Search Results (365)

Local Climate Zones (LCZs) have become a foundational framework for urban climate modeling, yet their use across model families has not been systematically evaluated. Crucially, the LCZ framework itself has served as a developmental basis, revealing the progression of urban canopy parameterizations (UCP) from early models to the diverse model families currently in use. This evolution is exemplified by systems like the Weather Research and Forecasting (WRF) model, where the application of LCZ has fundamentally shifted from an experimental add-on to a basic, built-in feature of its urban-modeling capabilities. This review synthesizes a decade of LCZ-based studies to clarify how LCZ improves surface representation, enhances comparability, and supports multiscale modeling workflows. It provides a comprehensive overview of peer-reviewed work up to the end of 2024, offering a baseline for understanding the field’s rapid recent growth. Using a structured evidence-mapping approach, we categorize applications into three maturity stages: testing and measurement, operational and planning-oriented applications, and expansions beyond urban climate to chemistry, hydrology, and Earth-system modeling. The assessment covers various iterations of mesoscale systems (WRF, SURFEX/TEB, COSMO), local-scale climatologies (MUKLIMO-3, UrbClim), microscale models (ENVI-met, CFD), and supporting tools including SUEWS, SOLWEIG, RayMan, VCWG, and CESM-CLMU. Results show clear divisions of labor: WRF and SURFEX/TEB anchor process-rich regional simulations; MUKLIMO-3 and UrbClim offer computationally efficient long-horizon or multi-city assessments; ENVI-met and CFD provide design-scale insight when parameterized with LCZ archetypes. Across all families, model skill is strongly constrained by LCZ data quality and by inconsistencies in LCZ to UCP translation. We conclude that advancing LCZ-based urban climate modeling will depend on improved LCZ products, standardized parameter libraries, and formalized cross-scale model couplings that allow existing tools to interoperate more reliably under growing urban-climate pressures. Full article

Increasing levels of chloride in surface water are associated with detrimental effects on water quality, aquatic ecosystems, infrastructure, and human health. Numerous mass-balance studies have inferred watershed transport processes by interpreting chloride inputs and outputs, but few represent internal dynamics explicitly. We constructed a coupled water/chloride mass balance model to gain insights into storage, residence time, and transport processes in a 10-km² urban watershed. The model, which operates over a 10-year period at a daily time scale, represents storage in a dynamic soil-moisture reservoir, quick-flow runoff from storm events, and slow-flow runoff that sustains streamflow in dry weather. The calibrated model accurately represented (a)the observed transition from a streamflow enrichment regime in cold months to a dilution regime in warmer months, (b) the observed tendency for late-summer concentrations to be higher after winters with heavy snowfall, and (c) a period-of-record downward trend in chloride concentration likely associated with a downward trend in annual snowfall. Estimated chloride inputs averaged 195 metric tons per year, while the average output was 270 metric tons per year. In contrast, estimated storage was only 107 metric tons. The estimated mean residence time in groundwater was 1.27 years. This short residence time indicates that efforts to reduce inputs will manifest as decreased concentrations in streamflow on a management-relevant time scale of several years. The coupled mass balance model yielded insights into internal watershed dynamics that would not be possible from simple input/output analysis; such models can be useful tools for gaining insight into small watershed hydrology and pollutant transport. Full article

Landscapes in semiarid regions are highly sensitive to climate change and anthropogenic activities, and their evolution directly influences ecosystem services and regional ecological security. Although previous research has examined land use changes, systematic quantitative analyses of long-term evolutionary trends and driving mechanisms, particularly the comprehensive relationships between key hydrological elements and landscape pattern evolution in water-scarce, semiarid watersheds, remain limited. To address the research gap in long-term, multifactor, and hydro–landscape integrated analysis, China’s Tuwei River watershed was selected as the study area in this study, and methods such as landscape pattern indices and gray relational analysis were employed to quantitatively reveal the spatiotemporal evolution of watershed landscape fragmentation from 1980 to 2020 and identify its dominant driving forces. The results revealed that (1) over the 40-year period, the land use structure of the watershed underwent significant restructuring, with developed land expanding by 1282%, cropland and bare land areas decreasing by 14.2% and 32.01%, respectively, and grassland and forestland areas increasing by 24.5% and 14.9%, respectively; (2) land-scape fragmentation continued to intensify, with the landscape fragmentation composite index (FCI) increasing by 37.6%, patch density (PD) continuously increasing, edge density (ED) and landscape shape index (LSI) increasing significantly, and landscape connectivity weakening; (3) natural and socioeconomic factors jointly drove landscape evolution, with temperature and mean annual flow contributing the most among natural factors and the urbanization rate and secondary industry output value serving as the core drivers among socioeconomic factors; and (4) the trend of landscape fragmentation was synchronized with changes in annual rainfall and runoff and exhibited a significant negative correlation with the groundwater level. In summary, through long-term, multifactor comprehensive analysis, the evolution characteristics and driving mechanisms of landscape patterns in the Tuwei River watershed were systematically revealed in this study. These findings not only deepen the understanding of landscape fragmentation processes under the dual pressures of climate change and anthropogenic activities but also provide scientific evidence for the sustainable management of landscapes and associated ecosystems in semiarid watersheds. Full article

Urban floods are becoming increasingly frequent and severe, highlighting the need for real-time information that supports safe evacuation decision-making. This study proposes and validates an unmanned aerial vehicle (UAV)-based methodology for real-time urban flood monitoring using an actual flood event caused by Typhoon Hinnamnor at the Seondeok Intersection in Gyeongju, Republic of Korea. The method comprises three simple steps: (1) collecting UAV images and data; (2) generating spatial and terrain information through photogrammetry; and (3) estimating flood extent, depth, and volume using GIS-based analysis. A total of 796 UAV images were processed, yielding a flooded area of 3847.36 m², a flood volume of 13,895.13 m³, and a maximum depth of 0.75 m. To assess performance, UAV-derived results were compared with XP-SWMM simulation outputs. Significant discrepancies were observed in flood extent, inundation volume, and flood persistence, indicating that hydrological models may not fully capture localized drainage failures or site-specific conditions in urban environments. These findings demonstrate that UAV-based monitoring provides a more accurate representation of actual flood and can supply high-resolution, rapidly obtainable information essential for real-time evacuation. This study provides empirical evidence of UAV applicability during the flood event itself and highlights its potential to enhance disaster-response capability, improve decision-making, and strengthen the resilience and sustainability of flood-prone urban areas. Full article

Urban lakes are essential for ecological balance and urban development. This study developed a comprehensive framework to evaluate the ecosystem health of urban lakes in China. Nineteen representative lakes from four lake zones were examined using three decades of remote-sensing data combined with hydrological, water-quality, and aquatic–biological investigations. An extended DPSIR model guided the selection of 52 indicators, and a hierarchical weighting scheme was used: the analytic hierarchy process determined criterion-level weights, while principal component analysis with Softmax normalization was used for indicator-level weights. The established index system was applied to Xuanwu Lake and Erhai Lake, and an obstacle-degree model was used to identify key ecological constraints from 2010 to 2020. Results showed that urban lakes in the Yunnan–Guizhou Plateau and Eastern Plain zones were mainly constrained by eutrophication and intensive urbanization, with state- and impact-related indicators contributing most to the health index. The framework captured the decline of Xuanwu Lake, driven by poor water exchange and external nutrient loading, and its subsequent improvement following governance interventions, as well as the post-2014 degradation of Erhai Lake driven by climate-induced hydrological stress and non-point source pollution, providing a practical tool for diagnosing constraints and supporting adaptive, region-specific lake management. Full article

Coastal forests are highly sensitive to both climate change and land use change, which can strongly affect hydrological processes and long-term water yield. This study quantifies the individual and combined impacts of climate change and land use/land cover (LULC) change on water yield in the Croatan National Forest (CNF), a coastal ecosystem in North Carolina, USA, from 2003 to 2070. To produce high-resolution climate projections, we extended the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach by applying a full statistical downscaling of temperature and precipitation from CMIP6–SSP5-8.5 scenarios using the Random Forest algorithm. Future LULC scenarios were generated using machine learning and Markov Chain-based modeling to predict spatial changes up to 2070. The downscaled climate and LULC data were integrated into the WaSSI hydrological model to simulate their potential effects on water yield under the following four scenarios: baseline, LULC change only, climate change only, and combined change. The results showed that climate change alone could reduce annual water yield by about 11%, while LULC change alone could increase it by roughly 3% due to lower evapotranspiration from forest-to-urban conversion. Under the combined scenario, water yield decreased by about 6%, indicating that climate change dominated, but LULC change could locally alter or influence its effects. Overall, the findings highlight that climate change could be the primary driver of reduced water yield in coastal forests, while LULC change mainly affects its spatial variability. This integrated framework improves the accuracy of regional hydrological projections and provides useful insights for climate adaptation and sustainable water resource management in coastal forest ecosystems. Full article

SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the simplification in the SWMM hydrological module of the rainfall–runoff process, coupled with measurement errors, introduces a high level of uncertainty in the calibration. This study investigates the influences of rainfall types on the highly uncertain SWMM model calibration by implementing the Bayesian inference theory. A Bayesian SWMM calibration framework was established, in which an advanced DREAM(zs) (Differential Evolution Adaptive Metropolis, Version ZS) sampling method was used. The investigation focused on eight key hydrological parameters of SWMM. The impact of rainfall types was analyzed using nine rainfall intensities and three rainfall patterns. Results show that rainfall events equivalent to a one-year return period (R5, 42.70 mm total depth) or higher generally yield the most accurate parameters, with posterior distribution standard deviations reduced by 40–60% compared to low-intensity rainfalls. Notably, three parameters (impervious area percentage [Imperv], storage depth of impervious area [S-imperv], and Manning’s coefficient of impervious area [N-imperv]) demonstrated consistent accuracy irrespective of rainfall intensity, with a coefficient of variation below 0.05 for Imperv and S-imperv across all rainfall intensities. Furthermore, it was found that rainfall events with double peaks resulted in more satisfactory calibration compared to single or triple peaks, reducing the standard deviation of the Width parameter from 168.647 (single-peak) to 110.789 (double-peak). The findings from this study could offer valuable insights for selecting appropriate rainfall events before SWMM model calibration for more accurate predictions when it comes to urban non-point pollution control strategies and watershed management. Full article

Landscape spatial patterns are critical drivers of ecological processes, including nutrient cycling from terrestrial to aquatic systems, which ultimately modulate microorganism biodiversity. The emergence of robust spatial analysis tools now makes it possible to disentangle these complex relationships through controlled scenario generation. This study assesses the influence of land use and land cover (LULC) configuration on the export of total nitrogen (TN) and total phosphorus (TP) in an anthropogenically impacted river basin. We characterized the baseline landscape and generated synthetic LULC scenarios using the rflsgen (version 1.2.2) R package. Landscape metrics were calculated with landscape metrics, and nutrient export was modeled with the Nutrient Delivery Ratio (NDR) module of InVEST. The results demonstrate that spatial arrangement of the landscape is a key determinant of nutrient dynamics. Agriculture and urban areas have the greatest impact on nutrient export. Nutrient delivery is maximized when these LULC classes are configured in large, compact, and simply-shaped patches with high connectivity, which facilitates efficient hydrological transport. Conversely, fragmented natural grasslands and aggregated forests with regular shapes are associated with lower nutrient export, highlighting their role as nutrient sinks. This integrative methodology provides a novel framework for reproducible spatial experiments, offering evidence-based insights for land-use planning aiming to mitigate eutrophication and enhance ecosystem health. Full article

This study presents a novel methodology for the detection and monitoring of changes in surface water bodies, with a particular emphasis on the near-eastern region of Antioquia, Colombia. The proposed approach integrates remote sensing and artificial intelligence techniques through the fusion of multi-source imagery, specifically Synthetic Aperture Radar (SAR) and optical data. The framework is structured in several stages. First, radar imagery is pre-processed using an autoencoder-based despeckling model, which leverages deep learning to reduce noise while preserving structural information critical for environmental monitoring. Concurrently, optical imagery is processed through the computation of normalized spectral indices, including NDVI, NDWI, and NDBI, capturing essential characteristics related to vegetation, water presence, and surrounding built-up areas. These complementary sources are subsequently fused into synthetic RGB composite representations, ensuring spatial and spectral consistency between radar and optical domains. To operationalize this methodology, a standardized and reproducible workflow was implemented for automated image acquisition, preprocessing, fusion, and segmentation. The Segment Anything Model (SAM) was integrated into the process to generate semantically interpretable classes, enabling more precise delineation of hydrological features, flood-prone areas, and urban expansion near waterways. This automated system was embedded in a software prototype, allowing local users to manage large volumes of satellite data efficiently and consistently. The results demonstrate that the combination of SAR and optical datasets provides a robust solution for monitoring dynamic hydrological environments, particularly in tropical mountainous regions with persistent cloud cover. The fused products enhanced the detection of small streams and complex hydrological patterns that are typically challenging to monitor using optical imagery alone. By integrating these technical advancements, the methodology supports improved environmental monitoring and provides actionable insights for decision-makers. At the local scale, municipal governments can use these outputs for urban planning and flood risk mitigation; at the regional level, environmental and territorial authorities can strengthen water resource management and conservation strategies; and at the national level, risk management institutions can incorporate this information into early warning systems and disaster preparedness programs. Overall, this research delivers a scalable and automated tool for surface water monitoring, bridging the gap between scientific innovation and operational decision-making to support sustainable watershed management under increasing pressures from climate change and urbanization. Full article

(1) Background: In the context of rapid urbanization and climate change, Chinese traditional villages are facing severe challenges such as deterioration of hydrological environment, weakened social resilience, and degradation of cultural heritage. (2) Methods: This paper took Baoyan Village in Zhenjiang City, Jiangsu Province as the research object and constructs a research framework of “assessment of historical hydrological resilience–diagnosis of current problems–construction of enhancement strategies”, aiming to explore the paths and driving mechanisms for enhancing the resilience of traditional villages. The spatio-temporal evolution of historical hydrological resilience in Baoyan Village was quantitatively evaluated by establishing a three-dimensional resilience index system of “ecological governance–social adaptation–cultural continuity”, combined with the Analytic Hierarchy Process (AHP) and GIS spatial overlay technology. (3) Results: The study found that ① The hydrological resilience zoning of Baoyan Village presented spatial differentiation characteristics of “core vulnerability-marginal resilience”, and the high-risk area was concentrated in the cultural building density area along the old Tongji River in the historical town area, indicating that this area requires key flood protection and resilience construction; ② this paper constructed a composite evaluation system of “Ecological Governance–cultural inheritance–social adaptation”, and the total score after evaluation was 0.67, indicating that the overall HHRI of Baoyan Village has declined. Specifically, the scores for Ecological Governance Resilience and Cultural Heritage Resilience were 0.48 and 0.46, respectively, reflecting a significant decrease compared to historical scenarios. Conversely, the score for Social Adaptation Resilience was recorded at 1.05, suggesting an improvement in this dimension. This enhancement can be attributed to advancements in water infrastructure and increased levels of community organizational support, which have bolstered the village’s capacity to withstand flooding events. ③ The integrity of weir fields, the transmission of traditional disaster prevention knowledge, and the stability of natural river channels are the main factors hindering the improvement of resilience systems. (4) Conclusions: Based on the assessment results, this study proposed the resilience enhancement path of “ecological space reconstruction-traditional water management wisdom activation–cultural resilience empowerment” for this case, and constructed a four-pronged driving mechanism consisting of government guidance, community participation, technology empowerment, and industrial synergy for implementation. Practice has shown that through specific strategies such as restoring the weir and field system, constructing sponge village units, and developing the rain and flood cultural experience industry, the key obstacle factors of the village can be effectively addressed, and the goals of flood safety and cultural inheritance can be achieved in a coordinated manner. This case provides an empirical reference that combines historical wisdom with modern technology for understanding the evolution of human–water relationships and the enhancement of resilience in traditional villages, and its research framework and methods are also of reference value for similar villages. Full article

Climate change is profoundly reshaping watershed hydrological regimes and threatening the sustainability of regional ecosystems, rendering traditional ecological restoration planning—primarily reliant on static baselines—insufficient to support long-term resilience under future environmental conditions. To enhance the sustainability of metropolitan ecological restoration, this study develops a climate-adaptive restoration framework for the Wuhan Metropolitan Area, structured around “climate scenario—hydrological simulation—zoning delineation—strategy formulation.” The framework aims to elucidate how projected hydrological shifts constrain the spatial configuration of ecological restoration. Under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the WEP-L (Water and Energy transfer Processes in Large river basins) distributed hydrological model was calibrated and validated using observed hydrological data from 2016–2020 and subsequently applied to simulate the spatiotemporal evolution of precipitation, evapotranspiration, runoff, and total water resources in 2035. Hydrological trend analyses were further conducted at the secondary watershed scale to assess the differentiated impacts of future hydrological changes across planning units. Based on these simulations, ecological sensitivity and ecosystem service assessments were integrated to identify priority restoration areas, forming a “five-zone × three-tier” sustainable restoration zoning system encompassing farmland restoration, forest ecological restoration, soil and water conservation restoration, river and lake wetland ecological restoration, and urban habitat improvement restoration, classified into general, important, and extremely important levels. A comprehensive “four-water” management scheme—addressing water security, water resources, water environment, and water landscape—was subsequently proposed to strengthen the sustainable supply capacity and overall resilience of metropolitan ecosystems. Results indicate that by 2035, hydrological processes in the Wuhan Metropolitan Area will exhibit pronounced spatial heterogeneity, with uneven changes in precipitation and runoff further intensifying disparities in regional water availability. These findings highlight the necessity of incorporating scenario-based hydrological constraints into sustainable ecological restoration planning. The proposed technical framework provides a transferable pathway for enhancing watershed ecosystem sustainability and resilience under climate change. Full article

The development of urban areas and the proliferation of impervious surfaces have significantly altered natural hydrological cycles, resulting in an increase in stormwater runoff and substantial risks to groundwater quality. This review synthesizes current research on the transport mechanisms of stormwater contaminants, including toxic elements, nutrients, pathogens, and emerging pollutants such as microplastics and pharmaceuticals, into aquifers. This study analyzes the physicochemical and biological processes that affect pollutant mobility and retention in urban soils, emphasizing the vulnerability of groundwater systems, particularly in areas with permeable soils and shallow water tables. The article evaluates a range of green infrastructure (GI) and low-impact development (LID) strategies—including rain gardens, bioswales, infiltration basins, constructed wetlands, and urban forestry—to assess how effectively they can mitigate stormwater pollution and improve groundwater protection. Case studies from North America illustrate the practical implementation and performance of GI systems, emphasizing the importance of site-specific design, monitoring, and adaptive management. The review also discusses global policy frameworks and community engagement strategies that support sustainable stormwater management. Ultimately, it advocates for an integrated, multidisciplinary approach that combines engineering, ecological science, and public policy to safeguard groundwater resources in the face of climate variability and urban expansion. Full article

Urban development and intensive human activities have led to increasingly prominent nitrogen pollution issues in the Baiyangdian Lake basin. Accurately identifying the sources of nitrate pollution is a crucial prerequisite for implementing targeted remediation strategies, while flooding further complicates this task by exacerbating the transport and mixing of multi-source pollutants within the basin. This study, conducted from August to October 2023 (encompassing flood and post-flood periods), established 20 sampling sites in the lake area and its major inflow rivers. By integrating hydrochemical parameters, nitrate dual-isotope tracers (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻), and the Bayesian mixing model (MixSIAR), we quantitatively revealed the contributions of nitrate sources and their response mechanisms to a major flood event. The results indicate that domestic sewage and livestock wastewater (Manure & Sewage, MS) were the dominant sources of nitrate, with an average contribution of 84.0%, which further increased to 90.3% after the flood. Soil nitrogen was a secondary source (average 12.3%), while contributions from chemical fertilizers and atmospheric deposition were negligible (<4%). The results quantified a flood-driven dynamic response process of the nitrate source structure, characterized by “dilution-mixing-pollution rebound-process transformation”: the initial flood stage (August) showed multi-source mixing; the post-flood period (September) witnessed a rapid rebound of sewage sources; and during the October, nitrification persisted, but the basin’s overall denitrification capacity was limited, indicating a risk of nitrogen accumulation. Spatially, rivers like the Fu River were identified as key input pathways. This study revises the traditional understanding by emphasizing the absolute dominance of sewage sources after extreme hydrological events and the risk of insufficient denitrification capacity. The findings provide a scientific basis for water quality management in Baiyangdian and similar lakes. Full article

Accurately estimating urban floodwater depth is a critical step in enhancing urban resilience and strengthening disaster prevention and mitigation capabilities. Traditional methods relying on hydrological monitoring stations and numerical simulations suffer from limitations such as sparse spatial coverage, insufficient validation data, limited accuracy, and delayed fast performance. In contrast, social media data—characterized by its vast volume and fast availability, can effectively compensate for these shortcomings. When processed using artificial intelligence (AI) algorithms, such data can significantly improve credibility, disaster perception speed, and water depth estimation accuracy. To address these challenges, this paper proposes a robust and widely applicable method for rapid urban flood depth perception. The approach integrates AI technology and social media data to construct an AI framework capable of perceiving urban physical parameters through multimodal big data fusion without costly model training. By leveraging the near real-time and widespread nature of social media, an automated web crawler collects flood images and their textual descriptions (including reference objects), eliminating the need for additional hardware investments. The framework uses predefined prompts and pre-trained models to automatically perform relevance verification, duplicate filtering, object detection, and feature extraction, requiring no manual data annotation or model training. With only a minimal amount of water depth annotated data and compressed cross-modal feature vectors as training input, a lightweight Multilayer Perceptron (MLP) achieves high-precision depth estimation based on reference objects. This method avoids the need for large-scale model fine-tuning, allowing rapid training even on devices without GPUs. Experiments demonstrate that the proposed method reduces the Mean Square Error (MSE) by over 80%, processes each image in less than 0.5 s (more than 20 times faster than existing large-model approaches), and exhibits strong robustness to changes in perspective and image quality. The solution is fully compatible with existing infrastructure such as surveillance cameras, offering an efficient and reliable approach for fast flood monitoring in urban hydrology and water engineering applications. Full article

Outcomes for urban green infrastructure (GI) and low-impact development (LID) vary; thus, we ask when and how public participation affects performance. We apply a four-dimensional framework—breadth (who participates), depth (decision influence), identity (values/place attachment), and potential (incentives/capacity)—to conduct a literature review of Web of Science, Scopus, and Google Scholar. After deduplication and screening, 107 English-language studies were coded and compared across cases. Across contexts, early and representative engagement combined with clearly specified decision rights was associated with designs better aligned with local hydrologic and social conditions. Processes that attend to identity were consistently linked to stewardship behaviors. Institutionalized incentives and capacity, such as dedicated funding, defined roles, and feedback mechanisms, coincided with more durable operations and maintenance (O&M). Conversely, broad outreach without decision influence or feedback tended to remain tokenistic, with technical complexity and resource limits attenuating public influence. Effects appeared configurational rather than linear, with particular combinations of the four dimensions more often associated with success. Embedding codesign and feedback across the project lifecycle, pairing equity safeguards with community partnerships, and resourcing participation through clearly defined roles and incentives may help translate participation into resilient ecological and social outcomes. Full article