Surface Water Depth Research Articles

Improved infiltration rate determination method for interlocking-block permeable pavements using standard ASTM test apparatus

ABSTRACT Current standard methods of measuring permeability of interlocking-block permeable pavements (IBPPs) do not correctly measure the actual infiltration rates of IBPPs. The test results vary with test area size and location. In this study, employing the standard ASTM-1781 test apparatus, an improved procedure that adopts an experiment-based numerical approach is developed to overcome the problem. It involves the following steps: (i) Perform falling-head infiltration tests using ASTM-1781 apparatus to obtain infiltration flow data; (ii) Develop computational fluid dynamic (CFD) finite-volume models for different infiltration test conditions; and (iii) Apply appropriate finite-volume model to calculate the effective IBPP infiltration rate under any given surface water depth on the IBPP. Experimental tests were conducted to validate the proposed method.

Estimating Vertical Distribution of Total Suspended Matter in Coastal Waters Using Remote-Sensing Approaches

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions.

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Simulation of flow characteristics in open channels with inflatable dam

Abstract Inflatable dams are structure cylindrical inflatable and deflated bile structures made of rubberized material placed on top of a rigid base and inflatable by air, water, or a combination of both. The present paper aims to measure the water surface elevation, and depth of flow in Shatt Al Ibrahim and comparison with design depth and study the flow state according to design discharge and also according to scarce discharge. The study effect of the height of the dam filled with water on upstream and downstream water depth and analyzed numerically by using HEC-RAS program one–one-dimensional flow in two states, steady flow and unsteady flow under different variables such as changing of discharge. Good results were obtained in two flow states. The results show that the proper solution for maintaining the required water levels is to construct inflatable dams, due to lower construction and maintenance costs, easy and simple of operation, simple and easy to inflate and deflate quickly to prevent upstream flooding. The results of the current study indicated an increase in the water surface height (WS) and water depth (Wd) across the channel after the implementation of inflatable dams compared to natural conditions. Specifically, the data indicate a significant increase in (WS) and (Wd) at “station 52”, where (WS) increased by 0.45 m with a 0.5 m dam and 1.43 m with a 1.5 m dam compared to the basic scenario. This confirms the adaptability of inflatable dams in adapting water levels to real-time requirements, underscoring their potential as a cost-effective solution to mitigate upstream floods and improve water resource management practices.

Analysis of seepage behavior of porous asphalt pavement based on scale test and three-dimensional simulation

This study implemented indoor scaled drainage testing and three-dimensional (3D) finite element simulations to analyze the seepage behavior of ultra-wide cross-section porous asphalt concrete (PAC) pavement with voids of 18 %, 20 % and 22 %. The seepage water level curves under the rainfall intensity of 0.5, 1, 2, 10, 50 and 100 years recurrence intervals were obtained by scaled test. The Seep3D software was used to analyze the seepage state of the ultra-wide cross-section porous pavement. Results indicated that void ratio was the paramount factor affecting the drainage capacity, which decreasing porosity significantly intensifies roadway surface water depth over 25 %. Water level curves obtained in scaled test showed similar trends to pore water pressure curves in 3D simulation. According to the theory of non-uniform gradual seepage, the pore water pressure can be used to effectively characterize the seepage state inside the porous pavement. Implementing cross drain with 10 m spacing maximally alleviated inundation by 23 %. Spacing intervals of 50 m, 30 m, and 20 m proportionately mitigated peak value by 4 %, 7 % and 12 % respectively.

Rainfall induced shallow flow water depths on vehicular pavements

This article investigates twenty-five experiments of one-dimensional shallow thin-surface flow on wide carriageway pavements, undertaken as part of wider investigations to avoid aquaplaning. Analysis of their predictive equations for surface water depths are assessed, along with informative background papers. Issues regarding rainfall intensity and the macrotexture structure of pavement materials and their inter-relationship are considered. Current international best-practice guidance is contrasted and observations for the practising highway engineer are provided, as well as recommendations for future research.

Design Of Resistivity Meter Data Storage System Based On Arduino Mega 2560 Laboratory Scale Measurement Results

<p class="Keywords"><span lang="EN-US">A resistivity meter can find out resistivity depth of rock structure, sediment layer, or the depth of water surface. However, the resistivity meter that is often used requires manually recording of the measurement results before inputting them into the processing data software, and this creates many steps in the geoelectric survey. This is because acquiring a resistivity meter with a data storage system entails additional costs, due to its expensive price. Consequently, many of the resistivity meters currently found in laboratories or used in the field are not equipped with a data storage system. This research aims to design a resistivity meter with a data storage system for measurement results, based on Arduino Mega 2560, for laboratory scale. The used sensors were INA219 currents sensor and voltage sensor. In addition, the tests were carried out to determine the level of accuracy of each sensor and testing for data storage system of measurement result. This research employed Schlumberger configuration in the development of resistivity meter. This research has successfully developed a resistivity meter device with a current sensor accuracy level of 97.28% using the INA219 sensor, and a voltage sensor accuracy level of 97.44%. It is noteworthy that the accuracy level is high, and the device is capable of performing readings effectively. This research has successfully designed a resistivity meter which has a measurement data storage.</span></p>

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships. Here, we propose a DL modeling framework, incorporating convolutional neural networks (CNNs) to efficiently replicate physics-based model outputs at high spatial resolution. The goal was to estimate groundwater head and surface water depth in the Sabgyo Stream Watershed, South Korea. The model datasets consisted of input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, and initial hydrological conditions. The initial conditions and target data were obtained from the fully distributed hydrological model HydroGeoSphere (HGS), whereas the other inputs were actual measurements in the field. By optimizing the training sample size, input design, CNN structure, and hyperparameters, we found that CNNs with residual architectures (ResNets) yielded superior performance. The optimal DL model reduces computation time by 45 times compared to the HGS model for monthly hydrological estimations over five years (RMSE 2.35 and 0.29 m for groundwater and surface water, respectively). In addition, our DL framework explored the predictive capabilities of hydrological responses to future climate scenarios. Although the proposed model is cost-effective for hydrological simulations, further enhancements are needed to improve the accuracy of long-term predictions. Ultimately, the proposed DL framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds.

Characterizing the stable oxygen isotopic composition of the southeast Indian Ocean

New seawater stable oxygen isotope (δ18O) samples were collected from the southeast Indian Ocean as part of the Coring to Reconstruct Ocean Circulation and Carbon dioxide Across 2 Seas (CROCCA-2S) expedition in November – December of 2018. These data fill a gap in the δ18O sampling coverage of the southern Indian basin, providing new insights into the hydrologic and oceanographic processes influencing the δ18O distribution of the region and its relationship to salinity in the upper ocean. Our surface ocean data (<100 m)—in combination with decades of observations from the broader south Indian Ocean—show distinct δ18O – salinity characteristics on either side of ∼85°E. The balance between evaporation and precipitation yields a strong, robust δ18O – salinity relationship west of 85°E (δ18O = 0.50(±0.01) * S – 17.2(±0.22)). However, within the mesoscale eddy field initiated by the Leeuwin Current further east (∼85–120°E), our observations fall along a mixing line between the southwest Indian Ocean and data collected from the Australian coastal margin, illustrating for the first time how the unique eastern boundary system of the south Indian Ocean drives regional-scale variability in the δ18O – salinity relationship of the surface ocean. A comparison between our observations in the shallow subsurface (100–1000 m) and those from neighboring surveys reinforces this upper ocean connection across the Indo-Australian basin. Antarctic Intermediate Water from the Indian Ocean can be isotopically distinguished from the more regional Tasman Intermediate Water occupying the South Australian Bight, suggesting exchange between the two regions is most prevalent at surface and mode water depths. In deeper waters (> ∼1500 m), we observe a notable 0.87‰ spread in δ18O. This variability may represent interactions between distinct deep water masses in the region, although additional data are needed to confirm. Overall, our data provide a new look at the hydrography and isotopic chemistry of the southeast Indian Ocean, emphasizing the impact of the region's mesoscale eddy field and its interconnectivity with neighboring basins.

Variation in water utilization by mammal diversity in Khao Phaeng Ma Non-hunting area, Thailand

Access to suitable water sources is important for mammals. This study aimed to compared mammal diversity and water use among water springs, standard artificial ponds, and water pans within the Khao Phaeng Ma Non-Hunting Area in 2020 and 2021. Two camera traps were installed at each water sources for 749 nights with a total of 12 camera traps of 6 water sources. A total of 19,467 photographs were recorded comprising 13,777 photographs of gaur (Bos gaurus, vulnerable and the most important species in the area), and 5690 photographs of other mammals. In the wet season, relative use was highest at standard artificial pond number 2, which is established in the forest plantation area (4 × 4 m spacing, 12–20 m height, and 60%–80 % crown cover) and has a high volume of water, and at water pan number 1, which mimics a natural water spring in the man-made grassland and can supply water to mammals throughout the year. In the dry seasons, relative use was highest at water pan numbers 1 and 2; at the same time, other water sources dried up. During the study period, the number of mammal species was highest at water pan number 1 (10 species, diversity index [H′] = 1.38), and water pan number 2 (11 species, H′ = 1.75). Grazers and browsers, including gaur, sambar deer (Rusa unicolor), northern red muntjac (Muntiacus vaginalis), omnivores (e.g. wild boar, Sus scrofa), and Asian black bear (Ursus thibetanus), used the water pan in the artificial grassland and standard artificial pond in the forest plantation rather than the water spring in the dry evergreen forest. Beside forest types, the use of water springs was associated with water period (months), while the use of standard artificial pond and water pans was associated with water surface area, water depth, altitude, species diversity, and species richness, and number of mammals photographed. The results show that water pans were more suitable for utilization by mammals than are other water sources.

Rehydration of degraded wetlands: Understanding drivers of vegetation community trajectories

AbstractDegradation of wetland ecosystems results from loss of hydrologic connectivity, nutrient enrichment, and altered fire regimes, among other factors. It is uncertain how drivers of wetland ecosystem processes and wetland vegetation communities interact in reversing the ecological trajectory from degraded to restored conditions. We analyzed biogeochemical and vegetation data collected in wetlands of the Florida Everglades at the start of (2015) and during (2018 and 2021) the initial stages of rehydration. Our objectives were to analyze the allocation of carbon and nutrients among ecosystem compartments and correlated trajectories of vegetation community change following rehydration, to identify the drivers of change, including fire, and analyze macrophyte species‐specific responses to drivers. We expected to see changes in vegetation toward more hydric communities that would differ based on wetland baseline conditions and the magnitude of the hydrologic change. During the study period, both length of inundation and surface water depth increased throughout wetlands in the region, and four fires occurred, which affected 51% of the sampling locations. We observed biogeochemical shifts in the wetland landscape, driven by both hydrology and fire. Total phosphorus concentrations in soil and flocculent detrital material decreased, while soil carbon:phosphorus and nitrogen:phosphorus mass ratios increased at sites further away from water management infrastructure. Transitions in vegetation communities were driven by an increase in hydroperiods and by the distinct changes in nutrient concentrations or soil stoichiometric ratios in each subregion. The abundance of macrophyte species typical of short‐hydroperiod prairies strongly decreased, while dominant long‐hydroperiod species, such as Eleocharis cellulosa, expanded. Fire facilitated the expansion of thickly vegetated plumes of invasive Typha at sites close to the water inflow sources. Overall, restored hydrology shifted vegetation community composition toward higher abundance of long‐hydroperiod species within six years. In contrast, removal of invasive vegetation controlled by soil phosphorus concentrations will likely require long‐term and interactive restoration strategies.

Performance analysis and multi-objective optimization of the offshore renewable energy powered integrated energy supply system

Offshore renewable energy technologies offer a promising prospect for energy supply on offshore islands. However, traditional wind and solar energy sources exhibit strong variability and cannot fully meet the energy demands of users. At the same time, the energy demand of users also has great volatility, so the supply side and demand side of energy cannot match well. Existing renewable energy systems often utilize thermal power generation as a stabilizer for the system, however, this approach is difficult to apply in offshore conditions. This study introduces an innovative multi-energy complementary system that integrates seawater freezing desalination, refrigeration and power-hydrogen-ammonia co-generation, driven by ocean thermal energy, wind energy, and solar energy. The system incorporates the use of a new developed small temperature difference ejector refrigeration cycle to stabilize the entire system and produce low-temperature refrigeration. By utilizing the seawater freezing desalination process to minimize energy conversion losses and balancing fluctuations in multiple energy inputs and user demands through ammonia-hydrogen co-production, this approach effectively minimizes energy waste, enhances energy utilization efficiency, and reduces excess power handling. The purpose is to optimize the configuration of the energy conversion subsystem within the multi-energy complementary multi-effect co-generation system, thus improving the economic feasibility and reliability of the system in providing energy and freshwater supply, ultimately achieving the goals of reducing energy waste, achieving high conversion efficiency, and minimizing equipment investment. This study conducts an economic analysis and optimization of a multi-energy complementary system, utilizing a 2 MW-level OTEC subsystem as a stabilizer. Both equipment investment and operating costs are considered in the economy analysis. The carbon emission reduction performance of this system is evaluated. The performance optimization results show that the system achieves an energy storage efficiency of 65.96 %, and the curtailment rate of this system is only 3.99 %, significantly lower than the 10 % curtailment rate in traditional system. The proposed system can meet the annual electricity demand of 33.09 million kWh, as well as a cooling demand of 546.81 million kWh. Under typical conditions, the ocean thermal energy conversion cycle exhibits a power efficiency of 0.85 % and a refrigeration efficiency of 29.10 %. Meanwhile, it annually reduces CO2 emissions by 2.54 million tons, demonstrating significant environmental benefits. This proposed system is mainly applicable to tropical waters with high surface water temperatures and depths of no less than 600 m worldwide. This study offers a practical and economically beneficial solution for energy and freshwater supply on offshore islands.

Ecological Niche and Interspecific Association of the Main Fishes in the Coastal Waters of Hainan Island, China

This study explored the ecological niche and interspecific relationships among the main fish species in the coastal waters of Hainan Island based on data from fishery stock surveys conducted in the spring and autumn of 2022. The methods of chi-square test, percentage of co-occurrence (PC), association coefficient (AC), cluster analysis, and redundancy analysis were used to analyze the interspecific associations and influence of environmental factors on the ecological niches of fish communities. According to the cluster analysis of niche breadth, 20 main fishes could be divided into broad, medium, and narrow niche species. There were four and eight broad niche species in spring and autumn, respectively. The ranges of niche overlap values were 0.001–0.91 in spring and 0–0.87 in autumn, indicating that the species differed and were similar in their ability to utilize resources, survive in habitats, and prey. According to the variance ratio and statistic value W, the main fishes in spring showed a significant positive association, whereas those in autumn showed a positive association, but not at a significant level, indicating that the main fishes in spring were more closely associated with each other. There were 56 species pairs in the 2 seasons that were significantly associated (χ2 ≥ 3.841). AC and PC tests revealed that the interspecific association was strong and tended to be positive. According to the redundancy analysis, environmental factors such as surface temperature, water depth, and pH significantly affected the main fishes in spring, while environmental factors such as dissolved oxygen, bottom temperature, surface salinity, and pH significantly affected those in autumn.

Hierarchical model to predict common carp and bigmouth buffalo abundance from electrofishing data

Catch per unit effort (CPUE) is used as an index of fish abundance under the premise that changes in CPUE result from changes in true density. However, catchability may also vary based on environmental conditions that affect observed CPUE. We developed a hierarchical model for estimating common carp ( Cyprinus carpio) and bigmouth buffalo ( Ictiobus cyprinellus) relative abundance with electrofishing survey data from six shallow lakes in northwest Iowa, USA, between 2018 and 2020. Common carp catchability was negatively associated with lake perimeter but unrelated to lake surface area, water depth, Secchi depth, temperature, and month of sampling. Bigmouth buffalo catchability was negatively associated with Secchi depth and water temperature and unrelated to other environmental variables. Hierarchical model posterior distributions of bigmouth buffalo density were less precise than Schnabel estimates, whereas common carp posterior distribution abundance estimates were more precise than Schnabel estimates. Our results indicate that hierarchical models can be a viable substitute for labor-intensive capture–mark–recapture methods to estimate unknown latent variables like relative abundance, and could be applied to other species, sampling gears, and management frameworks.

Nondestructive Detection and Early Warning of Pavement Surface Icing Based on Meteorological Information.

Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.

Principles of ADCP deployment methodologies

ADCPs are regularly used for resource characterization in wave and tidal energy sites; however, methodologies and best practices are entirely dependent on the site characteristics and the assets available. EMEC have used their widely varied experience at multiple sites and locations to develop a review of methodologies and the pros and cons of different deployment styles for data acquisition.When deploying ADCPs there are a number of site criteria that must be assessed, such as seabed type, bed flow speeds, surface flow speeds, recovery windows (slack and weather), and water depth and range. These criteria can significantly affect the success or failure of marine operations, not necessarily for deployment but certainly for recovery. When recovery operations fail then projects lose assets (ADCP units and bed frames), data, and money (in hire/unit costs, and vessel hire for example). When the data is not recovered then this can jeopardise project success, wasting time, effort and funding.Different deployment configurations and recovery techniques have been used for ADCP deployments. Equipment such as ground lines, acoustic releases, USBLs, and surface buoys are used to mark, locate, and/or recover devices. These are varied depending on a number of factors. Ground type, for example, can affect systems used since a ground line cannot be effectively used in delicate ecosystems since the dredging required to recover may disturb local flora and fauna. Also, for example, flow speed and vessel traffic will determine whether a surface buoy can be used.Based on multiple ADCP deployments to characterize resource for wave and tidal energy projects in Scotland, England, France and other sites, EMEC have reviewed different deployment methods and recovery techniques. There is a proposed set-up for deployments suitable for a number of active marine energy sites and suggestions for edits based on site characteristics and target data requirements.

Exploring spatial patterns and environmental risk factors for global maritime accidents: A 20-year analysis

The global shipping industry faces increasingly complex safety challenges due to the rapid growth of international maritime trade. This study develops a novel framework that combines spatial density analysis and machine learning (i.e., extreme gradient boosting model) to investigate the evolutionary patterns of global maritime accidents during 2001–2020 from both spatial and temporal dimensions, and then identifies key environmental risk factors affecting maritime safety. The results show that the number of global maritime accidents exhibits fluctuations between 2001 and 2019, with a significant decrease observed in 2020. Furthermore, the distribution of global maritime accidents shows significant spatial variation over different time periods. Denmark's sea areas have high accident rates between 2001 and 2005, while concentrated accidents are observed in the seas around the United Kingdom, Denmark, and China between 2006 and 2010. From 2011 to 2015, Europe's accident-prone areas increase, but fewer accidents are reported along China's east coast. The Strait of Malacca is also an accident-prone area from 2016 to 2020. In addition, wave height, sea surface temperature, wind speed, water depth, and precipitation are identified as key environmental risk factors affecting maritime safety. These findings can inform strategies and mitigation plans to improve navigational safety in the global shipping industry.

Disentangling Effects of Sea Surface Temperature and Water Depth on Hydroxylated Isoprenoid GDGTs: Insights From the Hadal Zone and Global Sediments

AbstractHydroxylated isoprenoid glycerol dialkyl glycerol tetraethers (OH‐GDGTs) preserved in marine sediments are thought to be controlled by sea surface temperature (SST). However, water depth may also exert a significant influence on OH‐GDGTs. Here, we investigated sedimentary OH‐GDGTs in the Kermadec and Atacama trench regions (2,560–9,560 m water depth). Sedimentary OH‐GDGTs in hadal trenches were dominated by OH‐GDGT‐0 (72 ± 8%), potentially reflecting an adaption of source organisms to ambient cold deep water. This result, combined with global data set, revealed that the predominance of OH‐GDGT‐0 is a ubiquitous phenomenon in deep‐sea sediments, leading to a considerable underestimation of RI‐OH′‐derived SSTs. By considering both SST and water depth effects, we developed more accurate OH‐GDGT‐based paleothermometers for both shallow regions and the global ocean, encompassing the full‐ocean‐depth range. Our findings highlight the importance of accounting for the effect of water depth on OH‐GDGTs and provide improved tools for reconstructing paleo‐SSTs.

Simulation of shallow landslides induced by typhoon in July 2018 in Gogoshima, Ehime Prefecture, Japan based on a simple model

Every year, many typhoons make landfall in Japan causing disasters on mountain slopes across the country. Rainfall induced landslides in Japan mostly occur at shallow depths. Developing simple models of rainfall-induced landslides provides a practical alternative to finite element methods in large-scale landslide modelling and prediction. Therefore, authors previously developed a simple method to calculate the advance of the wetting front into fine sand slopes subjected rainfall. However, during intense rainfall, the excess rainfall that cannot be absorbed into the soil will generate runoff on the slope surface, which has not been included in the previous model. Thus, this study integrated the model with a surface water modeling module to simulate the disasters more comprehensively. The surface water is controlled by 2D shallow water equations and the digital elevation model depression removal process. The combined model is able to provide the maps of the wetting front, surface water depth, and factor of safety of slope during rainstorms. The newly integrated model was used to simulate rainfall-induced disasters in the Gogoshima Island during the July 2018 typhoon. The simulation results disclosed that both moving of the wetting front and surface water flows contributed to shallow landslides along catchments in this island.

Rapid quantification of the surface overflow and underground infiltration in sewer pipes based on computer vision and continuous optimization

The overloading of the sewer network caused by unwarranted infiltration of stormwater may lead to waterlogging and environmental pollution. The accurate identification of infiltration and surface overflow is essential to predict and reduce these risks. To retrieve the limitations of infiltration estimation and the failure of surface overflow perception using the common stormwater management model (SWMM), a surface overflow and underground infiltration (SOUI) model is proposed to estimate the infiltration and overflow. First, the precipitation, water level of the manhole, surface water depth and images of the overflowing point, and volume at the outfall are collected. Then, the surface waterlogging area is identified based on computer vision to reconstruct the local digital elevation model (DEM) by spatial interpolation, and the relationship between the waterlogging depth, area and volume is established to identify the real-time overflow. Next, a continuous genetic algorithm optimization (CT-GA) model is proposed for the underground sewer system to determine the inflow rapidly. Finally, surface and underground flow estimations are combined to perceive the state of the urban sewer network accurately. The results show that, compared with the common SWMM simulation, the accuracy of the water level simulation is improved by 43.5% during the rainfall period, and the time cost of the computational optimization is reduced by 67.5%. The proposed method can effectively diagnose the operation state and overflow risk of the sewer networks in real time during rainfall seasons.