Abstract. 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.

A comprehensive framework is developed to assess the flood types, their spatiotemporal characteristics and causes based on the rainfall statistics, antecedent flow conditions, and atmospheric teleconnections. The Missouri River Basin (MRB) is used as a case study for the application of the framework. Floods are defined using the multivariate characteristics of annual peak, volume, duration, and timing. The temporal clustering of flood durations is assessed using a hierarchical clustering analysis, and low-frequency modes are identified using wavelet decomposition. This is followed by an identification of the synoptic scale atmospheric processes and an analysis of storm tracks that entered the basin and their moisture releases. Atmospheric teleconnections are distinctively persistent and well developed for long duration flood events. Long duration floods are triggered by high antecedent flow conditions which are in turn caused by high moisture release from the tracks. For short duration floods, these are insignificant and random across the MRB's in the recent half-century. The relative importance of hydroclimatic drivers (rainfall duration, rainfall intensity and antecedent flow conditions) in explaining the variance in flood duration and volume is discussed using an empirical log-linear regression model. The implication of analyzing the duration and volume of the floods in the context of flood frequency analysis for dams is also presented. The results demonstrate that the existing notion of the flood risk assessment and consequent reservoir operations based on the instantaneous peak flow rate at a stream gage needs to be revisited, especially for those flood events caused by persistent rainfall events, high antecedent flow conditions and synoptic scale atmospheric teleconnections.

Abstract:The Global Positioning System (GPS) reflected signal has been demonstrated to remotely sense the oceans, land surfaces and the cryosphere, including measuring snow depth, soil moisture, vegetation growth and wind direction. Since the Earth surface's characteristics are very complex, the surface reflectivity process and interaction with GPS signals is not well understood. In this study, we investigate the surface's reflectivity and variability of snow and ice surfaces interacting with GPS L1 and L2 signals in order to retrieve multipath signals and infer surface characteristics by using the direct and reflected polarizations of each signal. Firstly, the effects of both GPS satellite elevation angle and GPS receiver's antenna height variations on the multipath signal variability have been investigated by numerical formulations. Secondly, the specular reflection coefficients' features and the total surface polarization for liquid and solid surfaces are discussed. Moreover, the linear polarization and circular polarizations (co-polarized and cross-polarized) as well as their corresponding convolution functions are developed horizontally and vertically. The results show that the multipath signals are more sensitive to the satellite elevation angle variations than to changes in the GPS receiver's antenna height. The convolution function demonstrates that the snowy surface has a minimum reflectance in circular polarization but maximum reflectance in linear polarization. GPS signals reflecting from an ice-covered surface show a maximum value in circular polarization reflectance and a minimum for linear polarization reflectance. Moreover, the values for reflection from soils are between those for snow and ice in all polarization types. The placement of soil surface reflectance values between snowy and icy surface ones may be noteworthy in new remote sensing applications.

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Abstract. Frequency and duration of flood events are analyzed using Dartmouth Flood Observatory's (DFO) global flood database to detect significant trends and regime shifts 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 non-parametric Mann-Kendall trend test and Pettitt change-point analysis are used to evaluate three hypotheses (H1, H2, and H3) addressing potential monotonic trends and regime shifts in flood frequency, moments of the duration, and the frequency of a specific flood duration type. The results show that long duration flood frequency has increased across most spatial scales with significant change-point observed in the 2000s. In the tropics, floods have increased four-fold since the 2000s. This increase is 2.5 fold in the north mid-latitudes. There is no monotonic trend in the frequency of short duration floods across all global and latitudinal scales. There is also a significant increasing trend in the annual median and tails of flood durations globally and in each latitudinal belt. The possible causes of these trends are analyzed using a Generalized Linear Model framework and also discussed qualitatively. This analysis provides the framework for understanding simultaneously changing climate and socioeconomic conditions and how they relate to the frequency and persistence in the organization of global and local dynamical systems that cause hydrologic extremes.

We introduce the idea of simultaneous heavy precipitation events (SHPEs) to understand whether extreme precipitation has a spatial organization manifested as specified tracks or contiguous fields with inherent scaling relationships. For this purpose, we created a database of SHPEs using ground-based precipitation observations recorded by the daily Global Historical Climatology Network across the conterminous United States during 1900-2014. SHPEs are examined for their seasonality, spatial manifestation, orientation, and areal extent. We quantified the spatial distribution of the centroids and principal axes of SHPEs and their quasi-elliptical manifestations, azimuthal orientations, and areal extents on the ground. Four seasons, December-January-February (DJF), March-April-May (MAM), June-July-August (JJA), and September-October-November (SON) are considered to examine the spatial patterns and associated large-scale atmospheric circulations.

Weather regime based stochastic weather generators (WR‐SWGs) have recently been proposed as a tool to better understand multi‐sector vulnerability to deeply uncertain climate change. WR‐SWGs can distinguish and simulate different types of climate change that have varying degrees of uncertainty in future projections, including thermodynamic changes (e.g., rising temperatures, Clausius‐Clapeyron scaling of extreme precipitation) and dynamic changes (e.g., shifting circulation and storm tracks). These models require the accurate identification of WRs that are representative of both historical and plausible future patterns of atmospheric circulation, while preserving the complex space–time variability of weather processes. This study proposes a novel framework to identify such WRs based on WR‐SWG performance over a broad geographic area and applies this framework to a case study in California. We test two components of WR‐SWG design, including the method used for WR identification (Hidden Markov Models (HMMs) vs. K‐means clustering) and the number of WRs. For different combinations of these components, we assess performance of a multi‐site WR‐SWG using 14 metrics across 13 major California river basins during the cold season. Results show that performance is best using a small number of WRs (4–5) identified using an HMM. We then juxtapose the number of WRs selected based on WR‐SWG performance against the number of regimes identified using metastability analysis of atmospheric fields. Results show strong agreement in the number of regimes between the two approaches, suggesting that the use of metastable regimes could inform WR‐SWG design. We conclude with a discussion of the potential to expand this framework for additional WR‐SWG design parameters and spatial scales.

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.