Housing market dynamics of the post-Sandy Hudson estuary, Long Island Sound, and New Jersey coastline are explained by NFIP participation

How flooding affects home values can determine the path of economic recovery for communities and have lasting impacts on national and global financial systems. Yet, our understanding of how flood insurance, community risk perception, and past flooding events shape future housing prices (HPs) remains limited. To explore this, we used a socio-environmental (SE) model and studied the temporal impacts of flooding on mean housing values across 496 coastal census tracts of New York, Connecticut, and New Jersey, US, from 1970 to 2021. The modeling exercise demonstrated that the initial economic impact of Hurricane Sandy was largely absorbed by the National Flood Insurance Program (NFIP); however, the region then exhibited a long-term decline in home values, which was well described by an interrupted time series model. We found significant correlations between SE model parameters describing HP change and those describing tract-scale behaviors and perceptions, suggesting that the salience of past flooding events and NFIP participation may be important regional drivers of HPs. Tracts with greater post-flood change in active insurance policies exhibited larger decreases in mean home values than those with more stable NFIP participation. An improved understanding of relationships between HPs, flood insurance, and community perceptions could support more equitable distributions of resources and improved policy interventions to reduce flooding risk

Validating a large geophysical data set: Experiences with satellite-derived cloud parameters

We are validating the global cloud parameters derived from the satellite-borne HIRS2 and MSU atmospheric sounding instrument measurements, and are using the analysis of these data as one prototype for studying large geophysical data sets in general. The HIRS2/MSU data set contains a total of 40 physical parameters, filling 25 MB/day; raw HIRS2/MSU data are available for a period exceeding 10 years. Validation involves developing a quantitative sense for the physical meaning of the derived parameters over the range of environmental conditions sampled. This is accomplished by comparing the spatial and temporal distributions of the derived quantities with similar measurements made using other techniques, and with model results. The data handling needed for this work is possible only with the help of a suite of interactive graphical and numerical analysis tools. Level 3 (gridded) data is the common form in which large data sets of this type are distributed for scientific analysis. We find that Level 3 data is inadequate for the data comparisons required for validation. Level 2 data (individual measurements in geophysical units) is needed. A sampling problem arises when individual measurements, which are not uniformly distributed in space or time, are used for the comparisons. Standard 'interpolation' methods involve fitting the measurements for each data set to surfaces, which are then compared. We are experimenting with formal criteria for selecting geographical regions, based upon the spatial frequency and variability of measurements, that allow us to quantify the uncertainty due to sampling. As part of this project, we are also dealing with ways to keep track of constraints placed on the output by assumptions made in the computer code. The need to work with Level 2 data introduces a number of other data handling issues, such as accessing data files across machine types, meeting large data storage requirements, accessing other validated data sets, processing speed and throughput for interactive graphical work, and problems relating to graphical interfaces

Challenges to implementing bottom-up flood risk decision analysis frameworks: how strong are social networks of flooding professionals?

Recent developments in bottom-up vulnerability-based decision analysis frameworks present promising opportunities for flood practitioners to simplify complex decisions regarding risk mitigation and climate adaptation. This family of methodologies relies on strong social networks among flood practitioners and the public to support careful definition of stakeholder-relevant thresholds and vulnerabilities to hazards. In parallel, flood researchers are directly considering distinct atmospheric mechanisms that induce flooding to readily incorporate information on future climate projections. We perform a case study of flood professionals actively engaged in flood risk mitigation within Tompkins County, New York, USA, a community dealing with moderate flooding, to gage how much variance exists among professionals from the perspective of establishing a bottom-up flood mitigation study from an atmospheric perspective. Results of this case study indicate disagreement among flooding professionals as to which socioeconomic losses constitute a flood, disagreement on anticipated community needs, weak understanding of climate–weather–flood linkages, and some disagreement on community perceptions of climate adaptation. In aggregate, the knowledge base of the Tompkins County flood practitioners provides a well-defined picture of community vulnerability and perceptions. Our research supports the growing evidence that collaborative interdisciplinary flood mitigation work could reduce risk, and potentially better support the implementation of emerging bottom-up decision analysis frameworks for flood mitigation and climate adaptation

Potassium binding adjacent to cationic transition metal fragments: unusual heterobimetallic adducts of a calix[4]arene-based thione ligand

The synthesis of cationic rhodium and iridium complexes of a bis(imidazol-2-thione) functionalised calix[4]arene ligand and their surprising capacity for potassium binding is described. In both cases uptake of the alkali metal into the calix[4]arene cavity occurs despite adverse electrostatic interactions associated with close proximity to the transition metal fragment (Rh+∙∙∙K+ = 3.715(1) Å, Ir+∙∙∙K+ = 3.690(1) Å). The formation and constituent bonding of these unusual heterobimetallic adducts has been interrogated through extensive solution and solid-state characterisation, examination of the host-guest chemistry of the ligand and its upper-rim unfunctionalised calix[4]arene analogue, and computationally using DFT-based energy decomposition analysis (EDA)

Estimating dominant runoff modes across the conterminous United States

Effective natural resource planning depends on understanding the prevalence of runoff generating processes. Within a specific area of interest, this demands reproducible, straightforward information that can complement available local data and can orient and guide stakeholders with diverse training and backgrounds. To address this demand within the contiguous United States (CONUS), we characterized and mapped the predominance of two primary runoff generating processes: infiltration‐excess and saturation‐excess runoff (IE vs. SE, respectively). Specifically, we constructed a gap‐filled grid of surficial saturated hydraulic conductivity using the Soil Survey Geographic and State Soil Geographic soils databases. We then compared surficial saturated hydraulic conductivity values with 1‐hr rainfall‐frequency estimates across a range of return intervals derived from CONUS‐scale random forest models. This assessment of the prevalence of IE versus SE runoff also incorporated a simple uncertainty analysis, as well as a case study of how the approach could be used to evaluate future alterations in runoff processes resulting from climate change. We found a low likelihood of IE runoff on undisturbed soils over much of CONUS for 1‐hr storms with return intervals \u3c5 years. Conversely, IE runoff is most likely in the Central United States (i.e., Texas, Louisiana, Kansas, Missouri, Iowa, Nebraska, and Western South Dakota), and the relative predominance of runoff types is highly sensitive to the accuracy of the estimated soil properties. Leveraging publicly available data sets and reproducible workflows, our approach offers greater understanding of predominant runoff generating processes over a continental extent and expands the technical resources available to environmental planners, regulators, and modellers

Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: exploring the roles of topography, minority populations, and political dissimilarity

Current research on flooding risk often focuses on understanding hazards, de-emphasizing the complex pathways of exposure and vulnerability. We investigated the use of both hydrologic and social demographic data for flood exposure mapping with Random Forest (RF) regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US. Topographic characteristics best described flood claim frequency, but RF prediction skill was improved at both spatial scales when socioeconomic data was incorporated. Substantial improvements occurred at the tract-level when the percentage of minority residents, housing stock value and age, and the political dissimilarity index of voting precincts were used to predict insurance claims. Census tracts with higher numbers of claims and greater densities of low-lying tax parcels tended to have low proportions of minority residents, newer houses, and less political similarity to state level government. We compared this data-driven approach and a physically-based pluvial flood routing model for prediction of the spatial extents of flooding claims in two nearby catchments of differing land use. The floodplain we defined with physically based modeling agreed well with existing federal flood insurance rate maps, but underestimated the spatial extents of historical claim generating areas. In contrast, RF classification incorporating hydrologic and socioeconomic demographic data likely overestimated the flood-exposed areas. Our research indicates that quantitative incorporation of social data can improve flooding exposure estimates

Luminescent Pt(II) complexes using unsymmetrical Bis(2-pyridylimino)isoindolate analogues

A series of ligands based upon a 1,3-diimino-isoindoline framework have been synthesized and investigated as pincer-type (N∧N∧N) chelates for Pt(II). The synthetic route allows different combinations of heterocyclic moieties (including pyridyl, thiazole, and isoquinoline) to yield new unsymmetrical ligands. Pt(L1–6)Cl complexes were obtained and characterized using a range of spectroscopic and analytical techniques: 1H and 13C NMR, IR, UV–vis and luminescence spectroscopies, elemental analyses, high-resolution mass spectrometry, electrochemistry, and one example via X-ray crystallography which showed a distorted square planar environment at Pt(II). Cyclic voltammetry on the complexes showed one irreversible oxidation between +0.75 and +1 V (attributed to Pt2+/3+ couple) and a number of ligand-based reductions; in four complexes, two fully reversible reductions were noted between −1.4 and −1.9 V. Photophysical studies showed that Pt(L1–6)Cl absorbs efficiently in the visible region through a combination of ligand-based bands and metal-to-ligand charge-transfer features at 400–550 nm, with assignments supported by DFT calculations. Excitation at 500 nm led to luminescence (studied in both solutions and solid state) in all cases with different combinations of the heterocyclic donors providing tuning of the emission wavelength around 550–678 nm