Surface Water Extent Research Articles

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation

ABSTRACT Remote sensing (RS) imagery is becoming increasingly popular in surface water extent extraction thanks to the increasing availability of RS data and advancements in image processing algorithms, software, and hardware. Many studies proved that RS imagery can work independently or along with other approaches to identify flood extent. However, due to the insufficiency in the number of images from single-sourced RS and independent references for validation, most studies just depicted the inundation status near the peak of the inundation. The potential of those images to document flood events at different stages (e.g. rising and receding stages) has not been well investigated. To close that gap, this study investigated the efficacy of RS-based multi-spatiotemporal flood inundation mapping using multimodal RS images captured on different dates to describe the entire flooding process. Additionally, a Quantile-based Filling & Refining (QFR) workflow was proposed to resolve the blocking effects of dense vegetation in study areas. We tested the multimodal flood mapping workflow plus the QFR correction in four lock and dam sites on the Mississippi River by comparing the RS-based flood maps with HEC-RAS simulations. Our results demonstrated the usefulness of multimodal RS images in describing flooding events at different stages and showcased the potential of those images to serve as a reliable reference source in data-scarce areas. In addition, results showed that the standard water extraction plus post-processing will not guarantee accurate flood maps in densely vegetated areas. In contrast, map processed with QFR were noticeably more consistent with HEC-RAS maps, especially for flood maps generated with PlanetScope images, for which the median accuracy has been improved from below 0.5 to above 0.94 after the QFR postprocessing. Thanks to the simple structure, the proposed multimodal RS flood mapping workflow plus QFR correction procedures can be fully automated and can thus benefit near-real-time applications.

Climate Change Will Impact Surface Water Extents and Dynamics Across the Central United States.

Climate change is projected to impact river, lake, and wetland hydrology, with global implications for the condition and productivity of aquatic ecosystems. We integrated Sentinel-1 and Sentinel-2 based algorithms to track monthly surface water extent (2017-2021) for 32 sites across the central United States (U.S.). Median surface water extent was highly variable across sites, ranging from 3.9% to 45.1% of a site. To account for landscape-based differences (e.g., water storage capacity, land use) in the response of surface water extents to meteorological conditions, individual statistical models were developed for each site. Future changes to climate were defined as the difference between 2006-2025 and 2061-2080 using MACA-CMIP5 (MACAv2-METDATA) Global Circulation Models. Time series of climate change adjusted surface water extents were projected. Annually, 19 of the 32 sites under RCP4.5 and 22 of the 32 sites under RCP8.5 were projected to show an average decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under surface water dry conditions at these sites suggest greater impacts of drought events are likely in the future. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, relative to the 2017-2021 period, likely attributable to projected increases in winter and spring precipitation exceeding increases in projected temperature.

An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics.

Optimizing Height Above Nearest Drainage parameters to enable rapid flood mapping in North Carolina

Surface water flooding represents a significant hazard for many infrastructure systems. For example, residential, commercial, and industrial properties, water and wastewater treatment facilities, private drinking water wells, stormwater systems, or transportation networks are often impacted (i.e., in terms of damage or functionality) by flooding events. For large scale events, knowing where to prioritize recovery resources can be challenging. To help communities throughout North Carolina manage flood disaster responses, near real-time state-wide rapid flood mapping methods are needed. In this study, Height Above Nearest Drainage (HAND) concepts are combined with National Water Model river discharges to enable rapid flood mapping throughout North Carolina. The modeling system is calibrated using USGS stage-discharge relationships and FEMA 100-year flood maps. The calibration process ultimately provides spatially distributed channel roughness values to best match the available datasets. Results show that the flood mapping system, when calibrated, provides reasonable estimates of both river stage (or corresponding water surface elevations) and surface water extents. Comparing HAND to FEMA hazard maps both in Wake County and state-wide shows an agreement of 80.1% and 76.3%, respectively. For the non-agreement locations, flood extents tend to be overestimated as compared to underestimated, which is preferred in the context of identifying potentially impacted infrastructure systems. Future research will focus on developing transfer relationships to estimate channel roughness values for locations that lack the data needed for calibration.

Quantification of surface water extent and volume in the Inner Niger Delta (IND) over 2000–2022 using multispectral imagery and radar altimetry

Spatio-temporal dynamics of surface water reservoirs at regional and global scales remain poorly understood. Using satellite remote sensing provides a unique opportunity to address this problem. This study aims to (1) quantify the extent and volume of surface water and (2) compare our approach with other datasets. We utilized MODIS-based spectral indices to monitor temporal variations in inundation extent. By interpolating water levels across the surface water extent, we generated water level maps and quantified surface water volume from 2000 to 2022. Evaluation against ICESat-2 data involved 64 comparisons, with approximately 58% showing an R 2 value greater than or equal to 0.5, and 38% were higher than or equal to 0.7. Compared to the flooding model, our method aligns more closely with ICESat-2 data, contrary to the flooding model which tends to overestimate water levels and, consequently, water storage.

Detecting changes in surface water extent of the Weija dam, Accra, Ghana using MNDWI indices from Landsat satellite imagery

Detecting changes in surface water extent of the Weija dam, Accra, Ghana using MNDWI indices from Landsat satellite imagery

Comprehensive Landsat-Based Analysis of Long-Term Surface Water Dynamics over Wetlands and Waterbodies in North America

Wetlands are considered one of the most valuable ecosystems around the world and provide numerous environmental services, including water purification, flood protection, and habitat for a variety of species. Wetlands loss is an increasing trend due to anthropogenic activities and natural processes. As such, spatial knowledge regarding the extent and dynamics of surface water is demanding for wetland conservation and protection. The Landsat program, with five decades of historical Earth observation data, has a unique advantage for monitoring wetland surface water changes and dynamics with 30 m spatial resolution. We monitored 266 Ramsar wetland sites in North America for the past 40 years using the open-access Landsat data within the Google Earth Engine cloud computing platform. Landsat Collection 2 Level-2 surface reflectance products were preprocessed and cloud-screened, and a time series of spectral bands and indices were created. The unsupervised Dynamic Surface Water Extent method classified each image into water classes with different confidence levels. An average overall agreement of 92,97% and an average F-score of 96.31% were achieved in this study. Water occurrence maps, in addition to inundation class and change maps, were created for the entire North America, and quantified spatial information was calculated for Ramsar wetland sites.

Water Body Extraction Using a Sentinel-1 Scene and Artificial Neural Networks: Case Study – Carpina-PE Dam

Surface water is the most important resource and environmental factor for maintaining human survival and ecosystem stability, therefore accurate and timely information on surface water is urgently needed. In this study, an image classification approach using Artificial Neural Networks was proposed for mapping the surface water extent of the Carpina-PE Dam using radar image from the Sentinel-1 satellite, as well as its polarizations (VH and VV) and the generated water indices (SDWI and SWI). All datasets presented limitations in detecting small water bodies, such as narrow rivers, and overestimation in pasture areas, generating commission errors ranging from 16.5% to 28.9% and omission errors ranging from 1.47% and 3.5%, with emphasis on VH and VV polarizations. The overall classification accuracy ranged from 96% to 98% and R² values reached close to 1, where the best performance was seen for SDWI and SWI. The comparative experiments indicated that unitary radar polarizations with water spectral indices were useful for improving the accuracy of extracting water bodies in places with clouds, without significant variations, in addition to providing detailed information, with potential for continuous monitoring.

Detection of inland water bodies under dense biomass by CYGNSS

The reliable detection of inland water bodies under dense biomass is not possible with traditional spaceborne remote sensing systems. In this investigation, we demonstrate the capability of the National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission to detect and quantify the extent of small water bodies under dense biomass up to 400 ton/ha in complex heterogenous scenes such as tropical rainforests. A novel inland water body product based on L1 CYGNSS data is developed using a combination of peak surface reflectivity Γp and its spread with respect to time delay and Doppler shift, as characterized by the power ratio Pratio. Only L1 CYGNSS mission data has the potential to study Earth's surface water dynamics because raw Intermediate Frequency (IF) data availability is limited. Several coherence indices are used to verify that the L-band Global Positioning System (GPS) signals are being coherently reflected by surface water bodies and attenuated by the intervening vegetation canopy. These indices include full and computationally fast versions of the entropy associated with Delay Doppler Maps (DDMs), Efull and Efast, as well as the time derivative of the phase φpeak of the reflected GPS signal. The capability to accurately resolve surface water extent under dense vegetation has been shown to improve the estimation by current state-of-art surface water extension datasets used by global methane CH4 emission models in wetlands.

Integrating Multiple Geospatial Datasets for Precise and Efficient Surface Water Extent Mapping in Malaysia

Accurate mapping of surface water extent is essential for several applications, including water resources management, climate modelling, biodiversity conservation, agricultural irrigation, hydropower, and food security. Inland water management is critical in Malaysia due to dams, river basins, and heavy rainfall. In a tropical country, mapping surface water is challenging due to cloud cover and dense vegetation. This study proposes an approach that uses rule sets in ArcGIS Model Builder to automate the identification of water bodies. At the regional scale, the extent of surface waters can be mapped by integrating multiple geospatial datasets, including surface terrain, global surface water data, and synthetic aperture radar (SAR) imagery. Accuracy is calculated by comparing the results to high-resolution optical imagery via the SecureWatch® application. The accuracy of 200 stratified random points is 95%, with a kappa value of 0.90 for water body and land area classes. This study maps a 12,563.95 km2 study area covering several states in Malaysia. These results show that the integration of multiple geospatial datasets using rule sets and data modelling can map the extent of surface waters in Malaysia. The proposed approach could be applied in other tropical countries with similar topography.

Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model

AbstractFor many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases.

Combining Satellite Optical and Radar Image Data for Streamflow Estimation Using a Machine Learning Method

River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (RNIR/RSWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m3/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), RNIR/RSWIR (Model 5), and a combination of AREA_SAR and RNIR/RSWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and RNIR/RSWIR (Model 6) were 0.25 and 56.5 m3/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and RNIR/RSWIR (Model 8) and the combination of AREA_SAR, MNDWI and RNIR/RSWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m3/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions.

Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series

Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km2 occurred in 2005, and the maximum water area of 2614.96 km2 occurred in 2020, with an annual average variation of 193.92 km2. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture.

Monitoring water spread and aquatic vegetation using earth observational data for Nani-High Altitude Lake (N-HAL) of Uttarakhand State, India

The lakes are facing anthropogenic pressure and losing its natural characteristics. Hence, the evaluation of lake water quality is a thrust area of research. The Nani-High Altitude Lake (N-HAL) was aimed to study during pre- and post-monsoon (March and October) period (2017–2022) using earth observation data based on false color composite (FCC) and spectral indices. The Sentinel-2 A/B data of 10 m spatial resolution was considered for monitoring and mapping of high-altitude lake. The FCC images were used to map and monitor changes in water spread and aquatic vegetation. Further, three spectral indices, namely, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Turbidity Index (NDTI), were applied to delineate water bodies and aquatic macrophytic vegetation (recorded are the Potamogeton pectinatus, Potamogeton crispus, Polygonum glabrum, Polygonum amphibium and Polygonum hydropiper (Water pepper). Results reveal that the water surface extent has changed from 2017 to 2022, depending on the pre- and post-monsoon rainfall pattern during the study period. During a flood year on 18-20, October 2021, in the area resulting into a drastic reduction in the aquatic vegetation and suspended particles. This lake needs immediate conservation and management policies.

A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai–Tibet Plateau from Landsat Observations

Abstract The Qinghai–Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (∼30 m) with good accuracy. Multiple sensors’ observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000–20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66 × 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future. Significance Statement In this paper, we present a new methodology to estimate surface water and its intra-annual changes using Landsat data. Missing data and retrieval errors in the winter are major issues in the existing products (i.e., JRC dataset). This motivated us to develop a new machine learning algorithm to better improve the retrieval scheme. We show that our approach, based on a neural network classifier, delivers a significant improvement compared to the previous estimates. As shown in the literature, JRC data can hardly be used at the monthly level, whereas our retrieval appears to be exploitable at the monthly scale. This is essential to understand the trend in surface water, one of the key elements of the water cycle.

Monitoring monthly variation of Tonle Sap Lake water volume using Sentinel-1 imagery and satellite altimetry data

This work estimates the surface water volume variation of the Cambodian Tonle Sap Lake at a monthly scale from 2015-2022. To achieve this, radar Sentinel-1 imagery was processed using the Google Earth Engine platform to generate backscatter coefficient maps. The Otsu method was utilized to identify the optimal threshold to classify each backscatter coefficient map into water or non-water clusters. Additionally, altimetry data from three satellites (i.e., Sentinel-3, Jason-3, and Jason-CS/Sentinel-6) was processed to estimate Tonle Sap Lake’s water level variation using the AlTiS software. Surface water maps of the lake, derived from MODIS and clear-sky Sentinel-2 imagery, were used to validate the lake’s surface water extent time series, while in situ water level data collected at Prek Kdam station was used to validate the variation of the lake’s water height. Our results estimated that the lake’s open water area varies from 2200 to 6000 km2, while its water level ranges from 3.1 to 10.9 m. Combining the two time series, we estimated that Tonle Sap Lake’s water volume varies between approximately -7.2 and 9.4 km3 month-1, which shows high correlation with the variation of the water volume flowing through Chau Doc and Tan Chau stations (R = 0.9528 after removing the time lag). This study highlights the ability of satellite data for lake monitoring, which is very useful in remote areas where gauge stations are limited or unavailable. Future work aims to test the accuracy of the proposed methodology in other types of environments, particularly in mountainous regions of North Vietnam, where the terrain is very steep.

Monitoring Extreme Floods and Droughts in the Amazon Basin with Surface-Water-Based Indices

Abstract Extreme droughts and floods in the Amazon have great implications for ecosystems and societies. Over the last decade, the region has undergone major extreme events with no equivalent in the previous 100 years. Wetlands have been greatly impacted by these events. This study aims at presenting new indicators for wetlands based on water surface extent (WSE): duration of the flooded and nonflooded season, number of days of extreme events, delay of the start of the flooded season, and severity for each season. These indicators are more adapted for monitoring of wetlands than those based on precipitation, discharge, or groundwater information. They are computed for seven major Amazon subbasins for flooded and nonflooded seasons. These indicators improve our knowledge of the temporal behavior of the water surface during different extreme events, such as the 2015/16 drought and the 2014 flood that occurred in the Madeira basin. For the Negro basin and from the point of view of wetlands, the 2015 nonflooded season was 55% more severe than the average of the nonflooded season during the 2011–18 period. For the Paru, Trombetas, Negro, and Solimões basins, we found that a delay in the arrival of the flooded season led to a weak flood season in terms of severity. No correlation between the onset of the flooded season and its severity was found for the Madeira, Xingu, and Tapajós basins. Future hydrometeorological monitoring systems would benefit from including, in addition to variables such as river discharge and water elevation, precipitation, and vegetation dynamics, a severity index based on water surfaces as proposed in this study.

Pan-Greenland mapping of supraglacial rivers, lakes, and water-filled crevasses in a cool summer (2018) and a warm summer (2019)

The spatiotemporal distribution and variability of surface water remained poorly known at the pan-Greenland scale, and its dominant features (supraglacial rivers, lakes, and water-filled crevasses) were rarely studied collectively. We present pan-GrIS surface water extent and volume for a relatively cool (2018) and a relatively warm (2019) summer, using 10 m resolution Sentinel-2 imagery, a semi-automated multi-scale water extraction algorithm, and the Regional Atmospheric Climate Model (RACMO). While the 10 m resolution of Sentinel-2 imagery prevents inclusion of all water-filled crevasses and narrow rivers, our findings include: (1) strong interannual differences are observed in total surface water area (4903km2 vs. 9988 km2), volume (3.5 km3 vs. 6.8 km3), and mean elevation limit (1407 m a.s.l. vs. 1545 m a.s.l.) in response to a low (cool) (265 Gt/yr) and high (warm) (510 Gt/yr) runoff year; (2) large spatial contrasts in surface water extent, volume, elevation limit, and drainage pattern among the eight major GrIS basins; (3) supraglacial rivers dominate GrIS surface water appearance, accounting for 57%/48% of total surface water area/volume, respectively, over the two years (in contrast, water-filled crevasses and supraglacial lakes account for 33%/32% and 10%/20%, respectively); (4) ratio of remotely sensed water volume to RACMO-simulated cumulative surface runoff declines during the cool (2.6%) vs. the warm (1.8%) year; and from north (4.5%) to south (1.1%) Greenland. In summary, this study reveals strong temporal and spatial differences in GrIS surface water extent, volume, and drainage pattern and raises prospects for improved understanding of pan-Greenland Ice Sheet surface hydrology.

Assessment of UAS Photogrammetry and Planet Imagery for Monitoring Water Levels around Railway Tracks

High water levels near railway tracks can be a major factor affecting the safety of train passage. Water conditions near the tracks are normally monitored through visual inspections. However, this method is limited in spatial coverage and may not provide comparable information over time. We evaluated the utility of satellite imagery (Planet Dove constellation at 3 m pixel size) at the landscape level to assess overall water surface area along railway tracks. Comparatively, we evaluated the use of Structure- from-Motion 3D point clouds and high spatial detail orthomosaics (3 cm) generated from a commercial off-the-shelf Unmanned Aerial System (UAS) (DJI M300 RTK) for measuring vertical water level changes and extent of surface water, respectively, within the right-of-way of a railway line in Ontario, Canada, in areas prone to high water level and flooding. Test sites of varied lengths (~180 m to 500 m), were assessed four times between June and October 2021. Our results indicate that the satellite imagery provides a large-scale overview regarding the extent of open water in wetlands at long distances from the railway tracks. Analysis of the UAS derived 3D point cloud indicates that changes in water level can be determined at the centimeter scale. Furthermore, the spatial error (horizontal and vertical alignments) between the multi-temporal UAS data collections between sites was less than 3 cm. Our research highlights the importance of using consistent UAS data collection protocols, and the significant potential of commercial off-the-shelf UAS systems for water level monitoring along railway tracks.