The short-term predictions of annual and seasonal discharge derived by a modified TIPS (Tendency, Intermittency, Periodicity and Stochasticity) methodology are presented in this paper. The TIPS method (Yevjevich, 1984) is modified in such a way that annual time scale is used instead of daily. The reason of extracting a seasonal component from discharge time series represents an attempt to identify the long-term stochastic behaviour. The methodology is applied for modelling annual discharges at six gauging stations in the middle Danube River basin using the observed data in the common period from 1931 to 2012. The model performance measures suggest that the modelled time series are matched reasonably well. The model is then used for the short-time predictions for three annual step ahead (2013–2015). The annual discharge predictions of larger river basins for moderate hydrological conditions show reasonable matching with records expressed as the relative error from –8% to +3%. Irrespective of this, wet and dry periods for the aforementioned river basins show significant departures from annual observations. Also, the smaller river basins display greater deviations up to 26% of the observed annual discharges, whereas the accuracy of annual predictions do not strictly depend on the prevailing hydrological conditions.
From the year 2000, the environmental state and the impact of human activities on fluvial lakes in the central part of the Czech section of the Elbe River is evaluated. For that reason, three oxbow lakes were chosen: Lake Labiště pod Opočínkem (east of Pardubice), Lake Doleháj (near Kolín), and Lake Obříství (near Mělník). All of them are situated within an area with a well-developed chemical industry and the nearby lowlands are one of the most intensively farmed areas of the Czech Republic. In spite of the identical origin during the river canalization, major differences were found. E.g. very low oxygen saturation in Opočínek (mean saturation 46 %) was determined. Nutrient concentrations and their seasonal dynamics (nitrites, nitrates, ammoniated ions, and phosphates) differed in each lake as well. The lowest concentrations of heavy metals (Ag, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb and Zn) in sediment were found in the Doleháj. In samples from Obříství after a big flood in September 2002, the Index of Geoaccumulation increased only for Pb and decreased for Hg. This result indicated that the pollution has probably not been occurred during such floods. and Od roku 2000 jsou v oblasti středního Labe hodnoceny přírodní podmínky starých labských ramen a posuzován vliv antropogenní činnosti na ně. Z toho důvodu byly pro výzkum vybrány následující tři slepé opuštěné meandry: Labiště pod Opočínkem (východně od Pardubic), Doleháj (v blízkosti Kolína) a jezero Obříství (v blízkosti Mělníka). Všechna tato fluviální jezera leží v oblasti s velmi rozvinutým chemickým průmyslem, přilehlé nížiny jsou též jedny z nejintenzivněji obdělávaných zemědělských ploch v České republice. Ačkoliv jezera vznikla stejným způsobem při regulacích řeky, byly mezi nimi zjištěny značné rozdíly. Například lokalita Opočínek vykazovala značně nízké hodnoty nasycení vody kyslíkem (průměrné nasycení 46 %). Koncentrace živin a jejich sezónní dynamika (dusitany, dusičnany, amonné ionty a fosforečnany) se též v každém případě lišila. Nejnižší koncentrace těžkých kovů (Ag, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb a Zn) v sedimentu byly zaznamenány v Doleháji. Ve vzorcích z lokality Obříství po velké povodni v roce 2002 se zvýšil geoakumulační index jen v případě Pb, naopak hodnota u Hg poklesla. Výsledky proukázaly nepřítomnost výrazného znečištění po této povodni.
The characteristics of evapotranspiration estimated by the complementary relationship actual evapotranspiration (CRAE), the advection-aridity (AA), and the modified advection-aridity (MAA) models were investigated in six pairs of rural and urban areas of Japan in order to evaluate the applicability of the three models the urban area. The main results are as follows: 1) The MAA model could apply to estimating the actual evapotranspiration in the urban area. 2) The actual evapotranspirations estimated by the three models were much less in the urban area than in the rural. 3) The difference among the estimated values of evapotranspiration in the urban areas was significant, depending on each model, while the difference among the values in the rural areas was relatively small. 4) All three models underestimated the actual evapotranspiration in the urban areas from humid surfaces where water and green spaces exist. 5) Each model could take the effect of urbanization into account.
Optimal operation of reservoir systems is the most important issue in water resources management. It presents a large variety of multi-objective problems that require powerful optimization tools in order to fully characterize the existing trade-offs. Many optimization methods have been applied based on mathematical programming and evolutionary computation (especially heuristic methods) with various degrees of success more recently. This paper presents an implementation and comparison of multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II) for the optimal operation of two reservoirs constructed on Ozan River catchment in order to maximize income from power generation and flood control capacity using MATLAB software. The alternative solutions were based on Pareto dominance. The results demonstrated superior capacity of the NSGA-II to optimize the operation of the reservoir system, and it provides better coverage of the true Pareto front than MOPSO.
Hydrological models often require input data on soil-water retention (SWR), but obtaining such data is laborious
and costly so that SWR in many places remains unknown. To fill the gap, a prediction of SWR using a pedotransfer
function (PTF) is one of the alternatives. This study aims to select the most suitable existing PTFs in order to predict
SWR for the case of the upper Bengawan Solo (UBS) catchment on Java, Indonesia. Ten point PTFs and two continuous
PTFs, which were developed from tropical soils elsewhere, have been applied directly and recalibrated based on a small
soil sample set in UBS. Scatter plots and statistical indices of mean error (ME), root mean square error (RMSE), model
efficiency (EF) and Pearson’s correlation (r) showed that recalibration using the Shuffled Complex Evolution-University
of Arizona (SCE-UA) algorithm can help to improve the prediction of PTFs significantly compared to direct application
of PTFs. This study is the first showing that improving SWR-PTFs by recalibration for a new catchment based on around
50 soil samples provides an effective parsimonious alternative to developing a SWR-PTF from specifically collected soil
datasets, which typically needs around 100 soil samples or more.
This study shows a comprehensive simulation of water and sediment fluxes from the catchment to the reach scale. We describe the application of a modelling cascade in a well researched study catchment through connecting stateof-the-art public domain models in ArcGIS. Three models are used consecutively: (1) the hydrological model SWAT to evaluate water balances, sediment input from fields and tile drains as a function of catchment characteristics; (2) the onedimensional hydraulic model HEC-RAS to depict channel erosion and sedimentation along a 9 km channel onedimensionally; and (3) the two-dimensional hydraulic model AdH for simulating detailed substrate changes in a 230 m long reach section over the course of one year. Model performance for the water fluxes is very good, sediment fluxes and substrate changes are simulated with good agreement to observed data. Improvement of tile drain sediment load, simulation of different substrate deposition events and carrying out data sensitivity tests are suggested as future work. Main advantages that can be deduced from this study are separate representation of field, drain and bank erosion processes; shown adaptability to lowland catchments and transferability to other catchments; usability of the model’s output for habitat assessments.
The scenario forecasting technique for assessing changes of water balance components of the northern river basins due to possible climate change was developed. Three IPCC global emission scenarios corresponding to different possible scenarios for economic, technological, political and demographic development of the human civilization in the 21st century were chosen for generating climate change projections by an ensemble of 16 General Circulation Models with a high spatial resolution. The projections representing increments of monthly values of meteorological characteristics were used for creating 3-hour meteorological time series up to 2063 for the Northern Dvina River basin, which belongs to the pan-Arctic basin and locates at the north of the European part of Russia. The obtained time series were applied as forcing data to drive the land surface model SWAP to simulate possible changes in the water balance components due to different scenarios of climate change for the Northern Dvina River basin.
Assessment of soil water repellency (SWR) was conducted in the decomposed organic floor layer (duff) and
in the mineral soil layer of two Mediterranean pine forests, one in Italy and the other in Spain, by the widely-used water
drop penetration time (WDPT) test and alternative indices derived from infiltration experiments carried out by the
minidisk infiltrometer (MDI). In particular, the repellency index (RI) was calculated as the adjusted ratio between
ethanol and water soil sorptivities whereas the water repellency cessation time (WRCT) and the specifically proposed
modified repellency index (RIm) were derived from the hydrophobic and wettable stages of a single water infiltration
experiment. Time evolution of SWR and vegetation cover influence was also investigated at the Italian site. All indices
unanimously detected severe SWR conditions in the duff of the pine forests. The mineral subsoils in the two forests
showed different wettability and the clay-loam subsoil at Ciavolo forest was hydrophobic even if characterized by organic
matter (OM) content similar to the wettable soil of an adjacent glade. It was therefore assumed that the composition
rather than the total amount of OM influenced SWR. The hydraulic conductivity of the duff differed by a factor of 3.8–
5.8 between the two forested sites thus influencing the vertical extent of SWR. Indeed, the mineral subsoil of Javea
showed wettable or weak hydrophobic conditions probably because leaching of hydrophobic compounds was slowed or
prevented at all. Estimations of SWR according to the different indices were in general agreement even if some discrepancies
were observed. In particular, at low hydrophobicity levels the SWR indices gathered from the MDI tests were able
to signal sub-critical SWR conditions that were not detected by the traditional WDPT index. The WRCT and modified
repellency index RIm yielded SWR estimates in reasonable agreement with those obtained with the more cumbersome RI
test and, therefore, can be proposed as alternative procedures for SWR assessment.
In this paper a new black box approach for rainfall-runoff modelling at a daily scale is presented. The considered black box model is non-linear regression based on Parzen probability density function. When using only measured rainfall as an input to any black box model there is a serious problem with building in the necessary memory. A standard approach to tackle this issue is to force a black box with a large number of rainfall and runoff variables of the past. In practice however, any regression technique, will have difficulties handling so large (possibly dependent) input set. For that reason, a more hydrological approach is proposed. Two linear reservoirs are used to model the memory. The reservoir constants are found by simple piecewise linear regression. An application to the Beerze catchment in the Netherlands is shown. A good correspondence between measured and estimated runoff is achieved. and Príspevok prezentuje nový prístup k zrážkovo-odtokovému modelovaniu, ktorý vychádza z metódy čiernej skrinky. V prípade, ak sa pri predpovedi prietokov použijú v modeli tohto typu ako vstupy len zrážkomerné pozorovania, môžu nastať ťažkosti s dostatočným zohľadnením pamäte procesu. Štandardný prístup ako riešiť tento problém, je zahrnúť dostatočné množstvo zrážkových a odtokových premenných zohľadňujúcich minulosť procesu odtoku. V praxi však môžu vzniknúť problémy pri aplikácii regresných metód na takýto súbor vstupných údajov (pravdepodobne vzájomne závislých). Preto je v príspevku navrhnutý hydrologicky vhodnejší prístup, pričom boli navrhnuté dve lineárne nádrže na modelovanie pamäte procesu odtoku. Konštanty nádrží boli určené metódou lineárnej regresie. Bol navrhnutý nelineárny regresný model založený na aplikácii Parzenovej funkcie hustoty rozdelenia pravdepodobnosti. V príspevku je uvedená aplikácia tohto prístupu na povodí Beerze v Holandsku. Dosiahla sa dobrá zhoda medzi meranými a modelovanými hodnotami odtoku.
In this paper we describe the use of modified passive capillary samplers (PCSs) to investigate the water isotope variability of snowmelt at selected sites in Slovenia during winter 2011/2012 and during winter 2012/2013. First, PCS with 3 fibreglass wicks covering approximately 1 m2 were tested to determine sample variability. We observed high variability in the amount of snowmelt water collected by individual wick (185 to 345 g) and in the isotope composition of oxygen (δ18O −10.43‰ to −9.02‰) and hydrogen (δ2H −70.5‰ to −63.6‰) of the collected water. Following the initial tests, a more detailed investigation was performed in winter 2012/2013 and the variability of snowmelt on the local scale among the different levels (i.e. within group, between the close and more distant groups of wicks) was investigated by applying 30 fibreglass wicks making use of Analysis Of Variance (ANOVA) and a balanced hierarchical sampling design. The amount of snowmelt water collected by an individual wick during the whole experiment was between 116 and 1705 g, while the isotope composition varied from −16.32‰ to −12.86‰ for δ18O and from −120.2‰ to −82.5‰ for δ2H. The main source of variance (80%) stems from the variability within the group of wicks (e.g. within group) while other sources contribute less than 20% of the variability. Amount weighted samples for the 2012–2013 season show no significant differences among groups, but significant differences for particular sampling events were observed. These investigations show that due to the variability within the group of wicks, a large number of wicks (> 5) are needed to sample snowmelt.