Assessing the effect of soil parameterization in land use change impact modeling

This study addresses the importance of integrating the effect of land use on soil infiltration rate into land use change impact modeling. Based on a validated version 9.05.04 of the Water balance Simulation Model-WaSiM (statistical quality measures > 0.7), and field measurement of the infiltration rate under cropland and fallow, sixteen model simulations were performed. The impact of land use change is computed comparing LULC status of years 1990 and 2013. The effect of soil parameterization is computed using a refined soil map integrating land use change impact of soil infiltration rate and a classic soil map not considering this interaction. The results show differences in model results as an effect of soil parameterization approaches, indicating that the model is sensitive to the integration of LULC related effects on soil hydraulic conductivity. These differences are more pronounced with increasing modeling time steps (24 and 28 h). The signal-to-noise-ratio indicates that, results achieved in LULC impact assessment with a classic and a refined soil parameterization are very comparable except for interflow.

Dark-humus soils on the updated soil map of Russian Federation scale 1 : 2.5 M

The dark-humus soil type was included in the updated legend of the Soil Map of the Russian Federation at scale 1 : 2.5 M, converted to the system of Soil Classification of Russia. The soil profile starts with the dark-humus horizon gradually merging to the parent rock; any mid-profile diagnostic horizons are absent. Large areas of dark-humus soils are found in the forest-steppe, steppe and taiga zones of the European Russia, Western and Central Siberia, in the Trans-Baikal region, the Altai-Sayany Mountains, and the Caucasus. The type of dark-humus soils comprises both mesomorphic soils (of normal moisture conditions) and soils with additional surface or ground-water moisture. The main prerequisites for the formation of dark-humus soils are, on the one hand, the climatic conditions favorable for the dark-humus horizon formation, and, on the other hand, parent material - mostly derivates of hard rocks, restricting the development of mid-profile diagnostic horizons. In the updated map, the following initial legend units are partially or completely converted to dark-humus soils: several units of chernozems, dark-gray forest and gray forest non-podzolized soils, soddy-taiga base-saturated and slightly unsaturated soils, several mountain soils, a significant part of soddy-calcareous soils, as well as some mountainous forest-meadow soils. The diversity of dark-humus soils subtypes is determined by secondary carbonate features, weak signs of clay accumulation and podzolization, alteration of the mineral mass, gley and cryogenic phenomena.

Floodplain soils on the soil map of the Russian Federation, scale 1 : 2.5 M, 1988, in the Russian soil classification

The development of the digital model of the soil map of Russia derived of the map of the Soviet Russian Federation, 1988, compiled in Dokuchaev Soil Science Institute, comprises the transfer of soil names in the initial legend to those in the new classification system of Russian soils (2004). Floodplain soils (only native) are represented by seven legend units (out of 205) that were named in terms of soil classification of USSR, 1977, and part of their names indicated ‘landscapes’ rather than soils, which disagrees with the principles of the new classification system. Basing on numerous publications and following the rules of the new system, soils were renamed. Most of them were referred to alluvial soil types within the synlithogenic trunk (Fluvisols), and their new names indicate both their properties and their zonal attachment. In order to obtain more adequate patterns of soils in river valleys additional soils were introduced including stratified-alluvial soils in the trunk of primary pedogenesis (Regosols). Simultaneously, the composition of polygons in the database was revised in accordance with regional data; human-modified soils were introduced (agro-soils and urbo-soils).

Soil Loss and Erosion Potential Estimation of Jhimruk Watershed, Nepal

The Revised Universal Soil Loss Equation (RUSLE) model was used in a Geographic Information System (GIS) to estimate the soil loss of the Jhimruk Watershed, Lumbini Province, Nepal. This research also aimed to calculate the erosional soil loss status of the local governments lying inside the watershed. For this, remote sensing data obtained from various sources were used to generate the factor maps to calculate the soil loss through RUSLE. A 13 year mean annual precipitation data from the 8 meteorological stations in and around the watershed was used to calculate the rainfall erosivity (R) factor. For the soil erodibility factor (K), the soil map of the watershed was clipped from the digital soil map of the world provided by FAO. Aster Digital Elevation Model of 30m resolution was used for the generation of LS factor map. For the computation of C-factor, the landcover map of the watershed produced in Arc GIS 10.2.1 through supervised classification of the Sentinal imagery of 10m resolution was used. The values were assigned based on the literatures in the case of C and P factors.The mean annual soil loss of the watershed was found to be 13.4 tons per hectare per year (t/ha/yr.). However, the soil loss was calculated to be as high as 182 t/ha/yr. 68.82% of the total area of the watershed lie under very low erosion class and thus have low conservation priority whereas 7.73 % of the total area lie under extremely high erosion class and thus have a conservation priority class of 1.The mean erosion rate from the barren land was found to be highest (23.179 t/ha/yr.) followed by agricultural (21.40 t/ha/yr.) and forest area had the lowest erosion rate i.e. 7.90 t/ha/yr.

Using GIS towards the Characterization and Soil Mapping of the Caia Irrigation Perimeter

The Caia Irrigation Perimeter is an irrigation infrastructure implemented in 1968. As is often the case, the original soil map of this region (dated from 1961) does not have the detail needed to characterize a relatively small-sized zone, where intensive agricultural practices take place. Using FAO methodology and with the main goal of establishing a larger-scale soil map, adequate for the demands of a modern and intensive agriculture, we gathered the geological characterization of the study area and information about the topography, climate, and vegetation of the region. Using ArcGIS software, we overlapped this information and established a pre-map of soil resources. Based on this pre-map, we defined a set of detailed itineraries in the field, evenly distributed, in which soil samples were collected. In those distinct soil units, we opened several soil profiles, from which we selected 26 to analyze in the present study, since they characterized the existing diversity in terms of soil type and soil properties. Based on the work of verification, correction, and reinterpretation of the preliminary soil map, we reached a final soil map for the Caia Irrigation Perimeter, which is characterized by enormous heterogeneity, typical of Mediterranean soils, containing 23 distinct cartographic units, the most representative being the Distric Fluvisols with inclusions of Luvisols Distric occupying 29.9% of the total study area, and Calcisols Luvic with inclusions of Luvisols endoleptic with 11.9% of the total area. Considering the obtained information on soil properties; ArcGIS was used to develop a map in which it was possible to ascertain the impact of the continuous practice of irrigation in this area. This allows us to put forward relevant conclusions on the need to access and monitor specific Mediterranean soils in order to mitigate the environmental impact of irrigation practices.

Using GIS towards the Characterization and Soil Mapping of the Caia Irrigation Perimeter

The Caia Irrigation Perimeter is an irrigation infrastructure implemented in 1968. As is often the case, the original soil map of this region (dated from 1961) does not have the detail needed to characterize a relatively small-sized zone, where intensive agricultural practices take place. Using FAO methodology and with the main goal of establishing a larger-scale soil map, adequate for the demands of a modern and intensive agriculture, we gathered the geological characterization of the study area and information about the topography, climate, and vegetation of the region. Using ArcGIS software, we overlapped this information and established a pre-map of soil resources. Based on this pre-map, we defined a set of detailed itineraries in the field, evenly distributed, in which soil samples were collected. In those distinct soil units, we opened several soil profiles, from which we selected 26 to analyze in the present study, since they characterized the existing diversity in terms of soil type and soil properties. Based on the work of verification, correction, and reinterpretation of the preliminary soil map, we reached a final soil map for the Caia Irrigation Perimeter, which is characterized by enormous heterogeneity, typical of Mediterranean soils, containing 23 distinct cartographic units, the most representative being the Distric Fluvisols with inclusions of Luvisols Distric occupying 29.9% of the total study area, and Calcisols Luvic with inclusions of Luvisols endoleptic with 11.9% of the total area. Considering the obtained information on soil properties; ArcGIS was used to develop a map in which it was possible to ascertain the impact of the continuous practice of irrigation in this area. This allows us to put forward relevant conclusions on the need to access and monitor specific Mediterranean soils in order to mitigate the environmental impact of irrigation practices.

Added value of geophysics-based soil mapping in agro-ecosystem simulations

There is an increased demand for quantitative high-resolution soil maps that enable within-field management. Commonly available soil maps are generally not suited for this purpose, but digital soil mapping and geophysical methods in particular allow soil information to be obtained with an unprecedented level of detail. However, it is often difficult to quantify the added value of such high-resolution soil information for agricultural management and agro-ecosystem modelling. In this study, a detailed geophysics-based soil map was compared to two commonly available general-purpose soil maps. In particular, the three maps were used as input for crop growth models to simulate leaf area index (LAI) of five crops for an area of ∼ 1 km2. The simulated development of LAI for the five crops was evaluated using LAI obtained from multispectral satellite images. Overall, it was found that the geophysics-based soil map provided better LAI predictions than the two general-purpose soil maps in terms of correlation coefficient R2, model efficiency (ME), and root mean square error (RMSE). Improved performance was most apparent in the case of prolonged periods of drought and was strongly related to the combination of soil characteristics and crop type.