Bi-decadal drought assessment in Northwestern Algeria: integrating meteorological and remote-sensing indices.

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Ramzi Benhizia, Brahim Abdelkebir, Behnam Ata, Singo Mukovhe Vele, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8711142/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Drought is an escalating hazard in arid and semi‑arid regions with major consequences for agriculture, ecosystems, and water resources. This study presents a 2003–2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria (2003–2023) using CHIRPS precipitation and MODIS remote‑sensing products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) at 3‑, 6‑ and 12‑month timescales; vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identify recurrent drought episodes in 2007–2009, 2011–2012 and a pronounced dry phase from 2020–2023. Mann–Kendall results indicate widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen’s slope = − 0.10 yr⁻¹). Vegetation indices mirror these changes: VHI shows substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses are spatially heterogeneous. The strongest coupling between precipitation and vegetation occurs at the semi‑annual scale (SPI‑6 vs VHI, r = 0.578), suggesting that 6‑month precipitation anomalies best predict agricultural drought in this region. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and they point to an ongoing shift toward increased aridity with implications for water management and agricultural adaptation. Drought monitoring Standardized Precipitation Index (SPI) Vegetation Health Index (VHI) Mann-Kendall test Remote Sensing Northwest Algeria Climate Change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Drought is a recurring natural phenomenon that has challenged human societies throughout history (Giaquinto et al., 2023 ; Spinoni et al., 2021 ; Verma et al., 2023 ) Its impacts extend beyond ecosystems to multiple economic and social sectors from agriculture, the cornerstone of early civilizations, to modern industry, urban water systems, and transportation networks (Heim, 2002 ). The United Nations (UN) identifies drought as a major contributor to global water scarcity, currently affecting 40% of the world’s population and posing a serious risk to human security and well-being (Biswas et al., 2025; UNCCD & JRC, 2023). Between 1967 and 1991, drought affected nearly half of all weather-related disaster victims (He et al., 2020 ; Kogan., 1997), Estimates suggest that, by 2030, up to 700 million people, roughly translating to about 9% of the global population, could be displaced due to drought-induced water shortages (Ismail et al., 2022 ). Developing regions, such as Africa, are likely to suffer more devastating effects than their developed counterparts. Droughts in Africa cause major human and economic hardships, particularly in areas where more than 85% of the population depends on rain-fed farming (Bayable et al., 2025; Gebremeskel et al., 2019 ). The Near East and North Africa (NENA) region is predominantly arid due to its geographic and climatic characteristics (Francis et al., 2024). Approximately 75% of the area is desert, receiving less than 50 mm of rainfall annually and offering limited support for human activity (Bazza et al., 2018 ). The remaining areas experience semi-arid or Mediterranean climates, while sub-humid and humid conditions are confined to select coastal zones and high-altitude regions exposed to prevailing winds (Lionello, 2012 ). Regions within a country that fall under desert or arid climates are persistently dry and should be governed accordingly through 'dryland management plans' integrated into broader national drought management strategies (FAO, 2018 ). Algeria endured a devastating drought between 1945 and 1947 (Fellag et al., 2021 ). In the Ain Sefra region, located in southern Oran, official records indicate that around 3,000 people died from starvation out of a total population of 80,000 (Achite et al., 2025 ). The drought also led to the loss of 900,000 sheep, wiping out nearly 90% of the livestock. Two decades later, in early 1966, the country experienced its driest period since 1945, resulting in poor crop germination and significant declines in agricultural production (Kassoul, 2006). Over the past 30 years, Algeria has experienced prolonged and severe drought conditions, marked by a 30% reduction in average annual rainfall (Ceppi et al., 2025 ). This significant rainfall deficit has adversely affected ‘river flow patterns, leading to serious repercussions for the country’s socio-economic activities (FAO, 2018 ). The American Meteorological Society categorizes drought into four types: meteorological, hydrological, agricultural, and socio-economic (Benkhamallah et al., 2021 ; Ceppi et al., 2025 ). Each type reflects a different form of moisture shortage and corresponds to different environmental and societal impacts (Li et al., 2025 ). Among various meteorological variables, precipitation is widely recognized as the primary factor influencing the occurrence and severity of drought. Consequently, the term meteorological drought is often used to refer specifically to periods of precipitation deficit. To assess long-term variations in drought conditions based solely on precipitation data, the Standardized Precipitation Index (SPI) is commonly employed as a reliable analytical tool (Mckee et al., 1993 ). To address this, the U.S. National Oceanic and Atmospheric Administration (NOAA) introduced the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI), derived from satellite-based NDVI and thermal data. These indices enable effective large-scale drought monitoring by identifying vegetation stress related to water and temperature conditions. Their application has demonstrated strong correlations with crop yields across diverse ecological regions (Kogan, 1997 ). In contrast to NDVI and VCI, the Vegetation Health Index (VHI) also accounts for the influence of temperature on vegetation condition (Gebrechorkos et al., 2023 ). Together, These indices have been successfully applied to drought dynamic assessment over space and time through integrating meteorological deficit with vegetation and thermal responses, A recent study in Rwanda, with a focus on the drought-prone Eastern Province, investigated meteorological and agricultural droughts using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI), which integrates NDVI and LST data. Analyzing data from 31 meteorological stations (1983–2020) and remote sensing indices (2001–2020), the study revealed that the most severe droughts occurred between 2003 and 2017, especially in the Southern and Eastern Provinces. These droughts significantly reduced vegetation health and crop yields (Niyonsenga et al., 2024). The findings highlight the urgent need for spatiotemporal drought assessments and recommend proactive policies on drought mitigation, climate change adaptation, and sustainable water resource management in Rwanda (Niyonsenga et al., 2024). In Saudi Arabia's hyper-arid regions, a study compared remote sensing drought indices (VCI, TCI, VHI) with the meteorological SPEI index from 2001 to 2020. Results showed that VHI correlated best with SPEI, especially at longer timescales, making it a reliable tool for drought monitoring where ground data is limited (Ejaz et al., 2023). A recent study conducted in Annaba, Algeria, analyzed drought trends from 1981 to 2021 in the context of global warming and extreme weather events. Using standardized drought indices (SPI and SPEI) and extreme value analysis, the research highlighted the increasing severity of droughts when temperature is accounted for (via SPEI), compared to precipitation-only assessments (SPI). The findings underscore the importance of integrating temperature, precipitation, and evapotranspiration in drought evaluation to enhance prediction accuracy. This study contributes valuable insights into climate change adaptation and water resource management in arid and semi-arid regions (Ziari et al., 2024). A study focused on the Cheliff watershed in Northwestern Algeria examined drought patterns using Landsat satellite imagery and meteorological data. Given the region's alternating wet and dry periods influenced by both Atlantic and Mediterranean air masses, the research aimed to monitor drought at spatial and temporal scales. By comparing the standardized NDVI values with the Standardized Precipitation Index (SPI) from fifty meteorological stations for selected years (1987, 2000, 2006, 2011, and 2015), the study found a strong correlation between vegetation response and precipitation. This led to the development of a new satellite-based drought index, offering a valuable tool for spatial drought monitoring, particularly in areas with limited climate data (Abbes et al., 2018). A study on the upper Cheliff basin in Algeria (1982–2021) analyzed meteorological drought patterns using SPI and SPEI across multiple time scales. Both indices effectively captured drought variability, with SPEI detecting more short-term events. Five major drought periods were identified, and a strong correlation (R = 0.73–0.93) was observed between the indices. The findings highlight the importance of using both SPI and SPEI for accurate drought monitoring, especially in agriculturally significant areas (Messis et al., 2025 ). A study on the Oued Sebaou basin in northern central Algeria analyzed meteorological drought patterns from 1972 to 2010 using SPI data from 23 rain gauges. Seasonal and annual assessments, supported by GIS-based indices (PCI and MFI), revealed moderate precipitation concentration and spatial variability linked largely to longitude. The findings indicated a prolonged drought starting in the late 1980s, with over half the stations experiencing moderate to severe drought between 1986 and 2001. Decadal comparisons showed more wet conditions during 1972–1981 and 2002–2010, with extreme wet events more frequent in the latter period. These insights support improved watershed and drought management strategies (Zerouali et al., 2021 ). Despite this body of work, a comprehensive, long-term assessment integrating both meteorological and a full suite of vegetation health indices (VCI, TCI, and VHI) for the entirety of Northwest Algeria has been lacking. Many previous studies concluded before the recent, intense drought period post-2015, potentially missing a critical climatic shift. This research aims to fill this gap by providing a detailed assessment of drought dynamics in Northwestern Algeria over the two-decade period of 2003–2023. The specific objectives are to: (i) examin drought severity across multiple temporal scales through SPI at 3, 6, and 12-month intervals; (ii) assess vegetation condition and thermal stress patterns using three MODIS-derived indicators: VCI, TCI, and VHI; (iii) detect and evaluate significant temporal changes in drought severity through Mann–Kendall analysis and Sen's Slope estimation; and (iv) assess the degree of coupling between precipitation variability and vegetation dynamics through correlation analysis. By detecting recurrent drought episodes, spatially vulnerable areas, and vegetation sensitivity to climatic variability, the findings support improved drought monitoring and contribute to the development of regional drought warning systems, and shaping the formulation for adaptive management policies in arid and semi-arid environments. 1. Study area: The study area encompasses the northwest region of Algeria, extending approximately 500 km from west to east and varying in north-south width. Geographically, it lies between 2°10′10″ W and 3°10′11″ E longitude and 34°18′54″ and 36°48′12″ N latitude (Fig. 2 ). The climate ranges from semi-arid to Mediterranean and exhibits pronounced spatial and temporal variability in precipitation, which strongly influences regional hydrology (Hamitouche et al., 2024 ; Mahcer et al., 2024; H. Meddi et al., 2007; M. Meddi et al., 2013 ), Mean annual precipitation differs markedly across the northwest region; interior basins such as the Tafna average 240 mm yr⁻¹, whereas coastal and mountainous areas (e.g., northern parts of the Chelif basin) exceed 700 mm yr⁻¹ (Achite et al., 2021 ; Bougara et al., 2020 ; Hamitouche et al., 2024 ; H. Meddi & Meddi, 2007 ; M. Meddi et al., 2013 ). The region includes several major basins and wadis, three of which dominate regional hydrology. The Chelif basin (≈ 44,694 km²), Algeria’s largest watershed, originates in the Tell Atlas and drains to the Mediterranean Sea (Achite et al., 2021 ; Derdous et al., 2021; Elmeddahi et al., 2016 ; H. Meddi et al., 2014 ; Mehaiguene et al., 2015 ). The Tafna basin, is an important transboundary catchment on the Algeria–Morocco frontier that supports regional agriculture and biodiversity,(Bougara et al., 2021 ; Fettam et al., 2025 ) the Macta basin collects waters from the Habra and Sig rivers and drains into the Gulf of Arzew in the Mediterranean Sea; containing ecologically significant wetlands such as Lac El Macta, which are designated under the Ramsar Convention, underscoring its ecological importance (Elouissi et al., 2021 ; Ismail et al., 2022 ). 2. Materials and Methods The research methodology (Fig. 3 ) comprises six key stages: (i) satellite-derived data collection “vegetation/thermal” from MODIS and extraction of precipitation records from CHIRPS for the period 2003–2023, (ii) preprocessing of MODIS data to derive NDVI and LST metrics, (iii) calculation of the SPI from CHIRPS data; (iv) derivation of drought indices (VCI from NDVI, TCI from LST, and VHI combining VCI and TCI); (v) spatiotemporal analysis and mapping of all indices in Google Earth Engine (GEE); and (vi) statistical correlation and trend analysis to evaluate drought patterns and the relationship between meteorological and vegetation-based indices. 2.1 Data Acquisition and preprocessing: Multiple satellite datasets were processed using Google Earth Engine (GEE) to assess drought conditions across northwest Algeria from 2003 to 2023, The details regarding the source, variable, and resolution of these datasets are summarized in Table 1 . For precipitation data, we used CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), which combines satellite imagery estimates with station data at a spatial resolution of 0.05° (~ 5.3 km) (Funk et al., 2015 ; Mianabadi et al., 2022 ). The dataset was clipped to the study boundary, then, daily precipitation values were aggregated to 3-, 6-, and 12-month totals (SPI-3, SPI-6, SPI-12). Additionally, for data quality control and to ensure valid inputs for the SPI calculation, only time intervals with continuous and complete daily records were retained. SPI standardization was applied per pixel using long-term means and standard deviations, assuming an approximately normal distribution of precipitation anomalies. MODIS (Moderate Resolution Imaging Spectroradiometer) data were used to retrieve remote sensing data (2003–2023). More specifically, we obtained the MOD13Q1 V6.1 NDVI products with a 16-daytemporal resolution and a spatial resolution of 250 m (Dinan, 2021 ; Khan et al., 2023; Sazib et al., 2018 ). In addition, we gathered LST from MOD11A2 V6.1 products with an 8-days temporal resolution and a spatial resolution of 1000 m (Wan, 2013 ). Both datasets were aggregated into monthly composite means to achieve the temporal resolution alignment for drought analysis. Furthermore, MODIS data preprocessing included converting digital values to physical unites using standard scaling factors, respectively, while spatial analysis was limited to the study area boundaries. Table 1 Summary of the datasets used in the study. Dataset Name Source Variable Spatial Resolution Temporal Resolution CHIRPS CHIRPS v2.0 Precipitation 5.3km (0.05°) Daily MODIS NDVI MOD13Q1 V6.1 NDVI 250m 16-day MODIS LST MOD11A2 V6.1 LST 1000m 8-day 2.2 Calculation of Drought Indices: 2.2.1 Vegetation Condition Index (VCI): The VCI, introduced by Kogan (Kogan, 1995a, 1995b), is a remote sensing-based drought indicators that assesses vegetation condition by comparing the current NDVI values with their historical range for a given location and time. VCI reflects the local ecosystems dynamics and vegetation response during different growth cycles. The index scales the NDVI values into a standardized 0-100 range, where low values indicate stressed vegetation and high values imply optimal growing conditions. VCI was computed using Eq. (1): $$\:\begin{array}{c}VCI=100\times\:\frac{NDVIcurrent-NDVImin}{NDVImax-NDVImin}\:\#\left(1\right)\end{array}$$ Where: NDVIcurrent represents the current value of NDVI NDVImin is the minimum value of NDVI and NDVImax indicates the maximum value of NDVI during the research period (2003–2023). This study applied the formula on 250 m NDVI imagery that was scaled by 0.0001. Monthly composites were generated; per-pixel minimum and maximum NDVI values were extracted to standardize each composite. 2.2.2 Temperature Condition Index (TCI): The Temperature Condition Index (TCI), developed by Kogan in 1995, is the thermal analogue of VCI and assesses vegetation stress resulting from elevated surface temperatures. Drought assessment becomes particularly more effective, where soil moister reduction induces thermal stress on vegetation, TCI ranges from 0 to 100, low values indicate extreme thermal stress, while high values reflect favorable thermal conditions. (Kogan, 1995a; Liu & Kogan, 1996) TCI was computed using Eq. (2): $$\:\begin{array}{c}TCI=100\times\:\frac{LSTmax-LSTcurrent}{LSTmax-LSTmin}\#\left(2\right)\end{array}$$ Where: LST current represents the monthly mean land surface temperature values. LST min and LST max indicate the annual minimum and maximum values of LST. This study used LST at 1 km resolution. Digital numbers (DN) were converted to physical temperatures with a scale factor of 0.02. Monthly mean LST composites were generated from the 8-day product, after which annual pixelwise maximum and minimum LST values were calculated to define normalization bounds for TCI (Rhee et al., 2010 ). 2.2.3 Vegetation Health Index (VHI): VHI is a widely used remote-sensing drought metric that integrates thermal stress and vegetation health into a single composite drought indicator, VHI combines VCI (from NDVI) and TCI (from LST)), through integrating the moisture deficiency and heat stress effects on vegetation. (Kogan, 1995a; Liu & Kogan, 1996) VHI, proposed by Kogan (Kogan, 1995a, 1995b), is computed as a weighted mean of VCI and TCI, making it highly effective for monitoring agricultural drought in arid, semi-arid, and dry sub-humid climates (Bhuiyan et al., 2006; Kogan, 2001; Kogan et al., 2003), In this research we assigned equal weights to VCI and TCI (α = 0.5), consistent with conventional approaches due to the uncertainty regarding the contribution of each component to drought assessment (Liu & Kogan, 1996; Wan et al., 2004), VHI ranges from 0 to 100, ; values near 0 indicate extreme drought, while values near 100 indicate no drought. VHI was computed by Eq. (3): $$\:\begin{array}{c}VHI=\alpha\:\:\times\:\:VCI\:+\left(1-\:\alpha\:\right)\times\:\:TCI\#\left(3\right)\end{array}$$ Where: α is the weighting factor with a value of 0.5. VCI represents the vegetation condition index compared to its long-term range. TCI indicates the temperature condition index. The formula was applied using MODIS-derived VCI and TCI data for each month during 2003–2023. Firstly, the monthly VCI and TCI images datasets were combined; secondly, the VHI was computed for each pixel using the weighted formula. We classified VHI into five drought severity classes as shown in Table 2 . Table 2 Drought Severity Classification Scheme according to VHI values. No. Drought Class VHI 1 Extreme drought 40 2.3 Standardized Precipitation Index (SPI). SPI, presented firstly by Mckee in 1993 (Mckee et al., 1993 ) is widely recognized as a robust and flexible drought index. The WMO (world meteorological organization) played a pivotal role in its widespread adoption by recommending SPI as a standard meteorological drought index, promoting its global application in 2009. SPI detects precipitation anomalies across different timescales from short-term (e.g., 1 or 3 months) to long-term (e.g., 12 or 24 months) by fitting a probability distribution (typically Gamma) to a long-term precipitation record, which is then transformed into a normal distribution (Hayes et al., 2011; Lloyd-Hughes & Saunders, 2002; Mckee et al., 1993 ). We computed SPI at 3-, 6-, and 12-month time scales (SPI-3, SPI-6, SPI-12) to capture short-term, seasonal, and annual drought dynamics (Vicente-Serrano et al., 2010 ). Daily CHIRPS v2.0 precipitation (≈ 5.3 km) were temporally aggregated to the corresponding time scales, To ensure consistency and accuracy in the SPI calculations, only time intervals only periods with complete daily precipitation records (i.e., without missing data) or (no missing days) were included in the analysis. SPI was computed as Eq. (4): $$\:\begin{array}{c}SPI=\frac{P-\mu\:}{\sigma\:}\#\left(4\right)\end{array}$$ Where: \(\:P\) : represents the total precipitation during the given period. µ: represents the long-term mean precipitation. σ: is the standard deviation of that period. We computed SPI at each pixel across the study area. These calculated values were subsequently classified into drought intensity categories using a standardized classification approach, shown in Table 3 . Table 3 Drought classification scheme according to SPI following McKee et al. ( 1993 ). No. Drought Class SPI Range 1 Extreme drought ≤ -2.0 2 Severe drought -1.99 to -1.50 3 Moderate drought -1.49 to -1.00 4 Near normal -0.99 to + 0.99 5 Moderately wet + 1.00 to + 1.49 6 Severely wet + 1.50 to + 1.99 7 Extremely wet ≥ +2.0 2.4 Mann-Kendall Trend Analysis: The non-parametric Mann-Kendall (MK) test was employed to detect significant, consistent trends within the temporal records of VHI, TCI, VCI, and SPI at three temporal scales (3-, 6-, and 12-month intervals) throughout the period 2003–2023. This test is widely used in environmental studies due its robustness to extreme observations and its non-reliance on specific data distribution assumptions (Abu Arra et al., 2024; Kourouma et al., 2022; Wang et al., 2020). The MK test was executed on annual time series values of each index across the study area. The analysis tests the null hypothesis (H₀) indicating no trend against the alternative hypothesis (H₁) reflecting a continuous directional shift. The results are expressed in terms of a computed test statistic (S) and its corresponding p-value, Pixels with p-values under the value (α = 0.05) were classified as exhibiting significant trends (Genovese et al., 2025). Sen’s Slope Estimator was applied to assess the strength of the detected trends, The median slope is determined through applying a non-parametric method by computing slopes between all possible pairs of time series data points (Gutiérrez-Hernández & García, 2024), Sen’s Slop offers a robust metric for the temporal rate of change per time unit, positive values presents an increasing trend while the negative values indicates decreasing trends. The spatial patterns of trends were visualized through specialized thematic mapping, including Sen's Slope Maps, Mann-Kendall p-value Maps, and Trend Classification Maps, in addition, to evaluate the overall spatial extent and trend intensity, summary statistics were calculated for each drought index. Specifically, we calculated the total area that experienced significant increases or decreases, alongside the mean and standard deviation of the Sen's Slope values for these identified significant areas (Genovese et al., 2025). By employing a multi-index approach, regions exhibiting significant temporal changes in vegetation health, thermal stress, and precipitation deficits were identified. Through this comprehensive approach, valuable insights were obtained into the spatio-temporal trends in drought and potential desertification processes in Northwest Algeria over the period 2003–2023. 2.5 Correlation Analysis of Drought Indices: To evaluate the relationship between meteorological drought severity and vegetation response, a Pearson correlation analysis was performed using time series data of SPI and VHI (Kogan, 1995a, 2001; Mckee et al., 1993 ). In this analysis we focused on assessing variations in precipitation patterns through using SPI at multiple accumulation intervals and corresponding changes in vegetation health dynamics. In preparation for the correlation analysis, annual mean time series data for each drought index (SPI3, SPI6, SPI12, VHI) were compiled, representing average conditions over the 2003–2023 period and derived from the corresponding geospatial datasets (Dai, 2011; Peters et al., 1991; Vicente-Serrano et al., 2010 ). the use of annual mean values highlights interannual climate-vegetation associations while diminishing short-term variability. Following data preparation, each annual SPI time series (SPI3, SPI6, SPI12) was paired with its corresponding annual VHI series. All datasets were temporally aligned, and date formats were standardized to ensure temporal consistency across variables. The Pearson correlation coefficient (r) was computed for each SPI–VHI pair, which measures the strength and direction of each pair’s linear relationship, ranging from − 1 (perfect negative correlation) to + 1 (perfect positive correlation), with 0 indicating no linear correlation (Wilks, 2006). SPI is standardized to be of an approximately normal distribution by design (McKee et al., 1993 ), whereas VHI is a normalized composite vegetation index derived from vegetation greenness and thermal condition components. Since the indices are standardized and annual mean values were calculated, Pearson’s correlation was considered appropriate for exploring linear relationships between precipitation anomalies and vegetation dynamics. Scatter plots were employed to visualize these relationships, where each plotted point represents the annual values of both indices for a given year. Linear regression lines were fitted to each SPI–VHI pair, offering a visual representation of the relationship significance within the plotted data. Additionally, 95% confidence intervals were displayed as shaded regions to indicate the uncertainty associated with the regression estimates. The Pearson correlation coefficient (r) for each pair was reported in the plot titles, linking visual trends with their statistical significance (Peters et al., 2002; Vicente-Serrano et al., 2013). This comprehensive approach allowed us to assess both the strength and direction of relationships between SPI-derived precipitation anomalies at multiple timescales and vegetation health dynamics across the 2003–2023 study period. 3. Results This study applied SPI (3-, 6-, and 12-month timescales) together with remote-sensing indices (VCI, TCI, VHI) to assess spatial and temporal patterns of drought across northwest Algeria from 2003 to 2023. The indices were analyzed annually to detect interannual severity variations and identify patterns in climatic and vegetation stress. 3.1 Vegetation Condition Index (VCI): To assess vegetation changes (2003–2023), we computed annual median VCI from monthly data composites. VCI map (Fig. 4 ) shows distinct contrasts between well-vegetated coastal/northern areas and arid southern/southeastern regions, which are characterized by sparse, climate-sensitive vegetation. Over the two decades, a consistent spatial pattern was observed. Coastal provinces such as Mostaganem, Chlef, Ain Defla, Oran, and Ain-Temouchent showed higher VCI values, reflecting stable vegetation and good growth conditions. In contrast, interior provinces (Saida, Tiaret, Sidi Bel Abbes, and Mascara) showed lower values, indicating greater climatic vulnerability. Certain years recorded optimal vegetation conditions. In 2005, high VCI values were recorded in Mostaganem, Mascara, and Relizane; in 2010, Chlef, Oran, and Ain-Temouchent showed gains, along withTissemsilt and Saida. The year 2017, was particularly favorable, with widespread high VCI values across nearly all provinces, suggesting a notably wet year. Conversely, other years were marked by significant vegetation stress. In 2008, major declines occurred in (Saida, Tiaret, southern Mascara,Oran, and Relizane). Similar degradation appeared in 2012, notably in (Sidi Bel Abbes, central Mascara, and Saida). In 2018, severe drought triggered widespread declines, including in typically well-vegetated areas such as (Chlef, Ain Defla, and Relizane). In 2022, widespread stress affected (Tlemcen, Saida, Tiaret, and parts of Chlef and Mascara). These inter-annual patterns highlight the region’s vulnerability to climatic variability, with coastal and Tell Atlas areas showing greater resilience, whereas interior and steppe regions remain more vulnerable. 3.2 Temperature Condition Index TCI: This study used TCI to evaluate thermal stress on vegetation from 2003 to 2023 (Fig. 5 ). TCI reflects surface temperature: low values indicate high thermal stress, while high values indicate cooler, less stressful conditions, across both decades, TCI patterns followed ecological-climatic zones. Coastal and high-altitude provinces (Mostaganem, Chlef, Ain Defla, and Tissemsilt) often recorded higher TCI values, reflecting favorable thermal conditions. By contrast, interior provinces (Tiaret, Saida, Sidi Bel Abbes, and southern Mascara) consistently showed lower values, indicating higher thermal stress and drought vulnerability. In 2003, conditions were moderate to favorable in Oran, Chlef, and northern Ain Defla, though localized stress occurred in Tlemcen and Saida. By 2004, widespread thermal degradation affected Relizane, Mascara, and Saida. Coastal and high-altitude provinces of Mostaganem, Chlef, Ain Defla, and Tissemsilt often recorded higher TCI values, reflecting favorable thermal conditions (Fig. 5 ). By contrast, interior provinces (Tiaret, Saida, Sidi Bel Abbes, and southern Mascara) consistently showed lower values, indicating higher thermal stress and drought vulnerability. Major stress events were noted in 2012, when Tlemcen, Saida, and Mascara recorded the decade’s lowest TCI values, coinciding with vegetation decline. Stress peaked again in 2020 and 2022, with 2022 recording the lowest TCI of the two decades, severely affecting Sidi Bel Abbes, Mascara, Tiaret, and Saida and resulting in marked vegetation decline (Fig. 4 ). Overall, TCI reveals a progressive rise in the frequency and extent of thermal stress. Semi-arid and steppe provinces (Saida, Tiaret, and Mascara) were most affected, while coastal and sub-humid areas (Chlef and Mostaganem) were more resilient. 3.3 Vegetation Health Index VHI: By employing VHI, we assessed the degree of severity of the drought (2003–2023) and classified it into five categories: no drought, mild, moderate, severe, and extreme. VHI showed significant spatial and temporal variability throughout the twelve provinces in the study area, by combining vegetation and thermal stress indices. A clear spatial pattern emerged across the study period; southern and interior provinces were more vulnerable, whereas coastal and mountainous regions were more resilient. In the early years (2003–2004), large areas, especially Saida, Tiaret, and Mascara faced mild to moderate drought. However, the following years were characterized by significant fluctuations. Oran, Mostaganem, and Ain-Temouchent underwent little to no drought in 2007, 2010, and 2015, which were among the least affected years. Nevertheless, conditions improved. Similarly, the region experienced only a slight drought in 2021, mostly affecting northern Tissemsilt, Chlef, and Relizane. Conversely, the region experienced multiple severe drought episodes. In 2008, the drought became more severe, causing moderate to severe stress in Tlemcen, Sidi Bel Abbes, and Saida. Following this trend, 2012 was particularly severe for eastern Tissemsilt, southern Mascara, and central Relizane. Tiaret, Saida, and Sidi Bel Abbes were among the interior provinces where the drought intensified in 2017. Moreover, Tlemcen, Mascara, and southern Relizane were heavily effected in 2018. Furthermore, 2022, the most catastrophic year of the previous 20 years, saw a recurrence of severe drought, affecting almost every province, including typically resilient regions like Chlef, Tissemsilt, and Mostaganem. After this peak, Saida, Mascara, and Tiaret continued to experience moderate drought in 2023, representing a decreased intensity compared to 2022. Overall, the VHI time series demonstrates that mountainous and coastal areas were more resilient, while southern and interior provinces were more vulnerable. The years 2008, 2012, 2017, and 2022 were the years with the most severe drought, while 2007, 2010, 2015, and 2021 were the least affected years. 3.4 Spatial Distribution of SPI (2003–2023): We investigated the distribution and intensity of dry and wet periods in northwest Algeria utilizing SPI at multiple time scales: 3-month (short-term), 6-month (seasonal), and 12-month (long-term). The interannual variability observed during the study period is presented graphically in (Figs. 7, 8, and 9 ). SPI-3 results (Fig. 7) exhibit strong short-term variability, with frequent shifts between dry and wet periods. The years 2003, 2007, 2008, 2010, and 2012 showed widespread positive anomalies (moderately to extremely wet), particularly in coastal and northern provinces. In contrast, 2006, 2015, 2017, 2019, and a persistent period from 2019 to 2023 were dominated by negative SPI values, indicating considerable short-term rainfall deficits. While wet conditions generally occurred in specific areas, droughts were more uniformly distributed, particularly across the interior and southern areas. Figure 7 . Spatial distribution of SPI3 (2003–2023). SPI-6 patterns (Fig. 8) reveals a decrease in sensitivity compared to short-term variations, and highlight more persistent seasonal conditions. The early period (2003–2014) was characterized mainly by wet conditions, with 2008, 2012, and 2014 exhibiting pronounced positive anomalies with extensive spatial coverage of moderately to extremely wet conditions across the study region. A significant shift occurred in 2015, initiating an extended period of precipitation deficiency that persisted through 2023. Drought intensity varied over time, with moderate to severe conditions during 2015–2017 and 2019–2023. Both 2021 and 2023 were marked by critical droughts, with large parts of the region experiencing severe to extreme precipitation deficits. Drought conditions also showed clear spatial variability, with some areas facing more persistent or severe impacts than others. This, along with strong interannual variability in rainfall, confirms a hydroclimatic transition from predominantly wet to predominantly dry states since the mid-2010s. Figure 8. Spatial distribution of SPI6 (2003–2023). SPI-12 temporal analysis (Fig. 9 ) demonstrates a continuous wet period spanning 2003–2014, marked by predominantly moderate to severe wet conditions. exhibiting peak positive anomalies in 2007, 2009–2011, and 2013. During this period, above-average precipitation exhibited widespread spatial distribution across the region. Around 2015, there was a significant shift in hydroclimatic conditions occurred, marking the start of a persistent long-term drought that extended through 2023. Drought conditions intesified progressively from moderate drought in 2015 and 2017 to more severe conditions in 2019–2023. The most extreme drought conditions of the study period occurred in 2023, where large areas were effected by severe to extreme drought. Despite this overarching drying trends, 2018 stands out as an exceptionally wet year covering most of the study area. Analysis of the hydroclimate in northwest Algeria indicates that both the frequency and spatial extent of persistent droughts have increased, particularly since 2015. Emerging from that period, multiple indicators point to a trend toward a drier climate, although interannual variability remains high and occasional anomalously wet years still occur. 3.5 Spatiotemporal Trends in Drought Indices: To adress the presistence and the direction of the drought conditions in Northwest Algeria, we employed the Mann–Kendall trend test and Sen’s slope estimator to all meteorological and vegetation-based indices from 2003 to 2023 (Figs. 10 and 11 , Table 4 ). The SPI trend analysis revealed exclusively negative (drying) trends across all timescales, and no significant wetting trends were detected throughout the study area. SPI3 exhibited the most spatially extensive drying, with approximately 56% of the region of shwing significant decreasing trends (p < 0.05), with a mean Sen’s slope of − 0.10 yr⁻¹. Similarly, 55% of the study area exhibited significant drying at the seasonal scale (SPI-6, mean slope = -0.10 yr⁻¹), while the long-term SPI-12 showed significant drying over 39% of the area (mean slope = -0.11 yr⁻¹). The decreasing spatial extent of significant trends from short- to long-term timescales reflects the integrative nature of cumulative precipitation deficits. The consistent presence of significant negative trends, emphasizes the growth and the progressive worsening of meteorological drought conditions, particularly in the post-2015 period. Vegetation-derived indices revealed more complex patterns and consistently lower coverage of significant trends compared to meteorological indices. The trend analysis for TCI showed an almost complete absence of significant temporal changes, with less than 1% of pixels exhibiting statistically significant trends (p < 0.05). In contrast, VCI revealed a significant spatial variability, with 3.4% of the area experiencing vegetation stress (decline), while 2.7% exhibited significant improvement (greening). The spatial distribution appears relatively balanced, combined with similar magnitudes of change (mean Sen’s slopes of approximately ± 0.006 yr⁻¹), for both negative and positive VCI trends. Vegetation responses appear to be highly localized and shaped by factors such as land cover, topography, and anthropogenic management. VHI, as a composite indicator, showed the clearest signal of degradation A significant decline in vegetation health was detected across 4.9% of the region (mean slope = -0.006 yr⁻¹), compared to only 0.5% showing significant improvement. This clear imbalance, where degradation extends over an area nearly ten times larger than improvement, indicates a net loss of vegetation health across the study area. Table 4 Summary of Mann-Kendall Trend Analysis for Drought Indices (2003–2023). Index Trend Type Significant Pixels (n) Spatial Coverage (%) Mean Sen’s Slope (units · year⁻¹) SD of Sen’s Slope Interpretation SPI-3 Significant Decrease (Drying) 2,907 55.97 –0.1001 0.0123 Drying Significant Increase (Wetting) 0 0.00 0.00 NA NA SPI-6 Significant Decrease (Drying) 2,848 54.83 –0.0957 0.0144 Drying Significant Increase (Wetting) 0 0.00 0.00 NA NA SPI-12 Significant Decrease (Drying) 2,043 39.33 –0.1062 0.0124 Drying Significant Increase (Wetting) 0 0.00 0.00 NA NA TCI Significant Decrease (Cooling) 23,962 0.94 –0.0029 0.0007 Cooling Significant Increase (Warming) 2,402 0.09 + 0.0026 0.0006 Warming VCI Significant Decrease (Vegetation Stress) 87,111 3.40 –0.0058 0.0023 Vegetation Stress Significant Increase (Vegetation Health) 69,801 2.72 + 0.0057 0.0022 Vegetation Health VHI Significant Decrease (Degradation) 125,261 4.89 –0.0060 0.0014 Degradation Significant Increase (Improvement) 13,182 0.51 + 0.0057 0.0016 Improvement 3.6 Multi-Temporal Analysis of Drought Conditions and Vegetation Response Derived from Meteorological and Remote Sensing Indices (2003–2023) : Figure 12 offers a comprehensive view of drought evolution by plotting the time series of all meteorological and vegetation indices from 2003 to 2023. By integrating these indices across a multi-panel time series, this study provides an integrated assessment of drought propagation dynamics, allowing systematic analysis of drought persistence across temporal scales and its ecological ramifications on regional vegetation dynamics. 3.6.1 Meteorological Drought Indicators. The top three panels present SPI values calculations at 3-, 6-, and 12-month timescales, based on the methodology of McKee et al. ( 1993 ). Drought is classified using SPI standardized thresholds In accordance with WMO guidelines: moderate drought (SPI ≤ − 1.0, orange dashed line) and severe-to-extreme drought (SPI ≤ − 1.5, red dashed line),. SPI-3 (Panel 1): This index captures short-term shifts in precipitation patterns and related meteorological variability, with rapid shifts between dry and wet conditions. Notable drought episodes occur during 2007–2009, 2011–2012, and sporadically from 2015 to 2022, where many episodes crossed into severe drought conditions. In contrast, wet episodes notably observed in 2009, 2013, and 2019, underscores transient periods of above-average precipitation and reflect episodic recovery phases. SPI-6 (Panel 2): Reflecting mid-term precipitation deficits, this index exhibits less temporal variability compared to SPI-3, Sustained wet conditions were observed between 2003 and 2014. After 2015, a persistent drying period emerged from 2015 to 2023, this pattern aligns closely with the SPI-6 spatial distribution. The period (2021–2023) is marked as one of the most severe drought episodes over the entire study period. SPI-12 (Panel 3): this index demonstrates long-term hydro-climatic transitions, with a notable decline in annual variability, exhibiting a substantial decrease in interannual variability. with wet conditions observed during the 2003–2014 period, followed by a persistent drying trend forward. Extended multi-year drought events (e.g., 2004–2006, 2020–2023), suggest continuous precipitation deficits that may adversely affect groundwater recharge and reservoir levels. These patterns are consistent with negative trends detected by Mann–Kendall analysis, indicating a progressive transition toward aridity. 3.6.2 Vegetation Response Indicators. The bottom three panels present remotely sensed vegetation condition indices, scaled to ranges between 0 and 1, where stress levels are classified as moderate (≤ 0.4, orange line) and severe-to-extreme (≤ 0.2, red line), consistent with classification schemes widely adopted in vegetation stress assessment. VCI (Panel 4): The VCI time series exhibits notable interannual variability, Episodes of significant vegetation stress, marked by declines in VCI values ( 55) indicate temporary improvements in vegetation condition associated with increased precipitation, The index exhibits a temporal lag in responding to water availability, highlighting its utility in assessing drought effects. TCI (Panel 5): TCI displays significant temporal variations that tracks thermal stress patterns throughout the study period, TCI values ( 60) detected during 2009–2010 and 2018–2019 indicate reduced thermal stress and more optimal thermal conditions supporting vegetation growth. Overall, TCI temporal patterns display a negative and inverse association with SPI and VCI trends, illustrating the critical effect of the thermal stress in exacerbating drought severity and limiting vegetation recovery during dry periods. VHI (Panel 6): The VHI time-series indicates moderate to severe drought conditions (VHI < 40) exhibiting relatively reduced temporal variations,. Significant drought periods occurred during 2004–2006, 2007–2008, 2011–2012, and 2020–2023, revealing persistent vegetation stress accompanied by thermal extremes. VHI patterns show agreement with SPI, highlighting its effectiveness in extended drought monitoring and impact assessment. 3.7 Multi-timescale correlation analysis between the (SPI-3, SPI-6, SPI-12) and VHI: Figure 13 illustrates the relationship between SPI at three different timescales and VHI, The analysis reveals a clear positive relationship between SPI and VHI, confirming that vegetation condition declines during periods of rainfall deficits. The correlation is moderate at the short-term scale (SPI-3 vs. VHI, r = 0.386), highlighting a rapid vegetation response to short-term moisture variations. The correlation strengthens significantly at the semi-annual scale (SPI-6 vs. VHI, r = 0.578), suggesting that vegetation health in this region is most sensitive to precipitation anomalies integrated over a 6-month period. The correlation weakens at the annual scale (SPI-12 vs. VHI, r = 0.285), possibly due to ecological resilience mechanisms or the buffering role of soil moisture and groundwater over longer periods. The strong correlation at the SPI-6 timescale underscores its effectiveness as a predictor for agricultural and ecological drought in the study area. 4 Discussion This research presents an integrated assessment for drought patterns across Northwest Algeria from 2003 to 2023, linking meteorological indices with remotely sensed vegetation indicators. The findings reveal a statistically significant and persistent increase in drought conditions, marked by negative precipitation patterns over the short, medium, and long-term scales and associated patterns of vegetation stress. These findings suggests that Northwest Algeria is experiencing a continuous process of aridity, a trend that aligns with broader climate projections for the western Mediterranean basin. The following section frames these findings within the scientific literature, examines climatic and environmental drivers, assess ecosystem response dynamics, and outlines the implications for regional drought management and future research. 4.6 Comparative Context and Validation of Findings: The drought patterns detected in this study are consistent with previous regional studies, confirming the validity of our comprehensive methodological framework and indicating the strength of the chosen methodological approach. Although the association between vegetation response and precipitation anomalies varied spatio-temporally, statistically significant correlations between SPI and two vegetation-based indices (VCI and VHI) were observed in several periods and sub-regions. For instance, (Abbes et al., 2018) documented a strong correlation between SPI and NDVI-based indices in the Cheliff watershed, indicating that rainfall variation is a principal factor of vegetation dynamics. Our findings confirm a consistent response of VCI and VHI to SPI-driven moisture availability, supporting the drought–vegetation relationship, Moreover, the frequency and exacerbation of droughts after 2015 are consistent with the findings of (Messis et al., 2025 ), who reported 2015–2021 as one of the most severe dry episodes in the upper Cheliff basin. The strong correlation between SPI and VHI, particularly at the 6-month interval (r = 0.578), validates the integrated monitoring approach used in this research, demonstrating the reliability of vegetative health as an effective indicator for assessing agricultural drought severity. Our findings are consistent with research undertaken in other dry and semi-arid regions, such as by (Ejaz et al., 2023) in Saudi Arabia, which confirmed the reliability of VHI in regions with restricted data availability. Similarly, research such as that by (Bougara et al., 2021 ; Fettam et al., 2025 ) have corroborated the methodological choice of using SPI, demonstrating its statistical resilience through strong inter-index correlation coefficients within the Tafna watershed. This consistency between our findings and previous research highlights the dependability of the integrated approach for monitoring the multi-temporal dynamics of drought across the region. However, our findings reveal a critical departure that underscores the intensity of recent climate shifts. Whereas long-term studies like(Fettam et al., 2025 ) (1970–2019) and(Bougara et al., 2020 ) (1979–2011), which documented periods of stability or wetting trends at longer timescales, in addition, our analysis of SPI-12 during the period 2003–2023 reveals a significant and sustained decline trend. The difference clearly indicates that over the last 20 years, the historical balance between dry and wet cycles, which previously offered long-term climatic resilience, has been fundamentally disrupted during the last two decades. the outcomes of this study suggest that a major hydroclimatic regime shift appears to have occurred during the research time period, with a new era of aridity exceeding the region’s existing adaptive levels a critical shift that occurred primarily in the last decade (post-2015), whereas most previous studies concluded before these regional drought assessments. 4.7 Drivers of Aridification Chronic aridity in Northwest Algeria is linked to broader regional and global climatic dynamics, and the study area experienced a significant decrease in SPI values after 2015, which is consistent with the intensifying aridity in the Mediterranean region. Several studies indicate the decline in precipitation in the Maghreb region is strongly correlated with the positive NAO phase (Achite et al., 2021 ; Bouarfa et al., 2022 ). This long-term trend has historical roots, as (Derdous et al., 2021) identified the period post-1976 as a transition to persistent dryness, indicating a major hydroclimatic regime shift, that provides the broader historical context for the trends we observed. Our findings, notably, the strong correlation between VHI and TCI, indicates that precipitation alone cannot explain vegetation stress; this pattern is consistent with the results of (Mahcer et al., 2024), which point to an increase in regional temperatures. This warming trend intensifies atmospheric evaporative demand (AED), increasing potential evapotranspiration (PET) rates and decreasing in soil moisture retention, even when annual rainfall is near-normal (Trenberth et al., 2013; Vicente-Serrano et al., 2014 ), These circumstances are often referred to as a “hot drought” or “global‑change‑type drought”, where rising temperatures exacerbate hydrological deficits and ecological vulnerability (Allen et al., 2015 ; Mora et al., 2017 ) These dynamics mean that even normal or slightly below‑normal rainfall can exacerbate agricultural and ecological stress by accelerating soil moisture depletion and direct thermal stress on vegetation. Moreover, (Benhizia et al., 2024 ) observed a negative correlation between NDVI and temperature anomalies, which suggests that higher temperatures are a primary driver of vegetation condition, in addition to the effects of precipitation deficiency. This shift constitutes a major hydroclimatic change that has serious consequences on ecological resilience and the sustainability of water resources in the Mediterranean basin, particularly the Maghreb region. Furthermore, the effects of these climatic drivers are exacerbated by anthropogenic pressures, including deforestation, overgrazing, agricultural expansion, and urban growth, as reported by (Bouarfa et al., 2022 ; Derdour et al., 2022 ), which leads to land degradation and decreases the soil's capacity for holding water, making ecosystems more vulnerable to climate variability. While several studies consistently point to the inner steppe provinces (Saida, Tiaret, Mascara) as drought hotspots (Ceppi et al., 2025 ; Derdous et al., 2021; Elouissi et al., 2021 ; Oubadi et al., 2024 ) and North African steppe ecosystems (García-Vega et al., 2020 ), the combination of climatic vulnerability with anthropogenic pressures fits with long‑term trends of reduced precipitation and surface water availability across Algeria (Hamitouche et al., 2024 ), highlighting how hydroclimatic stress in this area builds up over time and is caused by many different things. 4.8 Vegetation Dynamics and Land Cover Mechanisms under Extended Drought Conditions. Vegetation indices show that drought impacts different locations in different ways, providing more detailed ecological insights than SPI alone, Meteorological drought was widespread, whereas vegetation indices (VCI, TCI, VHI) exhibited delayed and regional responses, showing delayed consequences due to soil moisture buffering (Bouarfa et al., 2022 ; Vicente-Serrano et al., 2010 ). However, during the extended drought from 2020 to 2023, these natural barriers disintegrated, causing extensive vegetation decrease, especially notable in VHI patterns. The increased sensitivity of VHI compared to VCI indicates the integrated impact of thermal stress and moisture deficits (Benhizia et al., 2024 ; Mahcer et al., 2024). Land cover extensively influences vegetation resilience. Forests and grasslands show considerably stronger adaptation ability, but croplands and shrublands in semi-arid zones are more vulnerable to drought (Mahcer et al., 2024), Similar findings in Morocco’s Doukkala and Tensift basins demonstrate that irrigated croplands respond differently from natural rangelands under drought conditions (Ayad et al., 2023 ; El-Bouhali et al., 2025 ; Habitou et al., 2020 ; Salih et al., 2024 ). Subsequently, drought consequences are determined not only by climate forcing but also by land-use mosaics and human management, highlighting the need for spatially explicit, LULC-informed drought risk frameworks (Ceppi et al., 2025 ). 4.9 Broader Context, Limitations, and Future Research The agreement between meteorological and vegetation‑based drought indicators across multiple time scales highlights an accelerating aridification trend in Northwest Algeria, This pattern illustrates conditions across North Africa and the Mediterranean, regions commonly identified as climate‑change hotspots (Essa et al., 2023a ; Stamou et al., 2025 ), The convergence of meteorological and biophysical drought indicators across multiple temporal scales further underscores the intensifying aridification trajectory in Northwest Algeria, mirroring broader climatic challenges across the southern Mediterranean basin (Bergaoui et al., 2024 ; Kenawy et al., 2025 ). The documented shift toward more frequent, intense, and prolonged drought episodes, amplified by rising temperatures and declining precipitation, poses growing threats to water security, agricultural productivity, and socio-economic stability across the region (Ben Mhenni et al., 2020 ; Mliyeh et al., 2024 ). The spatial and temporal patterns identified in this study are consistent with similar aridification trajectories reported in neighboring Mediterranean basins, such as the Macta basin (Berhail et al., 2021 ), Northeastern Algeria (Derradji et al., 2023 ), and El Tarf province (Mayouf & Hanafi, 2024 ). Comparative research in Morocco (Mliyeh et al., 2024 ) and Tunisia (Ben Mhenni et al., 2020 ) also reports parallel patterns of precipitation decline and vegetation stress, suggesting that Northwest Algeria’s drought intensification forms part of a larger regional climatic transition affecting the entire North African–Mediterranean corridor. While this research presents a comprehensive multi-index approach, certain methodological limitations merit attention. First, the use of SPI, which despite its robustness, reflects only precipitation deficits and does not fully capture the role of temperature-driven evapotranspiration (Liu et al., 2024 ; Vicente-Serrano et al., 2010 ). Given the documented warming trend and its role in creating "hot droughts," (Cook et al., 2018 ; Trenberth et al., 2013b ), future studies should integrate temperature-sensitive indices such as SPEI and PDSI. Comparative research in Algeria (Ziari & Medjerab, 2024b ) and other Mediterranean environments (Hadri et al., 2021 ; Worku, 2024 ) has already shown that SPEI can detects greater drought severity and duration under warming conditions than SPI alone. Second, this study relied on meteorological and vegetation-based indicators. Integrating satellite-derived soil-moisture datasets (e.g., SMAP and Sentinel-1 SAR)(Bauer-Marschallinger et al., 2019 ; Entekhabi et al., 2010 ) would offer a more direct measure of water availability for plants and could refine understanding of drought propagation from meteorological to agricultural and hydrological domains. Recent applications have shown promise in enhancing drought monitoring accuracy in North Africa (e.g.,Tunisia and Egypt)(Ramat et al., 2023 ) and in the MENA region (Nie et al., 2022 ), and highlight how incorporating soil-moisture and leaf-area data into land-surface models can substantially enhance drought monitoring accuracy and operational early-warning systems. Finally, a key research direction concerns the integration of biophysical drought indicators with socio-economic vulnerability assessments. While our indices characterize the physical dimensions of drought, they do not capture how different communities and agricultural systems experience or adapt to water scarcity (Naumann et al., 2014 ; Simelton et al., 2009 ). Coupling remote-sensing indicators with socio-economic and livelihood metrics, such as crop dependency, and adaptive capacity, is critical for an understanding of drought risk and adaptive capacity (Erian et al., 2011 ; Mertz et al., 2009 ). This transition from hazard characterization to impact assessment is critical for informing targeted drought-management strategies in the Maghreb region. The observed long-term drying and vegetation degradation trends documented here carry significant implications for regional water governance and adaptation planning. Consistent with broader regional assessments (Essa et al., 2023b ; Krim & Hassani, 2023 ), Northwest Algeria appears to be transitioning into a new climatic regime characterized by persistent water scarcity and heightened drought frequency. Addressing this shift requires moving from reactive to proactive risk management paradigms anchored in integrated national drought policies, early-warning systems, and adaptive governance frameworks (Sivakumar et al., 2014 ; Wilhite et al., 2014 ). 5 Conclusion The present study used meteorological (SPI) and satellite-based vegetation (VCI, TCI, VHI) indicators for carrying out a comprehensive spatiotemporal assessment of drought in Northwest Algeria from 2003 to 2023. Our analysis revealed a significant and accelerating trend towards aridification, particularly in the post- 2015 period. Key findings include: All SPI timeframes (3, 6, and 12 months) showed a statistically significant drying trend, indicating a continuous decrease in precipitation and an increase in the frequency and intensity of meteorological drought. The biophysical response to these climatic changes was successfully captured by vegetation indices (VCI, TCI, and VHI), which demonstrated recurrent periods of extensive vegetative stress that correlated with meteorological drought events, particularly in 2008, 2012, 2017, and 2022. the interior area, semi-arid provinces (e.g. Saida, Tiaret, Mascara) emerged as uniformly drought hotspots with much higher vulnerability than both coastal and mountainous regions. The strongest significant correlation was that between SPI-6 and VHI (r = 0.578), highlighting that the seasonal precipitation deficit dominated agricultural drought in the area, this makes the combination of these two indices as an useful tool for early warning systems. The integrated approach, combining precipitation and temperature effects via the VHI, proved more effective in capturing the full extent of vegetation degradation than single-factor indices, underscoring the growing role of thermal stress in a warming climate. Tying these results together, it is clear that this region is under increasing hydroclimatic pressure. The framework proposed in this paper, utilizing publicly accessible data and cloud computing, it provides a scalable,remotely and efficiency approach for operational drought monitoring. The findings are important in generating key spatially explicit information for policymakers to develop focused adaptation related strategies, enhance water resource management, and build resilience in vulnerable agricultural and ecological systems. As Northwest Algeria confronts a new climatic reality, a proactive, data-driven, and integrated approach to drought management is not just recommended; it is imperative. This study makes several significant scientific contributions to drought monitoring and assessment methodologies. First, it demonstrates the effectiveness of integrating meteorological indices (SPI) with satellite-derived vegetation indices (VCI, TCI, VHI) for comprehensive drought characterization in semi-arid regions. The practical implications of this research are substantial for drought early warning systems and water resource management. The validated approach provides stakeholders with a robust framework for timely drought detection and severity assessment, enabling proactive rather than reactive management strategies. Furthermore, the integration of multiple indices enhances the reliability of drought monitoring by capturing both meteorological and ecological dimensions of drought impacts. This multi-faceted approach is particularly valuable for agricultural planning and water allocation decisions in Northwestern Algeria and similar semi-arid environments. Declarations Conflict of Interest: On behalf of all authors, the corresponding author states that there is no conflict of interest. Funding: Project No. TKP2020-IKA-04 has been implemented with the support provided by theNational Research, Development, and Innovation Fund of Hungary, financed under the 2020 − 4.1.1-TKP2020 funding scheme Author Contribution Ramzi Benhizia: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing – original draft. Brahim Abdelkebir: Methodology, Validation, Writing – review & editing. Behnam Ata: Formal analysis, Software, Validation. Singo Mukovhe Vele: Investigation, Resources, Writing – review & editing. Kwanele Phinzi: Data curation, Resources, Writing – review & editing. György Szabó: Supervision, Project administration, Funding acquisition, Writing – review & editing. All authors read and approved the final manuscript. Acknowledgement The authors gratefully acknowledge the providers of CHIRPS precipitation data and MODIS satellite products for making these datasets freely available, which constituted a fundamental basis for this research. In addition, the authors extend their sincere gratitude to the Department of Landscape Protection and environmental geography (University of Debrecen) for providing the resources and institutional support essential for the successful execution of this study. The authors also appreciate the constructive feedback and valuable insights offered by academic colleagues during the development of the manuscript. Furthermore, this research was supported by Project No. TKP2021-NKTA-32, funded by the National Research, Development, and Innovation Fund of Hungary, and this financial support is gratefully acknowledged. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. 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illustrating the network of keywords related to drought assessment approaches and indicators\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/d20889fc1d1d50f82fc5e5e8.png"},{"id":101652913,"identity":"c30b8924-b231-4d7f-a6e6-248b06f522eb","added_by":"auto","created_at":"2026-02-02 09:36:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12166934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGeographical Location of the study area in northwestern Algeria: (a) Land cover map of the study area, and the elevation of the study area (b).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/310398b2c0a68e9841b13191.png"},{"id":101652921,"identity":"03fca047-673d-4095-973b-69edd549882f","added_by":"auto","created_at":"2026-02-02 09:36:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141340,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodological workflow used in this research.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/380aa69e95a03dcc62504b2e.png"},{"id":101652903,"identity":"de1bbe6b-5299-4e63-9cd3-1b0564ab02fe","added_by":"auto","created_at":"2026-02-02 09:36:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1104172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of the VCI for the period 2003-2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/e65683b745b6921c760bde9b.jpg"},{"id":101652907,"identity":"5b099a95-ab11-4f7f-95de-2007cc05a193","added_by":"auto","created_at":"2026-02-02 09:36:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1023827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of the TCI during 2003-2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/8eac598c9f61c4582a7d417b.jpg"},{"id":101652904,"identity":"5007ddc0-5898-407f-8095-4eb11dbb31a0","added_by":"auto","created_at":"2026-02-02 09:36:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1098155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial Distribution of Drought Severity in Northwest Algeria (2003–2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/7098ccab65156e68755a9cf0.jpg"},{"id":101754217,"identity":"7761bf08-9fff-4f81-b46b-5af2d1ebcad4","added_by":"auto","created_at":"2026-02-03 10:42:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":313977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of SPI3 (2003-2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/c40dac6ba010de9b85dc3057.png"},{"id":101652906,"identity":"acf4b71f-0738-478a-93e6-1971036226d1","added_by":"auto","created_at":"2026-02-02 09:36:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":321168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of SPI6 (2003-2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/91c67023064525c1ec6b24ad.png"},{"id":101753710,"identity":"12c3230f-e517-40a9-b678-fc261483adb6","added_by":"auto","created_at":"2026-02-03 10:40:34","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":324214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of SPI12 (2003-2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/a941e642f7a51df827b4dee5.png"},{"id":101753273,"identity":"9631c6b2-1c03-4ffd-88c3-0340bafecb58","added_by":"auto","created_at":"2026-02-03 10:39:34","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1640793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatiotemporal trend analysis of meteorological drought indices (SPI-3, SPI-6, SPI-12) across Northwest Algeria (2003–2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/4febc238b3eb83c4ce87c4c8.png"},{"id":101652910,"identity":"d4c8c796-f7f6-46f8-b8e1-f6a8b183d533","added_by":"auto","created_at":"2026-02-02 09:36:30","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":13031893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatiotemporal trend analysis of vegetation-based drought indices (VHI, VCI, TCI) across Northwest Algeria (2003–2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/09d2811fd4193c40ed0daecf.png"},{"id":101652911,"identity":"6cbaef1c-df0f-4a96-aa50-fb35a31dabad","added_by":"auto","created_at":"2026-02-02 09:36:30","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":2879566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTemporal evolution of meteorological\u003c/em\u003e \u003cem\u003e(SPI-3, SPI-6, SPI-12) and vegetation-based (VCI, TCI, VHI) drought indices across Northwest Algeria (2003–2023). Dashed lines indicate thresholds for moderate and severe drought/stress.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/40665078218d51df07401ff6.png"},{"id":101753337,"identity":"e144e186-ca46-47e4-8274-0f3d6c68364b","added_by":"auto","created_at":"2026-02-03 10:39:45","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":814085,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMulti-timescale analysis of the relationship between (SPI) and (VHI) using Pearson correlation and linear regression (2003–2023).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/69cfea792e8615578182df75.png"},{"id":102403996,"identity":"b4ee2a9f-6a16-40bc-8a1b-f810247db48e","added_by":"auto","created_at":"2026-02-11 10:52:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35788719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8711142/v1/d80ba45f-cb04-4f0f-baa5-5995d82ba171.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bi-decadal drought assessment in Northwestern Algeria: integrating meteorological and remote-sensing indices.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDrought is a recurring natural phenomenon that has challenged human societies throughout history (Giaquinto et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Spinoni et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Its impacts extend beyond ecosystems to multiple economic and social sectors from agriculture, the cornerstone of early civilizations, to modern industry, urban water systems, and transportation networks (Heim, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The United Nations (UN) identifies drought as a major contributor to global water scarcity, currently affecting 40% of the world\u0026rsquo;s population and posing a serious risk to human security and well-being (Biswas et al., 2025; UNCCD \u0026amp; JRC, 2023). Between 1967 and 1991, drought affected nearly half of all weather-related disaster victims (He et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kogan., 1997), Estimates suggest that, by 2030, up to 700\u0026nbsp;million people, roughly translating to about 9% of the global population, could be displaced due to drought-induced water shortages (Ismail et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Developing regions, such as Africa, are likely to suffer more devastating effects than their developed counterparts.\u003c/p\u003e \u003cp\u003eDroughts in Africa cause major human and economic hardships, particularly in areas where more than 85% of the population depends on rain-fed farming (Bayable et al., 2025; Gebremeskel et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Near East and North Africa (NENA) region is predominantly arid due to its geographic and climatic characteristics (Francis et al., 2024). Approximately 75% of the area is desert, receiving less than 50 mm of rainfall annually and offering limited support for human activity (Bazza et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The remaining areas experience semi-arid or Mediterranean climates, while sub-humid and humid conditions are confined to select coastal zones and high-altitude regions exposed to prevailing winds (Lionello, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Regions within a country that fall under desert or arid climates are persistently dry and should be governed accordingly through 'dryland management plans' integrated into broader national drought management strategies (FAO, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlgeria endured a devastating drought between 1945 and 1947 (Fellag et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Ain Sefra region, located in southern Oran, official records indicate that around 3,000 people died from starvation out of a total population of 80,000 (Achite et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The drought also led to the loss of 900,000 sheep, wiping out nearly 90% of the livestock. Two decades later, in early 1966, the country experienced its driest period since 1945, resulting in poor crop germination and significant declines in agricultural production (Kassoul, 2006). Over the past 30 years, Algeria has experienced prolonged and severe drought conditions, marked by a 30% reduction in average annual rainfall (Ceppi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This significant rainfall deficit has adversely affected \u0026lsquo;river flow patterns, leading to serious repercussions for the country\u0026rsquo;s socio-economic activities (FAO, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe American Meteorological Society categorizes drought into four types: meteorological, hydrological, agricultural, and socio-economic (Benkhamallah et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ceppi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Each type reflects a different form of moisture shortage and corresponds to different environmental and societal impacts (Li et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among various meteorological variables, precipitation is widely recognized as the primary factor influencing the occurrence and severity of drought. Consequently, the term meteorological drought is often used to refer specifically to periods of precipitation deficit. To assess long-term variations in drought conditions based solely on precipitation data, the Standardized Precipitation Index (SPI) is commonly employed as a reliable analytical tool (Mckee et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). To address this, the U.S. National Oceanic and Atmospheric Administration (NOAA) introduced the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI), derived from satellite-based NDVI and thermal data. These indices enable effective large-scale drought monitoring by identifying vegetation stress related to water and temperature conditions. Their application has demonstrated strong correlations with crop yields across diverse ecological regions (Kogan, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In contrast to NDVI and VCI, the Vegetation Health Index (VHI) also accounts for the influence of temperature on vegetation condition (Gebrechorkos et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, These indices have been successfully applied to drought dynamic assessment over space and time through integrating meteorological deficit with vegetation and thermal responses,\u003c/p\u003e \u003cp\u003eA recent study in Rwanda, with a focus on the drought-prone Eastern Province, investigated meteorological and agricultural droughts using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI), which integrates NDVI and LST data. Analyzing data from 31 meteorological stations (1983\u0026ndash;2020) and remote sensing indices (2001\u0026ndash;2020), the study revealed that the most severe droughts occurred between 2003 and 2017, especially in the Southern and Eastern Provinces. These droughts significantly reduced vegetation health and crop yields (Niyonsenga et al., 2024). The findings highlight the urgent need for spatiotemporal drought assessments and recommend proactive policies on drought mitigation, climate change adaptation, and sustainable water resource management in Rwanda (Niyonsenga et al., 2024). In Saudi Arabia's hyper-arid regions, a study compared remote sensing drought indices (VCI, TCI, VHI) with the meteorological SPEI index from 2001 to 2020. Results showed that VHI correlated best with SPEI, especially at longer timescales, making it a reliable tool for drought monitoring where ground data is limited (Ejaz et al., 2023).\u003c/p\u003e \u003cp\u003eA recent study conducted in Annaba, Algeria, analyzed drought trends from 1981 to 2021 in the context of global warming and extreme weather events. Using standardized drought indices (SPI and SPEI) and extreme value analysis, the research highlighted the increasing severity of droughts when temperature is accounted for (via SPEI), compared to precipitation-only assessments (SPI). The findings underscore the importance of integrating temperature, precipitation, and evapotranspiration in drought evaluation to enhance prediction accuracy. This study contributes valuable insights into climate change adaptation and water resource management in arid and semi-arid regions (Ziari et al., 2024). A study focused on the Cheliff watershed in Northwestern Algeria examined drought patterns using Landsat satellite imagery and meteorological data. Given the region's alternating wet and dry periods influenced by both Atlantic and Mediterranean air masses, the research aimed to monitor drought at spatial and temporal scales. By comparing the standardized NDVI values with the Standardized Precipitation Index (SPI) from fifty meteorological stations for selected years (1987, 2000, 2006, 2011, and 2015), the study found a strong correlation between vegetation response and precipitation. This led to the development of a new satellite-based drought index, offering a valuable tool for spatial drought monitoring, particularly in areas with limited climate data (Abbes et al., 2018).\u003c/p\u003e \u003cp\u003eA study on the upper Cheliff basin in Algeria (1982\u0026ndash;2021) analyzed meteorological drought patterns using SPI and SPEI across multiple time scales. Both indices effectively captured drought variability, with SPEI detecting more short-term events. Five major drought periods were identified, and a strong correlation (R\u0026thinsp;=\u0026thinsp;0.73\u0026ndash;0.93) was observed between the indices. The findings highlight the importance of using both SPI and SPEI for accurate drought monitoring, especially in agriculturally significant areas (Messis et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study on the Oued Sebaou basin in northern central Algeria analyzed meteorological drought patterns from 1972 to 2010 using SPI data from 23 rain gauges. Seasonal and annual assessments, supported by GIS-based indices (PCI and MFI), revealed moderate precipitation concentration and spatial variability linked largely to longitude. The findings indicated a prolonged drought starting in the late 1980s, with over half the stations experiencing moderate to severe drought between 1986 and 2001. Decadal comparisons showed more wet conditions during 1972\u0026ndash;1981 and 2002\u0026ndash;2010, with extreme wet events more frequent in the latter period. These insights support improved watershed and drought management strategies (Zerouali et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this body of work, a comprehensive, long-term assessment integrating both meteorological and a full suite of vegetation health indices (VCI, TCI, and VHI) for the entirety of Northwest Algeria has been lacking. Many previous studies concluded before the recent, intense drought period post-2015, potentially missing a critical climatic shift. This research aims to fill this gap by providing a detailed assessment of drought dynamics in Northwestern Algeria over the two-decade period of 2003\u0026ndash;2023. The specific objectives are to: (i) examin drought severity across multiple temporal scales through SPI at 3, 6, and 12-month intervals; (ii) assess vegetation condition and thermal stress patterns using three MODIS-derived indicators: VCI, TCI, and VHI; (iii) detect and evaluate significant temporal changes in drought severity through Mann\u0026ndash;Kendall analysis and Sen's Slope estimation; and (iv) assess the degree of coupling between precipitation variability and vegetation dynamics through correlation analysis. By detecting recurrent drought episodes, spatially vulnerable areas, and vegetation sensitivity to climatic variability, the findings support improved drought monitoring and contribute to the development of regional drought warning systems, and shaping the formulation for adaptive management policies in arid and semi-arid environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e1. Study area:\u003c/h3\u003e\n\u003cp\u003eThe study area encompasses the northwest region of Algeria, extending approximately 500 km from west to east and varying in north-south width. Geographically, it lies between 2\u0026deg;10\u0026prime;10\u0026Prime; W and 3\u0026deg;10\u0026prime;11\u0026Prime; E longitude and 34\u0026deg;18\u0026prime;54\u0026Prime; and 36\u0026deg;48\u0026prime;12\u0026Prime; N latitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The climate ranges from semi-arid to Mediterranean and exhibits pronounced spatial and temporal variability in precipitation, which strongly influences regional hydrology (Hamitouche et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mahcer et al., 2024; H. Meddi et al., 2007; M. Meddi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Mean annual precipitation differs markedly across the northwest region; interior basins such as the Tafna average 240 mm yr⁻\u0026sup1;, whereas coastal and mountainous areas (e.g., northern parts of the Chelif basin) exceed 700 mm yr⁻\u0026sup1; (Achite et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bougara et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hamitouche et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; H. Meddi \u0026amp; Meddi, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; M. Meddi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe region includes several major basins and wadis, three of which dominate regional hydrology. The Chelif basin (\u0026asymp;\u0026thinsp;44,694 km\u0026sup2;), Algeria\u0026rsquo;s largest watershed, originates in the Tell Atlas and drains to the Mediterranean Sea (Achite et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Derdous et al., 2021; Elmeddahi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; H. Meddi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mehaiguene et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Tafna basin, is an important transboundary catchment on the Algeria\u0026ndash;Morocco frontier that supports regional agriculture and biodiversity,(Bougara et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fettam et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) the Macta basin collects waters from the Habra and Sig rivers and drains into the Gulf of Arzew in the Mediterranean Sea; containing ecologically significant wetlands such as Lac El Macta, which are designated under the Ramsar Convention, underscoring its ecological importance (Elouissi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ismail et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe research methodology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) comprises six key stages: (i) satellite-derived data collection \u0026ldquo;vegetation/thermal\u0026rdquo; from MODIS and extraction of precipitation records from CHIRPS for the period 2003\u0026ndash;2023, (ii) preprocessing of MODIS data to derive NDVI and LST metrics, (iii) calculation of the SPI from CHIRPS data; (iv) derivation of drought indices (VCI from NDVI, TCI from LST, and VHI combining VCI and TCI); (v) spatiotemporal analysis and mapping of all indices in Google Earth Engine (GEE); and (vi) statistical correlation and trend analysis to evaluate drought patterns and the relationship between meteorological and vegetation-based indices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Acquisition and preprocessing:\u003c/h2\u003e \u003cp\u003eMultiple satellite datasets were processed using Google Earth Engine (GEE) to assess drought conditions across northwest Algeria from 2003 to 2023, The details regarding the source, variable, and resolution of these datasets are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For precipitation data, we used CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), which combines satellite imagery estimates with station data at a spatial resolution of 0.05\u0026deg; (~\u0026thinsp;5.3 km) (Funk et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mianabadi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The dataset was clipped to the study boundary, then, daily precipitation values were aggregated to 3-, 6-, and 12-month totals (SPI-3, SPI-6, SPI-12). Additionally, for data quality control and to ensure valid inputs for the SPI calculation, only time intervals with continuous and complete daily records were retained. SPI standardization was applied per pixel using long-term means and standard deviations, assuming an approximately normal distribution of precipitation anomalies.\u003c/p\u003e \u003cp\u003eMODIS (Moderate Resolution Imaging Spectroradiometer) data were used to retrieve remote sensing data (2003\u0026ndash;2023). More specifically, we obtained the MOD13Q1 V6.1 NDVI products with a 16-daytemporal resolution and a spatial resolution of 250 m (Dinan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khan et al., 2023; Sazib et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, we gathered LST from MOD11A2 V6.1 products with an 8-days temporal resolution and a spatial resolution of 1000 m (Wan, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Both datasets were aggregated into monthly composite means to achieve the temporal resolution alignment for drought analysis. Furthermore, MODIS data preprocessing included converting digital values to physical unites using standard scaling factors, respectively, while spatial analysis was limited to the study area boundaries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the datasets used in the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemporal Resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHIRPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHIRPS v2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3km (0.05\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMODIS NDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOD13Q1 V6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16-day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMODIS LST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOD11A2 V6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8-day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Calculation of Drought Indices:\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Vegetation Condition Index (VCI):\u003c/h2\u003e \u003cp\u003eThe VCI, introduced by Kogan (Kogan, 1995a, 1995b), is a remote sensing-based drought indicators that assesses vegetation condition by comparing the current NDVI values with their historical range for a given location and time. VCI reflects the local ecosystems dynamics and vegetation response during different growth cycles. The index scales the NDVI values into a standardized 0-100 range, where low values indicate stressed vegetation and high values imply optimal growing conditions.\u003c/p\u003e \u003cp\u003eVCI was computed using Eq.\u0026nbsp;(1):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}VCI=100\\times\\:\\frac{NDVIcurrent-NDVImin}{NDVImax-NDVImin}\\:\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eNDVIcurrent\u003c/em\u003e represents the current value of NDVI\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eNDVImin\u003c/em\u003e is the minimum value of NDVI and \u003cem\u003eNDVImax\u003c/em\u003e indicates the maximum value of NDVI during the research period (2003\u0026ndash;2023).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis study applied the formula on 250 m NDVI imagery that was scaled by 0.0001. Monthly composites were generated; per-pixel minimum and maximum NDVI values were extracted to standardize each composite.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Temperature Condition Index (TCI):\u003c/h2\u003e \u003cp\u003eThe Temperature Condition Index (TCI), developed by Kogan in 1995, is the thermal analogue of VCI and assesses vegetation stress resulting from elevated surface temperatures. Drought assessment becomes particularly more effective, where soil moister reduction induces thermal stress on vegetation, TCI ranges from 0 to 100, low values indicate extreme thermal stress, while high values reflect favorable thermal conditions. (Kogan, 1995a; Liu \u0026amp; Kogan, 1996)\u003c/p\u003e \u003cp\u003eTCI was computed using Eq.\u0026nbsp;(2):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}TCI=100\\times\\:\\frac{LSTmax-LSTcurrent}{LSTmax-LSTmin}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLST current\u003c/em\u003e represents the monthly mean land surface temperature values.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLST min\u003c/em\u003e and \u003cem\u003eLST max\u003c/em\u003e indicate the annual minimum and maximum values of LST.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis study used LST at 1 km resolution. Digital numbers (DN) were converted to physical temperatures with a scale factor of 0.02. Monthly mean LST composites were generated from the 8-day product, after which annual pixelwise maximum and minimum LST values were calculated to define normalization bounds for TCI (Rhee et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Vegetation Health Index (VHI):\u003c/h2\u003e \u003cp\u003eVHI is a widely used remote-sensing drought metric that integrates thermal stress and vegetation health into a single composite drought indicator, VHI combines VCI (from NDVI) and TCI (from LST)), through integrating the moisture deficiency and heat stress effects on vegetation. (Kogan, 1995a; Liu \u0026amp; Kogan, 1996) VHI, proposed by Kogan (Kogan, 1995a, 1995b), is computed as a weighted mean of VCI and TCI, making it highly effective for monitoring agricultural drought in arid, semi-arid, and dry sub-humid climates (Bhuiyan et al., 2006; Kogan, 2001; Kogan et al., 2003), In this research we assigned equal weights to VCI and TCI (α\u0026thinsp;=\u0026thinsp;0.5), consistent with conventional approaches due to the uncertainty regarding the contribution of each component to drought assessment (Liu \u0026amp; Kogan, 1996; Wan et al., 2004), VHI ranges from 0 to 100, ; values near 0 indicate extreme drought, while values near 100 indicate no drought.\u003c/p\u003e \u003cp\u003eVHI was computed by Eq.\u0026nbsp;(3):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}VHI=\\alpha\\:\\:\\times\\:\\:VCI\\:+\\left(1-\\:\\alpha\\:\\right)\\times\\:\\:TCI\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eα is the weighting factor with a value of 0.5.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVCI represents the vegetation condition index compared to its long-term range.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTCI indicates the temperature condition index.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe formula was applied using MODIS-derived VCI and TCI data for each month during 2003\u0026ndash;2023. Firstly, the monthly VCI and TCI images datasets were combined; secondly, the VHI was computed for each pixel using the weighted formula.\u003c/p\u003e \u003cp\u003eWe classified VHI into five drought severity classes as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrought Severity Classification Scheme according to VHI values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVHI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Standardized Precipitation Index (SPI).\u003c/h2\u003e \u003cp\u003eSPI, presented firstly by Mckee in 1993 (Mckee et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) is widely recognized as a robust and flexible drought index. The WMO (world meteorological organization) played a pivotal role in its widespread adoption by recommending SPI as a standard meteorological drought index, promoting its global application in 2009. SPI detects precipitation anomalies across different timescales from short-term (e.g., 1 or 3 months) to long-term (e.g., 12 or 24 months) by fitting a probability distribution (typically Gamma) to a long-term precipitation record, which is then transformed into a normal distribution (Hayes et al., 2011; Lloyd-Hughes \u0026amp; Saunders, 2002; Mckee et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe computed SPI at 3-, 6-, and 12-month time scales (SPI-3, SPI-6, SPI-12) to capture short-term, seasonal, and annual drought dynamics (Vicente-Serrano et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Daily CHIRPS v2.0 precipitation (\u0026asymp;\u0026thinsp;5.3 km) were temporally aggregated to the corresponding time scales, To ensure consistency and accuracy in the SPI calculations, only time intervals only periods with complete daily precipitation records (i.e., without missing data) or (no missing days) were included in the analysis.\u003c/p\u003e \u003cp\u003eSPI was computed as Eq.\u0026nbsp;(4):\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}SPI=\\frac{P-\\mu\\:}{\\sigma\\:}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e \u003c/span\u003e: represents the total precipitation during the given period.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026micro;: represents the long-term mean precipitation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eσ: is the standard deviation of that period.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWe computed SPI at each pixel across the study area. These calculated values were subsequently classified into drought intensity categories using a standardized classification approach, shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrought classification scheme according to SPI following McKee et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrought Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPI Range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le; -2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.99 to -1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate drought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.49 to -1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNear normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.99 to +\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;1.00 to +\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverely wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;1.50 to +\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge; +2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mann-Kendall Trend Analysis:\u003c/h2\u003e \u003cp\u003eThe non-parametric Mann-Kendall (MK) test was employed to detect significant, consistent trends within the temporal records of VHI, TCI, VCI, and SPI at three temporal scales (3-, 6-, and 12-month intervals) throughout the period 2003\u0026ndash;2023. This test is widely used in environmental studies due its robustness to extreme observations and its non-reliance on specific data distribution assumptions (Abu Arra et al., 2024; Kourouma et al., 2022; Wang et al., 2020).\u003c/p\u003e \u003cp\u003eThe MK test was executed on annual time series values of each index across the study area. The analysis tests the null hypothesis (H₀) indicating no trend against the alternative hypothesis (H₁) reflecting a continuous directional shift. The results are expressed in terms of a computed test statistic (S) and its corresponding p-value, Pixels with p-values under the value (α\u0026thinsp;=\u0026thinsp;0.05) were classified as exhibiting significant trends (Genovese et al., 2025).\u003c/p\u003e \u003cp\u003eSen\u0026rsquo;s Slope Estimator was applied to assess the strength of the detected trends, The median slope is determined through applying a non-parametric method by computing slopes between all possible pairs of time series data points (Guti\u0026eacute;rrez-Hern\u0026aacute;ndez \u0026amp; Garc\u0026iacute;a, 2024), Sen\u0026rsquo;s Slop offers a robust metric for the temporal rate of change per time unit, positive values presents an increasing trend while the negative values indicates decreasing trends.\u003c/p\u003e \u003cp\u003eThe spatial patterns of trends were visualized through specialized thematic mapping, including Sen's Slope Maps, Mann-Kendall p-value Maps, and Trend Classification Maps, in addition, to evaluate the overall spatial extent and trend intensity, summary statistics were calculated for each drought index. Specifically, we calculated the total area that experienced significant increases or decreases, alongside the mean and standard deviation of the Sen's Slope values for these identified significant areas (Genovese et al., 2025).\u003c/p\u003e \u003cp\u003eBy employing a multi-index approach, regions exhibiting significant temporal changes in vegetation health, thermal stress, and precipitation deficits were identified. Through this comprehensive approach, valuable insights were obtained into the spatio-temporal trends in drought and potential desertification processes in Northwest Algeria over the period 2003\u0026ndash;2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Correlation Analysis of Drought Indices:\u003c/h2\u003e \u003cp\u003eTo evaluate the relationship between meteorological drought severity and vegetation response, a Pearson correlation analysis was performed using time series data of SPI and VHI (Kogan, 1995a, 2001; Mckee et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In this analysis we focused on assessing variations in precipitation patterns through using SPI at multiple accumulation intervals and corresponding changes in vegetation health dynamics.\u003c/p\u003e \u003cp\u003eIn preparation for the correlation analysis, annual mean time series data for each drought index (SPI3, SPI6, SPI12, VHI) were compiled, representing average conditions over the 2003\u0026ndash;2023 period and derived from the corresponding geospatial datasets (Dai, 2011; Peters et al., 1991; Vicente-Serrano et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). the use of annual mean values highlights interannual climate-vegetation associations while diminishing short-term variability. Following data preparation, each annual SPI time series (SPI3, SPI6, SPI12) was paired with its corresponding annual VHI series. All datasets were temporally aligned, and date formats were standardized to ensure temporal consistency across variables. The Pearson correlation coefficient (r) was computed for each SPI\u0026ndash;VHI pair, which measures the strength and direction of each pair\u0026rsquo;s linear relationship, ranging from \u0026minus;\u0026thinsp;1 (perfect negative correlation) to +\u0026thinsp;1 (perfect positive correlation), with 0 indicating no linear correlation (Wilks, 2006).\u003c/p\u003e \u003cp\u003eSPI is standardized to\u0026ensp;be of an approximately normal distribution by design (McKee et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), whereas VHI is a normalized composite vegetation index derived from vegetation greenness and thermal condition components. Since the indices are standardized and annual mean values were calculated, Pearson\u0026rsquo;s correlation was considered appropriate\u0026ensp;for exploring linear relationships between precipitation anomalies and vegetation dynamics.\u003c/p\u003e \u003cp\u003eScatter plots were employed to visualize these relationships, where each plotted point represents the annual values of both indices for a given year. Linear regression lines were fitted to each SPI\u0026ndash;VHI pair, offering a visual representation of the relationship significance within the plotted data. Additionally, 95% confidence intervals were displayed as shaded regions to indicate the uncertainty associated with the regression estimates. The Pearson correlation coefficient (r) for each pair was reported in the plot titles, linking visual trends with their statistical significance (Peters et al., 2002; Vicente-Serrano et al., 2013).\u003c/p\u003e \u003cp\u003eThis comprehensive approach allowed us to assess both the strength and direction of relationships between SPI-derived precipitation anomalies at multiple timescales and vegetation health dynamics across the 2003\u0026ndash;2023 study period.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThis study applied SPI (3-, 6-, and 12-month timescales) together with remote-sensing indices (VCI, TCI, VHI) to assess spatial and temporal patterns of drought across northwest Algeria from 2003 to 2023. The indices were analyzed annually to detect interannual severity variations and identify patterns in climatic and vegetation stress.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Vegetation Condition Index (VCI):\u003c/h2\u003e \u003cp\u003eTo assess vegetation changes (2003\u0026ndash;2023), we computed annual median VCI from monthly data composites. VCI map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) shows distinct contrasts between well-vegetated coastal/northern areas and arid southern/southeastern regions, which are characterized by sparse, climate-sensitive vegetation.\u003c/p\u003e \u003cp\u003eOver the two decades, a consistent spatial pattern was observed. Coastal provinces such as Mostaganem, Chlef, Ain Defla, Oran, and Ain-Temouchent showed higher VCI values, reflecting stable vegetation and good growth conditions. In contrast, interior provinces (Saida, Tiaret, Sidi Bel Abbes, and Mascara) showed lower values, indicating greater climatic vulnerability. Certain years recorded optimal vegetation conditions. In 2005, high VCI values were recorded in Mostaganem, Mascara, and Relizane; in 2010, Chlef, Oran, and Ain-Temouchent showed gains, along withTissemsilt and Saida. The year 2017, was particularly favorable, with widespread high VCI values across nearly all provinces, suggesting a notably wet year. Conversely, other years were marked by significant vegetation stress. In 2008, major declines occurred in (Saida, Tiaret, southern Mascara,Oran, and Relizane). Similar degradation appeared in 2012, notably in (Sidi Bel Abbes, central Mascara, and Saida). In 2018, severe drought triggered widespread declines, including in typically well-vegetated areas such as (Chlef, Ain Defla, and Relizane). In 2022, widespread stress affected (Tlemcen, Saida, Tiaret, and parts of Chlef and Mascara). These inter-annual patterns highlight the region\u0026rsquo;s vulnerability to climatic variability, with coastal and Tell Atlas areas showing greater resilience, whereas interior and steppe regions remain more vulnerable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temperature Condition Index TCI:\u003c/h2\u003e \u003cp\u003eThis study used TCI to evaluate thermal stress on vegetation from 2003 to 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). TCI reflects surface temperature: low values indicate high thermal stress, while high values indicate cooler, less stressful conditions, across both decades, TCI patterns followed ecological-climatic zones. Coastal and high-altitude provinces (Mostaganem, Chlef, Ain Defla, and Tissemsilt) often recorded higher TCI values, reflecting favorable thermal conditions. By contrast, interior provinces (Tiaret, Saida, Sidi Bel Abbes, and southern Mascara) consistently showed lower values, indicating higher thermal stress and drought vulnerability. In 2003, conditions were moderate to favorable in Oran, Chlef, and northern Ain Defla, though localized stress occurred in Tlemcen and Saida. By 2004, widespread thermal degradation affected Relizane, Mascara, and Saida.\u003c/p\u003e \u003cp\u003eCoastal and high-altitude provinces of Mostaganem, Chlef, Ain Defla, and Tissemsilt often recorded higher TCI values, reflecting favorable thermal conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). By contrast, interior provinces (Tiaret, Saida, Sidi Bel Abbes, and southern Mascara) consistently showed lower values, indicating higher thermal stress and drought vulnerability.\u003c/p\u003e \u003cp\u003eMajor stress events were noted in 2012, when Tlemcen, Saida, and Mascara recorded the decade\u0026rsquo;s lowest TCI values, coinciding with vegetation decline. Stress peaked again in 2020 and 2022, with 2022 recording the lowest TCI of the two decades, severely affecting Sidi Bel Abbes, Mascara, Tiaret, and Saida and resulting in marked vegetation decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Overall, TCI reveals a progressive rise in the frequency and extent of thermal stress. Semi-arid and steppe provinces (Saida, Tiaret, and Mascara) were most affected, while coastal and sub-humid areas (Chlef and Mostaganem) were more resilient.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Vegetation Health Index VHI:\u003c/h2\u003e \u003cp\u003eBy employing VHI, we assessed the degree of severity of the drought (2003\u0026ndash;2023) and classified it into five categories: no drought, mild, moderate, severe, and extreme. VHI showed significant spatial and temporal variability throughout the twelve provinces in the study area, by combining vegetation and thermal stress indices. A clear spatial pattern emerged across the study period; southern and interior provinces were more vulnerable, whereas coastal and mountainous regions were more resilient. In the early years (2003\u0026ndash;2004), large areas, especially Saida, Tiaret, and Mascara faced mild to moderate drought. However, the following years were characterized by significant fluctuations. Oran, Mostaganem, and Ain-Temouchent underwent little to no drought in 2007, 2010, and 2015, which were among the least affected years. Nevertheless, conditions improved. Similarly, the region experienced only a slight drought in 2021, mostly affecting northern Tissemsilt, Chlef, and Relizane.\u003c/p\u003e \u003cp\u003eConversely, the region experienced multiple severe drought episodes. In 2008, the drought became more severe, causing moderate to severe stress in Tlemcen, Sidi Bel Abbes, and Saida. Following this trend, 2012 was particularly severe for eastern Tissemsilt, southern Mascara, and central Relizane. Tiaret, Saida, and Sidi Bel Abbes were among the interior provinces where the drought intensified in 2017. Moreover, Tlemcen, Mascara, and southern Relizane were heavily effected in 2018. Furthermore, 2022, the most catastrophic year of the previous 20 years, saw a recurrence of severe drought, affecting almost every province, including typically resilient regions like Chlef, Tissemsilt, and Mostaganem. After this peak, Saida, Mascara, and Tiaret continued to experience moderate drought in 2023, representing a decreased intensity compared to 2022. Overall, the VHI time series demonstrates that mountainous and coastal areas were more resilient, while southern and interior provinces were more vulnerable. The years 2008, 2012, 2017, and 2022 were the years with the most severe drought, while 2007, 2010, 2015, and 2021 were the least affected years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Spatial Distribution of SPI (2003\u0026ndash;2023):\u003c/h2\u003e \u003cp\u003eWe investigated the distribution and intensity of dry and wet periods in northwest Algeria utilizing SPI at multiple time scales: 3-month (short-term), 6-month (seasonal), and 12-month (long-term). The interannual variability observed during the study period is presented graphically in (Figs.\u0026nbsp;7, 8, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSPI-3 results (Fig.\u0026nbsp;7) exhibit strong short-term variability, with frequent shifts between dry and wet periods. The years 2003, 2007, 2008, 2010, and 2012 showed widespread positive anomalies (moderately to extremely wet), particularly in coastal and northern provinces. In contrast, 2006, 2015, 2017, 2019, and a persistent period from 2019 to 2023 were dominated by negative SPI values, indicating considerable short-term rainfall deficits. While wet conditions generally occurred in specific areas, droughts were more uniformly distributed, particularly across the interior and southern areas.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7\u003c/b\u003e. \u003cem\u003eSpatial distribution of SPI3 (2003\u0026ndash;2023).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSPI-6 patterns (Fig.\u0026nbsp;8) reveals a decrease in sensitivity compared to short-term variations, and highlight more persistent seasonal conditions. The early period (2003\u0026ndash;2014) was characterized mainly by wet conditions, with 2008, 2012, and 2014 exhibiting pronounced positive anomalies with extensive spatial coverage of moderately to extremely wet conditions across the study region.\u003c/p\u003e \u003cp\u003e A significant shift occurred in 2015, initiating an extended period of precipitation deficiency that persisted through 2023. Drought intensity varied over time, with moderate to severe conditions during 2015\u0026ndash;2017 and 2019\u0026ndash;2023. Both 2021 and 2023 were marked by critical droughts, with large parts of the region experiencing severe to extreme precipitation deficits. Drought conditions also showed clear spatial variability, with some areas facing more persistent or severe impacts than others. This, along with strong interannual variability in rainfall, confirms a hydroclimatic transition from predominantly wet to predominantly dry states since the mid-2010s.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 8.\u003c/b\u003e \u003cem\u003eSpatial distribution of SPI6 (2003\u0026ndash;2023).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSPI-12 temporal analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e) demonstrates a continuous wet period spanning 2003\u0026ndash;2014, marked by predominantly moderate to severe wet conditions. exhibiting peak positive anomalies in 2007, 2009\u0026ndash;2011, and 2013. During this period, above-average precipitation exhibited widespread spatial distribution across the region.\u003c/p\u003e \u003cp\u003eAround 2015, there was a significant shift in hydroclimatic conditions occurred, marking the start of a persistent long-term drought that extended through 2023. Drought conditions intesified progressively from moderate drought in 2015 and 2017 to more severe conditions in 2019\u0026ndash;2023. The most extreme drought conditions of the study period occurred in 2023, where large areas were effected by severe to extreme drought. Despite this overarching drying trends, 2018 stands out as an exceptionally wet year covering most of the study area. Analysis of the hydroclimate in northwest Algeria indicates that both the frequency and spatial extent of persistent droughts have increased, particularly since 2015. Emerging from that period, multiple indicators point to a trend toward a drier climate, although interannual variability remains high and occasional anomalously wet years still occur.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Spatiotemporal Trends in Drought Indices:\u003c/h2\u003e \u003cp\u003eTo adress the presistence and the direction of the drought conditions in Northwest Algeria, we employed the Mann\u0026ndash;Kendall trend test and Sen\u0026rsquo;s slope estimator to all meteorological and vegetation-based indices from 2003 to 2023 (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e11\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SPI trend analysis revealed exclusively negative (drying) trends across all timescales, and no significant wetting trends were detected throughout the study area. SPI3 exhibited the most spatially extensive drying, with approximately 56% of the region of shwing significant decreasing trends (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a mean Sen\u0026rsquo;s slope of \u0026minus;\u0026thinsp;0.10 yr⁻\u0026sup1;. Similarly, 55% of the study area exhibited significant drying at the seasonal scale (SPI-6, mean slope = -0.10 yr⁻\u0026sup1;), while the long-term SPI-12 showed significant drying over 39% of the area (mean slope = -0.11 yr⁻\u0026sup1;). The decreasing spatial extent of significant trends from short- to long-term timescales reflects the integrative nature of cumulative precipitation deficits.\u003c/p\u003e \u003cp\u003eThe consistent presence of significant negative trends, emphasizes the growth and the progressive worsening of meteorological drought conditions, particularly in the post-2015 period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVegetation-derived indices revealed more complex patterns and consistently lower coverage of significant trends compared to meteorological indices. The trend analysis for TCI showed an almost complete absence of significant temporal changes, with less than 1% of pixels exhibiting statistically significant trends (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, VCI revealed a significant spatial variability, with 3.4% of the area experiencing vegetation stress (decline), while 2.7% exhibited significant improvement (greening). The spatial distribution appears relatively balanced, combined with similar magnitudes of change (mean Sen\u0026rsquo;s slopes of approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 yr⁻\u0026sup1;), for both negative and positive VCI trends. Vegetation responses appear to be highly localized and shaped by factors such as land cover, topography, and anthropogenic management.\u003c/p\u003e \u003cp\u003eVHI, as a composite indicator, showed the clearest signal of degradation A significant decline in vegetation health was detected across 4.9% of the region (mean slope = -0.006 yr⁻\u0026sup1;), compared to only 0.5% showing significant improvement. This clear imbalance, where degradation extends over an area nearly ten times larger than improvement, indicates a net loss of vegetation health across the study area.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Mann-Kendall Trend Analysis for Drought Indices (2003\u0026ndash;2023).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrend Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignificant Pixels (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial Coverage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Sen\u0026rsquo;s Slope (units \u0026middot; year⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD of Sen\u0026rsquo;s Slope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPI-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease (Drying)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.1001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrying\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase (Wetting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPI-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease (Drying)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.0957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrying\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase (Wetting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPI-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease (Drying)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.1062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrying\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase (Wetting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease (Cooling)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCooling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase (Warming)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease (Vegetation Stress)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87,111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVegetation Stress\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase (Vegetation Health)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVegetation Health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVHI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Decrease (Degradation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDegradation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Increase (Improvement)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImprovement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6 Multi-Temporal Analysis of Drought Conditions and Vegetation Response Derived from Meteorological and Remote Sensing Indices (2003\u0026ndash;2023)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e12\u003c/span\u003e offers a comprehensive view of drought evolution by plotting the time series of all meteorological and vegetation indices from 2003 to 2023. By integrating these indices across a multi-panel time series, this study provides an integrated assessment of drought propagation dynamics, allowing systematic analysis of drought persistence across temporal scales and its ecological ramifications on regional vegetation dynamics.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Meteorological Drought Indicators.\u003c/h2\u003e \u003cp\u003eThe top three panels present SPI values calculations at 3-, 6-, and 12-month timescales, based on the methodology of McKee et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Drought is classified using SPI standardized thresholds In accordance with WMO guidelines: moderate drought (SPI\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;1.0, orange dashed line) and severe-to-extreme drought (SPI\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;1.5, red dashed line),.\u003c/p\u003e \u003cp\u003eSPI-3 (Panel 1): This index captures short-term shifts in precipitation patterns and related meteorological variability, with rapid shifts between dry and wet conditions. Notable drought episodes occur during 2007\u0026ndash;2009, 2011\u0026ndash;2012, and sporadically from 2015 to 2022, where many episodes crossed into severe drought conditions. In contrast, wet episodes notably observed in 2009, 2013, and 2019, underscores transient periods of above-average precipitation and reflect episodic recovery phases.\u003c/p\u003e \u003cp\u003eSPI-6 (Panel 2): Reflecting mid-term precipitation deficits, this index exhibits less temporal variability compared to SPI-3, Sustained wet conditions were observed between 2003 and 2014. After 2015, a persistent drying period emerged from 2015 to 2023, this pattern aligns closely with the SPI-6 spatial distribution. The period (2021\u0026ndash;2023) is marked as one of the most severe drought episodes over the entire study period.\u003c/p\u003e \u003cp\u003eSPI-12 (Panel 3): this index demonstrates long-term hydro-climatic transitions, with a notable decline in annual variability, exhibiting a substantial decrease in interannual variability. with wet conditions observed during the 2003\u0026ndash;2014 period, followed by a persistent drying trend forward. Extended multi-year drought events (e.g., 2004\u0026ndash;2006, 2020\u0026ndash;2023), suggest continuous precipitation deficits that may adversely affect groundwater recharge and reservoir levels. These patterns are consistent with negative trends detected by Mann\u0026ndash;Kendall analysis, indicating a progressive transition toward aridity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Vegetation Response Indicators.\u003c/h2\u003e \u003cp\u003eThe bottom three panels present remotely sensed vegetation condition indices, scaled to ranges between 0 and 1, where stress levels are classified as moderate (\u0026le;\u0026thinsp;0.4, orange line) and severe-to-extreme (\u0026le;\u0026thinsp;0.2, red line), consistent with classification schemes widely adopted in vegetation stress assessment.\u003c/p\u003e \u003cp\u003eVCI (Panel 4): The VCI time series exhibits notable interannual variability, Episodes of significant vegetation stress, marked by declines in VCI values (\u0026lt;\u0026thinsp;40) during 2007\u0026ndash;2008, 2011\u0026ndash;2012, and 2021\u0026ndash;2022, align closely with dry periods identified by SPI indices, Short-term recovery phases in 2009\u0026ndash;2010 and 2019\u0026ndash;2020 (VCI\u0026thinsp;\u0026gt;\u0026thinsp;55) indicate temporary improvements in vegetation condition associated with increased precipitation, The index exhibits a temporal lag in responding to water availability, highlighting its utility in assessing drought effects.\u003c/p\u003e \u003cp\u003eTCI (Panel 5): TCI displays significant temporal variations that tracks thermal stress patterns throughout the study period, TCI values (\u0026lt;\u0026thinsp;40) during 2007\u0026ndash;2008, 2010\u0026ndash;2012, and 2021\u0026ndash;2022, indicate periods of increased thermal stress associated with extreme drought conditions. Conversely, elevated TCI values (\u0026gt;\u0026thinsp;60) detected during 2009\u0026ndash;2010 and 2018\u0026ndash;2019 indicate reduced thermal stress and more optimal thermal conditions supporting vegetation growth.\u003c/p\u003e \u003cp\u003eOverall, TCI temporal patterns display a negative and inverse association with SPI and VCI trends, illustrating the critical effect of the thermal stress in exacerbating drought severity and limiting vegetation recovery during dry periods.\u003c/p\u003e \u003cp\u003eVHI (Panel 6): The VHI time-series indicates moderate to severe drought conditions (VHI\u0026thinsp;\u0026lt;\u0026thinsp;40) exhibiting relatively reduced temporal variations,. Significant drought periods occurred during 2004\u0026ndash;2006, 2007\u0026ndash;2008, 2011\u0026ndash;2012, and 2020\u0026ndash;2023, revealing persistent vegetation stress accompanied by thermal extremes. VHI patterns show agreement with SPI, highlighting its effectiveness in extended drought monitoring and impact assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Multi-timescale correlation analysis between the (SPI-3, SPI-6, SPI-12) and VHI:\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e13\u003c/span\u003e illustrates the relationship between SPI at three different timescales and VHI, The analysis reveals a clear positive relationship between SPI and VHI, confirming that vegetation condition declines during periods of rainfall deficits.\u003c/p\u003e \u003cp\u003eThe correlation is moderate at the short-term scale (SPI-3 vs. VHI, r\u0026thinsp;=\u0026thinsp;0.386), highlighting a rapid vegetation response to short-term moisture variations. The correlation strengthens significantly at the semi-annual scale (SPI-6 vs. VHI, r\u0026thinsp;=\u0026thinsp;0.578), suggesting that vegetation health in this region is most sensitive to precipitation anomalies integrated over a 6-month period. The correlation weakens at the annual scale (SPI-12 vs. VHI, r\u0026thinsp;=\u0026thinsp;0.285), possibly due to ecological resilience mechanisms or the buffering role of soil moisture and groundwater over longer periods. The strong correlation at the SPI-6 timescale underscores its effectiveness as a predictor for agricultural and ecological drought in the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis research presents an integrated assessment for drought patterns across Northwest Algeria from 2003 to 2023, linking meteorological indices with remotely sensed vegetation indicators. The findings reveal a statistically significant and persistent increase in drought conditions, marked by negative precipitation patterns over the short, medium, and long-term scales and associated patterns of vegetation stress. These findings suggests that Northwest Algeria is experiencing a continuous process of aridity, a trend that aligns with broader climate projections for the western Mediterranean basin. The following section frames these findings within the scientific literature, examines climatic and environmental drivers, assess ecosystem response dynamics, and outlines the implications for regional drought management and future research.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Comparative Context and Validation of Findings:\u003c/h2\u003e \u003cp\u003eThe drought patterns detected in this study are consistent with previous regional studies, confirming the validity of our comprehensive methodological framework and indicating the strength of the chosen methodological approach. Although the association between vegetation response and\u0026ensp;precipitation anomalies varied spatio-temporally, statistically significant correlations between SPI and two vegetation-based indices (VCI and VHI) were observed in several periods and sub-regions. For instance, (Abbes et al., 2018) documented a strong correlation between SPI and NDVI-based indices in the Cheliff watershed, indicating that rainfall variation is a principal factor of vegetation dynamics. Our findings confirm a consistent response of VCI and VHI to SPI-driven moisture availability, supporting the drought\u0026ndash;vegetation relationship, Moreover, the frequency and exacerbation of droughts after 2015 are consistent with the findings of (Messis et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who reported 2015\u0026ndash;2021 as one of the most severe dry episodes in the upper Cheliff basin.\u003c/p\u003e \u003cp\u003eThe strong correlation between SPI and VHI, particularly at the 6-month interval (r\u0026thinsp;=\u0026thinsp;0.578), validates the integrated monitoring approach used in this research, demonstrating the reliability of vegetative health as an effective indicator for assessing agricultural drought severity. Our findings are consistent with research undertaken in other dry and semi-arid regions, such as by (Ejaz et al., 2023) in Saudi Arabia, which confirmed the reliability of VHI in regions with restricted data availability. Similarly, research such as that by (Bougara et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fettam et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have corroborated the methodological choice of using SPI, demonstrating its statistical resilience through strong inter-index correlation coefficients within the Tafna watershed. This consistency between our findings and previous research highlights the dependability of the integrated approach for monitoring the multi-temporal dynamics of drought across the region.\u003c/p\u003e \u003cp\u003eHowever, our findings reveal a critical departure that underscores the intensity of recent climate shifts. Whereas long-term studies like(Fettam et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (1970\u0026ndash;2019) and(Bougara et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (1979\u0026ndash;2011), which documented periods of stability or wetting trends at longer timescales, in addition, our analysis of SPI-12 during the period 2003\u0026ndash;2023 reveals a significant and sustained decline trend. The difference clearly indicates that over the last 20 years, the historical balance between dry and wet cycles, which previously offered long-term climatic resilience, has been fundamentally disrupted during the last two decades. the outcomes of this study suggest that a major hydroclimatic regime shift appears to have occurred during the research time period, with a new era of aridity exceeding the region\u0026rsquo;s existing adaptive levels a critical shift that occurred primarily in the last decade (post-2015), whereas most previous studies concluded before these regional drought assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Drivers of Aridification\u003c/h2\u003e \u003cp\u003eChronic aridity in Northwest Algeria is linked to broader regional and global climatic dynamics, and the study area experienced a significant decrease in SPI values after 2015, which is consistent with the intensifying aridity in the Mediterranean region. Several studies indicate the decline in precipitation in the Maghreb region is strongly correlated with the positive NAO phase (Achite et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bouarfa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This long-term trend has historical roots, as (Derdous et al., 2021) identified the period post-1976 as a transition to persistent dryness, indicating a major hydroclimatic regime shift, that provides the broader historical context for the trends we observed.\u003c/p\u003e \u003cp\u003eOur findings, notably, the strong correlation between VHI and TCI, indicates that precipitation alone cannot explain vegetation stress; this pattern is consistent with the results of (Mahcer et al., 2024), which point to an increase in regional temperatures. This warming trend intensifies atmospheric evaporative demand (AED), increasing potential evapotranspiration (PET) rates and decreasing in soil moisture retention, even when annual rainfall is near-normal (Trenberth et al., 2013; Vicente-Serrano et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), These circumstances are often referred to as a \u0026ldquo;hot drought\u0026rdquo; or \u0026ldquo;global‑change‑type drought\u0026rdquo;, where rising temperatures exacerbate hydrological deficits and ecological vulnerability (Allen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mora et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) These dynamics mean that even normal or slightly below‑normal rainfall can exacerbate agricultural and ecological stress by accelerating soil moisture depletion and direct thermal stress on vegetation. Moreover, (Benhizia et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed a negative correlation between NDVI and temperature anomalies, which suggests that higher temperatures are a primary driver of vegetation condition, in addition to the effects of precipitation deficiency. This shift constitutes a major hydroclimatic change that has serious consequences on ecological resilience and the sustainability of water resources in the Mediterranean basin, particularly the Maghreb region.\u003c/p\u003e \u003cp\u003eFurthermore, the effects of these climatic drivers are exacerbated by anthropogenic pressures, including deforestation, overgrazing, agricultural expansion, and urban growth, as reported by (Bouarfa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Derdour et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which leads to land degradation and decreases the soil's capacity for holding water, making ecosystems more vulnerable to climate variability. While several studies consistently point to the inner steppe provinces (Saida, Tiaret, Mascara) as drought hotspots (Ceppi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Derdous et al., 2021; Elouissi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oubadi et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and North African steppe ecosystems (Garc\u0026iacute;a-Vega et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the combination of climatic vulnerability with anthropogenic pressures fits with long‑term trends of reduced precipitation and surface water availability across Algeria (Hamitouche et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), highlighting how hydroclimatic stress in this area builds up over time and is caused by many different things.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Vegetation Dynamics and Land Cover Mechanisms under Extended Drought Conditions.\u003c/h2\u003e \u003cp\u003eVegetation indices show that drought impacts different locations in different ways, providing more detailed ecological insights than SPI alone, Meteorological drought was widespread, whereas vegetation indices (VCI, TCI, VHI) exhibited delayed and regional responses, showing delayed consequences due to soil moisture buffering (Bouarfa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, during the extended drought from 2020 to 2023, these natural barriers disintegrated, causing extensive vegetation decrease, especially notable in VHI patterns. The increased sensitivity of VHI compared to VCI indicates the integrated impact of thermal stress and moisture deficits (Benhizia et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mahcer et al., 2024).\u003c/p\u003e \u003cp\u003eLand cover extensively influences vegetation resilience. Forests and grasslands show considerably stronger adaptation ability, but croplands and shrublands in semi-arid zones are more vulnerable to drought (Mahcer et al., 2024), Similar findings in Morocco\u0026rsquo;s Doukkala and Tensift basins demonstrate that irrigated croplands respond differently from natural rangelands under drought conditions (Ayad et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; El-Bouhali et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Habitou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Salih et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Subsequently, drought consequences are determined not only by climate forcing but also by land-use mosaics and human management, highlighting the need for spatially explicit, LULC-informed drought risk frameworks (Ceppi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.9 Broader Context, Limitations, and Future Research\u003c/h2\u003e \u003cp\u003eThe agreement between meteorological and vegetation‑based drought indicators across multiple time scales highlights an accelerating aridification trend in Northwest Algeria, This pattern illustrates conditions across North Africa and the Mediterranean, regions commonly identified as climate‑change hotspots (Essa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Stamou et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), The convergence of meteorological and biophysical drought indicators across multiple temporal scales further underscores the intensifying aridification trajectory in Northwest Algeria, mirroring broader climatic challenges across the southern Mediterranean basin (Bergaoui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kenawy et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The documented shift toward more frequent, intense, and prolonged drought episodes, amplified by rising temperatures and declining precipitation, poses growing threats to water security, agricultural productivity, and socio-economic stability across the region (Ben Mhenni et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mliyeh et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spatial and temporal patterns identified in this study are consistent with similar aridification trajectories reported in neighboring Mediterranean basins, such as the Macta basin (Berhail et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Northeastern Algeria (Derradji et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and El Tarf province (Mayouf \u0026amp; Hanafi, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comparative research in Morocco (Mliyeh et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Tunisia (Ben Mhenni et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also reports parallel patterns of precipitation decline and vegetation stress, suggesting that Northwest Algeria\u0026rsquo;s drought intensification forms part of a larger regional climatic transition affecting the entire North African\u0026ndash;Mediterranean corridor.\u003c/p\u003e \u003cp\u003eWhile this research presents a comprehensive multi-index approach, certain methodological limitations merit attention. First, the use of SPI, which despite its robustness, reflects only precipitation deficits and does not fully capture the role of temperature-driven evapotranspiration (Liu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Given the documented warming trend and its role in creating \"hot droughts,\" (Cook et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trenberth et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e), future studies should integrate temperature-sensitive indices such as SPEI and PDSI. Comparative research in Algeria (Ziari \u0026amp; Medjerab, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) and other Mediterranean environments (Hadri et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Worku, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) has already shown that SPEI can detects greater drought severity and duration under warming conditions than SPI alone.\u003c/p\u003e \u003cp\u003eSecond, this study relied on meteorological and vegetation-based indicators. Integrating satellite-derived soil-moisture datasets (e.g., SMAP and Sentinel-1 SAR)(Bauer-Marschallinger et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Entekhabi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) would offer a more direct measure of water availability for plants and could refine understanding of drought propagation from meteorological to agricultural and hydrological domains. Recent applications have shown promise in enhancing drought monitoring accuracy in North Africa (e.g.,Tunisia and Egypt)(Ramat et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and in the MENA region (Nie et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and highlight how incorporating soil-moisture and leaf-area data into land-surface models can substantially enhance drought monitoring accuracy and operational early-warning systems.\u003c/p\u003e \u003cp\u003eFinally, a key research direction concerns the integration of biophysical drought indicators with socio-economic vulnerability assessments. While our indices characterize the physical dimensions of drought, they do not capture how different communities and agricultural systems experience or adapt to water scarcity (Naumann et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Simelton et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Coupling remote-sensing indicators with socio-economic and livelihood metrics, such as crop dependency, and adaptive capacity, is critical for an understanding of drought risk and adaptive capacity (Erian et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mertz et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This transition from hazard characterization to impact assessment is critical for informing targeted drought-management strategies in the Maghreb region.\u003c/p\u003e \u003cp\u003eThe observed long-term drying and vegetation degradation trends documented here carry significant implications for regional water governance and adaptation planning. Consistent with broader regional assessments (Essa et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Krim \u0026amp; Hassani, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Northwest Algeria appears to be transitioning into a new climatic regime characterized by persistent water scarcity and heightened drought frequency. Addressing this shift requires moving from reactive to proactive risk management paradigms anchored in integrated national drought policies, early-warning systems, and adaptive governance frameworks (Sivakumar et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wilhite et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe present study used meteorological (SPI) and satellite-based vegetation (VCI, TCI, VHI) indicators for carrying out a comprehensive spatiotemporal assessment of drought in Northwest Algeria from 2003 to 2023.\u003c/p\u003e \u003cp\u003eOur analysis revealed a significant and accelerating trend towards aridification, particularly in the post- 2015 period. Key findings include:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAll SPI timeframes (3, 6, and 12 months) showed a statistically significant drying trend, indicating a continuous decrease in precipitation and an increase in the frequency and intensity of meteorological drought.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe biophysical response to these climatic changes was successfully captured by vegetation indices (VCI, TCI, and VHI), which demonstrated recurrent periods of extensive vegetative stress that correlated with meteorological drought events, particularly in 2008, 2012, 2017, and 2022.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethe interior area, semi-arid provinces (e.g. Saida, Tiaret,\u0026ensp;Mascara) emerged as uniformly drought hotspots with much higher vulnerability than both coastal and mountainous regions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe strongest significant correlation was that between SPI-6 and VHI (r\u0026thinsp;=\u0026thinsp;0.578), highlighting that the seasonal precipitation deficit dominated agricultural drought in\u0026ensp;the area, this makes the combination of these two indices\u0026ensp;as an useful tool for early warning systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe integrated approach, combining precipitation and temperature effects via the VHI, proved more effective in capturing the full extent of vegetation degradation than single-factor indices, underscoring the growing role of thermal stress in a warming climate.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTying these results together,\u0026ensp;it is clear that this region is under increasing hydroclimatic pressure. The framework proposed in this paper, utilizing publicly accessible data and cloud computing, it provides a scalable,remotely and efficiency approach for\u0026ensp;operational drought monitoring. The findings are important in generating key spatially explicit information for policymakers to develop focused adaptation related strategies, enhance water resource management, and build resilience in vulnerable agricultural and ecological systems. As Northwest Algeria confronts a new climatic reality, a proactive, data-driven, and integrated approach to drought management is not just recommended; it is imperative.\u003c/p\u003e \u003cp\u003eThis study makes several significant scientific contributions to drought monitoring and assessment methodologies. First, it demonstrates the effectiveness of integrating meteorological indices (SPI) with satellite-derived vegetation indices (VCI, TCI, VHI) for comprehensive drought characterization in semi-arid regions. The practical implications of this research are substantial for drought early warning systems and water resource management. The validated approach provides stakeholders with a robust framework for timely drought detection and severity assessment, enabling proactive rather than reactive management strategies. Furthermore, the integration of multiple indices enhances the reliability of drought monitoring by capturing both meteorological and ecological dimensions of drought impacts. This multi-faceted approach is particularly valuable for agricultural planning and water allocation decisions in Northwestern Algeria and similar semi-arid environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eProject No. TKP2020-IKA-04 has been implemented with the support provided by theNational Research, Development, and Innovation Fund of Hungary, financed under the 2020\u0026thinsp;\u0026minus;\u0026thinsp;4.1.1-TKP2020 funding scheme\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRamzi Benhizia: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing \u0026ndash; original draft. Brahim Abdelkebir: Methodology, Validation, Writing \u0026ndash; review \u0026amp; editing. Behnam Ata: Formal analysis, Software, Validation. Singo Mukovhe Vele: Investigation, Resources, Writing \u0026ndash; review \u0026amp; editing. Kwanele Phinzi: Data curation, Resources, Writing \u0026ndash; review \u0026amp; editing. Gy\u0026ouml;rgy Szab\u0026oacute;: Supervision, Project administration, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the providers of CHIRPS precipitation data and MODIS satellite products for making these datasets freely available, which constituted a fundamental basis for this research. In addition, the authors extend their sincere gratitude to the Department of Landscape Protection and environmental geography (University of Debrecen) for providing the resources and institutional support essential for the successful execution of this study. The authors also appreciate the constructive feedback and valuable insights offered by academic colleagues during the development of the manuscript. Furthermore, this research was supported by Project No. TKP2021-NKTA-32, funded by the National Research, Development, and Innovation Fund of Hungary, and this financial support is gratefully acknowledged.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbes M, Hamimed A, Lafrid A, Mahi H, Nehal L (2018a) Use of high spatial resolution satellite data for monitoring and characterization of drought conditions in the Northwestern Algeria. 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Revista de Gest\u0026atilde;o - RGSA 18(9):e06591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24857/rgsa.v18n9-078\u003c/span\u003e\u003cspan address=\"10.24857/rgsa.v18n9-078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Drought monitoring, Standardized Precipitation Index (SPI), Vegetation Health Index (VHI), Mann-Kendall test, Remote Sensing, Northwest Algeria, Climate Change","lastPublishedDoi":"10.21203/rs.3.rs-8711142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8711142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought is an escalating hazard in arid and semi‑arid regions with major consequences for agriculture, ecosystems, and water resources. This study presents a 2003\u0026ndash;2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria (2003\u0026ndash;2023) using CHIRPS precipitation and MODIS remote‑sensing products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) at 3‑, 6‑ and 12‑month timescales; vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann\u0026ndash;Kendall test and Sen\u0026rsquo;s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identify recurrent drought episodes in 2007\u0026ndash;2009, 2011\u0026ndash;2012 and a pronounced dry phase from 2020\u0026ndash;2023. Mann\u0026ndash;Kendall results indicate widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen\u0026rsquo;s slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.10 yr⁻\u0026sup1;). Vegetation indices mirror these changes: VHI shows substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses are spatially heterogeneous. The strongest coupling between precipitation and vegetation occurs at the semi‑annual scale (SPI‑6 vs VHI, r\u0026thinsp;=\u0026thinsp;0.578), suggesting that 6‑month precipitation anomalies best predict agricultural drought in this region. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and they point to an ongoing shift toward increased aridity with implications for water management and agricultural adaptation.\u003c/p\u003e","manuscriptTitle":"Bi-decadal drought assessment in Northwestern Algeria: integrating meteorological and remote-sensing indices.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 09:36:17","doi":"10.21203/rs.3.rs-8711142/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T18:03:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T14:23:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141034486000261722449790226542770486509","date":"2026-02-20T23:22:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T20:18:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T21:52:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271400297335352428108606531629978346682","date":"2026-01-30T22:00:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323452801664689438808131654511576963655","date":"2026-01-29T11:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339388200425302802408417624316795433385","date":"2026-01-29T09:17:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218629388521949095410274551456555937999","date":"2026-01-29T09:14:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T09:04:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-28T21:55:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-28T00:08:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2026-01-27T13:28:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2dfef655-13af-4be2-9f0b-bda276e587af","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-11T06:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 09:36:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8711142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8711142","identity":"rs-8711142","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}