National Repository of Grey Literature 5 records found  Search took 0.02 seconds. 
Influence of land cover and altitude on soilmoisturespatio-temporal variability
Šípek, Václav ; Hnilica, Jan ; Tesař, Miroslav
An understanding of spatial and temporal variation of soil moisture is essential for studying other hydrological, biological or chemical soil processes, such as water movement, microbial activity and biogeochemical cycling (Bruckner et al., 1999, Ridolfi et al., 2003). Although the world-wide total amount of water stored in the soil profile is negligible compared to ocean and glacier storages, it represents a crucial variable concerning the water resources and agricultural management. This is valid especially in the context of ongoing shift in climate. Soil water exhibits a tremendous heterogeneity in space and time (Gomez-Plaza et al., 2000). Therefore, spatial and temporal variations of soil moisture have always been the critical issue. The spatial variability is influenced by variety of factors encompassing the topographical effect on lateral water redistribution (Williams et al., 2003), radiation (Grayson et al., 1997, Geroy et al., 2011), soil texture and structure (Famiglietti et al., 1998, Pan and Peters-Lidard, 2008), vegetation (Teuling and Troch, 2005), climate (Lawrence and Hornberger, 2007), precipitation pattern (Keim et al., 2005) and antecedent soil moisture (Rosenbaum et al., 2012). The land use influence on the soil moisture content variation is of complex a character covering several above mentioned factors. However, it is determined namely by the different vegetation cover, which results in different rates of interception and transpiration. It also strongly influences the soil hydraulic properties, i.e. hydraulic conductivity and water retention characteristics (Zhou et al, 2008). Hence, the reaction of an area to a rainfall and also the temporal variability of the soil moisture content might be influenced by the present land cover. Nevertheless, the studies comparing the influence of several land covers in the longer periods are missing. This knowledge would be valuable especially in the context of extreme climatic events that are present nowadays. In central Europe, the period of major floods (1997, 2002, 2013) was followed by serious dry spells (2003, 2011–2012, 2015) (Trnka et al., 2015). This observed hydrological extremity raised the questions of sustainable water management. One of the possible management practices in consideration is represented by the land cover changes intended to hold more water in the landscape and simultaneously to attenuate the rainfall-runoff response. Moreover, previous studies have investigated that spatial and temporal variation of soil water under a certain land use type, and drawing significant research attention is lacking on the differences of dynamics of soil water conditions under different land use types. Thus, it is necessary to understand the comparisons of the dynamics of soil water conditions under different land use types (Niu et al., 2015) The main aim of the presented study is therefore to understand the soil moisture variability in the vegetation season under four different land covers (coniferous/deciduous forest, meadow, grassland). This analysis is conducted in five consecutive years, encompassing both dry and wet periods. The influence of altitude is also studied in the coniferous forest.
Statistical correction of daily precipitation sums from climate models
Hnilica, Jan ; Chára, Zdeněk (advisor) ; Jan, Jan (referee)
Climate change prediction and evaluation of its impact currently represent one of the key challenges for the science community. Regional climate models (RCM) have been recently established as a main source of the data for climate change assessment studies. Nevertheless, RCM outputs suffer from systematic errors caused primarily by their low spatial resolution and cannot be used directly without any form of bias correction. The bias correction is an actual topic in climatology and several correction methods were developed, ranging from the simple additive method to more advanced approaches (e.g. quantile mapping). However, despite this progress, the bias correction methods suffer from several difficulties, which bring another source of uncertainty into the climate change impact assessment studies. This thesis is focused on two problematic points connected with the bias correction of daily precipitation data. The first one is a non-stationarity between calibration and application periods. New correction methods are developed, showing an increased resistance to non-stationary conditions. The second problem is related to the correction of a dependence (i.e. correlation and covariance) structure of multivariate precipitation data. A new procedure is proposed, correcting the complete dependence structure of the model data. All newly introduced methods are validated using measured and RCM-simulated data; the validation demonstrates their suitable applicability.
Kernel density estimates used in stochastic precipitation generator
Hnilica, Jan ; Puš, V.
The kernel density estimates were tested to be suitable to describe the probability distribution of daily precipitation sums. For this purpose, a stochastic precipitation generator using the kernel density estimates was constructed and it was compared with the LARS-WG generator. The data from meteorological stations from the Cidlina river basin were used to evaluate the performances of the generators. It was found that the kernel density estimates capture the probability density better than histograms used in LARS-WG.
Statistical transformation of the precipitation data from regional climatic model to particular conditions of the Malse river basin
Hnilica, Jan ; Šípek, Václav
Paper describes procedure of statistical adaptation of the raw large-scale climate model data based on transformation between cumulative distribution functions of the model and real datasets. We developed two alternative ways and we validated those using data from Malse river basin.

See also: similar author names
3 Hnilica, Jiří
Interested in being notified about new results for this query?
Subscribe to the RSS feed.