Conference Agenda

This study introduces a Spatial constraint One-step Approach (SOA) and Temporal constraint One-step Approach (TOA) to improve the estimation of Terrestrial Water Storage Anomalies (TWSA) from GRACE and GRACE-FO satellite data. Traditional three-step or two-step post-processing methods sequentially apply spectral filtering and leakage correction, often causing signal attenuation, spatial leakage, and reliance on external models.

In contrast, the proposed one-step framework simultaneously estimates all signal components—including trends, seasonal cycles, and non-seasonal signals (NSS)—directly from unfiltered TWSAs within a region of interest. It incorporates full error covariance and models NSS using spatiotemporal constraints: TOA employs a Multi-Order Gauss-Markov process for temporal correlation, while SOA uses spatial covariance functions and a buffer zone to reduce boundary effects. Tikhonov regularization ensures solution stability.

Validation across major river basins and regions like Southeastern China shows that SOA/TOA outperforms conventional filters (e.g., DDK, IPF), reducing errors and improving agreement with mascon products and climate indices. The method also better identifies hydrological extremes (e.g., droughts, floods) and links them to climate drivers like ENSO, enhancing the monitoring and understanding of global water storage dynamics.

Groundwater dependent ecosystems (GDEs) are sustained by direct or indirect access to groundwater, relying on its flow or chemistry for their water needs. These ecosystems span aquatic, terrestrial, and subterranean realms, providing critical ecological functions, maintaining water quality, and supporting biodiversity and Indigenous land use. In Alberta’s boreal region, GDEs are abundant yet remain poorly mapped, limiting understanding of their extent and sensitivity to industrial development and hydrological change. Developing consistent, spatially explicit mapping tools is therefore essential for effective monitoring and management. This research develops and evaluates a remote sensing and machine learning (ML) framework for predicting GDE habitats across boreal Alberta, Canada, as part of a broader provincial effort toward consistent, high-resolution GDE mapping. Multi-sensor Earth observation and geospatial datasets were integrated using ensemble ML modelling to identify groundwater-dependent habitats. Specifically, the study aimed to (1) evaluate the performance of multiple ML algorithms and ensemble approaches for GDE prediction, (2) assess whether aquatic and terrestrial GDEs can be effectively modelled within a unified framework, and (3) identify the most influential environmental and remote sensing variables driving GDE occurrence. The resulting model ensemble achieved high predictive accuracy (AUC = 0.90), with wetland and hydrological variables emerging as dominant predictors. The approach provides a scalable, transferable methodology for regional GDE mapping to support groundwater management, ecosystem monitoring, and cumulative effects assessment across northern Alberta.

This study presents a hydrology-informed deep learning framework for supraglacial lake segmentation on the Greenland Ice Sheet using Sentinel-2 imagery. Traditional approaches to lake mapping rely primarily on spectral cues, which often struggle in regions with weak contrast, shadowing, or surface melt variability. To address these challenges, we incorporate physically meaningful hydrological features—flow accumulation, distance-to-drainage, and surface depressions—derived from high-resolution DEMs to guide the segmentation process. The proposed FiLM-based two-stream U-Net consists of an RGB stream for spectral–textural representation and a hydrology stream encoding surface meltwater routing patterns. Feature-wise linear modulation is applied at multiple levels of the RGB encoder–decoder to dynamically condition spectral features on hydrological context and improve spatial coherence.

Experiments on the SIGSPATIAL 2023 GISCUP dataset demonstrate that this architecture improves segmentation accuracy over a Sentinel-2-only baseline and a simple channel-concatenation model, particularly for small, fragmented, or spectrally ambiguous lakes. The combined use of hydrological cues and deep feature modulation reduces false positives in regions where meltwater is unlikely to accumulate and strengthens delineations along complex lake boundaries. These improvements highlight the value of integrating physically informed geospatial descriptors with modern segmentation networks for robust supraglacial lake detection.

Beyond methodological gains, the results support downstream applications including meltwater routing analysis, supraglacial drainage characterization, and improved understanding of seasonal lake evolution. Ultimately, this framework contributes to more reliable ice-sheet mass balance assessments and sea-level rise projections by enhancing the consistency and physical realism of supraglacial lake mapping at scale.

The rapid retreat and thinning of glaciers in the North-western Himalayas due to climate change have led to a significant increase in the number and size of glacial lakes. These high-altitude lakes, often dammed by unstable moraines, pose a growing threat of Glacial Lake Outburst Floods (GLOFs), which can cause catastrophic flash floods and endanger downstream communities. Accurate estimation of glacial lake bathymetry is crucial for GLOF risk assessment, but direct measurement is challenging due to inaccessibility and harsh conditions. This study presents a methodology for evaluating glacial lake bathymetry using remote sensing data, focusing on the Panikhar glacier lake in Ladakh, India. Time series analysis was conducted to map the lake's water spread from 2015 to 2024 using optical and synthetic aperture radar data. Three approaches were employed to estimate bathymetry: a radiative transfer model (RTM) based on multispectral reflectance, a topographical model using high-resolution digital elevation models, and empirical equations relating lake area to depth. The RTM approach relies on the optical properties of water, while the topographical model leverages the surrounding terrain to infer underwater topography. Empirical equations were drawn from established literature. Results were validated against physical bathymetry survey observations. Among the methods, topographical modeling demonstrated the highest potential for accurate depth estimation, as it directly incorporates the lake's topographic features. This study highlights the importance of integrating remote sensing techniques for effective GLOF hazard assessment in remote, high-altitude regions, offering a scalable solution for monitoring and mitigating risks associated with glacial lakes in the Himalayas.

Wildfires are an increasingly dominant disturbance in boreal and Arctic Canada, a trend projected to continue under a changing climate. The ecological and hydrological impacts of wildfires cascade into the abundant inland lakes in these interconnected northern landscapes, leading to post-fire changes in lake quality and colour. Previous in-situ studies on post-fire lake water quality in boreal regions have yielded inconsistent results, preventing a regional-scale understanding of the prevalence, magnitude, and duration of fire impacts on boreal lakes.

Here, we use harmonized Landsat time series to quantify fire-driven lake colour change and its controls across western boreal Canada. We studied 83 fires that burned 13,968 lakes during 2005 - 2015 and quantified lake colour dynamics through surface reflectance in the red wavelength, a proxy for suspended sediments and turbidity. Using a Difference-in-Difference approach, we found pervasive and long-lasting increases in lake colour driven by fire disturbance, beginning in the first post-fire summer and persisting for at least ten years, indicating sustained elevated suspended sediment concentrations and turbidity regardless of physiographic variations. The magnitude and temporal patterns of these changes varied, with burn severity and physiography as important controls. Severe burns in the Taiga and Shield zones underlain by extensive permafrost led to greater and more prolonged changes in lake colour.

These findings underscore the critical and growing role of wildfires in boreal lake quality change, with important implications for aquatic habitats and water resources in a fire-prone future.