Search Results (245)

Warming profoundly influences soil respiration in terrestrial ecosystems, thereby altering global carbon cycling. Understanding the trends and drivers of soil respiration changes in forest ecosystems under warming is essential for assessing regional carbon budgets and ecosystem carbon sink/source dynamics. In this study, a one-year warming experiment was conducted using open-top chambers in a Taxodium hybrid (Zhongshanshan) ecosystem in the northern Jiangsu coastal area, China. Treatments included control (CK) and warming (W), focusing on soil respiration components (soil respiration, Rs; heterotrophic respiration, Rh; autotrophic respiration, Ra) and associated soil hydrothermal and nutrient factors. Results showed that both warming and season significantly affected Rs, Rh, and Ra, all exhibiting a unimodal seasonal pattern peaking in summer. Warming increased winter Ra by 117.39% (p < 0.001). Bivariate models (temperature and moisture) explained more variation in respiration (R² = 0.720–0.893) than univariate models. Correlation analysis indicated that under control conditions, Rs components were significantly positively correlated with microbial biomass carbon (MBC), ammonium nitrogen (NH₄⁺-N), and available phosphorus (AP). After warming, these positive correlations with MBC and AP persisted; however, negative correlations emerged with soil organic carbon (SOC) and its stoichiometric ratios (C:N, C:P). Additionally, Ra showed negative correlations with easily oxidizable carbon (EOC), total nitrogen (TN), and N:P. Overall, these findings suggest that climate warming may enhance soil respiration in the Taxodium hybrid (Zhongshanshan) ecosystem by altering soil thermal-hydrological and nutrient factors, although further validation is needed. Full article

The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelized prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. Full article

The integration of remote sensors with nature-based solutions (NBS) offers new opportunities for suitable hydrological monitoring and risk reduction. This study implements 2D-1D numerical modelling tools to guide the allocation of both sensors and NBS interventions. Using the Cerisano catchment as a case study, the software IBER-SWMM (3.3.1–5.2 respectively) was employed to reproduce rainfall-runoff under different scenarios. Outputs were analysed to identify hydrologically sensitive zones where sensor deployment and NBS implementation would maximise monitoring efficiency and mitigation benefits. Results demonstrate how modelling supports effective decisions that are often limited by budget and site-specific constraints. Full article

Understanding drought and water availability requires integrating multiple components of the hydrological cycle. Satellite observations enable consistent monitoring of water storage, groundwater variability, and water budget components at continental scales. This study synthesises results from several satellite-based analyses to examine hydrological signals across Europe within the Köppen–Geiger climate zones. Indicators were analysed jointly, including the Combined Climatologic Deviation Index (CCDI), Water Budget (WB), Water Storage Deficit Index (WSDI), and Groundwater Drought Index (GDI). The comparison of these indices reveals consistent spatial and temporal patterns of water deficit across Europe, with the strongest drying signals observed in temperate and Mediterranean regions. In contrast, northern climatic zones show higher retention capacity. The integrated approach highlights relationships among groundwater variability, water storage anomalies, climate anomalies, and water budget dynamics, providing a broader perspective on hydrological responses to climate variability. The results demonstrate the value of multi-indicator satellite analysis for large-scale drought monitoring and water resource assessment. Full article

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins. Full article

This paper develops a rigorous inferential framework for a class of Gaussian stochastic processes driven by white noise with constant drift, whose temporal evolution is governed by a Caputo fractional derivative of order ${\displaystyle \alpha \in (1/2,1)}$. The model belongs to the family of fractional Volterra processes, where memory is generated by the dynamics themselves rather than by correlated noise. We derive explicit analytical expressions for the mean, variance, and covariance structure of the solution, thereby characterizing in a precise manner how the fractional order ${\displaystyle \alpha }$ governs both variance growth and the strength of temporal dependence. In particular, the process exhibits correlated increments and a power-law variance scaling of order ${\displaystyle t^{2\alpha -1}}$, highlighting the dual role of ${\displaystyle \alpha }$ as a regularity and memory parameter. Building on this structural analysis, we address the statistical problem of estimating the parameter vector ${\displaystyle (\mu ,\sigma ,\alpha )}$ from discrete-time observations. Two complementary procedures are proposed for the estimation of the fractional order: a variance-growth method based on log–log regression of empirical variances, and a wavelet-based estimator exploiting multi-scale scaling properties of the process. For the drift and diffusion parameters ${\displaystyle (\mu ,\sigma )}$, we construct explicit Gaussian pseudo-maximum likelihood estimators derived from the Volterra covariance structure of the increment process. We establish unbiasedness, ${\displaystyle L^2}$-convergence, strong consistency, and asymptotic normality for all estimators. Furthermore, we derive Berry–Esseen type bounds that quantify the rate of convergence toward the Gaussian law, providing sharp distributional approximations in a genuinely fractional and non-Markovian setting. A Monte Carlo study is carried out, using high-resolution Volterra discretizations, large-scale simulation budgets, covariance-structured linear algebra, and multi-scale diagnostic tools. The numerical experiments confirm the theoretical convergence rates, demonstrate the finite-sample reliability of the estimators, and illustrate the sensitivity of the process dynamics to the fractional order ${\displaystyle \alpha }$: smaller values of ${\displaystyle \alpha }$ produce stronger memory effects and higher variability, while values closer to one lead to smoother and more stable trajectories. The proposed methodology unifies statistical inference for long-memory Gaussian processes with fractional differential stochastic dynamics, offering a coherent analytical and computational framework applicable in areas such as quantitative finance, anomalous diffusion in physics, hydrology, and engineering systems with hereditary effects. Full article

Urban districts are increasingly exposed to overlapping heat stress and stormwater loads driven by warming trends, more intense rainfall, and continued growth of impervious surfaces. Pavements occupy a large share of the public right-of-way, so their material and structural design offers a scalable pathway for urban climate adaptation. Yet the literature on porous asphalt remains fragmented, with hydrological performance often assessed using infiltration or permeability metrics in isolation, while thermal studies frequently report surface cooling without consistently tracking the governing water budget or its persistence. To reconcile these disconnected strands, this review synthesizes a conceptual hydro-thermal balance framework in which runoff mitigation and heat moderation are treated as a coupled problem controlled by storage, drainage pathways, and evaporative demand. Within this framing, cooling is primarily water-limited: permeability enables wetting and redistribution, but the magnitude and duration of temperature reduction depend on how much water is retained near the surface and how long it remains available for evaporation, rather than on permeability alone. The review integrates the current understanding of mixture structure and pore connectivity, permeability–storage behavior, moisture availability and evaporation, and the operational factors that govern performance persistence. Laboratory and field evaluation approaches are summarized alongside modeling methods used to interpret coupled hydro-thermal responses under different climates. Practical constraints—including clogging, maintenance requirements, and durability risks under repeated moisture–temperature cycling—are discussed as mechanisms that can progressively suppress both infiltration and water availability, undermining long-term function without performance-based specifications and life-cycle planning. Finally, design and policy implications are outlined for integrating porous asphalt into coordinated heat-and-stormwater strategies, and research priorities are identified to advance standardization, long-term monitoring, and coupled hydro-thermal–mechanical assessment. Full article

Groundwater quantification is essential for sustainable water resources management, yet it is often hampered by limited data availability and difficulties in measuring spring discharges. This study investigates three carbonate aquifers in Central Italy’s Abruzzo region: the Genzana–Greco, Morrone, and Marsicano mountains. The aim is to resolve uncertainties in spring attribution, and groundwater flow patterns using isotopic analyses combined with field surveys. The Genzana–Greco aquifer was examined to clarify the sources of the Acquachiara spring and the previously unreported Germina spring, assessing whether recharge occurs locally or from the carbonate massif. In this case, the results indicate that the Germina, together with a similar known spring of Capolaia, share a common recharge sector, while the Acquachiara spring is mainly fed by higher-elevation carbonate areas, excluding significant contributions from local alluvial deposits. In the Morrone mountain aquifer, discharge gains along the Pescara River through the Gole di Popoli were quantified, and spring isotopic compositions were compared to the main basal spring Giardino to better define groundwater contributions. In this case study, the stable isotopes and tritium data confirm recharge from the central–southern massif and support the identification of basal springs and Pescara River gains as primary discharge points, with minimal influence from surface water. For the Marsicano mountain aquifer, the role of Lake Scanno in feeding the Villalago springs was investigated through isotopic analysis of inflows, downstream springs, and basal aquifer discharge points to constrain the hydrogeological water budget. The results highlight Lake Scanno’s role in the recharge of Villalago springs and delineate the Cavuto group as a major discharge system receiving inputs from central and northern sectors of the massif. Overall, the integration of isotopic tracers with hydrological measurements allowed a more precise characterization of aquifer recharge areas, Mean Residence Times, and groundwater flow paths, improving the understanding of regional water resources in a complex carbonate setting. Full article

The Jordan Valley is a heavily modified, data-limited transboundary river basin where water availability is constrained by both climate conditions and intensive human intervention. This study applies an integrated hydrological and hydrogeochemical modeling framework using the Soil and Water Assessment Tool (SWAT) to quantify basin-scale water availability and quality and to assess climate change impacts for the period 2000–2021. Results indicate that the basin is strongly evapotranspiration-dominated, with mean annual precipitation of 298.9 mm and precipitation-derived evapotranspiration accounting for 66.3% of rainfall. When externally supplied irrigation water is included, total evapotranspiration increases markedly, highlighting the strong dependence of agriculture on imported surface water and groundwater abstractions. Only a small fraction of total water input contributes to river discharge toward the Dead Sea, indicating a very limited internal water surplus. Hydrological dynamics are largely controlled by upstream dams and transboundary diversions, while nitrate and sediment simulations demonstrate a close coupling between hydrology, land use, and water quality. Climate projections suggest further reductions in water availability during the 21st century, exacerbating existing water scarcity. Overall, the study illustrates how intensive regulation and irrigation dependency constrain water availability in the Jordan Valley and in similar heavily modified transboundary river basins. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

Gridded meteorological data sources, such as reanalysis datasets, are increasingly used to estimate evapotranspiration, a key variable for surface water-budget analyses at regional and national scales and for assessing plant water requirements for irrigation. This study, conducted over mainland Portugal for the 44-year reference period from 1980 to 2023, first presents a comprehensive comparative analysis of the spatial patterns of potential ($Ep$) and reference ($Eto$) evapotranspiration at a 0.1° spatial resolution using daily data. Estimates derived from two high-resolution datasets (GLEAM and ERA5-Land) are compared with those obtained from the Thornthwaite, Hargreaves–Samani, and Penman–Monteith models. Secondly, trend analyses of $Eto$ magnitudes on a monthly and annual basis in a gridded format were conducted. The resulting spatial distributions of $Ep$ and $Eto$ show higher values in milder and flatter southern Portugal and lower values in the cooler and more mountainous northern regions, in agreement with existing knowledge. The Penman–Monteith model exhibited the highest reliability, while the Thornthwaite model generally underestimated evapotranspiration across the country, and the Hargreaves–Samani model showed underestimation in coastal areas. Trend analysis of $Eto$ indicates an overall increase in atmospheric evaporative demand over the full study period, with a more pronounced rise during the recent 22-year period (2002–2023) compared with the earlier period (1980–2001). These increases are statistically significant in August and October and may reflect a climate shift towards a progressively longer dry season. Understanding how changes in evapotranspiration affect hydrological processes—including surface water availability, river discharge, reservoir performance, and crop requirement—is critical. This study aims to contribute to addressing these emerging challenges. Full article

Clouds are essential for regulating the hydrological cycle and Earth’s radiation budget, and their fluctuations over the Tibetan Plateau (TP) have a significant effect on both regional climate dynamics and global atmospheric circulation. Using ground-based Vaisala CL51 ceilometer data and Fengyun-4A (FY-4A) satellite observations from October 2020 to June 2022, this study examines cloud occurrence frequency (COF), cloud vertical structure (including cloud base height (CBH), cloud top height (CTH), and cloud layer stratification), and related macroscopic properties over the Large High Altitude Air Shower Observatory (LHAASO). CL51 and FY-4A had cloud occurrence rates of 43.7% and 37.7%, respectively, over the observation period, with a strong correlation coefficient of 0.82. Given the impact of clouds on Cherenkov light observations by the LHAASO Wide Field of view Cherenkov Telescope Array (WFCTA), we specifically evaluated the cloud occurrence during the operational periods of the LHAASO-WFCTA, finding rates of 34.2% (CL51) and 28.0% (FY-4A), with the lowest rates occurring in the early morning. Due to monsoonal moisture inflow and dry northeasterly winds, seasonal COF changes showed clear peaks in summer (78.8%) and minima in winter (24.8%). Seasonal differences existed in the diurnal COF patterns, with nocturnal prominence in summer/autumn and daytime dominance in spring/winter. The CBH showed daily oscillations, peaking at 18:00 (local solar time) and troughing at 08:00 (local solar time), with seasonal CBH minima in summer/autumn and maxima in spring/winter. Low- and mid-level clouds predominated, with clear diurnal cycles: low- and mid-level clouds rose from morning until midday, while high-level clouds appeared after dusk. Vertical cloud structures were predominantly single-layered (81%), with multi-layered complexity peaking in the summer due to convective activity. The CTH distributions showed unimodal patterns in the fall and winter (1.5–3 km), while in the summer, they showed multimodal extents (up to 12 km). These results improve LHAASO-WFCTA observational scheduling, enhance climate model parameterizations, and deepen our understanding of the dynamics of the TP cloud. Full article

This study uses high-frequency monitoring across a river–barrier lake–reservoir continuum in the upper Minjiang River, southwestern China, to quantify the spatiotemporal dynamics and drivers of aquatic CO₂ partial pressure (pCO₂) and to identify the dominant controls under contrasting lotic and lentic conditions. River reaches were CO₂-supersaturated throughout the year, with higher pCO₂ in the wet season (mean 521 ppm) than in the dry season (421 ppm), indicating persistent CO₂ evasion to the atmosphere. In contrast, the downstream canyon-type reservoir showed a pronounced seasonal reversal. During the wet season, surface-water pCO₂ averaged 395 ppm, about 24% lower than that of the river and below atmospheric levels (~419 ppm); more than 55% of observations were undersaturated, with minima as low as 141–185 ppm, indicating temporary CO₂-sink behavior. In the dry season, mean pCO₂ increased to 563 ppm, exceeding both riverine and atmospheric levels and returning the reservoir to a CO₂ source. The reservoir pCO₂ variability was governed by the interaction of hydrology and metabolism: rising water levels and longer residence times likely enhanced CO₂ accumulation from the decomposition of inundated organic matter, while warm temperatures, high light and monsoon-driven nutrient inputs promoted phytoplankton growth that removed dissolved CO₂ and elevated dissolved oxygen, producing temporary sink behavior. In the river, short residence time and strong turbulence limited in-stream biological regulation, and pCO₂ variability was mainly driven by catchment-scale carbon inputs along the elevation gradient. Overall, our results demonstrate that dam construction and impoundment can substantially modify carbon cycling in high-mountain rivers. Under specific conditions (warm water, sufficient nutrients, high algal biomass), lentic environments may strengthen photosynthetic CO₂ uptake and temporarily transform typical riverine CO₂ sources into sinks, with important implications for carbon-budget assessments and reservoir management in mountainous basins. Full article