A Step Toward Resolving Spatiotemporal Distribution of Suspended Sediment Concentration Using Remote Sensing

It is critical to study Suspended Sediment Concentration (SSC) to improve our understanding of the impacts of wildfires, climate, and anthropogenic on the riverine environments. The processes that contribute to SSC occur over a range of spatiotemporal scales making it difficult to continuously measure and monitor. Traditional methods (e.g., field measurement) require significant resources and often lack the spatial and temporal resolution that is needed for important yet smaller-scale systems like streams. In this study, we create an open-source and low-cost method to estimate SSC using remotely sensed data. Our study aims to elucidate if a machine learning (ML) model is capable of estimating SSC from multispectral imagery using a dataset collected in the laboratory. Another objective is to determine which ML model most accurately estimates SSC. Our methods involve testing and comparing two Machine Learning (ML) models: Random Forest and Linear Regression. Preliminary results indicate that the Random Forest model has the capability of extracting the SSC signal from the imagery and providing superb agreement between experimental and ML-generated data (R2=0.99). Further, our results from the Linear Regression model indicate that the relationship between multispectral imagery and SSC is non-linear and requires a more robust ML model (low correlation of R2= -.71). Continuing work will compare these results with other ML models to develop a cross-validated method for widespread usage in SSC detection. This study contributes to a larger framework, and with a validated model we will be able to quantify the effects of SSC on the entire Snake and Columbia River system. Implications of this research include improving our understanding of SSC and detection methods that augment existing methodologies as tools for stakeholders, government agencies, land managers, and citizen scientists

Social Vulnerability of the People Exposed to Wildfires in U.S. West Coast States

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000–2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000–2010 to 2011–2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (\u3e40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans

Resolving a Human Crisis Through Remote Sensing: Equitable and Sustainable Use of Internationally Shared Water Resources

The transboundary Helmand river drains 41% of Afghanistan\u27s land to the Hamun lakes on the border of Afghanistan, Iran, and Pakistan. The Hamun lakes have become seasonally dry in the recent decade, leaving \u3e1 million people in desperate life conditions. This not only caused food and water insecurity in the region, but also deprived people of their only source of income driving many to resort to smuggling goods and drugs with global security implications. This human crisis mainly stems from the absence of in-situ measurements and monitoring systems in the basin that is necessary for developing sustainable water resource management strategies. In this study, we compile monthly time series of factors that impact the Hamun lakes storage using multispectral satellite imagery and global climate data over the past 40 years. Then we unravel the impact of climatic and anthropogenic drivers of the drying of the Hamun lakes by testing various machine learning models. Therefore, we will be able to determine how much the anthropogenic influences such as the expansion in agriculture and the construction of an artificial storage pond downstream affected the drying of the Hamun lakes. Our results will determine the sustainable water resource usage levels in this contentious, transboundary basin

A Multi-Fractal Approach for Modeling Unsaturated Hydraulic Conductivity

Accurate prediction of unsaturated hydraulic conductivity is indispensable for solving problems of fluid flow and solute transport. We present a novel approach to refine hydraulic conductivity estimation from soil water retention curve by considering multi-fractal properties of soil pore size distribution. On the basis of scaled saturation and pressure data, a test static has been developed for identification of soils with multi-fractal properties. The parameters of unsaturated hydraulic conductivity are tuned by Markov Chain Monte Carlo algorithm using a wide variety of soil textures. Results indicate that neglect to account for multi-fractal properties of soils leads to serious errors

Augmented Normalized Difference Water Index for Improved Surface Water Monitoring

We present a comprehensive critical review of well-established satellite remote sensing water indices and offer a novel, robust Augmented Normalized Difference Water Index (ANDWI). ANDWI employs an expanded set of spectral bands, RGB, NIR, and SWIR1-2, to maximize the contrast between water and non-water pixels. Further, we implement a dynamic thresholding method, the Otsu algorithm, to enhance ANDWI\u27s performance. Applied to a variety of environmental conditions, ANDWI with Otsu-thresholding offered the highest overall accuracy (accuracy = 0.98, F1 = 0.98, and Kappa = 0.96) compared to other indices (NDWI, MNDWI, AWEI, WI). We also propose a novel cloud filtering algorithm that substantially increases the number of useable images compared to the conventional cloud-free composites (124% increased observations in the studied area) and resolves inappropriate masking of water bodies and hot sands as clouds by conventional methods. Finally, we develop a Google Earth Engine App to readily delineate 16-day surface water bodies across the globe

Anthropogenic Stressors Compound Climate Impacts on Inland Lake Dynamics: The Case of Hamun Lakes

Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system – the terminal Hamun Lakes on the Iran-Afghanistan border – that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km2 versus 410 km2), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes\u27 dynamics indicate that the average lakes\u27 surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes\u27 desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region

Elevation-dependent intensification of fire danger in the western United States

Studies have identified elevation-dependent warming trends, but investigations of such trends in fire danger are absent in the literature. Here, we demonstrate that while there have been widespread increases in fire danger across the mountainous western US from 1979 to 2020, trends were most acute at high-elevation regions above 3000 m. The greatest increase in the number of days conducive to large fires occurred at 2500-3000 m, adding 63 critical fire danger days between 1979 and 2020. This includes 22 critical fire danger days occurring outside the warm season (May-September). Furthermore, our findings indicate increased elevational synchronization of fire danger in western US mountains, which can facilitate increased geographic opportunities for ignitions and fire spread that further complicate fire management operations. We hypothesize that several physical mechanisms underpinned the observed trends, including elevationally disparate impacts of earlier snowmelt, intensified land-atmosphere feedbacks, irrigation, and aerosols, in addition to widespread warming/drying

Elevation-dependent intensification of fire danger in the western United States

Elevation-dependent warming trends have been previously identified, but its effect on fire danger is still unclear. Here the authors show that there has been widespread increases in fire danger across the mountainous western US from 1979 to 2020 with most acute trends at high-elevation regions above 3000 m