Water Level Changes Research Articles

Vegetation and carbon sink response to water level changes in a seasonal lake wetland

Water level fluctuations are among the main factors affecting the development of wetland vegetation communities, carbon sinks, and ecological processes. Hongze Lake is a typical seasonal lake wetland in the Huaihe River Basin. Its water levels have experienced substantial fluctuations because of climate change, as well as gate and dam regulations. In this study, long-term cloud-free remote sensing images of water body area, net plant productivity (NPP), gross primary productivity (GPP), and Fractional vegetation cover (FVC) of the wetlands of Hongze Lake were obtained from multiple satellites by Google Earth Engine (GEE) from 2006 to 2023. The trends in FVC were analyzed using a combined Theil-Sen estimator and Mann-Kendall (MK) test. Linear regression was employed to analyze the correlation between the area of water bodies and that of different degrees of FVC. Additionally, annual frequencies of various water levels were constructed to explore their association with GPP, NPP, and FVC.The results showed that water level fluctuations significantly influence the spatial and temporal patterns of wetland vegetation cover and carbon sinks, with a significant correlation (P<0.05) between water levels and vegetation distribution. Following extensive restoration efforts, the carbon sink capacity of the Hongze Lake wetland has increased. However, it is essential to consider the carbon sink capacity in areas with low vegetation cover, for the lakeshore zone with a higher inundation frequency and low vegetation cover had a lower carbon sink capacity. These findings provide a scientific basis for the establishment of carbon sink enhancement initiatives, restoration programs, and policies to improve the ecological value of wetland ecosystem conservation areas.

Investigation on the failure mechanism of slope deformations induced by water level fluctuation with InSAR and numerical simulation: a case study

The Alagou Reservoir was officially closed for water storage in November 2014. Since September 2016, gradual slope collapse and deformation have been observed at elevations ranging from 600 to1050 m upstream of the left bank of the Alagou Reservoir dam site. The increasing annual deformation raises significant concerns about a potential landslide, which could lead to surge waves, posing a threat to both the dam and the surrounding environment. Due to difficult access and worsening conditions, thorough exploration of the site was challenging. Consequently, After emptying the reservoir, we conducted on-site investigations using satellite D-InSAR analysis, numerical modeling, and post-event monitoring. This multidisciplinary approach aimed to investigate the relationship between deformation behavior, triggering conditions, and reservoir water level changes. Our study provides valuable insights into the complex geological processes involved in slope deformation and failure mechanisms induced by water level fluctuations. We elucidated the interplay between deformation behavior and triggering conditions, which is essential for designing effective reinforcement treatments for deformed slopes. While InSAR has proven to be a cost-effective tool for monitoring surface deformation, it offers limited insights alone. Therefore, we recommend a multi-technique approach incorporating geophysical, geotechnical, and remote sensing data for a comprehensive understanding of slope stability.

Impact of Rivers on Seasonal Changes in Water Level

This study was conducted regarding the impact of rivers on Darbandikhan Lake's seasonal water level change. The aim of this study is to illustrate the impact of rivers on the lake through seasonal water level change from its tributaries. To achieve the research objectives, descriptive and quantitative methods were used. The findings showed that the highest water level of the lake was 467.72 m (a.s.l.) in Spring and that its storage capacity and area were 1.2554 billion m3 and 46.26 km2, respectively. In contrast, the lowest water level of the lake was 458.62 m (a.s.l.) in Autumn, with storage capacity and area of 908.2 million m3 and 32.38 km2, respectively. The river's share of discharge into the lake varied annually. The Tanjaro River averaged 6 m3/s (19.6%), the Chaqan River 0.47 m3/s (1.52%), the Zalm River 1.17 m3/s (3.77%), the Sirwan River 20.55 m3/s (66.28%), and the Zmkan River 2.74 m3/s (8.83%), all of which contributed to the lake annually. River discharge was influenced by physical factors such as topography, slope, and climatic elements, in addition to human factors such as water resource management policies, population and land use/land cover in the study area.

Wave–Tide–Surge Interaction Modulates Storm Waves in the Bohai Sea

Typhoons, extratropical cyclones, and cold fronts cause strong winds leading to storm surges and waves in the Bohai Sea. A wave–flow coupled numerical model is established for storm events observed in 2022 caused by three weather systems, to investigate how storm waves are modulated by wave–tide–surge interaction (WTSI). Wave response is basically controlled by water level change in coastal areas, where bottom friction or breaking dominates the energy dissipation, and determined by the current field in deep water by altering whitecapping. Wave height increases/decreases are induced by positive/negative water level or obtuse/acute wave–current interaction angle, leading to six types of field patterns for significant wave height (Hs) responses. For the three storm events, Hs basically changed within ±5% in central deep water, while the maximum increase/decrease reached 160%/−60% in the coastal area of Laizhou Bay/Liaodong Bay. Based on maximum Hs and its occurrence time, WTSI modulation is manifested as the superposition effect of wave–tide and wave–surge interactions in both space and time scales, and occurrence time depends more on tide than surge for all three storms. The enhancement/abatement of WTSI modulation happens for consistent/opposite changing trends of wave–tide and wave–surge interaction, with the ultimate result showing the side with a higher effect.

Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic

The U.S. South Atlantic coastal region is used by many marine birds for foraging, reproduction, and migration. We developed standardized indices of relative abundance from long–term (1980–2016), semi-structured monitoring data (eBird) for eight species: Brown Pelican (Pelecanus occidentalis), Double-Crested Cormorant (Nannopterum auritum), White Ibis (Eudocimus albus), Wood Stork (Mycteria americana), Piping Plover (Charadrius melodus), American Oystercatcher (Haematopus palliatus), Clapper Rail (Rallus crepitans), and Northern Gannet (Morus bassanus). Following a period of stable or declining abundance from the 1980s through the 1990s, most species have shown stable or slightly upward trends through the late 2000s; Brown Pelican and Piping Plover have shown some evidence of recent declines. Species–specific correlations between abundance indices developed from presence/absence data and those developed from count data were positive for all species and ranged from 0.53 to 0.86. Dynamic factor analysis identified common trends in abundance among several species, in particular, Brown Pelican, Double–Crested Cormorant, and White Ibis. Model performance was improved with inclusion of an indicator of sea level rise, but not forage fish abundance or temperature, indicating habitat availability mediated by changing water levels may explain some of the underlying abundance trends. Our results provide baseline information on long–term trends for several important coastal birds that can help inform research, monitoring and conservation efforts in the U.S. South Atlantic region.

Demand rerouting mechanisms with revenue management for intermodal barge transportation networks

Inland waterway transportation plays a crucial role in Europe's transportation network and economy. It is an efficient and sustainable mode of transportation, with lower emissions and energy consumption than other modes of transportation, such as road and air. However, the services provided by inland waterway transport can be significantly impacted by adverse weather conditions such as heavy rain, strong winds, and flooding. These disruptions can cause delays, cancellations, or even damage to vessels or infrastructure. To improve the system reliability, we propose a set of revenue management based (demand itinerary) rerouting mechanisms for intermodal barge transportation optimisation. Revenue Management policies including several customer categories and fare differentiation are applied. Sequential accept/reject decisions are made based on a probabilistic mixed integer programming model maximising the expected revenue of a carrier. A booking framework is defined over a rolling horizon and capacity allocation/reallocation decisions are made for a set of demands including the current and relevant past and potential future transportation requests. Several (demand) rerouting mechanisms are defined and implemented on different service network configurations. The service network status is regularly updated, in particular with respect to barge capacity variations due to changing water levels. An extensive set of experiments is performed and numerical results are analysed. The study results emphasise the added value but also the need for data availability and information sharing between the different stakeholders of Inland Waterway Transportation systems.

Coexistence through sustainable conservation strategies for sitatunga Tragelaphus spekii in African ecosystems

Sitatunga Tragelaphus spekii is an antelope species adapted to the dense swamps and marshes of Sub‐Saharan Africa, where traditional population survey techniques have been ineffective and encountered difficulties in making estimations. The species formerly occurred alongside waterways throughout the lowland forest zone of West and central Africa, extending into swamp systems in the savanna zones of central, East, and southern Africa. In most parts of Africa, the sitatunga population is declining, and attracting the attention of conservationists. Furthermore, its geographical range has been recorded to have shrunk. The present study reviewed major threats to sitatunga, assessed previous and current management approaches, and proposed new approaches to effectively manage its declining populations in Africa. To achieve the study objectives, published literature, reports, online information, expert knowledge, and personal experience were reviewed to acquire relevant information. Results indicated that sitatunga are threatened due to increased habitat loss, population isolation, political instability, water level changes, habitat fragmentation, illegal hunting, and diseases. Current wildlife management approaches raise many doubts as to their effectiveness. National‐level management may unsustainably segment management actions while the protected area approach manages only part of the range of wildlife. The current tenure system in most parts of Africa discourages human–wildlife co‐existence, whereas human–wildlife conflict management approaches only treat the symptoms and not the root cause of the problems. If wildlife, including sitatunga, are to persist in Africa, management approaches should be changed and include re‐focusing of the management context at the ecosystems and landscape level; assessing the genetic diversity of sitatunga; promoting better wetland management, including the aspect of human dimension in management; using non‐invasive techniques to genetically estimate the minimum population size; assessing inbreeding; and enhancing the implementation strategy of wildlife policy in African countries. Changing the attitude of the local community may take time, but it is a pivotal point if humans and wildlife are to coexist.

Numerical simulation of deposit-landslide instability induced by water level decrease: a case study of RS deposit in Lancang River

Changes in reservoir water level often trigger landslides along the reservoir banks, which are common geological hazards that significantly affect the construction and operation of hydropower projects. In this paper, the stability of the RS reservoir landslide in the southwestern region of China is investigated. A discrete element seepage analysis model is constructed based on the theory of changes in the infiltration surface of the deposit caused by water level drawdown. The model is then embedded into a continuous-discontinuous three-dimensional numerical simulation method to systematically investigate the influence of reservoir water level decrease on the stability of the RS deposit. The results indicate that, under the condition of a sudden drop in reservoir water level decrease, the deposit experiences destabilization failure. Its sliding velocities are mainly influenced by seepage forces and gravity, while sliding displacements are influenced by seepage forces and topographic conditions. The landslide process shows distinct stratification characteristics. The portion of the deposit that slides into the river channel is mainly distributed between an altitude of 2655 m and 2870 m. The research results provide a scientific basis for studying the mechanism of slope failure and landslide disaster chain under the condition of reservoir water level lowering.

Intelligent alarm system for river embankment seepage based on BILSTM

Currently, the alarm functions of existing levee seepage monitoring systems are limited to single-parameter monitoring and lack rate-of-change alarms and correlation alarms. This can lead to false alarms, missed alarms, equipment failures, or unnecessary downtime. To enhance the intelligence of levee safety monitoring and seepage alarms, a levee seepage intelligent alarm system based on a Bidirectional Long Short-Term Memory (BILSTM) network model was designed and implemented. Firstly, data cleaning and preprocessing are carried out on the engineering safety monitoring operation data to reduce the influence of dirty data such as outliers and repetitive values on the accuracy of alarms. Secondly, for the correlation between the piezometric tube water levels of the levee and the Yangtze River water levels, a correlation analysis based on Mutual Information (MI) theory was conducted to minimize the effect of piezometric tube water level change delays on correlation. Finally, the BILSTM model was used to predict trends in these potentially abnormal data intervals. Based on engineering application requirements, alarm thresholds were established, and a multi-level alarm module was developed. Field operation test results show that the proposed method can accurately predict the piezometric tube water levels of levees, achieving intelligent alarms within the engineering safety monitoring system.

Impact of Extreme Drought on Vegetation Greenness in Poyang Lake Wetland

The Poyang Lake Wetland, an internationally significant ecosystem, frequently experiences drought during the flood season. However, the total impact of extreme drought on wetland vegetation remains poorly understood. This study determined the standardised precipitation evapotranspiration index (SPEI) and analysed drought trends within the Poyang Lake Basin. Additionally, spatiotemporal variations in wetland vegetation under drought conditions were examined by analysing the mean normalised difference vegetation index (NDVI) values and categorising NDVI classifications. The key factors affecting wetland vegetation and its respective thresholds were determined. The Poyang Lake Basin has experienced increasing aridity over the past 3 years. In response to this trend, the wetland vegetation area in Poyang Lake expanded, whereas vegetation greenness declined. Notably, in the year following an extreme drought, Poyang Lake’s vegetation greenness was lower than that during the same period in previous years. Regardless, the correlation analysis showed no significant relationship between the SPEI values and the wetland vegetation greenness; however, water level changes significantly impacted the wetland vegetation, with a correlation coefficient of −0.89 (p < 0.001). A critical water level of 14 m was identified as the threshold at which sudden changes in the mean NDVI were observed. This research offers valuable insights into hydrological management strategies to protect Poyang Lake Wetland’s vegetation under drought conditions. Future studies should enhance the differentiation of drought tolerance among different wetland plant species, thereby achieving differentiated hydrological management.

Spatiotemporal variation of water level in wetlands based on multi-source remote sensing data and responses to changing environments

Under changing environmental conditions, water level is a crucial indicator for assessing the wetland hydrological cycle. However, due to some wetlands being located in remote and widely dispersed areas, acquiring data on wetland water level changes presents significant challenges, making wetland water level monitoring exceptionally difficult. Wetlands are extensively distributed in western Jilin Province, China, and are experiencing significant degradation due to various factors including natural conditions, agricultural activities, and social development. To address this challenge, this study proposes a monitoring method that combines Sentinel-3 radar altimetry satellites with optical remote sensing images to obtain wetland water level data. Additionally, the study takes into account the Chinese government's Interconnected River System Network Project (IRSNP), classifying wetlands in western Jilin Province into three different water recharge scenarios: direct recharge through main and branch canals, indirect recharge through ditches, and no recharge to isolated wetlands. This study analyses the relationship between wetland water level changes and climatic factors, and assesses how IRSNP can mitigate the negative impacts of climate factors on wetland water levels across different recharge scenarios. The results show that: (1) the wetland water level monitoring method, has high accuracy and feasibility. The average difference between the in-situ measured and satellite-monitored water levels was 0.254 m. (2) There was an overall increasing trend in wetland water levels directly influenced by IRSNP, an insignificant decreasing trend in wetland water levels indirectly influenced by IRSNP. (3) Increased precipitation and decreased evaporation are the predominant climatic factors contributing to rising wetland water levels. Conversely, lower relative humidity and higher temperatures primarily lead to declining water levels. The construction of IRSNP can mitigate the impact of climate change on water levels. Thus, under changing environmental conditions, the implementation of IRSNP has positively impacted wetland protection and provides valuable insights for understanding wetland water level changes and managing water resources effectively.

Functional diversity dynamics of waterbird communities driven by water levels at Shengjin Lake, a small river-connected shallow lake in the middle and lower Yangtze River floodplain.

Gate-controlled activities in lakes can directly or indirectly influence the assembly of waterbird communities. Shengjin Lake, a Ramsar site, is a typical river-connected and gate-controlled shallow lake in the lower and middle Yangtze River floodplain in China, comprising three sub-lakes (upper, middle, and lower) based on topographical features. We surveyed wintering waterbirds at Shengjin Lake from October 2022 to March 2023. We divided the winter water level period into nine phases based on the characteristics of water level changes. By measuring functional diversity, we aimed to provide insights into the differences in waterbird communities among the three sub-lakes under different water level conditions. Multiple linear regression was used to analyze the relationship between habitat factors and functional diversity. We further explored the relationship between specific functional traits and habitat factors through a combination of the R-mode linked to the Q-mode and the trait-environment correlation matrix (fourth-corner analyses) to explain the mechanism underlying waterbird community assembly. When the water level fluctuated in the range of 10.43-10.74 m (Huanghai elevation), the three sub-lakes had significant habitat differences and high habitat heterogeneity, increasing functional richness and functional dispersion of the upper and lower lakes, both of which significantly differed from those of the middle lake. Habitat heterogeneity and mudflat habitats have positive effects on functional diversity. The difference in functional diversity was primarily determined based on the foraging traits and strata of waterbirds. Habitat filtering of particular traits is a major driving force underlying the assembly of waterbird communities. Overall, we suggest that the minimum water level in the wintering period at Shengjin Lake should be regulated between 10.43 and 10.74 m. These findings provide reasonable suggestions for water level regulation and a theoretical basis for conserving waterbird diversity at Shengjin Lake.

Changes monitoring in Hongjiannao Lake from 1987 to 2023 using Google Earth Engine and analysis of climatic and anthropogenic forces

The research aimed to quantify the lake area dynamics, evaluate the changes in distance and rate of lake shorelines quantitatively and spatially and investigate the key factors influencing the Hongjiannao Lake (HL) area shrinkage. The study used remote sensing (RS) data from Landsat TM/ETM+ and OLI images and Google Earth Engine (GEE), a cloud platform for obtaining the lake surface area and island information from 1987 to 2023. A modified normalized difference water index (MNDWI) was applied to water area extraction. Digital Shoreline Analysis System (DSAS) was employed to assess net shoreline movement (NSM) and depict the lake shoreline length and rate changes. Furthermore, the water level was derived by ASTER GDEM V2 using the waterline method and lake boundaries. Six climatic features (temperature, precipitation, potential and actual evaporation, aridity index, and actual water difference) were investigated to find the driving factors of lake area shrinkage by correlation and factor analysis. The results reveal that during 1987–2023, the HL area has undergone four separate phases: stable (1987–1997), shrinkage (1998–2015), growth (2016–2019), and reduction (2020–2023). The most substantial negative change (−7.45%) in the HL area was observed in 1998. NSM analysis demonstrates that the lake has experienced both expansion and shrinkage at various times and locations. According to Water Balance Method, the water volume of HL exhibited variations, ranging from −0.1895 to −0.009 km³. The average yearly change in lake volume, water level, and area displayed similar characteristics with high inconstancy. Correlation and factor analysis of lake area and climatic factors demonstrate that higher precipitation, low temperatures, less potential evaporation level, lower actual evaporation rates, and more minor differences in water levels are associated with an increase in lake area. In contrast, the opposite conditions lead to a reduction in lake size.

Untangling the coupling effect of water quality and quantity on lake algal blooms in Lake Hulun from a dual perspective of remote sensing and sediment cores

Algal blooms and sediment diatoms are crucial indicators of lake water ecology, influenced by water quantity and quality. However, the coupled effects of water quality and quantity changes on algal blooms are still unclear, especially for lakes in cold and arid regions. This study assessed the long-term variations in algal blooms in Hulun Lake using a novel approach combining remote sensing and sediment core samples for diatom analysis. Two mutation points from the structural change test were identified in approximately 2000 and 2010 for algal bloom area (MBE) and sediment diatom richness, indicating asynchronous algal blooms. A structural equation model (SEM) demonstrated that water level (WL) changes were the dominant influencing factor, co-driving the variations in algal blooms and sediment diatoms in conjunction with total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). The results revealed nonlinear relationships between the lake WL, TN, Chla, and COD. The water level of 543 m emerged as a critical threshold affecting the relationship between water quality and quantity. Distinct differences in this relationship were observed when water levels were above or below this threshold. These variations became particularly pronounced during periods of high and low water levels. The results provide novel insights into the dynamics of algal blooms and can further support lake ecosystem conservation and management.

Hydroformer: Frequency Domain Enhanced Multi‐Attention Transformer for Monthly Lake Level Reconstruction With Low Data Input Requirements

AbstractLake level changes are critical indicators of hydrological balance and climate change, yet long‐term monthly lake level reconstruction is challenging with incomplete or short‐term data. Data‐driven models, while promising, struggle with nonstationary lake level changes and complex dependencies on meteorological factors, limiting their applicability. Here, we introduce the Hydroformer, a frequency domain enhanced multi‐attention Transformer model designed for monthly lake level reconstruction, utilizing reanalysis data. This model features two innovative mechanisms: (a) Frequency‐Enhanced Attention (FEA) for capturing long‐term temporal dependence, and (b) Causality‐based Cross‐dimensional Attention (CCA) to elucidate how specific meteorological factors influence lake level. Seasonal and trend patterns of catchment meteorological factors and lake level are initially identified by a time series decomposition block, then independently learned and refined within the model. Tested across 50 lakes globally, the Hydroformer excelled in reconstruction periods ranging from half to three times the training‐test length. The model exhibited good performance even when training data missing rates were below 50%, particularly in lakes with significant seasonal fluctuations. The Hydroformer demonstrated robust generalization across lakes of varying sizes, from 10.11 to 18,135 km2, with median values for R2, MAE, MSE, and RMSE at 0.813, 0.313, 0.215, and 0.4, respectively. Furthermore, the Hydroformer outperformed data‐driven models, improving MSE by 29.2% and MAE by 24.4% compared to the next best model, the FEDformer. Our method proposes a novel approach for reconstructing long‐term water level changes and managing lake resources under climate change.

FLOOD RISK MAPPING IN THE IRTYSH RIVER BASIN USING SATELLITE DATA

Floods are among the most frequent and devastating natural disasters, causing significant economic damage and loss of life worldwide. Effective flood risk management relies on accurate modeling techniques that can predict vulnerable areas and assess potential impacts. In this study, flood dynamics are simulated in the Irtysh River Basin near Ust-Kamenogorsk, a city in East Kazakhstan prone to seasonal flooding using high-resolution satellite imagery and digital elevation data. The primary objective is to visually model flood risks based on terrain characteristics. The study utilizes imagery sourced from the Mapbox platform, which combines data from MODIS, Landsat 7, Maxar, and the Google Earth Engine, providing access to Sentinel-2 surface reflectance imagery at 10-meter resolution. Elevation data from the Copernicus global digital elevation model, with a 30-meter resolution, is used to simulate flood progression. The flood simulation involves calculating flood depth relative to the terrain’s elevation, allowing for a pixel-by-pixel determination of submerged areas. Each simulation incrementally increases water levels to generate a sequence of images, showcasing the progression of flooding over time. The study describes hydraulic soil characteristics usage, and focuses on visualizing flood risk based on terrain data and water level changes. The simulation results indicate that flooding initially impacts riverbanks as water flow starts from the northwest of the city with critical infrastructure becoming vulnerable once water levels exceed 2 meters from the lowest elevation point. These findings highlight the potential of high-resolution satellite imagery and terrain data for flood risk assessment and improving urban flood preparedness. The results provide valuable insights into flood progression enabling more informed decision-making for disaster mitigation.

Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management

It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.

An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization

Due to climate change, the frequency and intensity of torrential rainfall in urban areas are increasing, leading to more frequent flood damage. Consequently, there is a need for a rapid and accurate analysis of urban flood response capabilities. The dual-drainage model has been widely used for accurate flood analysis, with minimum time step synchronization being commonly adopted. However, this method has limitations in terms of speed. This study applied the hyper-connected solution for an urban flood (HC-SURF) model with fixed-time step flow synchronization, validated its accuracy using laboratory observation data, and tested its effectiveness in real urban watersheds with various synchronization times. Excellent performance was achieved in simulating real phenomena. In actual urban watersheds, as the synchronization time increased, the errors in surcharge and discharge also increased due to the inability to accurately reflect water level changes within the synchronization time; however, overall, they remained minimal. Therefore, the HC-SURF model is demonstrated as a useful tool for urban flood management that can be used to advantage in real-time flood forecasting and decision-making.

Strandline records of high frequency, low magnitude drops in water level, glacial Lake Agassiz basin, Central Polk County, MN, USA

Glacial Lake Agassiz strandlines expressed in LiDAR-based digital elevation models are detailed records of past water level change that reflect outlet incision and switching. Detailed 1:24,000 scale surficial geology mapping of a small section of coastline east of Crookston, MN, reveals strandlines of the Lockhart, Emerson, and Nipigon phases. Other than the Campbell and youngest Tintah Beach, the strandlines are not identified using the naming convention established near the southern outlet (Herman, Norcross, Upham, Tintah) because of the increasing number of strandlines northwards. The strandlines, for the most part, naturally exist in elevation clusters, and are assigned to 11 strandline groups (SG). SGs 1–9 are of Lockhart age at elevations higher than the Tintah Campbell Gap (TCGap). Elevation gaps between SGs are ~1–3 m, greater than the 0.5–2 m gap between successive strandlines within a SG. The many (n = 70) small, low-relief strandlines are interpreted to record nearly continuous incision of the southern outlet sill during Lockhart Phase. Beach ridges are estimated to have formed every ~20 years. The TCGap is ~4 km wide in the study area. The Campbell Beach of Emerson Phase age consists of several embayments, beach ridges and the large Melvin Spit. Based on morphological comparison with the modern Gull Point Spit in the Lake Erie basin, the Melvin Spit took ~500 years to form, during which water level dropped 8 m. The Nipigon Phase strandlines are small discontinuous beach ridges. North-south elevation plots of the most continuous strandline in each SG records southward slopes of 1.1 to 0.19 m/km explained by differential glacioisostatic adjustment active during strandline development that decreases through time at lower elevations. The three highest elevation OSL dates agree with published ages for the older strandlines (~14.4 ± 1.5 ka), but younger dated Tintah, Campbell, and sub-Campbell beaches are 2–5 ka older than published ages. Ground penetrating radar (GPR) datasets acquired at 11 sites at frequencies between 100 and 500 MHz, representing a cumulative distance of 11 km, form the basis for a simple beach ridge formation model augmented with hand auger holes and shallow hand-dug pits. Six radar facies (RF) were identified, dominated by RF2 which consists of lakeward-dipping reflections with a downlapping lower boundary recording a depositional regressive system, and RF4 which consists of landward-dipping reflections with a downlapping lower boundary recording overwash deposits from proposed storm events. Spit orientations and GPR data indicate littoral drift was southwards with offshore bars possibly nucleating successive beach ridges upon ~1-m drops in water level. Detailed analysis of strandlines from paleo lakes offer promise for high resolution reconstructions of past water level history.

Cloud computing and spatial hydrology for monitoring the Buyo and Kossou reservoirs in Côte d'Ivoire

The Buyo and Kossou reservoirs are crucial for water supply, agricultural irrigation, and hydroelectric power generation in Côte d'Ivoire. However, climate change threatens the stability and availability of these water resources by increasing rainfall variability, extending drought periods, and intensifying extreme weather events. These challenges underscore the need for precise and continuous monitoring of water levels and surface areas to ensure sustainable management. Due to the scarcity of gauging stations, the objective of this study is to leverage cloud computing technologies along with altimetric and satellite data, for effective reservoir monitoring. Tools like the EO-Africa program's Innovation Lab and Google Earth Engine (GEE), along with advanced image processing software such as PyGEE-SWToolbox and AlTis, were used to process large datasets from the Sentinel-1, Sentinel-2, and Sentinel-3 satellites. These satellites delivered extensive, high-resolution imagery and altimetric data, crucial for monitoring changes in the reservoirs. The processed data were validated with in-situ measurements, yielding a Root Mean Square Error (RMSE) of less than 0.4 m and a correlation coefficient exceeding 0.90. The results highlighted water surface and level changes from 2016 to 2022, with downward trends and seasonal variations closely aligning with in-situ measurements. The study also revealed that the relationship between water levels and surface areas is influenced by both precipitation and the hydrological regimes of the Bandama and Sassandra rivers, demonstrating the complexity of water dynamics in these reservoirs. This research emphasizes the effectiveness of integrating spatial hydrology with cloud computing tools for fast and accurate monitoring of large reservoir. The use of these advanced technologies provides near real-time, reliable, and easily accessible data, offering a significant advantage for water resource management in Côte d'Ivoire.