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Keywords = self-calibrating Palmer Drought Severity Index

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25 pages, 11278 KiB  
Article
Analysis of Droughts and Floods Evolution and Teleconnection Factors in the Yangtze River Basin Based on GRACE/GFO
by Ruqing Ren, Tatsuya Nemoto, Venkatesh Raghavan, Xianfeng Song and Zheng Duan
Remote Sens. 2025, 17(14), 2344; https://doi.org/10.3390/rs17142344 - 8 Jul 2025
Viewed by 394
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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14 pages, 7591 KiB  
Article
A Paleo-Perspective of 21st Century Drought in the Hron River (Slovakia)
by Igor Leščešen, Abel Andrés Ramírez Molina and Glenn Tootle
Hydrology 2025, 12(7), 169; https://doi.org/10.3390/hydrology12070169 - 28 Jun 2025
Viewed by 493
Abstract
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 [...] Read more.
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
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12 pages, 1825 KiB  
Article
Selecting Tolerant Maize Hybrids Using Factor Analytic Models and Environmental Covariates as Drought Stress Indicators
by Domagoj Stepinac, Ivan Pejić, Krešo Pandžić, Tanja Likso, Hrvoje Šarčević, Domagoj Šimić, Miroslav Bukan, Ivica Buhiniček, Antun Jambrović, Bojan Marković, Mirko Jukić and Jerko Gunjača
Genes 2025, 16(7), 754; https://doi.org/10.3390/genes16070754 - 27 Jun 2025
Viewed by 272
Abstract
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 [...] Read more.
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 article belongs to the Special Issue Molecular Breeding and Genetics of Plant Drought Resistance)
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15 pages, 3429 KiB  
Article
Hydrological Dynamics and Climate Variability in the Sava River Basin: Streamflow Reconstructions Using Tree-Ring-Based Paleo Proxies
by Abel Andrés Ramírez Molina, Igor Leščešen, Glenn Tootle, Jiaqi Gong and Milan Josić
Water 2025, 17(3), 417; https://doi.org/10.3390/w17030417 - 2 Feb 2025
Cited by 1 | Viewed by 1385
Abstract
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 [...] Read more.
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
(This article belongs to the Section Hydrology)
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10 pages, 4032 KiB  
Communication
Driving Factors and Future Trends of Wildfires in Alberta, Canada
by Maowei Bai, Qichao Yao, Zhou Wang, Di Wang, Hao Zhang, Keyan Fang and Futao Guo
Fire 2024, 7(11), 419; https://doi.org/10.3390/fire7110419 - 18 Nov 2024
Cited by 1 | Viewed by 1984
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Effects of Climate Change on Fire Danger)
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17 pages, 11482 KiB  
Article
Analyzing the Spatiotemporal Dynamics of Drought in Shaanxi Province
by Junjie Zhu, Yuchi Zou, Defen Chen, Weilai Zhang, Yuxin Chen and Wuxue Cheng
Atmosphere 2024, 15(11), 1264; https://doi.org/10.3390/atmos15111264 - 22 Oct 2024
Viewed by 1236
Abstract
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 [...] Read more.
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
(This article belongs to the Section Climatology)
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30 pages, 20031 KiB  
Article
Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye
by Eyyup Ensar Başakın, Paul C. Stoy, Mehmet Cüneyd Demirel, Mutlu Ozdogan and Jason A. Otkin
Remote Sens. 2024, 16(20), 3799; https://doi.org/10.3390/rs16203799 - 12 Oct 2024
Cited by 8 | Viewed by 2958
Abstract
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 [...] Read more.
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
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12 pages, 9197 KiB  
Article
Exacerbated Tree Growth Decline of an Old Platycladus orientalis Forest after Rapid Warming at the Northern Edge of the Taihang Mountain of China
by Yan Li, Tongxin Wang, Yantao Dong, Xinxin Han, Yang Liu, Yumei Mu, Xiaoyan Ma, Pingsheng Leng and Zenghui Hu
Forests 2024, 15(9), 1666; https://doi.org/10.3390/f15091666 - 22 Sep 2024
Viewed by 1235
Abstract
Old trees are irreplaceable conservation resources with numerous ecological and socio-cultural values. While many forests have experienced significant declines under recent climate warming, the risk of growth declines in old trees remains unknown. Here, we tackle this problem by dendrochronological studies of 30 [...] Read more.
Old trees are irreplaceable conservation resources with numerous ecological and socio-cultural values. While many forests have experienced significant declines under recent climate warming, the risk of growth declines in old trees remains unknown. Here, we tackle this problem by dendrochronological studies of 30 old trees in a Platycladus orientalis forest at the northern boundary of the Taihang Mountain of China. We examined annual growth trajectories of trees at individual level and discovered four severe growth decline events over the last 150 years, including the periods of 1894–1899, 1913–1919, 1964–1967 and 2004–2018. The most recent growth decline event lasted for 15-year and involced 50% to 75% of the old trees. This decline was unprecedented in both its extent and duration. Furthermore, the growth–climate relationship of these old trees has changed since 1990. Before 1990, tree growth was significantly correlated with minimum winter; after 1990, tree growth became significantly correlated with the self-calibrating Palmer Drought Index. These results suggest that warming-induced droughts after 1990 could be the primary driver of the recent growth decline. If climate warming continues and drought stresses intensify, the old trees may face an increased risk of growth decline and even mortality. Full article
(This article belongs to the Section Forest Ecology and Management)
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15 pages, 5346 KiB  
Article
Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China
by Cheng Li, Yuli Gu, Hui Xu, Jin Huang, Bo Liu, Kwok Pan Chun and Thanti Octavianti
Agronomy 2024, 14(4), 817; https://doi.org/10.3390/agronomy14040817 - 14 Apr 2024
Cited by 1 | Viewed by 1994
Abstract
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 [...] Read more.
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
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17 pages, 5245 KiB  
Article
Multiscale Spatiotemporal Dynamics of Drought within the Yellow River Basin (YRB): An Examination of Regional Variability and Trends
by Lei Jin, Shaodan Chen and Mengfan Liu
Water 2024, 16(5), 791; https://doi.org/10.3390/w16050791 - 6 Mar 2024
Cited by 5 | Viewed by 2037
Abstract
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 [...] Read more.
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
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19 pages, 13001 KiB  
Article
Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data
by Wei Wei, Jiping Wang, Libang Ma, Xufeng Wang, Binbin Xie, Junju Zhou and Haoyan Zhang
Land 2024, 13(1), 95; https://doi.org/10.3390/land13010095 - 15 Jan 2024
Cited by 10 | Viewed by 2555
Abstract
Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including [...] Read more.
Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America. Full article
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25 pages, 6000 KiB  
Article
Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting
by Ömer Ekmekcioğlu
Water 2023, 15(19), 3413; https://doi.org/10.3390/w15193413 - 28 Sep 2023
Cited by 10 | Viewed by 2302
Abstract
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 [...] Read more.
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
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12 pages, 2264 KiB  
Article
Effect of Climate and Competition on Radial Growth of Pinus sylvestris var. mongolica Forest in Hulunbuir Sandy Land of Inner Mongolia, China
by Shuo Wen, Zhongjie Shi, Xiao Zhang, Leilei Pan, Semyung Kwon, Yuheng Li, Xiaohui Yang and Hanzhi Li
Plants 2023, 12(13), 2584; https://doi.org/10.3390/plants12132584 - 7 Jul 2023
Cited by 3 | Viewed by 1782
Abstract
(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 [...] Read more.
(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 (Tmax) 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
(This article belongs to the Special Issue Ecological Processes and Sandy Plant Adaptations to Climate Change)
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12 pages, 2890 KiB  
Article
Streamflow Reconstructions Using Tree-Ring-Based Paleo Proxies for the Sava River Basin (Slovenia)
by Glenn Tootle, Abdoul Oubeidillah, Emily Elliott, Giuseppe Formetta and Nejc Bezak
Hydrology 2023, 10(7), 138; https://doi.org/10.3390/hydrology10070138 - 28 Jun 2023
Cited by 8 | Viewed by 2272
Abstract
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 [...] Read more.
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
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12 pages, 3155 KiB  
Article
Investigating Drought Events and Their Consequences in Wildfires: An Application in China
by Song Yang, Aicong Zeng, Mulualem Tigabu, Guangyu Wang, Zhen Zhang, He Zhu and Futao Guo
Fire 2023, 6(6), 223; https://doi.org/10.3390/fire6060223 - 2 Jun 2023
Cited by 10 | Viewed by 2421
Abstract
Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China’s [...] Read more.
Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China’s fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models. Full article
(This article belongs to the Special Issue Effects of Climate Change on Fire Danger)
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