Evaluation of the influence of mountain peat bogs restoration measures on the groundwater level: case study Rokytka peat bog, the Šumava Mts., Czech Republic

In a period with frequently occurring hydrological extremes, research on areas with a high retention potential is brought into focus. The Šumava Mountains peat bogs are important parts of the landscape in the headwater area of the Otava river basin. The study objective is to describe the variability of discharges and the dynamics of groundwater level changes in various types of peat bogs, and to identify connections between observed physico-chemical water properties. This is assessed by basic statistical methods. The rainfall-runoff process and physico-chemical water properties can be affected by many factors. In this case, strong relations between the observed parameters were identified along with considerable differences in the involvement of various types of peat bog sites in the runoff process. It is evident that the peat bog pattern and its vegetation cover have an essential effect on the hydrological regime and water properties stored in a peat bog.

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Geochemical evidence for peat bog contribution to the streamflow generation process: case study of the Vltava River headwaters, Czech Republic

Hydrological Sciences Journal ◽

10.1080/02626667.2016.1140173 ◽

2016 ◽

Vol 61 (14) ◽

pp. 2579-2589 ◽

Cited By ~ 3

Author(s):

Jan Kocum ◽

Filip Oulehle ◽

Bohumír Janský ◽

František Bůzek ◽

Jakub Hruška ◽

...

Keyword(s):

Czech Republic ◽

Peat Bog ◽

Generation Process ◽

Geochemical Evidence ◽

Streamflow Generation

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Retention potential and hydrological balance of a peat bog: case study of Rokytka Moors, Otava River headwaters, sw. Czechia

Geografie ◽

10.37040/geografie2012117040395 ◽

2012 ◽

Vol 117 (4) ◽

pp. 395-414 ◽

Cited By ~ 3

Author(s):

Lukáš Vlček ◽

Jan Kocum ◽

Bohumír Janský ◽

Luděk Šefrna ◽

Andrea Kučerová

Keyword(s):

Ground Water ◽

Water Table ◽

Water Retention Capacity ◽

Peat Bogs ◽

Peat Bog ◽

Retention Capacity ◽

Factors Affecting ◽

Ground Water Table ◽

Significant Phenomenon

The paper summarizes findings about preservation and hydrological conditions of Rokytka Moors situated in the Vydra River headwaters, sw. Czechia. Special attention is paid to the evaluation of their water retention capacity. Due to the significant phenomenon of peat bogs in the study area, the assessment of factors affecting their retention potential represents a component in the discussion on flood protection and measures aimed at increasing runoff in dry periods. The main focus is directed at findings of runoff dynamics dependence on the ground water table in the peatland. Authors thus give attention to the assessment of the Rokytka Moors hydrological function, which represents a typical example of an peat bog in an environment of most of the evaluated parts of Šumava Mts. The research is based on a detailed pedological analysis of the Rokytka Brook catchment, on analysis of an peat bog ground water table time series and on data obtained by monitoring the water stage, discharge respectively, in the profile of the draining stream.

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Dynamics of methane fluxes from two peat bogs in the Ore Mountains, Czech Republic

Plant Soil and Environment ◽

10.17221/330/2012-pse ◽

2012 ◽

Vol 59 (No. 1) ◽

pp. 14-21 ◽

Cited By ~ 11

Author(s):

L. Bohdálková ◽

J. Čuřík ◽

Kuběna AA ◽

F. Bůzek

Keyword(s):

Czech Republic ◽

High Elevation ◽

Peat Bogs ◽

Peat Bog ◽

Vegetation Changes ◽

Closed Chamber ◽

Methane Fluxes ◽

Chamber Temperature ◽

Depth Water

Methane fluxes were studied at two high-elevation oligotrophic peat bogs in the Ore Mts., Czech Republic. The Bukova dolina Bog was drained 15 years ago and 2 years ago was partly restored, whereas the Brumiste Bog is an intact peatland. Draining led to a change of vegetation structure, dominated by Molinia caerulea, Carex sp., and forestation by Norway spruce. Methane fluxes were measured monthly from April to November 2011 using a closed chamber. Temperature and presence of Carex were significant controls on methane fluxes. Peat depth, water table and the presence of other plant species had no significant effect on CH4 emissions. Methane emissions ranged from 9 to 2700 mg/m2/day at the degraded and from 3 to 260 mg/m2/day at the intact bog. In general, the degraded peat bog emitted three times more methane compared to the intact peat bog, likely due to vegetation changes after long-term artificial draining.

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Influence of peat soils on runoff process: case study of Vydra River headwaters, Czechia

Geografie ◽

10.37040/geografie2016121020235 ◽

2016 ◽

Vol 121 (2) ◽

pp. 235-253 ◽

Cited By ~ 5

Author(s):

Lukáš Vlček ◽

Jan Kocum ◽

Bohumír Janský ◽

Luděk Šefrna ◽

Šárka Blažková

Keyword(s):

Groundwater Level ◽

Weather Conditions ◽

Peat Bog ◽

Flood Wave ◽

Peat Soils ◽

Rainfall Events ◽

Forest Coverage

This paper summarizes findings from the hydrological research in the Vydra River headwaters, the Šumava Mts., s-w Czechia, dealing with the hydrological function of local peat soils and their effect on the outflow from the basin. This study represents a part of a long-term research carried out at the Faculty of Science, Charles University in Prague. The paper shows how important it is to study the groundwater level in peat soils and its area in a catchment as well as to predict the outflow in distinct weather conditions. There were chosen four small experimental catchments with different peat and waterlogged forest coverage. Rainfall events were selected in various periods within a year with a varying groundwater level (maximum and minimum) in the peat bog. Within these situations flood wave volumes were calculated and all of them were compared regarding the peat bog extension. The presented research also compares various sources of data about peat soils areas and areas of waterlogged forest.

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Appendix 1. Case Study: Methanol Mass Poisoning Outbreak in the Czech Republic: Diagnosis, Treatment and Outcome

Chemical Health Threats - Issues in Toxicology ◽

10.1039/9781782623687-00263 ◽

2018 ◽

pp. 263-270

Author(s):

Sergej Zacharov ◽

Daniela Pelclova

Keyword(s):

Czech Republic ◽

The Czech Republic

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Soft Computing Techniques Applied to a Case Study of Air Quality in Industrial Areas in the Czech Republic

Advances in Intelligent Systems and Computing - Soft Computing Models in Industrial and Environmental Applications ◽

10.1007/978-3-642-32922-7_55 ◽

2013 ◽

pp. 537-546 ◽

Cited By ~ 2

Author(s):

Ángel Arroyo ◽

Emilio Corchado ◽

Verónica Tricio ◽

Laura García-Hernández ◽

Václav Snášel

Keyword(s):

Czech Republic ◽

Air Quality ◽

Soft Computing ◽

The Czech Republic ◽

Industrial Areas ◽

Soft Computing Techniques

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Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

Remote Sensing ◽

10.3390/rs13050907 ◽

2021 ◽

Vol 13 (5) ◽

pp. 907

Author(s):

Theodora Lendzioch ◽

Jakub Langhammer ◽

Lukáš Vlček ◽

Robert Minařík

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Spatial Data ◽

Groundwater Level ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Sampling Strategy ◽

Peat Bog ◽

Strong Impact ◽

Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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