TY - JOUR A1 - Zhang, Zhuodong A1 - Wieland, Ralf A1 - Reiche, Matthias A1 - Funk, Roger A1 - Hoffmann, Carsten A1 - Li, Yong A1 - Sommer, Michael T1 - Identifying sensitive areas to wind erosion in the xilingele grassland by computational fluid dynamics modelling JF - Ecological informatics : an international journal on ecoinformatics and computational ecolog N2 - In order to identify the areas in the Xilingele grassland which are sensitive to wind erosion, a computational fluid dynamics model (CFD-WEM) was used to simulate the wind fields over a region of 37 km(2) which contains different topography and land use types. Previous studies revealed the important influences of topography and land use on wind erosion in the Xilingele grassland. Topography influences wind fields at large scale, and land use influences wind fields near the ground. Two steps were designed to implement the CFD wind simulation, and they were respectively to simulate the influence of topography and surface roughness on the wind. Digital elevation model (DEM) and surface roughness length were the key inputs for the CFD simulation. The wind simulation by CFD-WEM was validated by a wind data set which was measured simultaneously at six positions in the field. Three scenarios with different wind velocities were designed based on observed dust storm events, and wind fields were simulated according to these scenarios to predict the sensitive areas to wind erosion. General assumptions that cropland is the most sensitive area to wind erosion and heavily and moderately grazed grasslands are both sensitive etc. can be refined by the modelling of CFD-WEM. Aided by the results of this study, the land use planning and protection measures against wind erosion can be more efficient. Based on the case study in the Xilingele grassland, a method of regional wind erosion assessment aided by CFD wind simulation is summarized. The essence of this method is a combination of CFD wind simulation and determination of threshold wind velocity for wind erosion. Because of the physically-based simulation and the flexibility of the method, it can be generalised to other regions. KW - Sensitive areas KW - Wind erosion KW - Computational fluid dynamics KW - Grassland KW - Surface roughness Y1 - 2012 U6 - https://doi.org/10.1016/j.ecoinf.2011.12.002 SN - 1574-9541 VL - 8 IS - 5 SP - 37 EP - 47 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Zhang, Zhuodong A1 - Wieland, Ralf A1 - Reiche, Matthias A1 - Funk, Roger A1 - Hoffmann, Carsten A1 - Li, Yong A1 - Sommer, Michael T1 - Wind modelling for wind erosion research by open source computational fluid dynamics JF - Ecological informatics : an international journal on ecoinformatics and computational ecolog N2 - The open source computational fluid dynamics (CFD) wind model (CFD-WEM) for wind erosion research in the Xilingele grassland in Inner Mongolia (autonomous region, China) is compared with two open source CFD models Gerris and OpenFOAM. The evaluation of these models was made according to software technology, implemented methods, handling, accuracy and calculation speed. All models were applied to the same wind tunnel data set. Results show that the simplest CFD-WEM has the highest calculation speed with acceptable accuracy, and the most powerful OpenFOAM produces the simulation with highest accuracy and the lowest calculation speed. Gerris is between CFD-WEM and OpenFOAM. It calculates faster than OpenFOAM, and it is capable to solve different CFD problems. CFD-WEM is the optimal model to be further developed for wind erosion research in Inner Mongolia grassland considering its efficiency and the uncertainties of other input data. However, for other applications using CFD technology, Gerris and OpenFOAM can be good choices. This paper shows the powerful capability of open source CFD software in wind erosion study, and advocates more involvement of open source technology in wind erosion and related ecological researches. KW - Computational fluid dynamics KW - Wind modelling KW - Open source KW - Wind erosion KW - Gerris KW - OpenFOAM KW - SAMT Y1 - 2011 U6 - https://doi.org/10.1016/j.ecoinf.2011.02.001 SN - 1574-9541 VL - 6 IS - 5 SP - 316 EP - 324 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Zhang, Zhuo-dong A1 - Wieland, Ralf A1 - Reiche, Matthias A1 - Funk, Roger A1 - Hoffmann, Carsten A1 - Li, Yong A1 - Sommer, Michael T1 - A computational fluid dynamics model for wind simulation: model implementation and experimental validation JF - Journal of Zhejiang University : an international journal ; Science A, Applied physics & engineering : an international applied physics & engineering journal N2 - To provide physically based wind modelling for wind erosion research at regional scale, a 3D computational fluid dynamics (CFD) wind model was developed. The model was programmed in C language based on the Navier-Stokes equations, and it is freely available as open source. Integrated with the spatial analysis and modelling tool (SAMT), the wind model has convenient input preparation and powerful output visualization. To validate the wind model, a series of experiments was conducted in a wind tunnel. A blocking inflow experiment was designed to test the performance of the model on simulation of basic fluid processes. A round obstacle experiment was designed to check if the model could simulate the influences of the obstacle on wind field. Results show that measured and simulated wind fields have high correlations, and the wind model can simulate both the basic processes of the wind and the influences of the obstacle on the wind field. These results show the high reliability of the wind model. A digital elevation model (DEM) of an area (3800 m long and 1700 m wide) in the Xilingele grassland in Inner Mongolia (autonomous region, China) was applied to the model, and a 3D wind field has been successfully generated. The clear implementation of the model and the adequate validation by wind tunnel experiments laid a solid foundation for the prediction and assessment of wind erosion at regional scale. KW - Wind model KW - Computational fluid dynamics (CFD) KW - Wind erosion KW - Wind tunnel experiments KW - Spatial analysis and modelling tool (SAMT) KW - Open source Y1 - 2012 U6 - https://doi.org/10.1631/jzus.A1100231 SN - 1673-565X VL - 13 IS - 4 SP - 274 EP - 283 PB - Zhejiang University Press CY - Hangzou ER - TY - JOUR A1 - Wilken, Florian A1 - Baur, Martin A1 - Sommer, Michael A1 - Deumlich, Detlef A1 - Bens, Oliver A1 - Fiener, Peter T1 - Uncertainties in rainfall kinetic energy-intensity relations for soil erosion modelling JF - Catena : an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution N2 - For bare soil conditions, the most important process driving and initiating splash and interrill erosion is the detachment of soil particles via raindrop impact. The kinetic energy of a rainfall event is controlled by the drop size and fall velocity distribution, which is often directly or indirectly implemented in erosion models. Therefore, numerous theoretical functions have been developed for the estimation of rainfall kinetic energy from available rainfall intensity measurements. The aim of this study is to assess differences inherent in a wide number of kinetic energy-rainfall intensity (KE-I) relations and their role in soil erosion modelling. Therefore, 32 KE-I relations are compared against measured rainfall energies based on optical distrometer measurements carried out at five stations of two substantially different rainfall regimes. These allow for continuous high-resolution (1-min) direct measurements of rainfall kinetic energies from a detailed spectrum of measured drop sizes and corresponding fall velocities. To quantify the effect of different KE-I relations on sediment delivery, we apply the erosion model WATEM/SEDEM in an experimental setup to four catchments of NE-Germany. The distrometer data shows substantial differences between measured and theoretical models of drop size and fall velocity distributions. For low intensities the number of small drops is overestimated by the Marshall and Palmer (1948; MP) drop size distribution, while for high intensities the proportion of large drops is overestimated by the MP distribution. The distrometer measurements show a considerable proportion of large drops falling at slower velocities than predicted by the Gunn and Kinzer (1949) terminal velocity model. For almost all rainfall events at all stations, the KE-I relations predicted higher cumulative kinetic energy sums compared to the direct measurements of the optical distrometers. The different KE-I relations show individual characteristics over the course of rainfall intensity levels. Our results indicate a high sensitivity (up to a range from 10 to 27 t ha(-1)) of the simulated sediment delivery related to different KE-I relations. Hence, the uncertainty associated with KE-I relations for soil erosion modelling is of critical importance. KW - Rainfall kinetic energy KW - Drop size distribution KW - Drop fall velocity KW - Soil erosion modelling KW - Optical distrometer Y1 - 2018 U6 - https://doi.org/10.1016/j.catena.2018.07.002 SN - 0341-8162 SN - 1872-6887 VL - 171 SP - 234 EP - 244 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Wieland, Ralf A1 - Dalchow, Claus A1 - Sommer, Michael A1 - Fukuda, Kyoko T1 - Multi-Scale Landscape Analysis (MSLA) a method to identify correlation of relief with ecological point data JF - Ecological informatics : an international journal on ecoinformatics and computational ecolog N2 - A common problem in ecology is identifying the relationship between relief and site properties obtainable only by point measurements. The method of Multi-Scale Landscape Analysis (MSLA) identifies such correlations. MSLA combines frequency filtering of the digital elevation model (DEM) with an estimation of the optimum filter coefficients using an optimization procedure. Tested using point data of soil decarbonation from a German young moraine landscape, MSLA provided significant results. Implemented within open source software SAMT. MSLA is comfortable and flexible to use, offering applications for numerous other spatial analysis problems. KW - Landscape structure KW - DEM KW - Fourier transformation KW - Wavelet transformation KW - Singular value decomposition KW - SAMT Y1 - 2011 U6 - https://doi.org/10.1016/j.ecoinf.2010.09.002 SN - 1574-9541 VL - 6 IS - 2 SP - 164 EP - 169 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Wehrhan, Marc A1 - Sommer, Michael T1 - A parsimonious approach to estimate soil organic carbon applying Unmanned Aerial System (UAS) multispectral imagery and the topographic position index in a heterogeneous soil landscape JF - Remote sensing / Molecular Diversity Preservation International (MDPI) N2 - Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset. KW - Unmanned Aerial System (UAS) KW - multispectral KW - Topographic Position Index KW - (TPI) KW - Multiple Linear Regression (MLR) KW - soil organic carbon (SOC) KW - agriculture KW - erosion KW - soil landscape KW - hummocky ground moraine Y1 - 2021 U6 - https://doi.org/10.3390/rs13183557 SN - 2072-4292 VL - 13 IS - 18 PB - MDPI CY - Basel ER - TY - GEN A1 - Wehrhan, Marc A1 - Rauneker, Philipp A1 - Sommer, Michael T1 - UAV-Based estimation of carbon exports from heterogeneous soil landscapes BT - a case study from the CarboZALF experimental area T2 - Sensors N2 - The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b(899). The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 451 KW - VI KW - soil landscape KW - carbon export KW - agriculture KW - multispectral KW - UAV Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407706 ER - TY - JOUR A1 - Wehrhan, Marc A1 - Rauneker, Philipp A1 - Sommer, Michael T1 - UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes-A Case Study from the CarboZALF Experimental Area JF - SENSORS N2 - The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b(899). The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part. KW - VI KW - soil landscape KW - carbon export KW - agriculture KW - multispectral KW - UAV Y1 - 2016 U6 - https://doi.org/10.3390/s16020255 SN - 1424-8220 VL - 16 PB - MDPI CY - Basel ER - TY - JOUR A1 - Wehrhan, Marc A1 - Puppe, Daniel A1 - Kaczorek, Danuta A1 - Sommer, Michael T1 - Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment BT - results from UAS-based high-resolution remote sensing JF - Biogeosciences : BG N2 - Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epige-jos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epige-jos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Sim(-2)), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales. Y1 - 2021 U6 - https://doi.org/10.5194/bg-18-5163-2021 SN - 1726-4170 SN - 1726-4189 VL - 18 IS - 18 SP - 5163 EP - 5183 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Webber, Heidi A1 - Lischeid, Gunnar A1 - Sommer, Michael A1 - Finger, Robert A1 - Nendel, Claas A1 - Gaiser, Thomas A1 - Ewert, Frank T1 - No perfect storm for crop yield failure in Germany JF - Environmental research letters N2 - Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments. KW - crop yield failure KW - extreme events KW - support vector machine KW - process-based crop model KW - Germany Y1 - 2020 U6 - https://doi.org/10.1088/1748-9326/aba2a4 SN - 1748-9326 VL - 15 IS - 10 PB - IOP Publ. Ltd. CY - Bristol ER -