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

Kocum, Jan; Oulehle, Filip; Jánský, Bohumír; Bůzek, František; Hruška, Jakub; Vlček, Lukáš

doi:10.1080/02626667.2016.1140173

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…Peat bogs, a subdivision of peatlands, in particular, are significant water-logged reservoirs with a specific hydrological regime [3], representing habitats dominated by sphagnum moss, stored organic matter, a high water table, and low pH [4,5]. The Sumava Mountains (Sumava Mts., Bohemian Forest) feature the most extensive peat bog complex in Central Europe due to their specific geology and morphology [6].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Peat bogs can significantly contribute to stormflow when the peat is fully saturated, that is, storm events exceeding a threshold of 10-15 mm [68]. As mentioned above, according to a geochemical study based on 2 years of monthly stream water sampling [55], peat bogs contribute only 10% to baseflow at the outlet of the entire Rokytka B. catchment. However, some zones of a peat bog area, such as springs or soil pipe systems connected to the stream, exhibit high fluctuations in discharge [69].…”

Section: Discussionmentioning

confidence: 99%

“…This year was very average in the sense of hydrometeorological features in recent years. Year 2008 was chosen also because of the fact that cooperation with the Czech Geological Survey (CGS) on geochemical analyses started this year ( [55], see Chapter 5.5). The total amount of precipitation in the Rokytka B. catchment in this year was 1485 mm.…”

Section: Hydroclimatic Conditionsmentioning

confidence: 99%

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Hydrological Function of a Midlatitude Headwater Peatland

Kocum¹,

Jánský²,

Vlček³

et al. 2018

Peat

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Peatland represents quite significant phenomenon in the headstream areas of Czech rivers. Considering the fact that these areas are crucial for streamflow generation process, it is very important to study the mechanism of runoff formation in a peatland and its hydrological function. Natural runoff process is affected by man already by its birth, thus in headwaters where numerous procedures related to runoff retardation and water retention increase in headstream areas could be realized. To understand and clarify the runoff generation process and the effect of various physicogeographic factors on its dynamics, the detailed analyses were carried out in the Vltava River headwaters (sw. Czechia) in recent years. It was necessary to consider the evaluation of peatland retention capacity, its hydraulic communication with draining watercourses and of runoff regime variability during various hydroclimatic conditions. The big attention was focused on findings of a runoff dynamics dependence on the groundwater table in the peatland and of the runoff chemistry and balance using isotopic hydrology methods. Natural tracers were applied at sprinkling plots to identify preferential flow and runoff formation at two opposite hillslopes in this peaty mountain headwater.

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“…Almost all headwaters of the Šumava Mts., including the Rokytka catchment, have been affected by bark beetle calamity and by storm activity; e.g., the storm Kyrill in 2007 uprooted many trees. These disturbances have been shown to have a long-term impact on runoff formation in headwater systems (Langhammer et al, 2015). However, it is unclear how these disturbances modify specific runoff formation processes like infiltration, lateral drainage (soil piping) or percolation in riparian soils.…”

Section: Introductionmentioning

confidence: 99%

Identification of runoff formation with two dyes in a mid-latitude mountain headwater

Vlček

Falátková

Schneider

2017

Hydrol. Earth Syst. Sci.

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Abstract. Subsurface flow in peat bog areas and its role in the hydrologic cycle has garnered increased attention as water scarcity and floods have increased due to a changing climate. In order to further probe the mechanisms in peat bog areas and contextualize them at the catchment scale, this experimental study identifies runoff formation at two opposite hillslopes in a peaty mountain headwater; a slope with organic peat soils and a shallow phreatic zone (0.5 m below surface), and a slope with mineral Podzol soils and no detectable groundwater (> 2 m below surface). Similarities and differences in infiltration, percolation and preferential flow paths between both hillslopes could be identified by sprinkling experiments with Brilliant Blue and Fluorescein sodium. To our knowledge, this is the first time these two dyes have been compared in their ability to stain preferential flow paths in soils. Dye-stained soil profiles within and downstream of the sprinkling areas were excavated parallel (lateral profiles) and perpendicular (frontal profiles) to the slopes' gradients. That way preferential flow patterns in the soil could be clearly identified. The results show that biomat flow, shallow subsurface flow in the organic topsoil layer, occurred at both hillslopes; however, at the peat bog hillslope it was significantly more prominent. The dye solutions infiltrated into the soil and continued either as lateral subsurface pipe flow in the case of the peat bog, or percolated vertically towards the bedrock in the case of the Podzol. This study provides evidence that subsurface pipe flow, lateral preferential flow along decomposed tree roots or logs in the unsaturated zone, is a major runoff formation process at the peat bog hillslope and in the adjacent riparian zone.

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

Cited by 10 publications

References 20 publications

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

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

Hydrological Function of a Midlatitude Headwater Peatland

Identification of runoff formation with two dyes in a mid-latitude mountain headwater

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