Hillslope response under variable microclimate

Regmi, Netra R.; McDonald, Eric V.; Rasmussen, Craig

doi:10.1002/esp.4686

Cited by 13 publications

(14 citation statements)

References 73 publications

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“…In addition, it has been proved that environmental conditions around slopes could be determined by elevation values (Paranunzio et al, 2019;Regmi et al, 2019). The elevation values of this study region were separated by 100 m, and arranged into nine levels, including 358-500 m, 500-600 m, 600-700 m, 700-800 m, 800-900 m, 900-1000 m, 1000-1100 m, 1100-1200 m and 1200-1284 m.…”

Section: Fig1 Study Areamentioning

confidence: 88%

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Zhang

Fu²,

Wang³

et al. 2021

Preprint

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Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree (FT), support vector machine (SVM) and classification regression tree (CART) and were integrated with bagging strategy. Then these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation (CAE) method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve (ROC) and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. among them the Bag-CART model has the highest AUC value of 0.874, however the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

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Section: Fig1 Study Areamentioning

confidence: 88%

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Zhang

Fu²,

Wang³

et al. 2021

Preprint

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“…This is related in part to gully orientation which largely determines the local microclimatic conditions through the extent of exposure to solar radiation. North-facing slopes receive less sunlight, resulting in cooler and wetter conditions, often with a denser vegetation cover compared to exposed and drier south-facing slopes [55][56][57]. In relation to C sequestration, it has been documented that afforestation under shady slopes (vs. sunny gentle slopes) maintained more SOC [50].…”

Section: Characteristics Of the Forest Gully Soilmentioning

confidence: 99%

Variations in Soil Properties and CO2 Emissions of a Temperate Forest Gully Soil along a Topographical Gradient

Walkiewicz

Bulak

Brzezińska

et al. 2021

Forests

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Although forest soils play an important role in the carbon cycle, the influence of topography has received little attention. Since the topographical gradient may affect CO2 emissions and C sequestration, the aims of the study were: (1) to identify the basic physicochemical and microbial parameters of the top, mid-slope, and bottom of a forest gully; (2) to carry out a quantitative assessment of CO2 emission from these soils incubated at different moisture conditions (9% and 12% v/v) and controlled temperature (25 °C); and (3) to evaluate the interdependence between the examined parameters. We analyzed the physicochemical (content of total N, organic C, pH, clay, silt, and sand) and microbial (enzymatic activity, basal respiration, and soil microbial biomass) parameters of the gully upper, mid-slope, and bottom soil. The Fourier Transformed Infrared spectroscopy (FTIR) method was used to measure CO2 emitted from soils. The position in the forest gully had a significant effect on all soil variables with the gully bottom having the highest pH, C, N concentration, microbial biomass, catalase activity, and CO2 emissions. The sand content decreased as follows: top > bottom > mid-slope and the upper area had significantly lower clay content. Dehydrogenase activity was the lowest in the mid-slope, probably due to the lower pH values. All samples showed higher CO2 emissions at higher moisture conditions, and this decreased as follows: bottom > top > mid-slope. There was a positive correlation between soil CO2 emissions and soil microbial biomass, pH, C, and N concentration, and a positive relationship with catalase activity, suggesting that the activity of aerobic microorganisms was the main driver of soil respiration. Whilst the general applicability of these results to other gully systems is uncertain, the identification of the slope-related movement of water and inorganic/organic materials as a significant driver of location-dependent differences in soil respiration, may result in some commonality in the changes observed across different gully systems.

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“…Overview of processes simulated in HydroLorica, including driving soil-forming factors in the model and landscape variables that are affected by each process. Humans are considered an additional soil-forming factor (Amundson and Jenny, 1991;Richter et al, 2015).…”

Section: Model Architecturementioning

confidence: 99%

“…The main (bio-)geomorphic processes affecting topography in loess areas are soil creep, tree throw, water erosion and tillage erosion. Soil creep is a bio-geomorphic process that causes a diffuse movement of soil material on a hillslope, driven by various factors such as (micro)climate, organisms and terrain (Pawlik and Šamonil, 2018;Regmi et al, 2019;Roering et al, 2002). The potential creep rate is a function of vegetation type and slope (Gabet et al, 2003).…”

Section: (Bio-)geomorphic Processesmentioning

confidence: 99%

Modeling soil and landscape evolution – the effect of rainfall and land-use change on soil and landscape patterns

Meij

Temme

Wallinga

et al. 2020

SOIL

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Abstract. Humans have substantially altered soil and landscape patterns and properties due to agricultural use, with severe impacts on biodiversity, carbon sequestration and food security. These impacts are difficult to quantify, because we lack data on long-term changes in soils in natural and agricultural settings and available simulation methods are not suitable for reliably predicting future development of soils under projected changes in climate and land management. To help overcome these challenges, we developed the HydroLorica soil–landscape evolution model that simulates soil development by explicitly modeling the spatial water balance as a driver of soil- and landscape-forming processes. We simulated 14 500 years of soil formation under natural conditions for three scenarios of different rainfall inputs. For each scenario we added a 500-year period of intensive agricultural land use, where we introduced tillage erosion and changed vegetation type. Our results show substantial differences between natural soil patterns under different rainfall input. With higher rainfall, soil patterns become more heterogeneous due to increased tree throw and water erosion. Agricultural patterns differ substantially from the natural patterns, with higher variation of soil properties over larger distances and larger correlations with terrain position. In the natural system, rainfall is the dominant factor influencing soil variation, while for agricultural soil patterns landform explains most of the variation simulated. The cultivation of soils thus changed the dominant factors and processes influencing soil formation and thereby also increased predictability of soil patterns. Our study highlights the potential of soil–landscape evolution modeling for simulating past and future developments of soil and landscape patterns. Our results confirm that humans have become the dominant soil-forming factor in agricultural landscapes.

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Hillslope response under variable microclimate

Cited by 13 publications

References 73 publications

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Variations in Soil Properties and CO2 Emissions of a Temperate Forest Gully Soil along a Topographical Gradient

Modeling soil and landscape evolution – the effect of rainfall and land-use change on soil and landscape patterns

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