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\"Flood inundation models enable us to make hazard predictions for floodplains, mitigating increasing flood fatalities and losses. This book provides an understanding of hydraulic modelling and floodplain dynamics, with a key focus on state-of-the-art remote sensing data, and methods to estimate and communicate uncertainty. Academic researchers in the fields of hydrology, climate change, environmental science and natural hazards, and professionals and policy-makers working in flood risk mitigation, hydraulic engineering and remote sensing will find this an invaluable resource. This volume is the third in a collection of four books on flood disaster management theory and practice within the context of anthropogenic climate change. The others are: Floods in a Changing Climate: Extreme Precipitation by Ramesh Teegavarapu, Floods in a Changing Climate: Hydrological Modeling by P.P. Mujumdar and D. Nagesh Kumar and Floods in a Changing Climate: Risk Management by Slodoban Simonović\"-- Provided by publisher.

Rainfall runoff and leaching are the main driving forces that nitrogen, an important non‐point source (NPS) pollutant, enters streams, lakes, and groundwater. Hydrological and transport processes thus play a pivotal role in NPS nitrogen pollution. Existing hydro‐environmental models for nitrogen pollution often over‐simplify the within‐watershed processes. It is unclear how such simplification affects the pollution assessment regarding the formation and distribution of denitrification hot spots—which is important for the design of land‐based countermeasures. To study this problem, we developed a model, DHSVM‐N, and its variant, DHSVM‐N_alt. DHSVM‐N is developed by integrating nitrogen‐related processes of SWAT into a comprehensive process‐based hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM). DHSVM‐N includes detailed representations of nitrate transport process at a fine spatial resolution with good landscape connectivity to accommodate interactions between hydrological and biogeochemical processes along the flow travel pathways. Because of the lack of spatially distributed observational data for validation, a model‐to‐model comparison study is conducted. Through comparison studies on a representative catchment using SWAT, DHSVM‐N and DHSVM‐N_alt, we quantify the critical roles of hydrological processes and nitrate transport processes in modeling the denitrification process. That is, the capabilities to give reasonable soil moisture estimates and to account for essential processes that take place along flow pathways are keys to simulate denitrification hot spots and their spatial variation. Furthermore, DHSVM‐N results show that terrestrial denitrification from hotspots alone can reach as high as 36% of the annual stream nitrate export of the watershed. Plain Language Summary Impacts on water quality of streams and lakes caused by non‐point nitrogen sources from agricultural activities have been the focus of investigations of nitrogen pollution. Typically, such investigations are carried out by employing existing hydro‐environmental models even though they often adopt simplified hydrological and transport processes. These simplified models may serve their purposes well, but when within‐watershed dynamics, such as the denitrification and its hotspot spatial distribution within a watershed, play important roles, the existing models could be subjected to large errors or uncertainties. To improve the modeling of watershed nitrogen transport, a new model, DHSVM‐N, which incorporates essential hydrological and transport processes, is developed by combining the widely used SWAT and DHSVM models. Because of the lack of spatially distributed observational data for validation, a model‐to‐model comparison study is conducted. DHSVM‐N and SWAT are found to give very different results in the levels and spatial distribution of denitrification, as well as nitrate export to streams. Key Points Key processes determining denitrification hot spots and spatial distribution are identified via systematic analysis using three models Landscape unit connectivity, routing and runoff flow paths are critical hydrological components for modeling denitrification hot spots A process‐based model, DHSVM‐N, is developed for studying nitrogen‐related processes and pollution at watershed scale

This paper reviews the basic concepts of path planning, classifies environmental modeling methods, analyzes the significance of V2X environment modeling, and summarizes the existing path planning algorithms. Different algorithm can be adjusted in time according to different environments to improve the efficiency of path planning. In addition, according to the advantages and disadvantages of different algorithms, each algorithm is fused, which can effectively avoid the shortcomings of each algorithm and improve the efficiency of the planning algorithm.

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Land cover (LC) maps are crucial to environmental modeling and define sustainable management and planning policies. The development of a land cover mapping continuous service according to the new EAGLE legend criteria has become of great interest to the public sector. In this work, a tentative approach to map land cover overcoming remote sensing (RS) limitations in the mountains according to the newest EAGLE guidelines was proposed. In order to reach this goal, the methodology has been developed in Aosta Valley, NW of Italy, due to its higher degree of geomorphological complexity. Copernicus Sentinel-1 and 2 data were adopted, exploiting the maximum potentialities and limits of both, and processed in Google Earth Engine and SNAP. Due to SAR geometrical distortions, these data were used only to refine the mapping of urban and water surfaces, while for other classes, composite and timeseries filtered and regularized stack from Sentinel-2 were used. GNSS ground truth data were adopted, with training and validation sets. Results showed that K-Nearest-Neighbor and Minimum Distance classification permit maximizing the accuracy and reducing errors. Therefore, a mixed hierarchical approach seems to be the best solution to create LC in mountain areas and strengthen local environmental modeling concerning land cover mapping.

The Amazon biome is being pushed by unsustainable economic drivers towards an ecological tipping point where restoration to its previous state may no longer be possible. This degradation is the result of self-reinforcing interactions between deforestation, climate change and fire. We assess the economic, natural capital and ecosystem services impacts and trade-offs of scenarios representing movement towards an Amazon tipping point and strategies to avert one using the Integrated Economic-Environmental Modeling (IEEM) Platform linked with spatial land use-land cover change and ecosystem services modeling (IEEM + ESM). Our approach provides the first approximation of the economic, natural capital and ecosystem services impacts of a tipping point, and evidence to build the economic case for strategies to avert it. For the five Amazon focal countries, namely, Brazil, Peru, Colombia, Bolivia and Ecuador, we find that a tipping point would create economic losses of US $256.6 billion in cumulative gross domestic product by 2050. Policies that would contribute to averting a tipping point, including strongly reducing deforestation, investing in intensifying agriculture in cleared lands, climate-adapted agriculture and improving fire management, would generate approximately US$ 339.3 billion in additional wealth and a return on investment of US$29.5 billion. Quantifying the costs, benefits and trade-offs of policies to avert a tipping point in a transparent and replicable manner can support the design of regional development strategies for the Amazon biome, build the business case for action and catalyze global cooperation and financing to enable policy implementation.

The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI’s capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments.

Coastal and marine systems are governed by fragile water-quality dynamics, where disturbances can trigger harmful algal blooms with significant ecological and societal consequences. These pressures have intensified interest in forecasting systems that can anticipate bloom development and support environmental management. This study presents a systematic review of simulation-based and predictive environmental modeling approaches used for marine forecasting of water quality and harmful algal bloom phenomena. Following PRISMA guidelines, 11,185 records were identified, 127 articles were screened in full text for eligibility, and 40 peer-reviewed studies published between 2015 and 2025 were included and synthesized using a structured extraction framework capturing modeling paradigms, forecast targets, data inputs, spatial and temporal scope, validation practices, operational context, and reported limitations. The reviewed literature indicates the dominance of predictive and hybrid modeling approaches, with forecasting efforts primarily focused on coastal systems and short-term applications. Harmful algal blooms and chlorophyll-a emerge as dominant forecast targets, commonly supported by satellite observations, in situ measurements, and environmental forcing variables. Despite substantial methodological advances, persistent challenges related to data availability and quality, validation rigor, system integration, and operational deployment remain evident across modeling paradigms. Overall, the findings suggest that while marine forecasting models have become increasingly sophisticated, their translation into reliable and operational systems remains uneven, highlighting the need for closer alignment.

Rising global temperatures are causing a poleward shift in species distribution. Range shift velocities are higher in the marine environment, with observed rates of 30–130 km per decade. Both protected and exploited species will be at risk if marine species management policies are not structured to anticipate these range shifts. The 2017 mass mortality event of the North Atlantic right whale showcases the detrimental impact of unanticipated climate-mediated behavior in a species protected by geographically and seasonally fixed policies. Based on the results of a demographic capture-recapture model, right whales may face extinction in fewer than 30 years unless protective policies are expanded to cover their shifting distribution. Increased support of long-term monitoring programs paired with environmental modeling research is critical to developing more proactive conservation management strategies and preventing further ecological crises.

The Mediterranean Basin is one of the most biodiverse areas in the world, harboring 25,000 plant species, of which 60% are endemic. Some of them have narrow distributions, such as Euphorbia orphanidis , which is only known from alpine screes on Mt. Parnassos in Greece. Its exact distribution in this mountain was, however, poorly known, and its phylogenetic origin was also unclear. We performed extensive field work in Mt. Parnassos and could register E. orphanidis only in five patches of limestone screes in the eastern part of this mountain range, emphasizing its very narrow distribution, which is likely limited by topography influencing water availability as indicated by environmental modeling. We also registered 31 accompanying species and thus characterized its habitat. Using nuclear ribosomal internal transcribed spacer and plastid ndhF–trnL and trnT–trnF sequences, we show that it belongs to E. sect. Patellares , despite not having connate raylet leaves typical for this section, and not to E. sect. Pithyusa as previously suggested. The relationships among the species of E. sect. Patellares are poorly resolved, suggesting their simultaneous divergence that dated to the late Pliocene, which coincided with the establishment of the Mediterranean climate. The relative genome size of E. orphanidis is in the range of that for the other members of E. sect. Patellares , suggesting that it is diploid. Finally, we performed multivariate morphological analyses to generate a comprehensive description of E. orphanidis . Based on its narrow distribution and the anticipated negative impact of global warming, we consider this species endangered. Our study demonstrates how microrelief can limit the distribution of plants in topographically heterogeneous mountain environments and likely plays an important, yet neglected, role in shaping the distribution patterns of plants in the Mediterranean Basin.