scholarly journals On the effect of the uncertainty in soil properties on the simulated hydrological state and fluxes at different spatio-temporal scales

2016 ◽  
Author(s):  
Gabriele Baroni ◽  
Matthias Zink ◽  
Rohini Kumar ◽  
Luis Samaniego ◽  
Sabine Attinger
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Spatial Scales ◽  
Strong Support ◽  
Grid Resolution ◽  
Temporal Scales ◽  
Soil Map ◽  
The Impact ◽  
Soil Spatial Variability

Abstract. Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of the uncertainties in soil properties. The methods are applied at the soil map of the upper Neckar catchment (Germany), as example. The uncertainties are propagated based on the distributed hydrological model mHM to assess the impact of the simulated state and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e., 500 m). However, several differences are identified by aggregating state and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed state and fluxes (e.g., soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer resolution soil information is (or not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of the uncertainty in soil properties. With that, soil map with additional information regarding the unresolved soil spatial variability would provide a strong support to hydrological modelling applications.

scholarly journals Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

2017 ◽  
Vol 21 (5) ◽  
pp. 2301-2320 ◽  
Author(s):  
Gabriele Baroni ◽  
Matthias Zink ◽  
Rohini Kumar ◽  
Luis Samaniego ◽  
Sabine Attinger
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Spatial Scales ◽  
Strong Support ◽  
Grid Resolution ◽  
Temporal Scales ◽  
Soil Map ◽  
The Impact ◽  
Soil Spatial Variability

Abstract. Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.

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

2021 ◽  
Author(s):  
José Rato Nunes ◽  
Luís Loures ◽  
António Lopez-Piñeiro ◽  
Ana Loures ◽  
Eric Vaz
Keyword(s):  
Soil Properties ◽  
Agricultural Practices ◽  
Soil Profiles ◽  
Soil Map ◽  
Mediterranean Soils ◽  
Soil Units ◽  
Total Study Area ◽  
The Impact ◽  
Irrigation Infrastructure

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.

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

2021 ◽  
Author(s):  
José Rato Nunes ◽  
Luís Loures ◽  
António Lopez-Piñeiro ◽  
Ana Loures ◽  
Eric Vaz
Keyword(s):  
Soil Properties ◽  
Agricultural Practices ◽  
Soil Profiles ◽  
Soil Map ◽  
Mediterranean Soils ◽  
Soil Units ◽  
Total Study Area ◽  
The Impact ◽  
Irrigation Infrastructure

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.

scholarly journals Regionalized Terrestrial Ecotoxicity Assessment of Copper-Based Fungicides Applied in Viticulture

2018 ◽  
Vol 10 (7) ◽  
pp. 2522 ◽  
Author(s):  
Ivan Viveros Santos ◽  
Cécile Bulle ◽  
Annie Levasseur ◽  
Louise Deschênes
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Regression Models ◽  
Management Practices ◽  
Metal Speciation ◽  
Agricultural Systems ◽  
Geographical Variability ◽  
Terrestrial Ecotoxicity ◽  
The Impact ◽  
Growing Region

Life cycle assessment has been recognized as an important decision-making tool to improve the environmental performance of agricultural systems. Still, there are certain modelling issues related to the assessment of their impacts. The first is linked to the assessment of the metal terrestrial ecotoxicity impact, for which metal speciation in soil is disregarded. In fact, emissions of metals in agricultural systems contribute significantly to the ecotoxic impact, as do copper-based fungicides applied in viticulture to combat downy mildew. Another issue is linked to the ways in which the intrinsic geographical variability of agriculture resulting from the variation of management practices, soil properties, and climate is addressed. The aim of this study is to assess the spatial variability of the terrestrial ecotoxicity impact of copper-based fungicides applied in European vineyards, accounting for both geographical variability in terms of agricultural practice and copper speciation in soil. This first entails the development of regionalized characterization factors (CFs) for the copper used in viticulture and then the application of these CFs to a regionalized life-cycle inventory that considers different management practices, soil properties, and climates in different regions, namely Languedoc-Roussillon (France), Minho (Portugal), Tuscany (Italy), and Galicia (Spain). There are two modelling alternatives to determine metal speciation in terrestrial ecotoxicity: (a) empirical regression models; and (b) WHAM 6.0, the geochemical speciation model applied according to the soil properties of the Harmonized World Soil Database (HWSD). Both approaches were used to compute and compare regionalized CFs with each other and with current IMPACT 2002+ CF. The CFs were then aggregated at different spatial resolutions—global, Europe, country, and wine-growing region—to assess the uncertainty related to spatial variability at the different scales and applied in the regionalized case study. The global CF computed for copper terrestrial ecotoxicity is around 3.5 orders of magnitude lower than the one from IMPACT 2002+, demonstrating the impact of including metal speciation. For both methods, an increase in the spatial resolution of the CFs translated into a decrease in the spatial variability of the CFs. With the exception of the aggregated CF for Portugal (Minho) at the country level, all the aggregated CFs derived from empirical regression models are greater than the ones derived from the method based on WHAM 6.0 within a range of 0.2 to 1.2 orders of magnitude. Furthermore, CFs calculated with empirical regression models exhibited a greater spatial variability with respect to the CFs derived from WHAM 6.0. The ranking of the impact scores of the analyzed scenarios was mainly determined by the amount of copper applied in each wine-growing region. However, finer spatial resolutions led to an impact score with lower uncertainty.

Measuring temporal and spatial scales of compound events in the United Kingdom

2020 ◽  
Author(s):  
Aloïs Tilloy ◽  
Bruce Malamud ◽  
Hugo Winter ◽  
Amelie Joly-Laugel
Keyword(s):  
Natural Hazards ◽  
Clustering Algorithm ◽  
Spatial Scales ◽  
Wind Gust ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Extreme Wind ◽  
Compound Events ◽  
Temporal And Spatial ◽  
The Impact

<p>Multi-hazard events have the potential to cause damages to infrastructures and people that may differ greatly from the associated risks posed by singular hazards. Interrelations between natural hazards also operate on different spatial and temporal scales than single natural hazards. Therefore, the measure of spatial and temporal scales of natural hazard interrelations still remain challenging. The objective of this study is to refine and measure temporal and spatial scales of natural hazards and their interrelations by using a spatiotemporal clustering technique. To do so, spatiotemporal information about natural hazards are extracted from the ERA5 climate reanalysis. We focus here on the interrelation between two natural hazards (extreme precipitation and extreme wind gust) during the period 1969-2019 within a region including Great Britain and North-West France. The characteristics of our input data (i.e. important size, high noise level) and the absence of assumption about the shape of our hazard clusters guided the choice of a clustering algorithm toward the DBSCAN clustering algorithm. To create hazard clusters, we retain only extreme values (above the 99% quantile) of precipitation and wind gust. We analyse the characteristics (eg., size, duration, season, intensity) of single and compound events of rain and wind impacting our study area. We then measure the impact of the spatial and temporal scales defined in this study on the nature of the interrelation between extreme rainfall and extreme wind in the UK. We therefore demonstrate how this methodology can be applied to a different set of natural hazards.</p>

scholarly journals Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 194
Author(s):  
Mohamed A. E. AbdelRahman ◽  
Yasser M. Zakarya ◽  
Mohamed M. Metwaly ◽  
Georgios Koubouris
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Nutrient Management ◽  
Prediction Method ◽  
Available Phosphorus ◽  
Detailed Knowledge ◽  
Agricultural Planning ◽  
Management Recommendations ◽  
Soil Spatial Variability

Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO3). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.

Impact of variability in measured and simulated tundra snowpack properties on heat transfer metrics

2021 ◽  
Author(s):  
Victoria Dutch ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Mel Sandells ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Field Measurements ◽  
Density Profiles ◽  
Community Land Model ◽  
Spatially Distributed ◽  
Snowpack Properties ◽  
The Impact

<p>Tundra snowpack properties are highly heterogenous over a variety of spatial scales and evolve over the course of the winter. Variations in snowpack properties such as snow density and microstructure control the transfer of heat through the snowpack. Thermal properties of the snowpack impact the subnivean environment; snow insulates the underlying soil, allowing films of liquid water to remain unfrozen, enabling biological processes to take place. In this study, field measurements from four field campaigns across two different winters (March and November 2018, January and March 2019) are used to capture and constrain the spatial variability of the snowpack. These include 1050 spatially distributed Snow MicroPenetrometer (SMP) profiles throughout the Trail Valley Creek catchment in the Northwest Territories, Canada. Bespoke coefficients for tundra snowpacks were calculated (based on the work of King et al., 2020) to convert raw SMP force measurements to densities. This allowed density changes of vertical profiles to be assessed and spatial variability in the thickness and properties of three snowpack layers (wind slab, indurated hoar and depth hoar) to be quantified. 105 needleprobe measurements from 37 snowpits were used to contrast the density and thermal conductivity of snowpack layers, as well as thermal conductivities estimated from recalibrated SMP density profiles. These in-situ measurements will be compared to 1-D simulations of snowpack properties from the Community Land Model (PTCLM 5.0) over the two winter seasons. The impact of snowpack layering on snow heat transfer metrics will be investigated using both 2-layer (wind slab: depth hoar) and 3-layer (wind slab: indurated hoar: depth hoar) snowpack configurations. The spatial variability of heat transfer metrics across the Trail Valley Creek catchment will also be considered.</p>

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