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Milan Onderka and Vladimír Chudoba


The ways how water from rain or melting snow flows over and beneath the Earth‘s surface affects the timing and intensity at which the same water leaves a catchment. Several mathematical techniques have been proposed to quantify the transit times of water by e.g. convolving the input-output tracer signals, or constructing frequency response functions. The primary assumption of these techniques is that the transit time is regarded time-invariant, i.e. it does not vary with temporarily changing e.g. soil saturation, evaporation, storage volume, climate or land use. This raises questions about how the variability of water transit time can be detected, visualized and analyzed. In this paper we present a case study to show that the transit time is a temporarily dynamic variable. Using a real-world example from the Lower Hafren catchment, Wales, UK, and applying the Continuous Wavelet Transform we show that the transit time distributions are time-variant and change with streamflow. We define the Instantaneous Transit Time Distributions as a basis for the Master Transit Time Distribution. We show that during periods of elevated runoff the transit times are exponentially distributed. A bell-shaped distribution of travel times was observed during times of lower runoff. This finding is consistent with previous investigations based on mechanistic and conceptual modeling in the study area according to which the diversity of water flow-paths during wet periods is attributable to contributing areas that shrink and expand depending on the duration of rainfall. The presented approach makes no assumptions about the shape of the transit time distribution. The mean travel time estimated from the Master Transit Time Distribution was ~54.3 weeks.

Open access

Veronika Bačová-Mitková and Milan Onderka

Analysis of extreme hydrological Events on THE danube using the Peak Over Threshold method

The Peak Over Threshold Method (POT) was used as an alternative technique to the traditional analysis of annual discharge maxima of the Danube River. The POT method was applied to a time-series of daily discharge values covering a period of 60 years (1931-1990) at the following gauge stations: Achleiten, Kienstock, Wien, Bratislava and Nagymaros. The first part of the paper presents the use of the POT method and how it was applied to daily discharges. All mean daily discharges exceeding a defined threshold were considered in the POT analysis. Based on the POT waves independence criteria the maximum daily discharge data were selected. Two theoretical log-normal (LN) and Log-Pearson III (LP3) distributions were used to calculate the probability of exceeding annual maximum discharges. Performance of the POT method was compared to the theoretical distributions (LN, LP3). The influence of the data series length on the estimation of the N-year discharges by POT method was carried out too. Therefore, with regard to later regulations along the Danube channel bank the 40, 20 and 10-year time data series were chosen in early of the 60-year period and second analysed time data series were selected from the end of the 60-year period. Our results suggest that the POT method can provide adequate and comparable estimates of N-year discharges for more stations with short temporal coverage.

Open access

Milan Onderka, Marek Rodný and Yvetta Velísková

Suspended Particulate Matter Concentrations Retrieved from Self-Calibrated Multispectral Satellite Imagery

Inland waters are known to be laden with high levels of suspended particulate matter (SPM). Remotely sensed data have been shown to provide a true synoptic view of SPM over vast areas. However, as to date, there is no universal technique that would be capable of retrieving SPM concentrations without a complete reliance on time-consuming and costly ground measurements or a priori knowledge of inherent optical properties of water-borne constituents. The goal of this paper is to present a novel approach making use of the synergy found between the reflectance in the visual domain (~ 400-700 nm) with the near-infrared portion of the spectrum (~ 700-900 nm). The paper begins with a brief discourse of how the shape and spectral dependence of reflectance is determined by high concentrations of SPM. A modeled example is presented to mimic real-world conditions in fluvial systems, with specific absorption and scattering coefficients of the virtual optically active constituents taken from the literature. Using an optical model, we show that in the visual spectral domain (~ 400-700 nm) the water-leaving radiance responds to increasing SPM (0-100 g m-3) in a non-linear manner. Contrarily to the visual spectra, reflectance in the near infrared domain (~ 700-900 nm) appears to be almost linearly related to a broad range of SPM concentrations. To reduce the number of parameters, the reflectance function (optical model) was approximated with a previously experimentally verified exponential equation (Schiebe et al., 1992: Remote sensing of suspended sediments: the Lake Chicot, Arkansas project, Int. J. Remote Sensing, 13, 8, 1487-1509). The SPM term in Schiebe's equation was expressed as a linear function of top-of-atmosphere reflectance. This made it possible to calibrate the reflectance in the visual domain by reflectance values from the near-IR portion of the spectrum. The possibility to retrieve SPM concentrations from only remote sensing data without any auxiliary ground mea-surements is tested on a Landsat ETM + scene acquired over a reservoir with moderately turbid water with SPM concentrations between 15-70 g m-3. The retrieved concentrations (on average) differ from in-situ measurement by ~ 10.5 g m-3.

Open access

Pavla Pekárová, Milan Onderka, Ján Pekár, Peter Rončák and Pavol Miklánek

Prediction of Water Quality in the Danube River Under extreme Hydrological and Temperature Conditions

One of the requirements imposed by the Water Framework Directive (WFD, 2000/60/EC) is to analyze and predict how quality of surface waters will evolve in the future. In assessing the development of a stream's pollution one must consider all sources of pollution and understand how water quality evolves over time. Flow and water temperature regime of a stream or river are the main factors controlling the extent to which deterioration of a stream's water quality can propagate under constant input from pollution sources. In addition, there is ever increasing public concern about the state of the aquatic environment. Decision makers and scientists involved in water management call for studies proposing simulation models of water quality under extreme natural hydrologic and climatic scenarios. Also, human impact on water resources remain an issue for discussion, especially when it comes to sustainability of water resources with respect to water quality and ecosystem health. In the present study we investigate the long-term trends in water quality variables of the Danube River at Bratislava, Slovakia (Chl-a, Ca, EC, SO2-, Cl-, O2, BOD5, N-tot, PO4-P, NO3-N, NO2-N, etc.), for the period 1991-2005. Several SARIMA models were tested for the long-term prediction of selected pollutant concentrations under various flow and water temperature conditions. In order to create scenarios of selected water quality variables with prediction for 12 months ahead, three types of possible hydrologic and water temperature conditions were defined: i) average conditions - median flows and water temperature; ii) low flows and high water temperature; and iii) high flows and low water temperature. These conditions were derived for each month using daily observations of water temperature and daily discharge readings taken in the Danube at Bratislava over the period 1931-2005 in the form of percentiles (1th-percentile, median, 99th-percentile). Once having derived these extreme-case scenarios, we used selected Box-Jenkins models (with two regressors - discharge and water temperature) to simulate the extreme monthly water quality variables. The impact of natural and man-made changes in a stream's hydrology on water quality can be readily well simulated by means of autoregressive models.