Determining Hydraulic Conductivity from Soil Characteristics with Applications for Modelling Stream Discharge in Forest Catchments

Jutras, Marie-France; Arp, Paul A.

doi:10.5772/20309

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…rugged terrain and steep slopes. This may be due to several reasons, for instance rain events during soil moisture measurements (Western et al, 2002), varying soil conditions across the study area (Jutras and Arp, 2011;Oltean et al, 2016), or the chosen flow accumulation algorithm (Kopecký and Čížková, 2010). In addition, the soil moisture state itself is also an important factor, as water must first build up in the soil for it to flow from ridges to depressions, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Whereas, at finer scales and in shallow soils, wetness indices may encounter challenges (Penna et al, 2009;le Roux et al, 2013;Ågren et al, 2014). This is due to the fact that often indices are based on low-quality terrain data that portray relatively poorly or ignore completely (1) local topography, such as minor flow channels and depressions (Sørensen and Seibert, 2007;Ågren et al, 2014), and (2) variation in soil factors, for example soil texture, permeability, and soil depth (Jutras and Arp, 2011;Oltean et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Kemppinen

Niittynen

Riihimäki

et al. 2017

Earth Surf Processes Landf

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Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R2 = 0.47 and RMSE 9.34 VWC%, and for the latter R2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m2 digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Kemppinen

Niittynen

Riihimäki

et al. 2017

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…Figure 2. Best-fitted log 10 K sat versus actual data from New Brunswick and Nova Scotai, Canada, as seen in [1] and [16] (left), best-fitted log 10 K sat versus von Post humification index from literature sources (right).…”

Section: Quantitative Backgroundmentioning

confidence: 99%

“…For the well-drained upland soil conditions (Scenario 1), K sat values would be sufficiently high so that soil moisture conditions would fluctuate around the field capacity, depending on season as well as rainfall and snow melt events The above watershed-based K sat evaluations have shown that the effective K sat values for downward and lateral flow generally vary by a factor of 2 to 3 in comparison to corresponding values generated via Eqs. 1-3 [1]. As illustrated via Table 5 and subsequent figures, these variations lead to uncertainties in quantifying how water percolates through watersheds as run-off, interflow and baseflow (Fig.…”

Section: Hydraulic Conductivitymentioning

confidence: 99%