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Browsing by Author "Nearing, Grey"

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    Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations
    (Springer, 2019) Yatheendradas, Soni; Kirschbaum, Dalia; Nearing, Grey; Vrugt, Jasper A.; Baum, Rex L.; Wooten, Rick; Lu, Ning; Godt, Jonathan W.; University of Maryland College Park; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; University of Alabama Tuscaloosa; University of California Irvine; United States Department of the Interior; United States Geological Survey
    In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the factor of safety, F-s) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated F-s values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantle's properties (hydraulic, geotechnical, and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference truth Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and null periods using multivariate frequency distribution-based metrics such as the false discovery and false omission rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicates their inability to accurately characterize hillslope stability.
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    Comment on "A blueprint for process-based modeling of uncertain hydrological systems" by Alberto Montanari and Demetris Koutsoyiannis
    (American Geophysical Union, 2014-07-24) Nearing, Grey; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; University of Alabama Tuscaloosa
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    The Efficiency of Data Assimilation
    (American Geophysical Union, 2018) Nearing, Grey; Yatheendradas, Soni; Crow, Wade; Zhan, Xiwu; Liu, Jicheng; Chen, Fan; University of Alabama Tuscaloosa; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; University of Maryland College Park; United States Department of Agriculture (USDA); National Oceanic Atmospheric Admin (NOAA) - USA
    Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real-world application of Bayes' theorem is approximate, and therefore, we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of trade-offs between improving (usually expensive) remote sensing observing systems versus improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the ensemble Kalman filter used to assimilate remote sensing soil moisture retrievals from Advanced Microwave Scattering Radiometer for Earth (AMSR-E) into the Noah land surface model.
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    Estimating information entropy for hydrological data: One-dimensional case
    (American Geophysical Union, 2014-06-19) Gong, Wei; Yang, Dawen; Gupta, Hoshin V.; Nearing, Grey; Beijing Normal University; Tsinghua University; University of Arizona; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; Science Applications International Corporation (SAIC); University of Alabama Tuscaloosa
    There has been a recent resurgence of interest in the application of Information Theory to problems of system identification in the Earth and Environmental Sciences. While the concept of entropy has found increased application, little attention has yet been given to the practical problems of estimating entropy when dealing with the unique characteristics of two commonly used kinds of hydrologic data: rainfall and runoff. In this paper, we discuss four important issues of practical relevance that can bias the computation of entropy if not properly handled. The first (zero effect) arises when precipitation and ephemeral streamflow data must be viewed as arising from a discrete-continuous hybrid distribution due to the occurrence of many zero values (e. g., days with no rain/no runoff). Second, in the widely used bin-counting method for estimation of PDF's, significant error can be introduced if the bin width is not carefully selected. The third (measurement effect) arises due to the fact that continuously varying hydrologic variables can typically only be observed discretely to some degree of precision. The Fourth (skewness effect) arises when the distribution of a variable is significantly skewed. Here we present an approach that can deal with all four of these issues, and test them with artificially generated and real hydrological data. The results indicate that the method is accurate and robust.
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    Using the National Water Model as a Hypothesis-Testing Tool
    (2017) Hooper, Richard P.; Nearing, Grey; Condon, Laura S.; University of Alabama Tuscaloosa
    High performance computing has enabled the creation of high-resolution, continental-scale models such as the NOAA’s National Water Model (NWM) which predicts hourly streamflow at 2.7 million locations in the conterminous US using the National Hydrographic Dataset Plus (NHDPlus) as the reference geofabric. This high resolution model provides a novel opportunity to bridge the gap between the scale of process research (at hillslope to headwater catchment scale) and the operational scale of river basins where predictions are needed for water resources management and hazard. We present a study design that uses the NWM in a hypothesis-testing (i.e., rejectionist) framework to assess process representations included in both “physically based” models, such as ParFlow-CLM, and conceptual models, such as the USGS Precipitation Runoff Modeling System. An information theoretic framework is proposed to assess the ability of either of these approaches to extract information from available data to make reliable predictions of water transport in the subsurface and surface.
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    Watershed-estuary dynamics in the mobile river watershed-mobile bay estuary (mr-mb) continuum examined by combined geochemical and satellite approaches
    (University of Alabama Libraries, 2020-12) Stewart, Jackson Buford; Dimova, Natasha T.; University of Alabama Tuscaloosa
    The Mobile River System acts as a vital economic, social, and cultural center for Alabama and the northern Gulf of Mexico region. With increasing levels of urbanization and land-use and development, the ecological integrity of this system becomes more vulnerable to imbalance due to pollution, anthropogenic alteration of stream course, and other land-use activities. This study focuses on tracking suspended sediment material transported by the Mobile River System, from upstream origin of erosion through the Mobile Estuary, and into Mobile Bay, by combining established geochemical and remote sensing methods and approaches. To accomplish this, I posed three research questions, (Q1) where in the upper reaches of the system are the suspended particulates originating? (Q2) what is the magnitude of flux for suspended sediment (SS) and associated major and trace metals from the river system? and (Q3) can the distribution of the suspended sediment material be effectively tracked within the Mobile Bay basin? The origin of sediment, i.e. identifying sources from the two major tributaries of the Mobile River, the Alabama and the Tombigbee Rivers (Q1) was determined using geochemical fingerprinting of radioisotopes and trace metals. The concatenation of fingerprinting properties of each tributary and of downstream suspended sediments in a geochemical mixing model resulted in 61% of suspended sediment material originating from the Tombigbee Basin, and 39% from the Alabama Basin. The flux of material out of the river system into Mobile Bay (Q2) was determined through compositional analysis of suspended sediment material collected by passive suspended sediment capture within the Mobile-Tensaw Delta. Using this sampling approach, SS flux entering the Delta varied between 981 g/s during low flow regime and 23,509 g/s during high flow. Associated trace metal fluxes were below EPA regulated limits. (Q3) Calibration of existing remote sensing algorithms with in-situ data from Mobile Bay proved successful in generating remote sensing algorithms which can track sediment movement in Mobile Bay across seasonal and hydrologic conditions.