribution) and redcedar encroachment that was estimated by using a combination of neural network and Markov-chain cellular automata model; iii) 16 future scenarios (2020–2099) with different combinations of extreme climate (5th and 95th percentiles of aridity index distribution) and four hypothetical encroachment scenarios (0.5, 2.4, 4.6, and 11.9%). The most-likely climate projection indicates that a warming pattern will be expected with a 4.1°C increase in average over the 100-year period, and this will be associated with lower-than-normal precipitation (P). Nevertheless, the concurrent increase in temperature and CO2 concentration is likely to induce stomata closure by reducing potential (ETp) and actual (ETa) evapotranspiration losses. Projected P and ETa are expected to decrease by 10 and 14% while recharge (R) and discharge (D) are expected to increase by 38 and 30% for the period 2020-2050. For the period 2051-2099, the projected P and ETa are expected to decrease by 8 and 32% while R and D are expected to increase by 140.2 and 40%. Finally, a sensitivity analysis of 16 combined climate and land use scenarios is presented and discussed. The scenario modeling approach presented in this paper can support decision-making by stakeholders for optimal management of water resources.

Predicting the impacts of climate change on water resources remains a challenging task and requires a good understanding of the dynamics of the forcing terms in the past. In this study, the variability of precipitation and drought patterns is studied over the Mediterranean catchment of the Medjerda in Tunisia based on an observed rainfall dataset collected at 41 raingauges during the period 1973–2012. The standardized precipitation index and the aridity index were used to characterize drought variability. Multivariate and geostatistical techniques were further employed to identify the spatial variability of annual rainfall. The results show that the Medjerda is marked by a significant spatio-temporal variability of drought, with varying extreme wet and dry events. Four regions with distinct rainfall regimes are identified by utilizing the K-means cluster analysis. A principal component analysis identifies the variables that are responsible for the relationships between precipitation and drought variability. [ABSTRACT FROM AUTHOR]

Reanalysis and earth observation data have enormous potential to support water resources management, particularly in river basins where data availability is poor or where available stations are unequally distributed. Despite this potential, these datasets continue to be underused around the world. In this article, we combine a recently established global water resources reanalysis that was developed within the EartH2Observe research project, and in-situ data in the Magdalena Cauca Macrobasin in Colombia. Through rigorous hydrological modelling we evaluate the contribution of this new information to the derivation of water availability indices used to support water policy and management. Results confirm the complementarity of the reanalysis, with water management indices derived from the reanalysis data showing good comparison to values obtained using in-situ data only. Indeed, the estimation of simple indices such as an Aridity Index and a Water Regulation Index show to be a suitable method for assessing and comparing the different reanalysis datasets. We also explore the value of the reanalysis and earth observations datasets in the study of climate and land use change scenarios in the basin. While considering the associated uncertainties, results show that on average rainfall is projected to increase in the basin and thus available water resources. Deforestation will increase the water balance due to lower evapotranspiration. However, reduced fog deposition in deforested cloud forest areas will lead to a decrease in the water balance. The promising results of the comparison have bolstered the confidence of the national hydro-meteorological agency to include the evaluated reanalysis datasets in the national water resources evaluation, complementing available in-situ datasets. [ABSTRACT FROM AUTHOR]

Calculated values of aridity index (AI) were used as a base for the analysis of precipitation and temperature impact on maize yield in Vojvodina. An investigation of maize production with irrigation was carried out at the experiment field of Institute of Field and Vegetable Crops in Novi Sad on calcareous chernozem soil on the loess terrace, in the period 1997-2007. Standardized values of temperatures (T') and precipitation (P') were used for AI calculation. Highly significant correlation (r = 0.86**) was found between AI and average yields of maize in Vojvodina. There were no significant correlations between AI and maize yield obtained in field experiments without irrigation (r = 0.40) and with irrigation (r = -0.24). Low values of correlation coefficient between AI and maize yields obtained in field experiments without irrigation and correlation coefficient values between AI and average yields of maize in Vojvodina indicate that production technology of maize in Vojvodina could be improved. Higher values of maize yield in irrigation conditions than in conditions without irrigation (20% or 2.04 t ha-1) confirm justification of maize production with irrigation under climatic conditions of Vojvodina.

Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere-biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past three decades in the Upper Heihe River Basin (UHRB), a complex multiple use watershed in arid northwestern China. We apply empirical orthogonal function (EOF) and singular value decomposition (SVD) analysis to isolate and identify the spatial patterns of satellite-derived leaf area index (LAI) and their close relationship with the variability of an aridity index (AI = Precipitation/Potential Evapotranspiration). Results show that UHRB has become increasingly warm and wet during the past three decades. In general, the rise of air temperature and precipitation had a positive impact on mean LAI at the annual scale. At the monthly scale, LAI variations had a lagged response to climate. Two major coupled spatial change patterns explained 29% and 41% of the LAI dynamics during 1983-2000 and 2001-2010, respectively. The strongest connections between climate and LAI were found in the southwest part of the basin prior to 2000, but they shifted towards the north central area afterwards, suggesting that the sensitivity of LAI to climate varied over time, and that human disturbances might play an important role in altering LAI patterns. At the basin level, the positive effects of regional climate warming and precipitation increase as well as local ecological restoration efforts overwhelmed the negative effects of overgrazing. The study results offer insights about the coupled effects of climatic variability and grazing on ecosystem structure and functions at a watershed scale. Findings from this study are useful for land managers and policy makers to make better decisions in response to climate change in the study region. [ABSTRACT FROM AUTHOR]

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Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective. [ABSTRACT FROM AUTHOR]