Precipitation Index Research Articles

Variability of ENSO teleconnections indices, and its impacts on moroccan agriculture

In Morocco, drought periods have become more frequent and severe due to climate change, which has had a substantial effect on freshwater supply and agricultural output. The El Niño-Southern Oscillation (ENSO) and the influence of climatic variability were the main subjects of the current study, which made use of daily precipitation, maximum and minimum temperature data collected at 50 locations around the nation from 1990 to 2022. The study analysed Palmer Drought Severity Index (PDSI) over Morocco to evaluate the impact of climate indices such as the Oceanic Niño Index (ONI) and the North Atlantic Oscillation (NAO) on drought severity. It also investigated the changes in precipitation, temperature, and extreme event-related indices. Rainfall is used as a proxy for El Nino and La Nina, supported by a negative and significant correlation between Oceanic Nino Index (ONI) and rainfall. Attempted to understand the impact of the NAO on Morocco's agricultural production, particularly during the winter months. During negative NAO phases, winter temperatures can lead to reduced wheat yields by 35–45% during the El Niño years and decreased by 4–7% during the La Niña years compared to the actual yield. Relying on the NAO plays a crucial role in modulating the precipitation patterns, which implies that crop requires adequate soil moisture with no extreme temperature during the growing season in order to produce higher yield. ENSO events can indeed lead to these extreme conditions in selected locations, potentially impacting crop yields.

Extreme Precipitation Events During the Wet Season of the South America Monsoon: A Historical Analysis over Three Major Brazilian Watersheds

Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas have significant socioeconomic impacts. The Madeira, São Francisco, and Paraná river basins, three major watersheds in Brazil, are especially vulnerable to wet and drought periods due to their importance as freshwater ecosystems and sources of water for consumption, energy generation, and agriculture. The scarcity of surface meteorological stations in these basins makes meteorological studies challenging, often using reanalysis and satellite data. This study aims to identify extreme weather (wet) and climate (wet and drought) events during the extended wet season (October to March) from 1980 to 2022 and evaluate the performance of two gridded datasets (CPC and ERA5) to determine which best captures the observed patterns in the Madeira, São Francisco, and Paraná river basins. Wet weather events were identified using the 95th percentile, and wet and drought periods were identified using the Standardized Precipitation Index (SPI) on a 6-month scale. In general, CPC data showed slightly superior performance compared to ERA5 in reproducing statistical measures. For extreme day precipitation, both datasets captured the time series pattern, but CPC better reproduced extreme values and trends. The results also indicate a decrease in wet periods and an increase in drought events. Both datasets performed well, showing they can be used in the absence of station data.

Application of the Standardized Precipitation Index for Forecasting Seasonal Drought in Vietnam

Drought is a natural disaster that causes significant damages to the economy, society and environment. Forecasting droughts is crucial for disaster risk management and water security. This study introduces a new method for forecasting seasonal droughts in Vietnam using the Standardized Precipitation Index (SPI) which is based on a combination of dynamic modeling and statistical methods. The precipitation forecast products from regional climate models (RegCM, clWRF) with inputs from Climate Forecast System (CFSv2) were used to calculate the SPI index after statistical calibration. Results show that calibrating model precipitation with statistical methods such as Artificial Neural Networks (ANN) and Multivariate Linear Regression (MLR) significantly improves the precipitation forecast accuracy. Evaluation indices indicate that the SPI calculated with the post-calibration model precipitation has a good reproducibility for drought events, particularly mild droughts. However, there remains some limitations on accurate forecast of the intensity and spatial distribution of moderate drought events in certain regions such as Central Vietnam.

Future Increase in Extreme Precipitation: Historical Data Analysis and Influential Factors

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province.

Assessing Drought Patterns in Al-Baha: Implications for Water Resources and Climate Adaptation

Due to growing water demands and changing hydro-meteorological variables brought on by climate change, drought is becoming an increasingly serious climate concern. The Al-Baha region of Saudi Arabia is the subject of this study because it is susceptible to both agricultural and meteorological droughts. This study investigates how climate change affects patterns of drought in Al-Baha by analyzing four drought indices (Agricultural Standardized Precipitation Index (aSPI), the Standardized Precipitation Index (SPI), the Rainfall Deficiency Index (RDI), and the Effective Reconnaissance Drought Index (eRDI)) for the years 1991–2022. Analysis of rainfall data was carried out to classify drought events according to their duration, frequency, and severity. Results showed that severe droughts occurred in 2009, 2010, 2012, 2016, and 2022, with 2010 being the worst year. Results also indicated a notable decrease in precipitation, which has resulted in extended dry spells. Several indices indicate that this tendency has significant ramifications for agriculture, particularly in areas where farming is a major economic activity. In addition, the possible occurrence of hydrological drought was also observed based on the negative values for the Reservoir Storage Index (RSI) in Al-Baha. Projections for the future under two Representative Concentration Pathways (RCPs) showed notable variations in temperature and precipitation. Both the RCP4.5 (low emission) and the RCP8.5 (high emission) projection scenarios indicate that drought conditions will likely worsen further. Depending on the emission scenario, it is projected to show a temperature increase of 1–2 °C, whereas the variability in precipitation projections indicates significant uncertainty, with a reduction change in the range of 1.2–27% between 2050 and 2100. The findings highlight the urgent need for proactive adaptation strategies, effective water resource management, and the development of sophisticated drought prediction tools. Addressing these challenges is crucial for sustaining agriculture and managing water scarcity in Saudi Arabia in the face of increasing drought risk.

A Synoptic‐Dynamic View of the Millennium Drought (2001–2009) in Southeastern Australia

AbstractAustralia has had several severe droughts in its recent history. Most studies have linked these droughts to large‐scale modes of variability, whereas few studies have investigated droughts from the perspective of weather systems. The current study examines a wide range of weather systems focusing especially on heavy rainfall events, which are important to meteorological drought. Two distinct phases (development and recovery) are identified for the Millennium Drought based on the cumulative standardized precipitation index. Differences in precipitation from heavy rainfall events between the two drought phases are most pronounced in autumn and summer. The pronounced reduction in precipitation from autumn heavy rainfall events during the development phase is due to fewer, less intense, faster‐moving warm conveyor belts. In contrast, increased precipitation from autumn heavy rainfall events in the recovery phase is explained by an interaction between warm conveyor belts and upper‐level anticyclonic potential vorticity with a persistent anticyclonic circulation over the Tasman Sea acting to slow the eastward propagation of rainfall‐producing weather systems. In summer, however, the difference in precipitation from heavy rainfall events in the two drought phases is due to changed moisture content within warm conveyor belts. In the recovery phase, the higher moisture content is associated with greater moisture transport over the Tasman Sea between Australia and New Zealand.

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

ABSTRACTThis paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

Machine learning for improved drought forecasting in Chhattisgarh India: a statistical evaluation

Meteorological drought is one of the major natural hazards that affects the ecosystem of the Central Indian state of Chhattisgarh. This study delves into the analysis, comparison, and prediction of drought trends spanning the period from 1993 to 2023 in the study area. Employing a comprehensive methodology, utilization of the Modified Mann–Kendall test to analyze drought trends, while assessing drought severity through the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) has been done. Further research entails assessing the link between SPI and SPEI utilizing the Pearson correlation coefficient and simple linear regression techniques. Additionally, the Support Vector Machine (SVM) and Random Forest (RF) methods were used for predictive modelling feasibility. The findings helped to deepen our understanding of drought dynamics in the region, providing important insights for drought mitigation and adaptation efforts. This study emphasizes the importance of using a variety of statistical techniques and machine learning algorithms to thoroughly analyze, compare, and forecast drought patterns, thereby informing evidence-based decision-making for sustainable water resource management and agricultural planning in Chhattisgarh, India.

Trends of Climate Extremes and Their Relationships with Tropical Ocean Temperatures in South America

South America has experienced significant changes in climate patterns over recent decades, particularly in terms of precipitation and temperature extremes. This study analyzes trends in climate extremes from 1979 to 2020 across South America, focusing on their relationships with sea surface temperature (SST) anomalies in the Pacific and Atlantic Oceans. The analysis uses precipitation and temperature indices, such as the number of heavy rainfall days (R10mm, R20mm, R30mm), total annual precipitation (PRCPTOT), hottest day (TXx), and heatwave duration (WSDI), to assess changes over time. The results show a widespread decline in total annual precipitation across the continent, although some regions, particularly in the northeast and southeast, experienced an increase in the intensity and frequency of extreme precipitation events. Extreme temperatures have also risen consistently across South America, with an increase in both the frequency and duration of heat extremes, indicating an ongoing warming trend. The study also highlights the significant role of SST anomalies in both the Pacific and Atlantic Oceans in driving these climate extremes. Strong correlations were found between Pacific SST anomalies (Niño 3.4 region) and extreme precipitation events in the northern and southern regions of South America. Similarly, Atlantic SST anomalies, especially in the Northern Atlantic (TNA), exhibited notable impacts on temperature extremes, particularly heatwaves. These findings underscore the complex interactions between SST anomalies and climate variability in South America, providing crucial insights into the dynamics of climate extremes in the region. Understanding these relationships is essential for developing effective adaptation and mitigation strategies in response to the increasing frequency and intensity of climate extremes.

Analyzing the impact of climatic conditions on rainfed wheat yield in northwest Iran: a parametric and nonparametric approach

ABSTRACT It is essential to comprehend the relationship between agricultural yields and climatic conditions, especially concerning food security and the possible threats to crop output. Wheat is a crucial agricultural crop that covers a significant amount of rainfed production regions in Iran. This study utilized parametric and nonparametric approaches to assess rainfed wheat yield. The study centered on the Tabriz area in northwest Iran, examining precipitation patterns concerning rainfed wheat cultivation. The study focused on analyzing seasonal and distinct rainfall patterns during the cultivation period, utilizing widely recognized drought metrics such as the standardized precipitation index and the standardized precipitation–evaporation index. The study findings indicate a notable upward trend in rainfed wheat output over the analyzed period. The Mann–Kendall test resulted in a p-value of 0.031, indicating statistical significance for the observed rising trend. We conducted trend removal and normalized rainfed wheat yield figures based on seasonal precipitation to study the data more thoroughly. The second phase of the growing season was particularly notable, spanning from the completion of germination to the beginning of blooming. Instead of analyzing precipitation for the full growing season, concentrating on autumn precipitation or the time from germination to blooming might improve yield forecasts and determinations.

Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data.

In this research, we demonstrate the effectiveness of a convolutional neural network (CNN) model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in a mountainous watershed. Our methodology harnesses image-based inputs, incorporating spatial distribution maps of key environmental variables, including temperature, snowmelt, snow cover, snow depth, volumetric soil water content, total evaporation, total precipitation, and leaf area index. The proposed CNN architecture, while drawing inspiration from classical designs, is specifically tailored for the task of streamflow prediction. The model's performance, assessed during both the training and testing phases, demonstrates high accuracy, reflected quantitatively in metrics such as RMSE, MAPE, R2, and NSE. Notably, the model exhibits enhanced accuracy in predicting lower flow rates, particularly in autumn and winter, as evidenced by an average RMSE of 2.02 m3/s for flows below 13.8 m3/s. In contrast, the model's precision decreases in high flow rate scenarios, predominantly in spring and early summer. The implementation of forward feature selection (FFS) has further optimized the model, pinpointing total evaporation and volumetric soil water as key parameters, thus enabling a more efficient model with accuracy comparable to the initial, more complex version. This research underscores the practical utility of an image-based approach using CNN models for streamflow prediction. Moreover, the adoption of the freely available and universally accessible ERA5-Land dataset highlights its effectiveness as a valuable and cost-efficient tool for streamflow prediction.

The effect of ontogeny, competition, site, and climate on background mortality for trees of nine species in Canadian boreal forest.

Background tree mortality can be defined as the death of trees that naturally occurs as stands develop, in the absence of major or sudden stand disturbances. The phenomenon is often linked to ontogeny and competition and generally affects individual trees, unlike catastrophic mortality, which affects most trees in the stand. To forecast stand characteristics and to estimate how stand development could change in response to changing climate, it is necessary to quantify background mortality and to identify the most important factors involved. Using data from 10,045 permanent sample plots, we modeled background tree mortality for the 9 most abundant tree species of the eastern Canadian boreal forest. We used explanatory variables related to stand and tree ontogeny, competition, site characteristics and climate to calibrate the models. We found that an increase in age, competition and the presence of partial cut increased the mortality risk. However, the effect of DBH and site-related variables varied among species. We also found that higher temperatures, less precipitation, and higher aridity index values increased background tree mortality. According to mortality simulations under different future climate scenarios, background tree mortality could increase in the next decades for 6 of the 9 tree species studied.

Using human observations with instrument-based metrics to understand changing rainfall patterns.

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.

Assessing climatic change impacts on mangrove structural dynamics on the northern coasts of the Persian Gulf

Abstract. Monitoring long-term trends in structural changes within mangrove ecosystems is essential for understanding their response to climate change and devising effective adaptation strategies in coastal regions. This study examines changes in mangrove patchiness within the Hara Biosphere Reserve (HBR) along the northern coasts of the Persian Gulf (PG) over a 31-year period (1986-2017), focusing on variations in rainfall patterns and drought occurrences. Employing a 35-year time series of monthly Standardized Precipitation Index (SPI) values alongside satellite imagery analysis, trends in both the number of patches (NP) and the Largest Patch Index (LPI) were assessed at the mangrove stand level. The analysis reveals a significant correlation between structural changes in mangroves and drought events. Pre-1998, characterized by wetter conditions, witnessed a decrease in both NP and LPI, indicating patch expansion and habitat extension. Conversely, post-1998, during drought periods, both indices increased, indicating habitat degradation due to heightened drought intensity. Pre-1998 structural changes in the HBR signify habitat expansion, with increased patch extent and core areas reflecting enhanced structural integrity. However, post-1998, a concerning trend of habitat degradation emerged, with increased NP and LPI attributed to intensified droughts. These findings highlight a transitional period marked by favourable conditions for mangrove growth followed by habitat degradation linked to increased drought intensity. This underscores the vulnerability of mangrove ecosystems to climate change impacts, particularly exacerbated droughts, necessitating urgent efforts for conservation and management to preserve biodiversity and ecosystem services in coastal regions.

Diverging Projections of Future Droughts in High‐End Climate Scenarios for Different Potential Evapotranspiration Methods: A National‐Scale Assessment for Poland

ABSTRACTIt has been broadly reported that future climate change will most likely affect the spatio‐temporal distribution of water resources and consequently droughts. There is a prevailing notion that an increase in temperature and frequency of heat waves are expected to result in more intense droughts in the coming years. In this study, we aimed to evaluate the effect of the potential evapotranspiration (PET) method selection on future drought projections over Poland. In our study, simulations of the Soil and Water Assessment Tool (SWAT) model were conducted, utilising an ensemble of six EURO‐CORDEX projections, spanning the period from 2006 to 2100 under the RCP8.5 scenario. Two model setups with two different PET methods (Penman‐Monteith—PM and Hargreaves—HAR) were used. For drought conditions evaluation we selected the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) for meteorological drought, Standardized Streamflow Index (SSI) for hydrological drought, and Standardized Soil Moisture Index (SMI) for agricultural drought. The meteorological and hydrological droughts were calculated using a 12‐month time aggregation window, while agricultural drought was calculated using a 3‐month window. Climate projections revealed that by 2080s annual mean temperature and precipitation increase is expected by up to +3.4°C and +10.3% respectively. Under future climate conditions duration and severity of meteorological droughts are projected to decrease. PM method leads to a higher PET increases (1.35 mm year−1) than the HAR method (1.1 mm year−1) throughout the century which entail diverging signal of change for agricultural and hydrological droughts. PM‐ and HAR‐based simulations indicate increase in the total duration and cumulative severity of agricultural droughts, buthowever, for HAR‐based projections, the increase is much less. For hydrological droughts the signal of change is similar for both PET methods, but considerably distinct in magnitude. Considering the entire simulation period, by the end of the century cumulative severity of hydrological droughts is projected to decrease, with a much more pronounced decline for HAR (70% reduction) than for the PM method (35% reduction). Our study demonstrated that methodological choices are crucial to the assessment of future drought risk under climate change.

Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia

Analyzing agricultural and hydrological drought at different timescales is essential for designing adaptation strategies. This study aimed to assess agricultural and hydrological drought in the Abbay Basin of Ethiopia by using multiple indices, namely the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and drought severity index (DSI). Climate extremes were assessed over the Abbay Basin between 1981 and 2022. The results indicate that the years 1982 and 2014 were the most drought-prone, while the year 1988 was the wettest year in the Abbay Basin. The results revealed the presence of extremely dry and severely dry conditions, potentially impacting agricultural output in the region. Agricultural drought was identified during the main crop seasons (June to September). The VCI results indicated the presence of extremely wet and severely wet conditions. In 2012, 65% of the area was affected by extreme drought conditions, while nearly half of the Basin experienced extreme drought in 2013 and 2022. The DSI results indicated the occurrence of agricultural drought, although the spatial coverage of extreme dry conditions was lower than that of the other indices. In 2003, 78.49% of the Basin experienced moderate drought conditions, whereas severe drought affected 20% of the region. In 2010, about 90% of the Basin experienced moderate drought. This study provides valuable insights for agricultural communities, enabling them to mitigate the impact of drought on crop yields by utilizing different adaptation strategies. An adequate knowledge of agricultural and hydrological drought is essential for policymakers to assess the potential effects of drought on socioeconomic activities and to recognize the significance of implementing climate change adaptation measures.

Evaluation of meteorological drought and its impact on crop yield over Afar region, northeast Ethiopia

Drought is a natural hazard caused by a prolonged precipitation deficit that cannot meet human, livestock, and environmental demands. In Ethiopia, frequent and severe droughts increasingly affect the socio-economic and environmental sectors. This study evaluates the current and future projected meteorological drought and its impact on crop yield over the Afar region in northeast Ethiopia. We used surface stations, satellite climate estimates, downscaled atmospheric reanalysis, and regional climate model datasets. We evaluated the occurrence of drought using the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) calculated at 3-month and 12-month time scales. The drought vs. regional cereal yield is correlated to explain yield variability in the region. Results showed that more intense droughts were analyzed in 1984, 1985, 2002, 2008, 2009, 2010, 2015, and 2016. Among these years, 1984, 2002, 2008, 2009, and 2015 were the driest years across all locations in the study area. The regression of SPI and SPEI with yield showed that the indices significantly explained (r2 = 0.56 for SPI and 0.18 for SPEI) the observed yield variation. Spatially, more intense drought prevails over the northern, northwestern, and southwestern parts of Afar, where these parts are more prone to severe drought. The projected drought pattern showed increases in the intensity and frequency of drought in the middle and end of the century. The findings of this study are helpful for stakeholders working on drought mitigation in the region. Keywords: Climate dataset, meteorological drought indices, drought projection, pastoral community.

Assessment of the coherence of groundwater levels in coastal aquifers with climate change and anthropogenic activity

This study aimed to assess the coherence between groundwater levels and various factors during two distinct periods, 2002–2020 and 2025–2050, in Miandoab aquifer in northwestern of the Iran. Partial wavelet coherence and multi-wavelet coherence analyses were employed to assess the coherence between individual parameters and their simultaneous coherence. The factors considered in the study were derived from remote sensing data, including Gravity Recovery and Climate Experiment data and Landsat data, which were utilized to examine water storage anomalies and anthropogenic activity, respectively. Additionally, General Circulation Models were employed to predict groundwater levels under future climate change scenarios via a feedforward neural network. To streamline the modeling process and categorize piezometers, with each group reflecting different patterns, clustering techniques were applied to group multiple piezometers. There were four final clusters, and representative piezometers from each cluster were chosen as targets for modeling and future predictions. Finally, the differences in coherence between past and future periods were compared and analyzed. The results revealed decreasing trends in groundwater level, precipitation and soil moisture index in 2025–2050; however, there were increasing trends in normalized difference vegetation index and temperature. In addition, wavelet analysis indicated that during the period 2025–2050, the delay in interaction between groundwater level and various factors decreased to 0–4 months, whereas longer delays were observed for the period 2002–2020. The analysis of multi- wavelet coherence showed that the combination of climate change and anthropogenic activity may have more significant coherence (0.9–1) with groundwater level than the combination of gravity recovery and climate experiment data and soil moisture index. The results highlight the greater significance of gravity recovery and climate experiment data in terms of coherence with groundwater levels compared to other factors.

Spatio-temporal variation of meteorological, hydrological and agricultural drought vulnerability: Insights from statistical, machine learning and wavelet analysis

The study of how agricultural drought (AD) is responsible for meteorological drought (MD) and hydrological drought (HD) is crucial for drought prevention and the socio-economic development of a nation. This is due to AD constitutes a significant threat to the nation's food productivity and security. In depth comprehension and mitigation of drought incidents depend on understanding their frequency and propagation patterns. In this study, spatio-temporal variation of three types of droughts have been assessed in the sub-tropical environment of eastern India. In this perspective, seasonal i.e., pre-monsoon, monsoon, post-monsoon, and winter MD, HD, and AD were assessed considering Standardized Precipitation Index (SPI), Standardized Water Level Index (SWI), and Standardized Soil Moisture Index (SSMI) statistical tool respectively in sub-tropical agro-climatic zone of eastern India. In addition to this, spatial drought vulnerability of MD, HD and AD was assessed using Analytic Hierarchy Process (AHP) considering suitable factors for each drought type, and overall drought vulnerability was assessed using “Random Forest (RF)” and “Artificial Neural Network (ANN)” methods. Furthermore, drought periodicity has been measured using a wavelet power spectrum analysis. The result of seasonal drought revealed that pre-monsoon season has more frequent drought occurrences than other seasons among the applied three types of droughts. The outcomes of overall drought vulnerability revealed that RF gives the optimum result followed by ANN i.e., 0.841 and 0.828, respectively, for validation purposes. The periodicity of drought ranges from 0.25 to 4 as obtained from wavelet analysis. In general, this research on how AD spreads from MD and HD is crucial for drought resilience, drought management, and food security among the stakeholders and policymakers for achieving the SDGs.

Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.