Search Results (86)

Due to its global coverage, high temporal and enhanced spatial resolution (compared with the ERA5 product), and open availability, the ERA5-Land global atmospheric reanalysis dataset offers a promising framework for various applications in environmental modeling. In this study, the ERA5-Land post-processed daily precipitation product was compared against ground-based observations to evaluate its reliability in representing regional precipitation patterns and its capacity to reproduce landslide-triggering rainfall conditions. The investigation was conducted across five landslide locations in Pannonian Croatia over a 35-year period (1990–2024). It encompassed a comparative statistical analysis of daily and monthly precipitation, an evaluation of rainfall day and event frequency distributions, and a reconstruction of specific historical landslide-triggering events. The results confirm that ERA5-Land exhibits a systematic dual bias, overestimating low-intensity precipitation frequencies while underestimating high-intensity rainfall peaks. Concurrently, the pronounced drizzle effect directly increases the duration of rainfall events, altering landslide-triggering rainfall conditions. The findings align with previous research, confirming that ERA5-Land data can effectively capture broad, long-term regional climate trends and track antecedent rainfall indices. However, its integration into operational landslide early warning systems would benefit from statistical bias-correction or localized calibration to reduce systematic errors. Full article

Groundwater is an essential natural resource for human societies and ecosystems. Traditional groundwater monitoring relies on in situ wells, which are susceptible to discontinuity, influencing water resource management. To overcome this deficiency, this study proposes a remote sensing-based groundwater level (GWL) monitoring system that uses machine learning (ML) algorithms and remotely sensed hydrological parameters to reconstruct well-specific GWL time series. Four machine learning algorithms, including K-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a weight-mean Ensemble strategy, were adopted to construct the models at each well individually for monitoring GWL. Specifically, the GWL data for ~770 wells across the conterminous United States (CONUS) were modeled using remotely sensed precipitation (P), evapotranspiration (ET), terrestrial water storage anomaly (TWSA), and soil moisture (SM) datasets during the period from 2004 to 2019. Afterwards, the performances of models were evaluated during an independent period from 2020 to 2023. The results show that the Ensemble model outperforms the individual baseline models evaluated in this study (i.e., KNN, RF, and XGBoost), achieving a mean coefficient of determination (R²) of 0.81, root mean square error (RMSE) of 0.34 m, normalized RMSE (NRMSE) of 11.8%, and Nash–Sutcliffe efficiency (NSE) of 0.78. The results demonstrate that the proposed system can effectively reconstruct GWL dynamics for most wells. This can be a compensation for missing records for hydrologically significant wells, which are those with historical groundwater observations. Full article

Radar echo extrapolation is a fundamental task in precipitation nowcasting. However, existing models based on RNNs, CNNs, and Transformers are often constrained by error accumulation, the loss of temporal information, and quadratic computational complexity. Consequently, these limitations can lead to prediction blurring and the distortion of fine-grained details in extrapolated radar images. To address these challenges, we propose RadarEchoMamba, a novel extrapolation framework built on the efficient Mamba model. RadarEchoMamba uses a pyramidal architecture to capture multi-scale features, and its core is a Bidirectional Spatiotemporal Mamba backbone that models dependencies within the observed historical input window. Furthermore, we introduce a spatiotemporal prior module driven by the input sequence to guide the reconstruction of high-resolution features during the decoding phase. This design enables the model to generate detail-rich predictions, improving upon the blurring issues prevalent in traditional extrapolation models. Experimental results on the South China and Shanghai-2020 datasets show the effectiveness of our method. Compared with strong Transformer-based baselines, the lightweight variant uses fewer parameters and achieves lower measured inference latency, while maintaining competitive prediction quality. Its main advantages are observed in visual-fidelity metrics, with improved SSIM and reduced LPIPS on the South China dataset. Full article

This study presents a meta-analysis of relatively high-resolution paleohydrological proxies derived from geological archives in Sardinia and in the Italian Peninsula–Sicily over the last 2000 years, with particular emphasis on the Medieval Warm Period (MWP) and the Little Ice Age (LIA). The investigated climate proxies, ranging from annual-decadal to centennial resolution, include terrestrial and marine sediment cores, glaciers, pollen spectra, speleothems, lake-level fluctuations, as well as sedimentary and geomorphological inventories. Such datasets were analyzed through holistic and stratigraphic approaches along West–East and North–South transects across the central Mediterranean. Limited temporal resolution and incomplete stratigraphic continuity of several paleoclimatic records from the investigated regions thwart full reconstructions of paleohydrological trends. Nevertheless, the presented meta-analysis has enabled: (1) the recognition of reliable paleoclimatic correlations between the two regions, which exhibit long-lasting anti-phase hydroclimatic trends (wetter conditions in Sardinia and drier conditions in central Italy during the MWP, with the opposite pattern during the LIA); and (2) the identification of the North Atlantic Oscillation (NAO) as the primary driver of these paleohydrological variations. The significance of this anti-phase pattern is discussed in the context of the North–South and West–East climatic dipoles identified in the Mediterranean region during the middle to late Holocene. Furthermore, we assessed the potential of the investigated paleohydrological network to: (1) compare reconstructed hydrological patterns with mean temperature and precipitation records derived from empirical and model-based climate reconstructions in southern Europe and the Mediterranean; and (2) identify gaps in data coverage that currently limit our understanding of high-resolution spatiotemporal hydrological variability and dynamics.The hydroclimatic pattern in Sardinia and in the Italian Peninsula–Sicily has exhibited marked spatio-temporal divergences, with major hydroclimatic transitions coincident with well-known solar minima over the last millennium, thus suggesting a possible cause-and-effect relationship. The interpretations presented in this study provide a framework for understanding how changes in the paleoclimatic variability of water resources may have influenced different regions of Italy since the Middle Ages, potentially affecting societal transitions as well as historical and socioeconomic dynamics. Comparison of the multidecadal-to-centennial reconstructions of paleohydrological patterns is presented for both areas, pending the development of new, higher-resolution, and more precisely dated proxies from the Italian records. Their importance is emphasized in order to improve reconstructions of past climate variability and to enhance assessments of future climate trajectories. Full article

The calibration accuracy of key weather radar antenna parameters, including beam pointing, beamwidth, and antenna gain, directly affects quantitative precipitation estimation (QPE) and multi-radar network products. Conventional calibration approaches such as external field beacons and far-field tests are often constrained by site conditions and high implementation costs, making them difficult to apply routinely in operational radar networks. To address this limitation, this study proposes a robust solar calibration method for key antenna parameters of weather radars based on a dedicated Volume Coverage Pattern for Sun calibration, hereafter referred to as VCPSun. The proposed method uses a high-density solar scanning strategy with midpoint time alignment and feed-forward control of solar apparent motion. Combined with solar sample identification, propagation path correction, two-dimensional Gaussian surface fitting, and deconvolution of solar-source broadening and scan-smearing effects, the method enables reliability retrieval of beam pointing, beamwidth, and antenna gain. A high-frequency intensive observing experiment was conducted using a China New Generation Weather Radar, model SA-D (CINRAD/SA-D), deployed at the Changsha Meteorological Radar Calibration Center, with independent far-field test results used for validation. The results show that the retention rate of quality-controlled solar samples reached 85.7%, supporting stable reconstruction of the main-lobe power pattern. The retrieved mean beam pointing biases for both polarizations were within ±0.05°. After correction, the relative differences in beamwidth with respect to far-field measurements were respectively 3.26% and 1.52% for the H-polarization azimuth and elevation directions and 2.09% and 1.84% for the V-polarization azimuth and elevation directions, with the overall mean relative difference being less than 3.5%. The antenna gain differences relative to the independent far-field reference values were within 0.2 dB, at −0.062 dB for H-polarization and −0.144 dB for V-polarization. Comparative analysis with historical one-dimensional SunCheck records and an ablation test of the beamwidth correction chain further demonstrate that high-density two-dimensional sampling and physical deconvolution corrections improve the robustness and quantitative accuracy of the solar-based retrieval. These results demonstrate the feasibility of reliable in situ calibration of key antenna parameters for operational weather radars. The proposed method provides a potential technical pathway for in situ quantitative assessment of antenna performance in S-band CINRAD/SA-D radars, although further validation using additional radars and longer observation periods is required prior to network-wide application. Full article

To address the scarcity of natural archives in historical climate reconstruction, this study utilized the late Ming Dynasty text Jiu Jing Yi Shi (Reminiscences of the Old Capital) as a primary data source to extract botanical and phenological information, aiming to quantitatively reconstruct climate parameters for the Beijing region circa 1644 CE. Using botanical textual research, 11 out of 20 recorded plant names were identified to the species level, 2 to the genus level, and 7 were classified as non-native species. Breaking from the traditional reliance solely on woody plants, we innovatively incorporated three herbaceous species into the coexistence analysis framework to enhance the accuracy of climate reconstruction. By comprehensively comparing four climate indicators—mean annual temperature (MAT), mean temperature of the coldest month (MTCM), mean temperature of the warmest month (MTWM), and annual precipitation (AP)—across three critical nodes (1368 CE, 1644 CE, and the present), this research revealed a “decline-then-rise” trajectory in Beijing’s temperature over the past 600 years, alongside corresponding variations in precipitation patterns. Results indicated that the cooling event in the Beijing region between 1368 CE and 1644 CE was synchronous with global cooling trends during the same period and demonstrated a climatic transition from maritime to continental characteristics in the region. This work not only expands the application of historical literature in paleoclimatology but also provides critical scientific evidence for understanding centennial-scale climate evolution in the East Asian monsoon region and predicting future climate trends. Full article

Against the backdrop of a gradual shift in the focus of cultural heritage (CH) conservation and utilization toward the integrated system formed by CH and its surrounding environment as well as regional systems, research on the coordinated protection of nature and culture to promote regional high-quality development has become a new trend. However, systematic summaries of the spatial–temporal distribution of CH in cross-regional typical geomorphic units at the river basin scale and their correlation with the natural environment remain insufficient. This study takes 387 Cultural Relics Protection Units in the Three Gorges of the Yangtze River (the Three Gorges region) as the research objects, utilizing GIS spatial analysis technology to examine the impact of the natural environment on CH across different periods and types. The theory of time-depth is introduced to reveal the layering mechanisms and underlying cultural logics. Coupled with the Minimum Cumulative Resistance (MCR) model, this study constructs a cultural corridor network and proposes spatial planning strategies. The findings are as follows: (1) The absolute core area for the distribution of CH across all periods remains the gentle slope zone near the river, characterized by elevations below 500 m, slopes within 25°, and distances from water systems within 1 km. However, the adaptive scope exhibits a diachronic evolution from core accumulation to peripheral expansion. (2) Different types of CH exhibited distinct natural adaptation strategies and vertical accumulation. Settlement Sites in the Before Qin Dynasty Period formed the foundational layer of survival rationality, while Ordinary Tombs in the Qin–Yuan Dynasty Period reinforced sedentism. Ancient Architecture in the Ming–Qing Dynasty Period underwent a transformation from “adapting to nature” to “reconstructing nature” as a product of environmental construction. Modern and Contemporary Significant Historical Sites and Representative Buildings in the After Qing Dynasty Period are characterized by a ruptured insertion on steep slopes, inscribing revolutionary memory onto space. The main stream of the Yangtze River serves as the core area of continuous deposition, while the extremely steep slopes form a distinctive stratigraphic accumulation of precipitous terrain. (3) Based on these distribution patterns, the study further proposes a spatial framework for CH called “One Corridor, Three Wings.” This framework uses the main stream of the Yangtze River as the spatial–temporal axis, linking the four core overlapping nodes of Fengjie, Wushan, Badong, and Xiling, supplemented by three secondary cultural clusters of the red heritage sites in southern Badong, the ancient town along the Daning River in Wushan, and the fortress sites in the Xiling–Yiling area. This research not only reveals the evolutionary path of CH in the Three Gorges region, but also provides a scientific basis for the systematic conservation and differentiated utilization of regional CH. Furthermore, it serves as a planning foundation and strategic reference for planning the Yangtze River National Cultural Park, as well as for the integrated preservation and utilization of river basin CH and linear CH with the aim of coordinated natural and cultural conservation. Full article

This study presents the development and validation of a consistent rainfall database for Maranhão State, Brazil, covering historical records from 1987 to 2023 obtained from 100 rainfall stations (90 from ANA and 10 from INMET). A total of 314 missing records across 74 stations were corrected using the Regional Weighting method, restricted to stations within the same Homogeneous Precipitation Region (HPR). The consistency of the reconstructed series was verified using the Double Mass method, which yielded coefficients of determination (R²) above 0.97 for all stations, confirming the robustness of the procedure. Statistical analyses with the Mann–Kendall test and Sen’s Slope estimator did not identify significant long-term trends, although weak positive slopes were detected in some regions (e.g., HPR3: +9.98 mm/year; HPR6: +3.70 mm/year), while HPR10 showed a negative slope (−0.99 mm/year). The novelty of this work lies in consolidating the first homogeneous and validated rainfall database for Maranhão, providing a reliable foundation for assessing regional climate variability. The results provide a solid foundation for future applications, including drought monitoring, agricultural planning, water resource management, and adaptation strategies under climate change scenarios. Full article

Hydrological modelling in lowland catchments is often constrained by flat terrain and sparce meteorological station networks, which limits the accuracy of spatial interpolation of precipitation and temperature. In these conditions, the nearest available station may be located tens of kilometres away, making interpolated meteorological inputs highly uncertain and prone to systematic bias. This study aims to improve interpolated meteorological data for hydrological applications by developing and evaluating a practical bias correction approach suitable for low-relief regions with insufficient station density. Long-term temperatures and precipitation records from 18 meteorological stations in Lithuania (1961–2020) were used as reference data. Meteorological fields were reconstructed using Ordinary Kriging and Spline interpolation and evaluated against observations at monthly and daily time scales using correlation (r), Root Mean Square Error (RMSE), Percent Bias (PBIAS), Nash–Sutcliffe Efficiency (NSE), and Probability of Detection (POD) for precipitation. Bias correction was applied to interpolated datasets using inverse distance weighting (IDW) based on one to four neighbouring stations, reflecting typical distances of 50–70 km between observation sites. The results show that while the interpolation method strongly influences precipitation accuracy, bias correction substantially reduces systematic errors without altering temporal structure. The most robust improvements were obtained using two to three neighbouring stations and an IDW power parameter of one, particularly under flat terrain conditions. When applied as input to the HBV rainfall–runoff model for three representative lowland catchments, bias-corrected interpolated meteorological data consistently improved runoff simulations, bringing model performance closer to that achieved using historical station observations. The findings demonstrate that targeted bias correction is an effective and computationally simple strategy for improving interpolated meteorological data in data-sparse lowland regions. The proposed approach provides practical guidance for hydrological modelling where dense observation networks are unavailable and reliance on interpolation is unavoidable. Full article

As global climate change intensifies and extreme weather events become more frequent, understanding the historical spatial distribution of vegetation is of critical importance. However, most vegetation studies are temporally limited to the post-1980 period due to satellite data constraints. To bridge this gap, we integrated tree-ring width chronologies from the International Tree-Ring Databank with Landsat-derived Enhanced Vegetation Index (EVI) data and evaluated three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—to reconstruct annual, spatially explicit EVI for the period 1850–1985 in Diqing, Yunnan, China. RF regression was the best among the three with highest adjusted R² (0.90) and lowest Root Mean Square Error (0.032). The RF-based reconstruction indicated a consistent increase in regional EVI from 1991 to 2005. Breakpoint analysis identified three distinct sub-periods, each with unique spatiotemporal variation patterns. In current times, the EVI value shows a significant positive correlation with average temperatures in June, July, August, and December. In the contemporary period, it also correlates significantly and positively with winter average temperatures, March average precipitation, and spring average precipitation. The spatial pattern for the past 100 years reflects the succession of the local vegetation ecosystem and provides an insight into the influences of natural disturbances (low-temperature damages and droughts) on vegetation growth. This study demonstrates the feasibility of reconstructing high-resolution, long-term vegetation spatial dynamics using tree-ring proxies and machine learning. Full article

Forests play a critical role in Earth’s ecosystem, yet monitoring their long-term, large-scale spatiotemporal dynamics remains a significant challenge. This study addresses this gap by developing an integrated framework to map annual forest distribution in Hunan, China, from 1999 to 2023 at a high resolution of 30 m. Our methodology combines multi-temporal satellite imagery (Landsat 5/7/8/9) with key environmental variables, including digital elevation models, temperature, and precipitation data. To efficiently reconstruct historical maps, training samples were automatically derived from a reliable 2023 forest product using a transferable logic, drastically reducing manual annotation effort. Comprehensive evaluations demonstrate the robustness of our approach: (1) Qualitative analyses reveal superior spatial detail and temporal consistency compared to existing global forest maps. (2) Rigorous quantitative validation based on ∼9000 reference samples confirms high and stable accuracy (∼92.4%) and recall (∼91.9%) over the 24-year period. (3) Furthermore, comparisons with government forestry statistics show strong agreement, validating the practical utility of the data. This work provides a valuable, accurate long-term dataset that forms a scientific basis for critical downstream applications such as ecological conservation planning, carbon stock assessment, and climate change research, thereby highlighting the transformative potential of multi-source data fusion and automated methods in advancing geospatial monitoring. Full article

A Dendrochronological study of teak (Tectona grandis) was conducted at two sites in northern Chhattisgarh, central India, and resulted in the development of two tree-ring width chronologies. We examined the relationships between tree-ring chronologies and gridded monthly and daily climate variables (mean temperature, total precipitation and drought indices) as well as monthly soil moisture. We performed spatial correlations using monthly climate data and used the nearest climate grid point for daily climate correlations. Both chronologies showed negative correlations with temperature and positive correlations with soil moisture, rainfall, and drought indices. These relationships highlight the dominant role of soil moisture availability in influencing teak growth in the monsoon-dominated climate of Chhattisgarh. Based on this relationship, we reconstructed average soil moisture from February to October, extending the gridded soil moisture record by 62 years (1920–1981 CE). This reconstruction represents the first tree-ring-based long-term soil moisture record from central India. Our findings provide a comprehensive hydroclimatic perspective for a region lacking historical tree-ring data and demonstrate the potential of teak as a proxy for investigating long-term soil moisture variability. Further research using older samples from this species will enhance understanding of past climate variability and hydroclimatic changes in central India. Full article

This study investigates how the traditional Chinese “philosophy of self-cultivation and awakening” (xiu-wu) can be systematically harnessed to foster Higher-Order Thinking Skills (HOTS) among undergraduates. Through historical–philosophical reconstruction and conceptual analysis, the study distills three recurring instructional principles—gradual cultivation (jian-xiu), gradual awakening (jian-wu), and sudden awakening (dun-wu), and their dialectical synthesis, and re-casts them as design parameters for thinking-centered instruction. These principles are then translated into a macro-level instructional metaphor, the Bridge-Building Model, which sequences curricular elements as bridge piers (the teaching process of “gradual cultivation”), bridge deck (student-constructed “an isolated fragments of knowing”), and final closure (holistic knowledge). The model integrates constructivist, behaviorist and intuitive dimensions: repetitive, scaffolded tasks foster behavioral automaticity; guided reflection precipitates incremental insight; and calibrated “epistemic shocks” elicit sudden reorganization of conceptual schemata. The framework clarifies the locus, timing and contingency of each phase while acknowledging the metaphysical indeterminacy of ultimate “holistic” mastery. By translating classical Chinese pedagogical insights into operational design heuristics, the paper offers higher-education instructors a culturally grounded, theoretically coherent blueprint for systematically nurturing HOTS without sacrificing the spontaneity essential to creative cognition. Full article

Forest productivity is a critical indicator of forest ecosystem vitality and carbon budget status. Understanding its historical trends and driving mechanisms is essential for assessing forest responses to climate change. Currently, widely used methods for productivity reconstruction, including forest inventories, eddy covariance observations, and remote sensing models, have temporal limitations and cannot adequately meet the demands of long-term ecological research. Tree-ring data, with their advantages of annual resolution and extended time series, have become an important tool for reconstructing historical forest productivity. Research has demonstrated that tree-ring width, stable isotopes, wood density, and anatomical properties are closely related to forest productivity. Mechanistic studies indicate that the climate–canopy–stem coupling relationship exhibits three key nonlinear characteristics: the bidirectional threshold effect of precipitation, the inverted U-shaped temperature response, and the carbon allocation “legacy effect”. Correlation analyses show that the optimal response period between tree rings and productivity is concentrated primarily in the growing season or summer, reflecting the critical regulatory role of temperature and moisture on tree growth. Based on this understanding, existing research has focused predominantly on mid- to high-latitude temperate forests in the Northern Hemisphere that are sensitive to climate, with tree-ring chronologies from arid regions showing stronger correlations with forest productivity. Given current progress and existing limitations, future research should address the impact of stand dynamics on reconstruction accuracy, strengthen linkages between vegetation indices and tree-ring data, integrate belowground productivity, and deepen understanding of the physiological mechanisms underlying forest productivity. Full article

With the advancement of remote sensing-based river discharge monitoring in data-scarce regions, reconstructing daily streamflow series from remote sensing data has become a critical hydrological challenge. To address the sparsity of remote sensing inversions and the discontinuity of discharge observations, we propose a physics-enhanced deep learning model—Physics-enhanced Seq-to-Seq Attention LSTM (PSAL)—to achieve high-accuracy daily streamflow reconstruction. The model incorporates input structures aligned with hydrological mechanisms, providing a physically meaningful basis for interpretability and enabling physics-guided learning. Results show that (1) PSAL achieves high reconstruction accuracy across five representative gauging sites on the Jinsha River (mean NSE = 0.81). Among lagged output configurations from T-1 to T-7 days, the T-7 setting yields the best performance (mean NSE = 0.85). (2) Compared with a baseline Seq-to-Seq Attention LSTM model without physics-enhanced features, PSAL significantly improves reconstruction skill (mean ΔNSE = 0.76). Feature ablation analysis further reveals that precipitation, as a key driver of runoff, has a strong influence on model performance (mean ΔNSE = 0.32). This study presents a novel approach that integrates physical knowledge with data-driven methods for streamflow reconstruction in remote sensing-dominated, data-scarce regions, offering theoretical support and methodological guidance for digital twin watershed development and historical hydrological data infilling. Full article