Recent trends in the frequency and duration of global floods

Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985–2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1–7, moderate: 8–20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann–Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. Results show that flood frequency and the tails of the flood duration (long duration) have increased at both the global and the latitudinal scales. In the tropics, floods have increased 4-fold since the 2000s. This increase is 2.5-fold in the north midlatitudes. However, much of the trend in frequency and duration of the floods can be placed within the long-term climate variability context since the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were the main atmospheric teleconnections explaining this trend. There is no monotonic trend in the frequency of short-duration floods across all the global and latitudinal scales. There is a significant increasing trend in the annual median of flood durations globally and each latitudinal belt, and this trend is not related to these teleconnections. While the DFO data come with a certain level of epistemic uncertainty due to imprecision in the estimation of floods, overall, the analysis provides insights for understanding the frequency and persistence in hydrologic extremes and how they relate to changes in the climate, organization of global and local dynamical systems, and country-scale socioeconomic factors

Hydroclimate Drivers and Atmospheric Dynamics of Floods

Our preliminary survey showed that most of the recent flood-related studies did not formally explain the physical mechanisms of long-duration and large-peak flood events that can evoke substantial damages to properties and infrastructure systems. These studies also fell short of fully assessing the interactions of coupled ocean-atmosphere and land dynamics which are capable of forcing substantial changes to the flood attributes by governing the exceeding surface flow regimes and moisture source-sink relationships at the spatiotemporal scales important for risk management. This dissertation advances the understanding of the variability in flood duration, peak, volume, and timing at the regional to the global scale, and quantifies their causal mechanisms that include land surface conditions, large-scale climatic patterns, and mesoscale atmospheric teleconnections. Analyzing recent trends in the frequency and duration of global floods indicates that there is a significant upward trend in the statistics of their annual probability distribution, and large-scale climate teleconnections play a major role in modulating this trend. A comprehensive hydroclimate-informed framework is then presented to identify the physical causative relationship between floods of varying duration and peak and their regional land surface conditions, rainfall statistics, and moisture transports and convergences. Then, flood duration is modeled using the antecedent exceeding flow, large-scale atmospheric circulation patterns, interrelated ocean-atmospheric conditions, and dynamics of moisture transport systems in a physically informed Bayesian network framework. Statistical scaling relationships of floods with basin-wide geomorphologic characteristics and precipitation variability are then estimated. This is followed by an understanding of the spatial manifestation of widespread simultaneous heavy precipitation events (SHPEs), including quantifying their risk footprints. Subsequently, an experimental study has been designed to study the flood risk propagation in the river network along with deriving the relationship between infrastructure failure probability and the probability of SHPEs. Ultimately, this full conditional probability based framework provides a multi-angle precautional insight on better management and maintenance of critical infrastructure systems such as flood control dams, water supply reservoirs, bridges, and power plants. This dissertation contributes the following aspects to the growing literature on the hydroclimatology of floods and its impacts: 1) Global trends in the duration and frequency of observed floods, and their driving atmospheric teleconnection together with their impacts are revealed, 2) A comprehensive hydroclimate-informed framework is developed to assess the variability of flood duration, peak, volume, and timing at different spatiotemporal scales conditioned on large-scale climate and atmospheric teleconnections, 3) The cause-effect relationship between floods of varying duration (and peak), regional preceding dry/wet conditions and large-scale atmospheric circulations are statistically modeled, 4) Statistical scaling of flood duration, peak, and volume with the regional geomorphologic and precipitation patterns are established, 5) Spatial manifestation of widespread heavy precipitation events are derived and their projected geometric attributes on the ground are modeled and predicted, and 6) Flood risk propagation across the river network and vulnerabilities of critical infrastructure systems such as the flood control dams to the specific driver of flood (e.g., simultaneous extreme rainfall) are quantified

Snow-covered surface variability and DEM generation using aerial photogrammetry in Mount Odin, Canada

Seasonal snow-covered surface has a critical role in global water resource supplement especially providing fresh water for humankind and flora's consumptions as well as local underground water storages. The in situ measurements of seasonal snow-covered variability are extensively prodigal and costly particularly in existence of severe climate conditions such as high latitude regions and polar areas. It is therefore necessary to apply remote sensing techniques and observations to estimate accurately the snowpack melting and accumulation for different seasons. In this paper, we estimate snow-covered surface variability for four different seasons of year in Mount Odin, Canada using aerial photos. In order to do this, firstly Digital Elevation Model (DEM) with respect to Earth Gravitational Model 1996 (EGM96) for each flight mission of A, B, C and D from these aerial photos by applying Bundle Adjustment (BA) triangulation is being generated precisely. Moreover, the displacement of each two DEMs is computing in order to determine snow-covered surface variability between each two flight missions. The results demonstrate that flight mission C has the highest elevation topographically compare to the missions A, B and D while mission C was planned in February 2011 in existence of vast snow throughout Mount Odin area as well as mission C's DEM which has higher elevation values than the others. The proposed methodology and problem solution and the case study information with the details of each flight mission are discussed in expatiation

Numerical evaluation and application-oriented analysis for forward and inverse rational function models of terrain-independent case in satellite imagery

Terrain-independent Rational Polynomial Coefficients (RPCs) are considered as most important part of the optical satellite and aerial imagery data processing especially those ones with high resolution since the proposed RPCs by the aerospace companies have some limitations in particular for using directly by the geoscientists in environmental studies and other Earth observation applications. While the inverse RPCs have more advantageous rather than direct ones, in this study, a new approach is presented in order to provide the inverse RPCs from direct ones and also to satisfy satellite imagery products users. In order to do this, first a spatial 3D-cubic is going to be fitted to the study area approximately including necessary altimetry layers numbers. Next, a range of virtual control points are being created in those altimetry layers randomly and then these points are going to be shifted to the image space by means of given direct RPCs. Hence, the inverse RPCs computes from the direct ones by space resection technique. Finally, the ground coordinates for the corresponding points have derived from different space intersection methodologies, direct RPCs and also inverse ones. Moreover, comparative tests have been developed to assess the effects of different altimetry layers numbers and also the number of virtual control points on the quality of derived inverse RPCs. It is demonstrated here that the precision of derived RPCs are increasing as much as the number of altimetry layers and control points increase. The proposed methodology, computations, data processing and results evaluation are discussed in details

Coupled flow accumulation and atmospheric blocking govern flood duration

We present a physically based Bayesian network model for inference and prediction of flood duration that allows for a deeper understanding of the nexus of antecedent flow regime, atmospheric blocking, and moisture transport/release mechanisms. Distinct scaling factors at the land surface and regional atmospheric levels are unraveled using this Bayesian network model. Land surface scaling explains the variability in flood duration as a function of cumulative exceedance index, a new measure that represents the evolution of the flood in the basin. Dynamic atmospheric scaling explains the cumulative exceedance index using the interaction between atmospheric blocking system and the synergistic model of wind divergence and atmospheric water vapor. Our findings underline that the synergy between a large persistent low-pressure blocking system and a higher rate of divergent wind often triggers a long-duration flood, even in the presence of moderate moisture supply in the atmosphere. This condition in turn causes an extremely long-duration flood if the basin-wide cumulative flow prior to the flood event was already high. Thus, this new land-atmospheric interaction framework integrates regional flood duration scaling and dynamic atmospheric scaling to enable the coupling of ‘horizontal’ (for example, streamflow accumulation inside the basin) and ‘vertical’ flow of information (for example, interrelated land and ocean-atmosphere interactions), providing an improved understanding of the critical forcing of regional hydroclimatic systems. This Bayesian model approach is applied to the Missouri River Basin, which has the largest system of reservoirs in the United States. Our predictive model can aid in decision support systems for the protection of national infrastructure against long-duration flood events.info:eu-repo/semantics/publishedVersio

Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping

Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) and Earth observation to a new stage. GeoAI has enhanced traditional geospatial analysis and mapping, altering the methods for understanding and managing complex human–natural systems. However, there are still challenges in various aspects of geospatial applications related to natural, built, and social environments, and in integrating unique geospatial features into GeoAI models. Meanwhile, geospatial and Earth data are critical components in geocomputation and GeoAI studies, as they can effectively reveal geospatial patterns, factors, relationships, and decision-making processes. This editorial provides a comprehensive overview of geocomputation and GeoAI applications in mapping, classifying them into four categories: (i) buildings and infrastructure, (ii) land use analysis, (iii) natural environment and hazards, and (iv) social issues and human activities. In addition, the editorial summarizes geospatial and Earth data in case studies into seven categories, including in-situ data, geospatial datasets, crowdsourced geospatial data (i.e., geospatial big data), remote sensing data, photogrammetry data, LiDAR, and statistical data. Finally, the editorial presents challenges and opportunities for future research

Replication Data for: Coupled Flow Accumulation and Atmospheric Blocking Govern Flood Duration

The data associated with the recent paper in the npj Climate and Atmospheric Science (ISSN 2397-3722). The npj Climate and Atmospheric Science is a high-quality Nature Research journal published by Springer Nature in partnership with the Center of Excellence for Climate Change Research. Najibi, N., N. Devineni, M. Lu, and R.A. Perdigão, 2019, “Coupled flow accumulation and atmospheric blocking govern flood duration”, npj Climate and Atmospheric Science. DOI: 10.1038/s41612-019-0076-6. https://doi.org/10.1038/s41612-019-0076-6 This database is created to be used only for research purposes

Replications Data for: Hydroclimate Drivers and Atmospheric Teleconnections of Long-duration Floods: An Application to Large Reservoirs in the Missouri River Basin

The original data used in this study are available from the U.S. Geological Survey (USGS) National Water Information System (NWIS) [http://waterdata.usgs.gov/nwis] dataset. The daily streamflow data then have been processed and provided for 13 USGS stations in the Missouri River Basin, and embedded in this MS Excel Spreadsheet [in unit of cfs]. When you use the data, please also cite this article: Najibi, N., Devineni, N. and Lu, M., 2017. Hydroclimate drivers and atmospheric teleconnections of long duration floods: An application to large reservoirs in the Missouri River Basin. Advances in Water Resources, 100, pp.153-167. doi: 10.1016/j.advwatres.2016.12.004. This database is created to be used only for research purposes