Civil & Environmental Engineering Research Works

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    Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Code
    (2022-07-11) Ghaedi, Hamed; Baroud, Hiba; Ferreira, Celso; Perrucci, Daniel; Reilly, Allison; Reilly, Allison
    A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.
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    Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Dataset
    (2022-07-11) Ghaedi, Hamed; Baroud, Hiba; Perrucci, Daniel; Ferreira, Celso; Reilly, Allison; Reilly, Allison
    A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.
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    Investigating the Impacts of the Political System Components in Iran on the Existing Water Bankruptcy
    (MDPI, 2021-12-10) Ketabchy, Mehdi
    Iran is suffering from a state of water bankruptcy. Several factors have contributed to the current water resources bankruptcy, ranging from anthropogenic impacts, such as an inefficient agricultural sector and aggressive withdrawal of groundwater, to climatological impacts. This paper suggests that water resources mismanagement in Iran should be evaluated beyond the policy-makers decisions, as it recognizes that the bankruptcy has been intensified due to the structural and institutional form of the political system in Iran. This study discusses the roots of the water bankruptcy and identifies four major shortcomings caused by the political system: (1) the absence of public engagement due to the lack of a democratic and decentralized structure; (2) adopting ideological policies in domestic and foreign affairs; (3) conflicts of interest and the multiplicity of governmental policy-makers and sectors; and (4) a state-controlled, resource-dependent economy. Through the development of a generic causal model, this study recommends a systematic transition towards a democratic, decentralized, non-ideological, and economically diverse political governance as the necessary–but not necessarily sufficient–adaptive and sustainable solution for mitigating the impacts of water resources bankruptcy in Iran. The insights highlighted in this paper could be employed to inform water resources decision-makers and political actors in other non-democratic and ideological political structures struggling with a water resources crisis or bankruptcy.
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    SMAP soil moisture assimilated Noah-MP model output
    (2021) Ahmad, Jawairia; Forman, Bart; Kumar, Sujay
    The data archived here includes the NASA Noah-MP (version 4.0.1) land surface model output used in the investigation of the impact of passive microwave-based soil moisture retrieval assimilation on soil moisture estimation in South Asia (Ahmad et al., 2021). SMAP soil moisture retrievals are assimilated into the Noah-MP land surface model to improve the estimation of soil moisture and other related states. The open loop (OL) represents Noah-MP’s modeling capabilities using MERRA2 and IMERG precipitation. Two different types of data assimilation runs were executed using the MERRA2 and IMERG precipitation boundary conditions, i.e., with CDF-matching (DA-CDF) and without CDF matching (DA-NoCDF). The key findings in this paper include: 1) assimilation results without any CDF-matching yielded the lowest error in estimated soil moisture, 2) the best goodness-of-fit statistics were achieved for the IMERG-forced DA-NoCDF soil moisture experiment, 3) biases associated with unmodeled hydrologic processes such as irrigation were corrected via assimilation, and 4) the highest influence of assimilation was observed across croplands.
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    Nuclear envelope laminopathies: evidence for developmentally inappropriate chromatin-nuclear envelope interactions
    (Springer Nature, 2013-03-18) Perovanovic, Jelena; Jaiswal, Jyoti; Markovic, Nikola; Hoffman, Eric
    During terminal differentiation of cells, there is typically a transition of the nuclear envelope from the Lamin B protein to Lamin A/C proteins. This is commensurate with exit from the cell cycle, and maintenance of the transcriptional programs associated with the terminally differentiated cells. Dominant missense mutations in Lamin A/C cause a broad spectrum of human genetic disorders, where specific point mutations are associated with defects in specific organs or tissues. We have previously presented a model where Lamin A/C mutations disrupt developmentally appropriate interactions between chromatin and the nuclear envelope and lead to poor coordination of E2F cell cycle pathways and terminal differentiation pathways [1]. One of the phenotypes caused by Lamin A/C mutations is Emery Dreifuss Muscular Dystrophy (EDMD). An X-linked recessive phenocopy of EDMD is caused by loss of function of emerin – a binding partner to Lamin A/C at the nuclear envelope. Here, we tested the hypothesis that emerin plays a role in chromatin remodeling via stabilizing nuclear lamina-heterochromatin interactions necessary for appropriate and time dependent muscle differentiation. We used WT and emerin null mouse myogenic stem cells to study transcriptional and epigenetic changes during in vitro exit from the cell cycle and differentiation to the myogenic lineage. Specific cell cycle (E2F) and myogenic genes were analyzed by qPCR and ChlP-qPCR to determine mRNA timing and H3K9me3 enrichment on gene promoters. Nuclear lamina-chromatin colocalization was determined and quantified by confocal imaging and Matlab. Our results showed that TK1 and other cell cycle genes are inappropriately persistently expressed in emerin null cells during differentiation causing delayed exit from cell cycle. Transcripts marking commitment to the myogenic lineage (myogenin and Mef5A) showed delayed activation on both mRNA and protein level. Epigenetic imprints predicted observed deviations from transcriptional timing in emerin null cells, with persistent suppressive chromatin state on myog promoter upon myogenic induction and failure to appropriately establish repressive histone marks (H3K9me3) on Tk1 promoter (cell cycle). Finally, we showed that the early cell cycle exit and terminal differentiation of emerin null myoblasts were accompanied by decreased H3K9me3 staining at the nuclear periphery (lamin A/C immunostaining). Myogenic cells lacking emerin exhibit perturbations in terminal commitment to the myogenic lineage. Our transcriptional, chromatin remodeling and gene promoter accessibility data show that both exit from cell cycle and terminal commitment to myogenesis are disrupted due to inappropriate heterochromatin-nuclear lamina interactions in EMD myogenic cells.