Water Depth Research Articles

Autonomous navigation and collision prediction of port channel based on computer vision and lidar.

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430m), average time consumption (from 30 to 22s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways.

Influence of environmental filtering and spatial processes on macroinvertebrate community in urban lakes in the Taihu Lake Basin, China.

Studies identifying the relative importance of multiple ecological processes in macroinvertebrate communities in urban lakes at a basin scale are rare. In this study, 14 urban lakes in the Taihu Lake Basin were selected to explore the relative importance of environmental filtering and spatial processes in the assembly of macroinvertebrate communities. Our findings revealed significant spatiotemporal variations in macroinvertebrate communities, both between lakes and across seasons. We found that environmental filtering exerted a greater influence on taxonomic total beta diversity and its individual components (species turnover and nestedness) compared to spatial processes. Key environmental variables such as water depth, water temperature, total dissolved solids, chlorophyll a, and lake surface area were found to be crucial in shaping macroinvertebrate communities within these urban lakes. The observed high spatial heterogeneity in environmental conditions, along with intermediate basin areas, good connectivity and short distances between lakes, and the high dispersal ability of dominant taxa, likely contributed to the dominance of environmental filtering in macroinvertebrate community assembly. Our study contributes to a better understanding of the underlying mechanisms governing macroinvertebrate community assembly in urban lakes, thereby providing valuable insights for studies on community ecology and water environmental protection in urban lakes.

Water depth affects submersed macrophyte more than herbivorous snail in mesotrophic lakes

Water depth affects submersed macrophyte more than herbivorous snail in mesotrophic lakes

Sight and blindness: The relationship between ostracod eyes, water depth, and light availability in the Arctic Ocean

Sight and blindness: The relationship between ostracod eyes, water depth, and light availability in the Arctic Ocean

Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data

In shallow water, Sentinel-2 multispectral imagery has only four visible bands and limited quantization levels, which easily leads to the occurrence of the same spectral profile but different depth (SSPBDD) phenomenon, resulting in a one-to-many relationship between water depth and spectral profile. Investigating the impact of this relationship on water depth inversion models is the main objective of this paper. The Stumpf model and three machine learning models (Random Forest, Support Vector Machine, and Mixture Density Network) are employed, and the performance of these models is analysed based on the spatial distribution of the training dataset and the input information composition of these models. The results show that the root mean square errors (RMSEs) of the depth inversion of Random Forest and Support Vector Machine are significantly affected by the spatial distribution of the training dataset, while minimal effects are observed for the Stumpf model and the Mixture Density Network model. The SSPBDD phenomenon is widespread in Sentinel-2 images at all depths, particularly between 5 m and 15 m, with most of the depth maximum difference of the SSPBDD pixels ranging from 0 to 5 m. The SSPBDDs phenomenon can significantly reduce the inversion accuracy of any model. The number and the depth maximum difference of the SSPBDDs pixels are the main influencing factors. However, by increasing the visible spectral information and the spatial neighbourhood information in the input layer of machine learning models, the inversion accuracy and stability of the models can be improved to a certain extent. Among the models, the Mixture Density Network achieves the best inversion accuracy and stability.

Modeling of Vehicle Mobility in Shallow Water With Data-Driven Hydrodynamics Model

Abstract In this study, a data-driven hydrodynamics model is proposed to enable quick prediction of vehicle mobility in shallow water, considering the effect of tire–soil interaction. To this end, a high-fidelity coupled vehicle–water interaction model using computational fluid dynamics (CFD) and multibody dynamics (MBD) solvers is developed to characterize the hydrodynamic loads exerted on a vehicle operated in shallow water, and it is used to generate training data for the data-driven hydrodynamics model. To account for the history-dependent hydrodynamic behavior, a long short-term memory (LSTM) neural network is introduced to incorporate effects of the historical variation of vehicle motion states as the input to the data-driven model, and it is used to predict hydrodynamic loads online exerted on vehicle components in the MBD mobility simulation. The impacts of hydrodynamic loads on the vehicle mobility capability in shallow water are examined for different water depths and incoming flow speeds using the high-fidelity coupled CFD-MBD model. Furthermore, it is demonstrated that the vehicle–water interaction behavior in scenarios not considered in the training data can be predicted using the proposed LSTM data-driven hydrodynamics model. However, the use of non-LSTM layers, which do not account for the sequential variation of vehicle motion states as the input, leads to an inaccurate prediction. A substantial computational speedup is achieved with the proposed LSTM-MBD vehicle–water interaction model while ensuring accuracy, compared to the computationally expensive high-fidelity coupled CFD-MBD model.

Postglacial evolution of the Lake Constance shelf and its relation to a 10-km long chain of submerged Neolithic mounds

Postglacial evolution of the Lake Constance shelf and its relation to a 10-km long chain of submerged Neolithic mounds

Environmental conditions influencing the early colonization stage of Ludwigia hexapetala , an aquatic plant recently invasive in Italy

Freshwater ecosystems are among the most susceptible to biological invasions. The South American Ludwigia hexapetala is an aquatic plant that is becoming an increasing threat in many European waterbodies, recently including Italy. This study aimed to define the main parameters influencing the early colonization stage of L. hexapetala by overlapping the percentage cover of this species with environmental parameter data collected at 24 aquatic sites from six waterbodies in north-central Italy. At each site, chemical and physical characteristics of the water (temperature, pH, dissolved oxygen, conductivity, nitrates, phosphates, ammonia, depth, transparency), grain size of the substrate and level of anthropogenic disturbance were evaluated. The results showed that although L. hexapetala prefers shallow, warm, alkaline, moderately rich in ions and nutrients (especially phosphates) and oxygen-poor waters, it can grow in a wide range of environmental conditions. Moreover, as a typical invasive alien species, it spreads opportunistically in disturbed, unstable sites. Thus, L. hexapetala can invade freshwater habitats with different environmental conditions and subjected to anthropogenic disturbance. However, the results suggest that water depth may be a limiting factor in the early colonization stage of this species, which does not seem to be able to colonise waters deeper than 1 m in investigated sites, while it has been observed in significantly deeper waters in other European countries with a longer invasion history. Detecting the environmental parameters that most influence the growth of L. hexapetala becomes crucial both to identify the sites most at-risk of invasion in which to initiate timely monitoring actions for the species, and to be able to develop better management and control actions for this alien species in sites that have already been invaded.

Excessive Eutrophication as a Chemical Barrier for Fish Fauna Dispersion: A Case Study in the Emblematic Tietê River (São Paulo, Brazil)

The Tietê River receives most of the effluents and diffuse wastes from the São Paulo metropolis (21.9 million inhabitants). The study aimed to assess the extent to which environmental changes affected the fish fauna. We compared, in rainy and dry seasons, three sites in Tietê and three in tributaries with much better water quality conditions. No physical barriers exist between the sites. Fish were sampled with gillnets (exposed overnight) and the assemblage’s ecological attributes calculated. Water depth, transparency, temperature, electrical conductivity, dissolved oxygen, turbidity, total dissolved solids, pH, redox potential, total phosphorus, total nitrogen, and chlorophyll were simultaneously determined. Low oxygen concentrations (near zero in the rainy period), and the opposite for nutrients and total solids, separated the predominantly hypereutrophic Tietê from the tributaries (PCA). Differences in the fish assemblages were also evidenced (nMDS), including a higher richness per sample in tributaries (11 to 14 spp.) compared to Tietê (3 to 4 spp.). Siluriformes with accessory breathing dominated in Tietê and the highly tolerant detritivorous Prochilodus lineatus (the main commercial fish) was the only species found in all sites. The species correlated positively with oxygen in the tributaries and with turbidity, redox potential, and nutrients in Tietê (DistLM) (rainy season). Recovery measures are urgently required.

Water quality characteristics and ecological risk evaluation of a landscaped river replenished by three reclaimed water sources in Qingdao, China.

The water crisis may be solved by utilizing reclaimed water. Three reclaimed water sources have restored the lower sections of the Licun River, forming a landscaped river. In this paper, the river's water quality was monitored for a year, and the ecological concerns were analyzed using luminescent bacteria, chlorella, and zebrafish. The results indicated that although basic water quality indicators like COD and ammonia fluctuated along the river, the classification of water quality was primarily affected by factors such as flow rate and water depth. Under experimental conditions, the toxic inhibitory effect of river water on luminescent bacteria, chlorella, and zebrafish was related to the treatment process of reclaimed water. It was found that the reclaimed water produced by the MBR, along with the UV disinfection process, showed no detectable toxicity. In contrast, the MBBR process, when combined with coagulation, sedimentation, filtration, ozonation, and chlorination, seemed to be the source of this toxicity. Along the river, the results of water quality assessments and ecological risk assessments were different, indicating that both should be conducted to evaluate rivers replenished with reclaimed water.

Detached eddy simulation of hydrokinetic turbine wake in shallow water depths

Detached eddy simulations (DES) have been performed for flow over a solitary hydrokinetic turbine for two different tip-speed ratios λ = 3.64 and 6.15 and three different tip-clearances (δ = 0.1D, 0.2D, and 0.35D) to elucidate the effect of air-water interface on the turbine performance and wake recovery in shallow water conditions. The power predictions show good agreement with experimental data for λ = 3.64, but a large 12% error for λ = 6.15. The interface introduced unsteadiness in the power predictions, which increased with λ. The interface did not affect the mean power predictions for lower λ significantly, but reduced power for higher λ. The two-phase simulations for λ = 6.15 showed significant improvement in wake deficit and near-wake TKE prediction compared to the single-phase simulations. The improved predictions were identified because the interface causes early tip vortex entanglement and TKE burst. The DES predictions also show turbulence anisotropy maps consistent with the experimental data. Analysis reveals that turbulence is one-dimensional, dominated by streamwise fluctuations behind the blade tip region; two-dimensional, dominated by transverse and spanwise fluctuations in the TKE burst region; and approaches three-dimensional isotropy in the fully developed wake region. Detailed analysis of the vortical structures and wake characteristics reveals that the interface significantly affects the vortex advection, breakdown, and wake evolution. In particular, they enhance the wake deficit, which slows down the advection of the tip vortex filaments, causing an early vortex entanglement. The tip vortices show interaction with vortices generated underneath the interface, but these interactions seem localized. The tip vortex breakdown was identified due to instabilities generated during vortex entanglement and interaction with interface vortices for λ = 6.15 case, and because of tip and root vortex interaction for λ = 3.64. Although the two-phase simulations are encouraging compared to the single-phase case, TKE is still underpredicted in the near-wake region, possibly because of turbulence or rotating interface modeling issues. Two-phase simulations also predict significantly higher swirl, which needs further investigation.

Study on the Typical Environmental Factors in the Middle Part of Zhoushan Fishery Based on HY-1C/D and Other Multi-Source Data

This study utilizes satellite data, including HY-1C/D, along with reanalysis data, to unveil the typical environmental characteristics of the sea surface in the middle of Zhoushan fishery. The article addresses three main issues. The first one is the development of an ocean primary productivity (OPP) inversion algorithm model. The second one is the study of chlorophyll-α (Chl-α) concentration and OPP distribution characteristics in Zhoushan fishery using China’s domestically produced ocean satellite HY-1C/D CZI data. The last one is the revelation of the characteristics of typical environmental factors on the sea surface at Zhoushan fishery by combining HY-1C/D with multi-source data. The results show the following: (1) The middle part of Zhoushan fishery exhibits significant seasonal and regional variations in Chl-α concentration and OPP. Chl-α concentration ranges mainly between 0.2 and 2.9 µg/L, with higher concentrations in spring and summer and lower Chl-α concentrations in autumn and winter. Spatially, Chl-α concentration gradually decreases from west to east. The OPP in the study area ranges from 100 mg·m−2d−1 to 1000 mg·m−2d−1, with high OPP values distributed on the western side, ranging from 400 mg·m−2d−1 to 1000 mg·m−2d−1, and gradually decreasing seaward. The highest OPP occurs in summer and the lowest in winter. (2) The correlation analysis between Chl-α concentration and OPP revealed a strong positive relationship. Consequently, this study developed an empirical model for estimating OPP based on Chl-α concentration and validated its feasibility. The model applies to areas with Chl-α concentrations ranging from 0.2 to 4 µg/L. (3) The convergence of freshwater injection, multiple ocean currents, and seasonal upwelling in the study area brings about a rich supply of nutrients. Additionally, the region is characterized by suitable conditions, including optimal Chl-α concentrations, OPP, SST, salinity, currents, and geological water depths. The synergistic effect of these factors together contributed to the formation of Zhoushan fishery.

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

Identification of potential landfill site suitability for urban solid waste disposal of Balurghat Municipality of Dakshin Dinajpur District using GIS and multi-criteria decision-making approach

The production of urban solid waste is one of the most dangerous environmental health risks on the planet. By selecting suitable landfill sites, it is possible to resolve the issue of health risks and environmental pollution driven by the unscientific disposal of solid waste. The current research is centered on determining suitable landfill locations for solid waste disposal within the Balurghat municipality through the integration of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) approach. Ten factors were taken into consideration according to their weightage including elevation (0.022), Lithology (0.027), Slope (0.032), Land use and land cover LULC (0.040), Depth of water (0.057), Population density (0.075), Distance from sensitive places (0.107), Distance from road (0.154), Distance from river (0.205) and Land value (0.282). After conducting thorough field visits, an analytical hierarchy process (AHP) under the MCDM approach using GIS reveals acceptable sites that were noted and referenced. Final results are validated through a receiver operating characteristic (ROC) curve using randomly selected 80 potential landfill-suitable site location points with higher accuracy, and a total of 93% of the area under the curve (AUC) has been found worthy to validate the spatial result. The present investigation revealed that only 0.65% of land is very highly suitable, 31.06% of land is highly suitable, 52.54% of land is moderately suitable and 14.74% of land is not suitable for landfilling in Balurghat Municipal area and surroundings respectively. The highly suitable site belongs to the Mamna, Katna, Kuaran, Amtali, Noksha, Bannapara, and Malancha villages of the study area which is a very negligible area. So, the planners should adopt alternative strategies like source minimization, composting, sanitary landfills and awareness programmes for sustainable solid waste management. Hence, the Balurghat Municipality administration, urban planners, and administrative authorities stand to gain valuable insights from the findings of this study as they endeavour to enhance the management of urban solid waste in the area.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Lump Kink interactional and breather-type waves solutions of (3+1)-dimensional shallow water wave dynamical model and its stability with applications

The shallow water equations are used to describe the behavior of water waves in various shallow regions such as coastal areas, lakes, rivers, etc. These equations are derived by making simplifying assumptions about the water depth relative to the wavelength of the waves. In this paper, the generalized exponential rational function method (gERFM) is used to construct novel wave solutions of the (3+1)-dimensional shallow water wave ((3+1)-dSWW) dynamical model. These solutions encompass distinct kinds of waves, such as solitary waves, solitons, Kink and anti-kink solitons, lump Kink interactional waves, traveling breathers-type waves and multi-peak solitons. The dynamical behavior of these wave solutions is discussed, examining the influence of free parameters on the resulting wave shapes. Furthermore, to provide a scientific elucidation of the obtained results, the solutions are presented graphically, making it easy to distinguish the dynamical features, which have practical implications in different areas of applied sciences and engineering. The stability of this dynamical model is revealed via modulational instability analysis, signifying that all analytical results are stable. The obtained results show that the given technique is universal and efficient. Through comparing the projected technique with the existing techniques, the obtained results demonstrate that the given technique is universal, pithy and efficient.

Ecomorphology of yellowtail lambari (Characiformes: Characidae) in the neotropical semi-arid region, Brazil

The ecomorphological patterns of the Astyanax lacustris in different environments (lotic and lentic) of the semiarid region of Pernambuco were analyzed using 25 linear morphometric measurements on 240 specimens (120 males and 120 females), with standard lengths ranging from 24.25 to 91.50 mm. These data were converted into 16 ecomorphological attributes and correlated with environmental characteristics. The principal component analysis (PCA) explained 81.64% (first and second components) of the data variation, highlighting sexual dimorphism in specimen size and noticeable variation among individuals of the same sex in different environments. Specimens from lotic environments showed higher caudal peduncle compression index (CPCI) values, while the pectoral fin aspect ration (PFAR) was more pronounced in lentic environments. Overall, females exhibited a wide morphometric variation. Analysis of linear morphometry in different water bodies indicated an intrinsic variation related to hydrodynamics linked to sexual dimorphism and water flow speed. Further studies are suggested to investigate whether factors such as macrophytes and different levels of water depth and flow contribute to this morphometric variation of species in the semi-arid region.

Multitemporal Seagrass Mapping and Monitoring of Posidonia Meadows and Banquettes for Blue Carbon Conservation: The Poseidon Project

Abstract. In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.

Concept Design of a 15 MW TLP-Type Floating Wind Platform for Korean Offshore Installation

Offshore wind farms on the east offshore of Korea to produce multi-GW power from floating wind platforms are being planned. The objectives of the present study are to develop a new TLP-type floating wind platform with a 15 MW turbine for the planned site and to confirm the feasibility of the TLP design under extreme typhoon environments. The concept design of the 15 MW TLP floating platform was completed for installation at a water depth of 137 m. The platform was vertically moored with highly pretensioned wire rope tendons. The platform and tendons were designed to withstand extreme conditions for up to 50 years. Additionally, a platform with an integrated turbine was designed to be wet-towable from the quayside without dedicated vessels to minimize the pre-service cost and risk. An extreme response analysis was conducted to evaluate the platform motion, acceleration, airgap and tendon tension for wave variation, intact and damaged condition of the tendon, environment heading change, and water level variation. The platform design results were validated using the design criteria from the industry standards and recommendations, and the design was verified to comply with the design requirements for the planned sites.

Response of Functional Traits of Aquatic Plants to Water Depth Changes under Short-Term Eutrophic Clear-Water Conditions: A Mesocosm Study

Aquatic plants play a key role in the structuring and functioning of shallow lake ecosystems. However, eutrophication often triggers shifts in plant communities and species diversity, especially in the early stages when the water is still clear. Additionally, water depth is an important factor regulating aquatic plant communities. We conducted a 50-day mesocosm study to investigate how water depth (50 cm and 100 cm) affected the functional traits (vertical expansion versus horizontal colonisation) of 20 aquatic plants under eutrophic clear-water conditions. Among the selected species, the submerged plants Hydrocotyle vulgaris and Limnophila indica exhibited higher plant height or biomass in deeper water, while the emergent plants Myriophyllum aquaticum showed the opposite trend. Additionally, Ludwigia peploides subsp. stipulacea exhibited better vertical growth than the remaining species, and the submerged species Vallisneria denseserrulata had better horizontal colonisation. There was a positive correlation between plant height and rhizome length, indicating the absence of a trade-off between vertical growth and horizontal expansion. Our findings suggest an overall resilience of aquatic plants to varying water depths within our study range and highlight the importance of analysing functional traits when selecting appropriate species in freshwater ecosystem restoration, particularly in the face of climate change-induced water depth fluctuations.