A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

We present a multi-sensor Bayesian passive microwave retrieval algorithm for flood inundation mapping at high spatial and temporal resolutions. The algorithm takes advantage of observations from multiple sensors in optical, short-infrared, and microwave bands, thereby allowing for detection and mapping of the sub-pixel fraction of inundated areas under almost all-sky conditions. The method relies on a nearest-neighbor search and a modern sparsity-promoting inversion method that make use of an a priori dataset in the form of two joint dictionaries. These dictionaries contain almost overlapping observations by the Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) F17 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows that it is capable of capturing to a good degree the inundation diurnal variability due to localized convective precipitation. At longer timescales, the results demonstrate consistency with the ground-based water level observations, denoting that the method is properly capturing inundation seasonal patterns in response to regional monsoonal rain. The calculated Euclidean distance, rank-correlation, and also copula quantile analysis demonstrate a good agreement between the outputs of the algorithm and the observed water levels at monthly and daily timescales. The current inundation products are at a resolution of 12.5 km and taken twice per day, but a higher resolution (order of 5 km and every 3 h) can be achieved using the same algorithm with the dictionary populated by the Global Precipitation Mission (GPM) Microwave Imager (GMI) products.Comment: 12 pages, 9 Figure

Multi-Satellite Remote Sensing of Land-Atmosphere Interactions: Advanced Data-Driven Methodologies for Passive Microwave Retrievals of Flood and Precipitation

University of Minnesota Ph.D. dissertation. August 2018. Major: Civil Engineering. Advisors: Efi Foufoula-Georgiou, Ardeshir Ebtehaj. 1 computer file (PDF); xii, 183 pages.Satellite Earth observations are increasing at an unprecedented rate, not even conceivable three decades ago, as new satellites have been launched and planned. However, the past quarter-century of outstanding progress in the fundamental technology of remote sensing has not translated into comparable advances in remote sensing of the water cycle. First, this dissertation presents a multi-satellite multi-sensor Bayesian methodology for prognostic detection of two key components in the terrestrial water cycle: (1) the extent of flooded regions at a sub-daily basis, which improves the flood forecasting by identifying the soil saturated zones, and (2) the precipitation phase (rainfall or snowfall). Remote sensing of snowfall is still very new and challenging despite that snowfall accounts for the majority of total precipitation events over mid- to high latitudes and its spatial distribution conditions the snowpack dynamics and hydrological responses. The proposed approach relies on a nearest-neighbor search based on a weighted distance metric and a modern sparsity-promoting inversion method using observations from optical, short-infrared, and microwave bands, thereby allowing the detection under all-sky (clear and cloudy) conditions. Last, this dissertation quantifies the effects of snow cover, particularly the snow depth, on the radiometric signal of snowfall in an attempt to mitigate challenges in passive microwave detection and estimation of snowfall

A Prognostic Nested k-Nearest Approach for Microwave Precipitation Phase Detection over Snow Cover

This is the accepted manuscript of an article published in Journal of Hydrometeorology. The publisher's version of the article can be found here: https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-18-0021.

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100 kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5 mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain

Human Amplified Changes in Precipitation-Runoff Patterns in Large River Basins of The Midwestern United States

Complete transformations of land cover from prairie, wetlands, and hardwood forests to homogenous row crop agriculture scattered with urban centers are thought to have caused profound changes in hydrology in the Upper Midwestern US since the 1800s. Continued intensification of land use and drainage practices combined with increased precipitation have caused many Midwest watersheds to exhibit higher streamflows today than in the historical past. While changes in crop type and farming practices have been well documented over the past few decades, changes in artificial surface (ditch) and subsurface (tile) drainage systems have not. This makes it difficult to quantitatively disentangle the effects of climate change and artificial drainage intensification on the observed hydrologic change, often spurring controversial interpretations with significant implications for management actions. In this study, we investigate four large (23,000–69,000 km2) Midwest river basins that span climate and land use gradients to understand how climate and agricultural drainage have influenced basin hydrology over the last 79 years. We use daily, monthly, and annual flow metrics to document streamflow changes and discuss those changes in the context of climate and land use change. While we detect similar timing of precipitation and streamflow changes in each basin, overall the magnitude and significance of precipitation changes are much less than we detect for streamflows. Of the basins containing greater than 20 % area drained by tile and ditches, we observe 2 to 4 fold increases in low flows and 1.5 to 3 fold increases in high and extreme flows. Monthly precipitation has increased slightly for some months in each basin, mostly in fall and winter months (August – March), but total monthly streamflow has increased in all months for the Minnesota River Basin (MRB), every month but April for the Red River Basin (RRB), September-December and March in the Illinois River Basin (IRB), and no months in the Chippewa River basin (CRB). Using a water budget, we determined that the soil moisture/groundwater storage term for the intensively drained and cultivated MRB, IRB, and RRB, has decreased by about 200 %, 100 %, and 30 %, respectively while increased by roughly 30 % in the largely forested CRB since 1975. We argue that agricultural land use change, through wetland removal and artificial drainage installation, has decreased watershed storage and amplified the streamflow response to precipitation increases in the Midwest. Highly managed basins with large reservoirs and urban centers, such as the Illinois River basin (IRB), may be able to buffer some of these impacts better than largely unregulated systems such as the Minnesota River (MRB) and Red River of the North (RRB) basins. The reported streamflow increases in the MRB, IRB, and RRB are large (18 %–318 %), and should have important implications for channel adjustment and sediment and nutrient transport. Acknowledging both economic benefits and apparent detrimental impacts of artificial drainage on river flows, sediments, and nutrients, we question whether any other human activity has comparably altered critical zone activities, while remaining largely unregulated and undocumented. We argue that better documentation of existing and future drain tile and ditch installation is greatly needed

Watertown: New Directions in Building Connectivity Between People and Their River

Report completed by students enrolled in CEGE 8602: Stream Restoration Practice, taught by Dr. Vaughn Voller and Chris Paola in fall 2015.This project was completed as part of the 2015-2016 Resilient Communities Project (rcp.umn.edu) partnership with Carver County. The Carver County Water Management Organization is investigating the feasibility of removing or restructuring a dam on the Crow River near downtown Watertown. The purpose of the removal/redesign is to improve the fishery in the river, reduce bank erosion, and potentially create an engineered whitewater recreation attraction to boost tourism in the area. The goal of this project was to assess the feasibility and potential impacts of removing the dam. Carver County project lead Paul Moline worked with a team of students in CEGE 8602: Stream Restoration Practice, to assess the potential impacts, benefits, and drawbacks of removing the dam. The students found that while removing the dam would decrease water elevation and impact flow velocity, it would also improve fish passage, better connect residents to the river, and provide safer recreation opportunities. The final report is available.This project was supported by the Resilient Communities Project (RCP), a program at the University of Minnesota whose mission is to connect communities in Minnesota with U of MN faculty and students to advance local sustainability and resilience through collaborative, course-based projects. RCP is a program of the Center for Urban and Regional Affairs (CURA). More information at http://www.rcp.umn.edu