Search Results (73)

The French Broad River in eastern Tennessee is a critical water resource for the Tennessee Valley Authority’s hydropower and drought relief, yet its instrumental record spans less than a century. To evaluate new dendrochronological tools and examine long-term streamflow trends, we extended the stream record by 1500 years using linear regression and machine learning reconstruction models informed by the tree-ring-derived self-calibrating Palmer Drought Severity Index (scPDSI). Linear regression models provided skillful reconstruction and stable performance across calibration and validation periods. Random Forest and Deep Learning achieved higher skill but lost some of their skill advantage with validation periods, indicating overfitting. All models captured drought years more reliably than flood years, reflecting the sensitivity of scPDSI to soil moisture but its limitations for high-flow extremes in the Appalachian region. Trend analyses identified a significant change point in 1271 CE, separating a drought-dominated early period (500–1272 CE) from a wetter, less variable regime (1273–1970 CE). An emerging trend shows higher average flow interrupted by severe single-year droughts, consistent with regional evidence and projected changes to hydrologic regimes in Appalachia. These findings provide a millennial perspective on hydrologic extremes and guidance on using paleohydrology tools for water resource planning in a changing climate. Full article

Exploring the impact of dry–wet change on the Manchu rise has important implications for revealing the impact of climate change on ethnic dynamics. In this study, we used tree rings of Carya cathayensis and historical data to study this dynamic in Northeast Asia using function fitting and step-by-step elimination analysis. The results show a mean reconstructed scPDSI_4–10 of 0.822 from 1583 to 1644, which is 0.287 higher than the mean from 1548 to 2022 (0.535), and during 25 slightly wet years. This indicates that dry–wet change provided a favorable natural environment for the Manchu rise, under which the group’s area continued to expand and change shape in complex ways, and the population increased rapidly in the control region. However, in some years, the closer the scPDSI_4–10 was to the multi-year mean (0.774) of the deviation from the mean (0.535) of the scPDSI_4–10, the faster the control region expanded and the more the population increased. These results provide a reference for understanding the relationship between ethnic groups’ dynamics and climate change. Full article

This study assesses the impact of extreme droughts on the surface and atmospheric water balance of the Peruvian Amazon basin during 2003–2024. It extends previous work by incorporating multiple datasets for precipitation (CHIRPS, MSWEP, and ERA5) and evapotranspiration (ERA5, GLDAS, Amazon-Paca, and observations from the Quistococha flux tower) and comparing three drought indices: Maximum Cumulative Water Deficit (MCWD), Standardized Precipitation Evapotranspiration Index (SPEI), and self-calibrated Palmer Drought Severity Index (scPDSI). The study focuses on the Peruvian–Ecuadorian Amazon basin, particularly on the Amazon and Madre de Dios river basins, closing at Tamshiyacu and Amaru Mayu stations, respectively. The results confirm four extreme drought years (2004–2005, 2009–2010, 2022–2023, and 2023–2024) with major precipitation deficits in dry seasons and significant reductions in runoff and total water storage anomalies (TWSAs), physically manifesting as negative surface balances indicating net terrestrial water depletion and negative atmospheric balances reflecting reduced moisture convergence, with residuals signaling hydrological uncertainties. The study highlights significant imbalances in the water cycle during droughts and underscores the need to use multiple indicators and datasets to accurately assess hydrological responses under extreme climatic conditions in the Amazon basin. Full article

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article

The Hron River is a vital waterway in central Slovakia. In evaluating observed streamflow records for the past ~90 years, the Hron River displayed historically low hydrologic summer (April–May–June–July–August–September (AMJJAS)) streamflow for the 10-, 20-, and 30-year periods ending in 2020. When using self-calibrated Palmer Drought Severity Index (scPDSI) proxies developed from tree-ring records, skillful regression-based reconstructions of AMJJAS streamflow were developed for two gauges (Banská Bystrica and Brehy) on the Hron River. The recent observed droughts were compared to these reconstructions and revealed the Hron River experienced extreme drought in the 21st century. A further comparison of observed wet (pluvial) periods revealed that the most extreme robust streamflow periods in the observed record were frequently exceeded in the reconstructed (paleo) record. The Hron River has recently been experiencing decline, and we hypothesize that this decline may be associated with anthropogenic influences, the natural climatic cycle, or the changing climate. Full article

Background/Objectives: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. Methods: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. Results: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = −0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = −0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. Conclusions: This could be considered an indication that there were two different acting mechanisms in which drought affected yield. Full article

This study reconstructs historical streamflow in the Sava River Basin (SRB), focusing on hydrological variability over extended timescales. Using a combination of Machine Learning (ML) and Deep Learning (DL) models, streamflow patterns were reconstructed from self-calibrated Palmer Drought Severity Index (scPDSI) proxies. The analysis included nine ML models and two DL architectures, with a post-prediction bias correction applied uniformly using the RQUANT method. Results indicate that ensemble methods, such as Random Forest and Gradient Boosted Tree, along with a six-layer DL model, effectively captured streamflow dynamics. Bias correction improved predictive consistency, particularly for models exhibiting greater initial variability, aligning predictions more closely with observed data. The findings reveal that the 2000–2022 period ranks as the lowest 23-year flow interval in the observed record and one of the driest over the past ~500 years, offering historical context for prolonged low-flow events in the region. This study demonstrates the value of integrating advanced computational methods with bias correction techniques to extend hydrological records and enhance the reliability of reconstructions. By addressing data limitations, this approach provides a foundation for supporting evidence-based water resource management in Southeastern Europe under changing climatic conditions. Full article

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts. Full article

Drought, as a natural disaster with wide-ranging impacts and long duration, has an adverse effect on the global economy and ecosystems. In this paper, four remote sensing drought indices, namely the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Water Index (NDWI), are selected for drought analysis. The correlation analysis is carried out with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and based on the optimal index (CWSI), the spatiotemporal characteristics of drought in Shaanxi Province from 2001 to 2021 were studied by SEN trend analysis, Mann–Kendall test, and a center of gravity migration model. The results show that (1) the CWSI performs best in drought monitoring in Shaanxi Province and is suitable for drought studies in this region. (2) Drought in Shaanxi Province shows a decreasing trend from 2001 to 2021; the main manifestation of this phenomenon is the decrease in the occurrence of severe drought, with severe drought covering less than 10% of the area in 2010 and subsequent years. The most severely affected regions in the province are the northern Loess Plateau region and Guanzhong Plain region. In terms of the overall trend, only 0.21% of the area shows an increase in drought, primarily concentrated in the Guanzhong Plain region and the outskirts of the Qinling–Bashan mountainous region. (3) Drought conditions are generally improving, with the droughts’ center of gravity moving northeastward at a rate of 3.31 km per year. The results of this paper can provide a theoretical basis and a practical reference for drought control and decision-making in Shaanxi Province. Full article

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems. Full article

Knowledge of the responses of winter wheat yield to meteorological dryness/wetness variations is crucial for reducing yield losses in Henan province, China’s largest winter wheat production region, under the background of climate change. Data on climate, yield and atmospheric circulation indices were collected from 1987 to 2017, and monthly self-calibrating Palmer drought severity index (sc-PDSI) values were calculated during the winter wheat growing season. The main results were as follows: (1) Henan could be partitioned into four sub-regions, namely, western, central-western, central-northern and eastern regions, based on the evolution characteristics of the time series of winter wheat yield in 17 cities during the period of 1988–2017. Among them, winter wheat yield was high and stable in the central-northern and eastern regions, with a remarkable increasing trend (p < 0.05). (2) The sc-PDSI in February had significantly positive impacts on climate-driven winter wheat yield in the western and central-western regions (p < 0.05), while the sc-PDSI in December and the sc-PDSI in May had significantly negative impacts on climate-driven winter wheat yield in the central-northern and eastern regions, respectively (p < 0.05). (3) There were time-lag relationships between the sc-PDSI for a specific month and the atmospheric circulation indices in the four sub-regions. Furthermore, we constructed multifactorial models based on selected atmospheric circulation indices, and they had the ability to simulate the sc-PDSI for a specific month in the four sub-regions. These findings will provide scientific references for meteorological dryness/wetness monitoring and risk assessments of winter wheat production. Full article

Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management and fostering ecological conservation alongside high-quality development. This study utilizes meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI), for a detailed spatiotemporal analysis of drought conditions. It examines the effectiveness of these indices in the basin’s drought monitoring, offering a comprehensive insight into the area’s drought spatiotemporal dynamics. The findings demonstrate the following: (1) SPEI values exhibit distinct fluctuation patterns at varying temporal scales, with more pronounced fluctuations at shorter scales. Drought years identified via the 12-month SPEI time scale include 1965, 1966, 1969, 1972, 1986, 1997, 1999, 2001, and 2006. (2) A modified Mann–Kendall (MMK) trend test analysis of the scPDSI time series reveals a worrying trend of intensifying drought conditions within the basin. (3) Correlation analysis between SPEI and scPDSI across different time scales yields correlation coefficients of 0.35, 0.54, 0.69, 0.76, and 0.62, highlighting the most substantial correlation at an annual scale. Spatial correlation analysis conducted between SPEI and scPDSI across various scales reveals that, within diverse temporal ranges, the correlation peaks at a 12-month time scale, with subsequent prominence observed at 6 and 24 months. This observed pattern accentuates the applicability of scPDSI in the monitoring of medium- to long-term drought phenomena. Full article

The current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and projections were made for different horizons, including short-term (1-month: t + 1), mid-term (3-months: t + 3 and 6-months: t + 6), and long-term (12-months: t + 12) periods. The original sc-PDSI time series was subjected to the partial autocorrelation function to identify the input configurations and, accordingly, one- (t − 1) and two-month (t − 2) lags were used to perform the forecast of the targeted outcomes. This research further incorporated the recently introduced variational mode decomposition (VMD) for signal processing into the predictive model to enhance the accuracy. The proposed model was not only benchmarked with the standalone XGBoost but also with the model generated by its hybridization with the discrete wavelet transform (DWT). The overall results revealed that the VMD-XGBoost model outperformed its counterparts in all lead-time forecasts with NSE values of 0.9778, 0.9405, 0.8476, and 0.6681 for t + 1, t + 3, t + 6, and t + 12, respectively. Transparency of the proposed hybrid model was further ensured by the Mann–Whitney U test, highlighting the results as statistically significant. Full article

(1) Background: The forest of Pinus sylvestris var. mongolica is an important semi-arid ecosystem in Hulunbuir sandy land that plays a key role in the carbon cycle and wind erosion control. It is crucial to explore the main factors affecting the radial growth of trees of P. sylvestris var. mongolica. (2) Methods: The study established the tree-ring chronology of P. sylvestris var. mongolica and analyzed the relationships among the radial growth, competition index, and climate variables using correlation analysis and a linear mixed effect model to explore the influence of competition and climate on radial growth of P. sylvestris var. mongolica. (3) Results: The results indicated that tree growth is mainly affected by the maximum average temperature (T_max) and precipitation in June and July of the current year and that tree growth significantly decreased with increasing competition pressure. Analysis of the linear mixed effect model showed that tree age, competition intensity, self-calibrating Palmer drought severity index (scPDSI) from May to July, and vapor pressure deficit (VPD) have a significant impact on radial growth. (4) Conclusions: The competition plays a dominant role in radial growth of P. sylvestris var. mongolica compared to climate factors. This study helps to understand the growth mechanism of P. sylvestris var. mongolica forests under climate change and provides a scientific basis for effective management of semi-arid forests. Full article

The Sava River Basin (SRB) extends across six countries (Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Albania, and Montenegro) and is a major tributary of the Danube River (DR). The Sava River (SR) originates in the alpine region of Slovenia, and, in support of a Slovenian government initiative to increase clean, sustainable energy, multiple hydropower facilities have been constructed within the past ~20 years. Given the importance of this river system for varying demands, including energy production, information about past (paleo) drought and pluvial periods would provide important information to water managers and planners. Seasonal (April–May–June–July–August–September—AMJJAS) streamflow data were obtained for two SRB gauges (Jesenice and Catez) in Slovenia. The Jesenice gauge is in the extreme headwaters of the SR, upstream of any major water control structures, and is considered an unimpaired (minimal anthropogenic influence) gauge. The Catez gauge is located on the SR near the Slovenia–Croatia border, thus providing an estimate of streamflow leaving Slovenia (entering Croatia). The Old World Drought Atlas (OWDA) provides an annual June–July–August (JJA) self-calibrating Palmer Drought Severity Index (scPDSI) derived from 106 tree-ring chronologies for 5414 grid points across Europe from 0 to 2012 AD. In lieu of tree-ring chronologies, this dataset was used as a proxy to reconstruct (for ~2000 years) seasonal streamflow. Prescreening methods included the correlation and temporal stability of seasonal streamflow and scPDSI cells. The retained scPDSI cells were then used as predictors (independent variables) to reconstruct streamflow (predictive and/or dependent variables) in regression-based models. This resulted in highly skillful reconstructions of SRB seasonal streamflow from 0 to 2012 AD. The reconstructions were evaluated, and both low flow (i.e., drought) and high flow (i.e., pluvial) periods were identified for various filters (5-year to 30-year). When evaluating the most recent ~20 years (2000 to present), multiple low-flow (drought) periods were identified. For various filters (5-year to 15-year), the 2003 end-year consistently ranked as one of the lowest periods, while the 21-year period ending in 2012 was the lowest flow period in the ~2000-year reconstructed-observed-historic period of record. The ~30-year period ending in 2020 was the lowest flow period since the early 6th century. A decrease in pluvial (wet) periods was identified in the observed-historic record when compared to the paleo record, again confirming an apparent decline in streamflow. Given the increased activities (construction of water control structures) impacting the Sava River, the results provide important information to water managers and planners. Full article