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Struzik P.,Institute of Meteorology and Water Management
Journal of Applied Remote Sensing

Snow observations from space play an important role in hydrological and climatological studies. They are especially important in remote areas with low (or none) population and sparse conventional observations at the ground. At the mid latitudes, they are needed especially for assimilation of spatially distributed data concerning snow water equivalent or snow depth derived from microwave satellite data to snowmelt models. This paper presents discussion on several problems with satellite derived snow observations focusing on the JAXA GCOM-W1 snow depth product. This product was analyzed for the period of October 1, 2012, to April 30, 2013, for an area of Poland and verified against ground observations. Benefits and disadvantages of these products were discussed in comparison to other satellite microwave products concerning snow properties. Problems with proper validation against "ground truth" were also highlighted. The possible alternative use of AMSR2 microwave data was also presented. © 2014 The Authors. Source

Kochanek K.,Polish Academy of Sciences | Strupczewski W.G.,Polish Academy of Sciences | Bogdanowicz E.,Institute of Meteorology and Water Management
Hydrological Processes

The annual peak flow series of Polish rivers are mixtures of summer and winter flows. As Part II of a sequence of two papers, practical aspects of applicability of seasonal approach to flood frequency analysis (FFA) of Polish rivers are discussed. Taking A Two-Component Extreme Value (TCEV1) model as an example it was shown in the first part that regardless of estimation method, the seasonal approach can give profit in terms of upper quantile estimation accuracy that rises with the return period of the quantile and is the greatest for no seasonal variation. In this part, an assessment of annual maxima (AM) versus seasonal maxima (SM) approach to FFA was carried out with respect to seasonal and annual peak flow series of 38 Polish gauging stations. First, the assumption of mutual independence of the seasonal maxima has been tested. The smoothness of SM and AM empirical probability distribution functions was analysed and compared. The TCEV1 model with seasonally estimated parameters was found to be not appropriate for most Polish data as it considerably underrates the skewness of AM distributions and upper quantile values as well. Consequently, the discrepancies between the SM and AM estimates of TCEV1 are observed. Taking SM and TCEV1 distribution, the dominating season in AM series was confronted with predominant season for extreme floods. The key argument for presumptive superiority of SM approach that SM samples are more statistically homogeneous than AM samples has not been confirmed by the data. An analysis of fitness to SM and AM of Polish datasets made for seven distributions pointed to Pearson (3) distribution as the best for AM and Summer Maxima, whereas it was impossible to select a single best model for winter samples. In the multi-model approach to FFA, the tree functions, i.e., Pe(3), CD3 and LN3, should be involved for both SM and AM. As the case study, Warsaw gauge on the Vistula River was selected. While most of AM elements are here from winter season, the prevailing majority of extreme annual floods are the summer maxima. The upper quantile estimates got by means of classical annual and two-season methods happen to be fairly close; what's more they are nearly equal to the quantiles calculated just for the season of dominating extreme floods. © 2011 John Wiley & Sons, Ltd. Source

Strupczewski W.G.,Polish Academy of Sciences | Kochanek K.,Polish Academy of Sciences | Bogdanowicz E.,Institute of Meteorology and Water Management
Natural Hazards and Earth System Sciences

The use of non-systematic flood data for statistical purposes depends on the reliability of the assessment of both flood magnitudes and their return period. The earliest known extreme flood year is usually the beginning of the historical record. Even if one properly assesses the magnitudes of historic floods, the problem of their return periods remains unsolved. The matter at hand is that only the largest flood (XM) is known during whole historical period and its occurrence marks the beginning of the historical period and defines its length (L). It is common practice to use the earliest known flood year as the beginning of the record. It means that the L value selected is an empirical estimate of the lower bound on the effective historical lengthM. The estimation of the return period of XM based on its occurrence (L), i.e. M̂ = L, gives a severe upward bias. The problem arises that to estimate the time period (M) representative of the largest observed flood XM. From the discrete uniform distribution with support 1,2, ⋯, M of the probability of the L position of XM, one gets L̂ =M/2. Therefore M̂ = 2L has been taken as the return period of XM and as the effective historical record length as well this time. As in the systematic period (N) all its elements are smaller than XM, one can get M̂ = 2(L+N). The efficiency of using the largest historical flood (XM) for large quantile estimation (i.e. one with return period T =100 years) has been assessed using the maximum likelihood (ML) method with various length of systematic record (N) and various estimates of the historical period length M̂ comparing accuracy with the case when systematic records alone (N) are used only. The simulation procedure used for the purpose incorporates N systematic record and the largest historic flood (XMi) in the periodM, which appeared in the Li year of the historical period. The simulation results for selected two-parameter distributions, values of their parameters, different N and M values are presented in terms of bias and root mean square error (RMSEs) of the quantile of interest are more widely discussed. © Author(s) 2014. Source

Wierzbicki G.,Warsaw University of Life Sciences | Ostrowski P.,Warsaw University of Life Sciences | Mazgajski M.,Institute of Meteorology and Water Management | Bujakowski F.,Warsaw University of Life Sciences

At the bottoms of river valleys, there can be found landforms developed by overbank flow such as crevasse channels and crevasse splays, usually cut off from the main river channel by the natural levées. Humans construct embankments - artificial levées which completely divide integrated parts of the river valley and thus gain new areas for agriculture and settlement purposes. However, during extremely high water stages these areas suffer from flooding, very often caused by levée breach. The objective of the study is to answer the research question: Can we use geomorphological analysis of the floodplain to predict extreme flood effects in a large river valley with an artificial levée system? The study has been conducted in a reach of the Vistula River (60. km downstream from Warsaw, Poland) that was affected by catastrophic flood event in May and June 2010. Specific emphasis has been put on using Very High Resolution (VHR) Multispectral Remote Sensing and LIDAR (LIght Detection And Ranging) data. Work is divided into three stages: (1) Identification of floodplain landforms from palaeofloods on the VHR multispectral satellite imagery; (2) Outline of the 2010 flood event (on the basis of river stage data and Acoustic Doppler Current Profiler measurements) and a detailed study of its geomorphologic effects on the floodplain (on the basis of aerial imagery and LIDAR data); (3) Comparison of landforms created in palaeofloods and in the 2010 flood event. The results of the study show that geomorphological effects of the recent catastrophic flooding are strikingly similar to palaeoflood landforms developed before the construction of an artificial levée system. The main conclusion is that overbank flow in some reaches of the floodplain causes (and will cause) similar effects as it has done in the past. Analysis of palaeoflood landforms enables prediction of these effects and can therefore prove useful for flood risk management. Post-flood transformation of palaeoflood landforms by human impact and natural geomorphological processes induces the use of special methods for the identification of the landforms and study of their spatial features. VHR multispectral remote sensing enables such analysis in many places of the Earth. LIDAR data provide new possibilities for the accurate estimation of overbank deposition and erosion rate on the floodplain. © 2012 Elsevier B.V. Source

Zalewska T.,Institute of Meteorology and Water Management | Saniewski M.,Institute of Meteorology and Water Management
Oceanological and Hydrobiological Studies

137Cs activity concentrations were determined in macrophytes and macrozoobenthic organisms from the southern Baltic Sea. Cesium isotope content was analysed in macroalgae species (green, red and brown algae representatives) and in some species of vascular plants. The analyzed macroinvertebrate organisms included bivalves and a crustacean. Concentration factors (CF) were calculated using the determined 137Cs concentration in the flora and fauna organisms against that in seawater, and the bioaccumulative properties were compared. The study pointed out that the most important factors in the cesium bioaccumulation process occurring in plants are related to morphology. The highest CF values were obtained in algae Polysiphonia fucoides, Ectocarpus siliculosus and Cladophora glomerata. Decidedly lower CF values were observed in the vascular plants and macrozoobenthos representatives. Copyright © of Institute of Oceanography, University of Gdansk, Poland. Source

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