Water Level Data Research Articles

Structural effects of reedbed grazing and its cessation on reed-nesting songbird densities

Reedbeds are crucial breeding habitats for vulnerable songbird species. Irrespective of their protection status, these habitats may be threatened by organic matter accumulation, progressively leading to structural homogenization and habitat succession towards woodland. Managers prevent excessive litter build-up with various interventions opening-up the reedbeds, such as grazing, which may be detrimental or suitable for some reed bird species. We assessed the effects of extensive grazing by horses and its cessation in the medium and long terms on reedbed structure, and the consequences on reed-nesting songbird densities in Estagnol Nature Reserve, a protected wetland near the French Mediterranean coast. We compared reedbed structural features between grazed, newly ungrazed and old-ungrazed plots. During nine years, we censused four songbird species in spring and collected water level data in the same survey plots. Grazing reduced reedbed extent, rejuvenated the vegetation with more short green stems on a thinner litter, and produced higher structural heterogeneity and discontinuity compared to long-lasting non grazing. Newly ungrazed plot showed intermediate effects. All surveyed songbird total densities were similar among plots while species numbers and densities differed. Grazed reedbed was more attractive to Moustached Warblers (Acrocephalus melanopogon) and Great Reed Warblers (Acrocephalus arundinaceus), likely due to the large edges and the high vegetation structural heterogeneity. However, Moustached Warblers were more negatively affected by higher water level in grazed reedbed, presumably because flooding prevents litter foraging. The newly ungrazed reedbed was not preferred by any species. All warbler species were found under low density in the old-ungrazed reedbed, where Reed Buntings (Emberiza schoeniclus) were exclusively found. Food availability related to thick litter layer may explain this predilection. Common Reed Warblers (Acrocephalus scirpaceus) were found everywhere in similar densities. We conclude that reedbed management by extensive horse grazing and also its long-term cessation may benefit several songbird species. Within a context of wetland degradation and disappearance, both management strategies are useful to establish and maintain suitable habitats for reed-nesting songbird communities.

Geomorphic Response of the Georgia Bight Coastal Zone to Accelerating Sea Level Rise, Southeastern USA

Synthesis of geologic and chronologic data generated from Holocene sedimentary sequences recovered along the inner continental shelf, shoreface, and modern coastal zone of the Georgia Bight reveal a synchronous sequence of paleoenvironmental events that occurred in response to rate of sea level rise tipping points. During the early Holocene (11.7–8.2 cal kyr BP), the paleoshoreline was overstepped and submerged by rapidly rising seas that averaged ~5 mm yr−1. Rates of rise during the middle Holocene (8.2–4.2 cal kyr BP) averaged ~2 mm yr−1 and this deceleration resulted in the formation of coastal environments and sedimentary sequences that were subsequently reworked as the shoreface continued its landward and upward migration. The modern coastal zone emerged commensurate with the late Holocene (4.2–0 cal kyr BP), when the rate of sea level rise averaged <1 mm yr−1. Analysis of water level data collected at six NOAA tide gauge stations located along the Georgia Bight coast indicates the rate of relative sea level rise has increased from a historical average of 3.6 ± 0.2 mm yr−1 (<1972 to 2022) to 6.6 ± 0.8 (1993 to 2022) and during the 21st century it has averaged 9.8 ± 0.3 mm yr−1 (2003 to 2022). The current rate of sea level rise is nearly double the early Holocene rate of rise. Based upon a novel application of the principle of uniformitarianism (i.e., the past is the key to the future), the likely geomorphic trajectory of the Georgia Bight coastal zone under conditions of 21st century accelerating sea level rise will be one of increasing instability (e.g., coastal erosion) and flooding (e.g., overwash, breaching). Evidence of an emerging instability within the coastal zone has been previously reported throughout the region and supports the trajectory of geomorphic change proposed herein. This will ultimately result in the submergence of existing landscapes and replacement by estuarine and marine environments, which may hasten in pace and scale given the current rate of sea level rise is expected to continue accelerating throughout this century. These findings have not been previously reported and should be considered by coastal practitioners responsible for conceptualizing risk, as well as the formulation and implementation of adaptation action plans designed to mitigate threats to the built and natural environment induced by climate change.

Wildlife Roadkill Driven by Hydrological Regime in a Subtropical Wetland

This study explored the role of the hydrological regime as a trigger factor for wildlife roadkill along a 22 km road crossing the Taim Wetland, a Ramsar site of international importance in South Brazil. The north–south crossing of BR-471, a federal highway, causes fatalities to numerous animals due to collisions with vehicles. An 11-year long-term time series study encompassing monthly roadkill incidents, water level, and rainfall was analyzed by considering three different periods related to a Wildlife Protection System (WPS): (i) 3 initial years before the WPS implementation (BWPS); (ii) 4 intermediate years after the WPS implementation (AWPS), which includes fences, fauna tunnels, cattle guard stocks, bumps, and speed control; (iii) 4 final years during partial destruction of the WPS (PDWPS). A pseudo-2D full hydrodynamic cell model was employed to fill missing water level data. The model had a good to very good performance (NSE: 0.73 to 0.87; R2: 0.79 to 0.90). The relationship between roadkill incidents and the WPS periods (BWPS, AWPS, and PDWPS) was modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), considering rainfall and water level as predictor variables. The analysis revealed a higher incidence of wildlife roadkill in BWPS compared to AWPS and PDWPS, suggesting the effectiveness of the implemented measures. Critical season and interplay between water levels, rainfall, and the roadkill were assessed. Mammals was the most common roadkill class identified (~92%), followed by reptiles (13%) and birds (2%), with no change in these percentual in the BWPS, AWPS, and PDWPS. Among mammals, capybara (Hydrochoerus hydrochaeris) and coypu (Myocastor coypu) were the most frequent victims (~93% of mammals). Winter, followed by autumn, recorded the highest number of roadkill incidents (>60%), and this pattern remained consistent during the three periods. While rainfall did not emerge as a determining variable for roadkill, water levels above certain thresholds (>3.3 m) drastically diminished the effectiveness of the WPS, mainly due to fauna tunnel submersion. These findings offer valuable insights for enhancing wildlife conservation strategies in this protected area by incorporating hydrological information providing a baseline for designing WPS in similar environments.

Modeling seasonal salinity variations in a large West African lagoon (Nokoué, Benin): Major drivers and mechanisms

Nokoué lagoon is a shallow water body connected to the Gulf of Guinea through the long and narrow Cotonou channel. The salinity dynamics in the lagoon is investigated using the 3D SYMPHONIE numerical model. We first validate the model using salinity and water level data. Simulated and observed salinity and water level variations compare well demonstrating that the reference model simulation correctly reproduces the dynamics of the lagoon. By performing several simulations with varying external forcings and freshwater fluxes, the main drivers of salinity variability in the lagoon are identified. We first focus on the salinization phase of the lagoon at the end of the tropical wet season (between November and February), and we investigate the total change in salinity and the salt fluxes involved in these variations. The high frequency salt fluxes associated with the ocean tide import salt from the ocean via the Cotonou channel to the south of the lagoon. Baroclinic fluxes, associated with the influence of salt on density and associated pressure gradient, increase this local salinity input in the south, but also play a major role in the dispersion of salt throughout the lagoon. Two rivers provide a permanent freshwater inflow in the north/ northeast of the lagoon that limits the salinization of the whole lagoon. Finally, wind-generated high frequency recirculation, partially prevents the salinisation of the North-East of the lagoon. During the desalinization period (between May and September), the lagoon salinity variations are highly sensitive to the magnitude of river inflow. At a constant river flux rate, the lagoon attains an equilibrium state in salinity. As this equilibrium is reached, both the overall salinity level and the time needed to achieve it decrease notably with higher fluxes. Beyond 100 m3 s-1, only a small area near the Cotonou channel retains higher salinity, while surpassing 500 m3 s-1 results in complete desalination of the entire Nokoué lagoon.

A Dual-Threshold Algorithm for Ice-Covered Lake Water Level Retrieval Using Sentinel-3 SAR Altimetry Waveforms.

Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016-2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes.

Development of a Microservice-Based Storm Sewer Simulation System with IoT Devices for Early Warning in Urban Areas

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.

Reservoir Sedimentation Assessment Using Geospatial Technology: A case Study of Dukan Reservoir, Sulaimani Governorate, Kurdistan Region, Iraq

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storage capacity of the reservoir within two elevations. According to the study results, the live storage capacity of Dukan Reservoir at elevation 511.78 m had decreased from 8000 MCM to 7007.77MCM and 6923.53 MCM using NDWI and MNDWI respectively, due to sedimentation, resulting in a capacity loss of 14.59% and 15.83% for NDWI and MNDWI respectively. The annual sedimentation was 13.78 MCM and 14.95 MCM for NDWI and MNDWI, respectively. Joglekar's equation and Khosla's formula have demonstrated that the sedimentation rate in the Dukan reservoir exceeds the critical rate. The findings of this study will inform the development of sediment management strategies aimed at preserving the reservoir's capacity.

Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm

AbstractThis study proposes a novel, new ensemble model (NEM) designed to simulate the maximum water level increases caused by storm surges in a frequently cyclone‐affected coastal water of Hong Kong, China. The model relies on storm and water level data spanning 1978–2022. The NEM amalgamates three machine learning algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost (XGB), employing a stacking technique for integration. Six parameters, determined using the Random Forest and Recursive Feature Elimination algorithms (RF‐RFE), are used as input features for the NEM. These parameters are the nearest wind speed, gale distance, nearest air pressure, minimum distance, maximum pressure drop within 24 hr, and large wind radius. Model assessment results suggest that the NEM exhibits superior performance over RF, GBDT, and XGB, delivering high stability and precision. It reaches a coefficient of determination (R2) up to 0.95 and a mean absolute error (MAE) that fluctuates between 0.08 and 0.20 m for the test data set. An interpretability analysis conducted using the SHapley Additive exPlanations (SHAP) method shows that gale distance and nearest wind speed are the most significant features for predicting peak water level increases during storm surges. The results of this study could provide practical implications for predictive models concerning storm surges. These findings present essential tools for the mitigation of coastal disasters and the improvement of marine disaster warning systems.

Uncertainties on the combined use of ICESat and ICESat-2 observations to monitor lake levels

Lake water level is an important variable to indicate lake hydrological balances and climate change impacts. Benefiting from the launch of the laser altimeters ICESat and ICESat-2, higher spatial-resolution elevation measurements have opened new possibilities for monitoring lake levels globally over the past two decades. However, uncertainties on the combined use of two-generation satellite laser measurements have not yet been investigated specifically. This study aimed to summarize the important technique notes on water level data processing by integrating the ICESat and ICESat-2 altimetry measurements. We mainly focused on the effect of geoid height, water masks for extracting altimetry footprints, and the 9-year data gap between the two generations of satellites on water level change estimates. We compared the influences of the above three factors in different situations by selecting typical lakes worldwide as study cases. The results showed that: (1) In the combination of ICESat and ICESat-2 products, geoid heights need to be recalculated for each footprint based on its longitude and latitude in order to replace the geoid values of the original products when calculating orthometric heights. It is necessary because the default geoids in both generations of products (ICESat and ICESat-2) exhibit a systematic deviation; (2) To balance the accuracy and efficiency, the small water mask in the low-level year is recommended to extract the potential footprints in comparison with the laborious processing of time-varying water masks; (3) The 9-year data gap between ICESat and ICESat-2 observations may cause inevitable overestimations or underestimations of the long-term change rate of lake levels with a non-linear trajectory, yet it has few effects on lakes with (near) linear trending or fluctuating changes.

Design of mechanical digitalization automatic water level recorder for river water level monitoring

The Welang River Pasuruan has an Automatic Water Level Recorder (AWLR) tool that measures water levels at that location. This tool operates using mechanical movement to rotate the roller on the AWLR, causing the float to move up and down. This movement results in ink marks on the water level validator paper. The recorded water level data is then processed into an Excel chart. This study aims to digitize the AWLR tool so that the monitoring and calculation of the water level is more precise and can be monitored in online. The water level detector will work in real time using a rotary encoder sensor and data will be sent to database and monitor water level monitoring through a series of data processing which later the data will be sent via MiFi. The method of testing the 2-component connecting system is the accuracy of reading the water level and the accuracy of sending data. The results will be displayed every 1 second on the monitoring dashboard and a standard deviation of 0.0002792 and an error of 0.43% is obtained which indicates the accuracy of the reading is quite accurate and real time.

Prediction of drought in lake mead

Lake Mead is a reservoir on the Colorado River on the border of Nevada and Arizona. It is the largest reservoir in the United States. Due to the drought caused by climate change and the increasing water demand of the population served by Lake Mead, the capacity of this reservoir has been greatly reduced. The continuous water shortage has had a huge impact on the surrounding cities, and people are preparing to adopt wastewater recycling. To alleviate the shortage of water sources. this paper uses the water level data of the past years to establish a grey forecast Grey Model (1,1) model to predict the changes in the water level of Lake Mead in the next 30 years and compares them based on the prediction of short-term and long-term drought data. based on the previous survey of wastewater sources and collection methods, it is believed that the wastewater recovery system is mainly affected by four factors: financial support, collection pipe network coverage, treatment technology, and wastewater classification capabilities.

Application of Machine Learning for Daily Forecasting Dam Water Levels

The evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. Multiple studies have shown that machine learning, a data-driven approach to forecast hydrological processes, brings about more reliable data and is more efficient than traditional models. In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. Multiple input combinations were investigated to improve the model’s sensitivity, and statistical indicators were used to assess the reliability of the developed model. The study of multiple models with multiple input scenarios suggested that the bagged trees model trained with seven days of lagged input provided the highest accuracy. The bagged tree model achieved an RMSE of 0.13953, taking less than 10 seconds to train. Its efficiency and accuracy made this model stand out from the rest of the trained model. With the deployment of this model on the field, the dam water level predictions can be made to help mitigate issues relating to water supply.

Risk assessment method of level-induced water scarcity in the city located along rivers: Application in Nanchang, China

In the context of global environmental changes, the problem of low water levels (LWLs) is becoming increasingly prominent and posing a significant threat to urban water security. At the same time, there have been noticeable changes in basin hydrological conditions, leading to compromised consistency of water level data based on historical monitoring. Assessing the adaptability of urban water intake projects to water level changes has become a challenging problem currently. Firstly, we have designed a new indicator with good consistency - velocity of water level descending (VWD). Secondly, we have interpreted the connotation of the risk of urban level-induced water scarcity (LIWS) based on the perspective of the coupling system of “water source - water intake engineering - the subsystem of urban water demand” (CSWWU). Thirdly, we constructed a risk assessment model of urban LIWS to quantify the possibility and consequences of adverse scenarios, aiming to enhance the city’s ability to respond to extreme events. Taking Nanchang as an example, we generated 972 preset scenarios, calculated the probability of all scenarios, and drew the risk map of LIWS. The results showed that: (1) When the water level of Nanchang Station descends to 10.33, 10.14 and 10.04 m, the water supply deficit of the urban water plant can be balanced by the saved water volume, which is released by activating three types of emergency measures, respectively. (2) The city faces the greatest risk of LIWS in January and February of each year, which is the key period of risk management. (3) By drawing a risk map, we have comprehensively evaluated the important scenarios about urban LIWS in the future. The risk assessment method for urban LIWS is based on the new indicator, which can provide theoretical support for urban emergency water supply management and promote local governments to fully utilize the resilience of coupling systems to adapt to constantly changing society and environment.

Hydrological characteristics of Mahakam cascade lake as a reference for the sustainability management

Mahakam cascade lakes consist of Lake Semayang, Melintang, and Jempang is a floodplain lake. Mahakam cascade lakes include the 15 national priority lakes which are the most urgent situation. This lake is characterized by dynamic water level fluctuation, resulting in high fishery productivity. The lake’s hydrological characteristic is the main component in controlling the lake ecosystem, therefore it is crucial to evaluate the influenced factors. The objective of this research is to describe the lake’s hydrological characteristics, including inlet-outlet discharge, sedimentation, water level pattern, and land cover change in the catchment area. Further, provide a reference for the conservation of lake ecosystems. We performed a field campaign to measure river discharge and collect water samples for sediment-load laboratory analysis. We analyzed daily water level data recorded from 1989 to 2010. We obtained the time series land cover from Landsat data and performed GIS spatial analysis. Our results revealed that first, the Rebak Rinding River has the biggest contribution to lake sedimentation (3,859 tons/day, 77% of the total sediments entering the lake). Second, the water level tended to increase during the medium and extreme flood seasons. Contrary, a relatively small decrease in the water level during the medium-receding season. Third, the catchment area is 51,573 km2 (66 times larger than the lake water bodies). From 2000 to 2019, the forest decreased by 2,063 km2. As a consequence, increased erosion-sensitive area by 2,695 km2. We emphasized preserving the catchment area is vital to reduce the sedimentation rate and water fluctuation. We recommend the Rebak Rinding sub-catchment as the priority area for controlling deforestation, and erosion on the plantation, mining, and agricultural area.

Sea level rise and the increasing frequency of inundation in Australia’s most exposed estuary

The large tidal lake systems along the Southeast Australian coast are amongst the most vulnerable estuaries in Australia to the effects of sea level rise. In these lakes, reduced tide ranges compared with the ocean, in combination with modest flood extremes, have allowed development to occur in close vertical proximity to the current mean sea level. In this study, we examine water levels within Lake Macquarie, Australia’s most exposed estuary to sea level rise. We analyse water level data from the entrance channel and the lake to investigate recent changes to the frequency and duration of inundation or flooding of low-lying streets and examine the potential impacts of future rises in sea level. Our analysis shows that the numbers of days each year when water levels exceed those of low-lying streets, while subject to some variability, have increased significantly over recent decades. The increasing frequency of inundation is attributed to both mean sea level rise and an increase in tide range over the period of available data, which is thought to be associated with scour processes related to ongoing morphological adjustment to entrance training works undertaken over a century ago. Comparison of the projected behaviour of lake and open coast water levels under sea level rise shows the lake has significantly greater sensitivity to sea level rise. Projected inundation frequency for a given amount of sea level rise within the lake is double that of open coast sites, exposing infrastructure in the estuary to increasing risk of damage.

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America.

The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.

Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects

Inter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measured data to establish a model. Therefore, based on a data-driven method, this paper diagnoses and restores the important parameters of the water diversion projects, including the elevation of pipeline and water level data, which can be used to establish the adaptive hydraulic transition model of the water diversion projects. Firstly, the abnormal data of the elevation of pipeline were identified using expert data annotation and support vector classification (SVC), with the identification accuracy of abnormal data being as high as 91%. Then, the single and continuous abnormal data were restored using an interpolation method and multiple linear regression algorithm (MLR), and the restored data were found to be consistent with the judgment of expert knowledge. Secondly, K-medoids was used to classify the complex multi-dimensional water level data, combined with the 3-sigma method to identify the outliers in each class. The gradient boosting decision tree algorithm (GBDT) trained on normal data restored outliers in a predictive manner, and the mean absolute percentage error (MAPE) was 0.003%, 0.025% and 0.091% in each class, respectively, showing the best accuracy compared with other models.

Characterizing the 2022 Extreme Drought Event over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations and In Situ Data

With advancements in remote sensing technology and the increasing availability of remote sensing platforms, the capacity to monitor droughts using multiple satellite remote sensing observations has significantly improved. This enhanced capability facilitates a comprehensive understanding of drought conditions and early warnings for extreme drought events. In this study, multiple satellite datasets, including Gravity Recovery and Climate Experiment (GRACE), the Global Precipitation Measurement (GPM) precipitation dataset, and the Global Land the Data Assimilation System (GLDAS) dataset, were used to conduct an innovative in-depth characteristic analysis and identification of the extreme drought event in the Poyang Lake Basin (PLB) in 2022. Furthermore, the drought characteristics were also supplemented by processing the synthetic aperture radar (SAR) image data to obtain lake water area changes and integrating in situ water level data as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index dataset, which provided additional instances of utilizing multi-source remote sensing satellite data for feature analysis on extreme drought events. The extreme drought event in 2022 was identified by the detection of non-seasonal negative anomalies in terrestrial water storage derived from the GRACE and GLDAS datasets. The Mann–Kendall (M-K) test results for water levels indicated a significant abrupt decrease around July 2022, passing a significance test with a 95% confidence level, which further validated the reliability of our finding. The minimum area of Poyang Lake estimated by SAR data, corresponding to 814 km2, matched well with the observed drought characteristics. Additionally, the evident lower vegetation index compared to other years also demonstrated the severity of the drought event. The utilization of these diverse datasets and their validation in this study can contribute to achieving a multi-dimensional monitoring of drought characteristics and the establishment of more robust drought models.

Effects of rising seas and geomorphological changes in the islands of northern Bay of Bengal

The geomorphology of the coastal islands of the northern Bay of Bengal is complex and dynamic in nature. During contemporary times, the geomorphology of coastal islands has been severely influenced by rising sea levels, imposing environmental stress on natural systems. Coastal island progradation and landscape loss scenarios exhibit complex behavior and are influenced by natural processes of fluvial and marine systems. Landscape geomorphic features continuously transform one into another by the natural causal mechanisms and response along with ethnographic interventions at the major islands of the Meghna estuary, which largely impacted ecosystem services, food security, and livelihood patterns of the coastal community. This paper provides historical geomorphological change statistics, the stage of development or evolution of coastal islands, dynamics progradation scenarios, landscape net loss or gain, rate of hereinafter sea-level rise, coastal island stability, impacts of sea level rise on the coastal island geomorphology, and landscape ecology. The geomorphological change of coastal islands have been assessed from ancient cartographic maps (i.e. Rennell's Map in 1776, Tassin's Map in 1840, and Topographic Survey Map in 1943) and remotely sensed satellite imagery of Landsat mission (1972–2021) by using advance RS and GIS tools via overlay analysis techniques. Using time series tidal gauge water level data (1993–2022), the present study analyzes historical trends in sea level rise and assesses its impacts on coastal ecosystems. The study finding demonstrated that coastal islands geomorphology of the northern Bay of Bengal continuously change by fluvial and marine process. Among the coastal island in the study area, the largest island Bhola was displayed noticeable erosion 192.96 km2 with an annual rate of erosion 2.47 km2/yr during the time span between 1943 and 2021. In the northern Bay of Bengal, the results from tidal gauge water level analysis revealed that the yearly maximum sea level rise rate was 9.7 mm/yr, with a strong positive correlation (R2 = 0.30), at a 95% confidence level. The study outcome helps in coastal risk assessment, mitigation strategies, development planning, and coastal resources management.

Reconstructing flood level timeseries at seasonal wetlands in Ireland using Sentinel-1

Wetlands are vital habitats which play a critical role in global biodiversity. Their functioning is intrinsically linked with their hydrological regime and so the monitoring of water level data is critical to their management and conservation. However, monitoring the hydrology of wetlands can be challenging. Their large number and wide distribution can make them impractical to monitor using traditional field instrumentation. In this context, this paper presents a novel approach for monitoring water levels in gauged and ungauged seasonally flooded wetlands using Sentinel-1 Synthetic Aperture Radar (SAR) data. The methodology, which was fully automated, used multitemporal sequences of SAR imagery to reconstruct hydrometric data at 44 seasonally flooded wetland sites across the Republic of Ireland. The procedure downloaded, processed and checked the suitability of SAR images for water classification. Suitable images were classified and filtered to remove common sources of misclassification, and the flood area in each image was cross-referenced against a predefined stage-area relationship (derived from a digital terrain model) to determine water level. The methodology was calibrated using observed field data and performance evaluated in two ways: 1) generic calibration whereby the process was optimised collectively to achieve the best accuracy for ungauged sites, and 2) site-specific calibration whereby the process was optimised for each site individually to achieve the best possible accuracy for each gauged site. The generic calibration yielded an average Nash-Sutcliffe Efficiency of 0.80 for both calibration and validation datasets while the site-specific optimisation produced results of 0.87 for both calibration and validation datasets. Results show the methodology was capable of accurately reproducing water levels in easonal wetlands for floods as small as 3 Ha. This demonstrates its potential as a viable tool for the remote monitoring of ecologically significant wetlands, and a means of providing the observational data necessary for their long-term sustainable management.