Modelling of nitrogen turnover and leaching in Saxony

Gebel, Michael; Halbfass, Stefan; Bürger, Stephan; Friese, Holm; Naumann, S.

doi:10.5194/adgeo-27-139-2010

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…The starting point of the computation of retention are the modelling results of diffuse and point related nitrogen input per grid element, described in detail in Gebel et al (2010) and Halbfaß et al (2010). The grid elements represent the spatial properties of a watershed area.…”

Section: Resultsmentioning

confidence: 99%

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Modelling of long term nitrogen retention in surface waters

2010

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Abstract. In order to derive measures to reduce nutrient loadings into waters in Saxony, we calculated nitrogen inputs with the model STOFFBILANZ on the regional scale. Thereby we have to compare our modelling results to measured loadings at the river basin outlets, considering long term nutrient retention in surface waters. The most important mechanism of nitrogen retention is the denitrification in the contact zone of water and sediment, being controlled by hydraulic and micro-biological processes. Retention capacity is derived on the basis of the nutrient spiralling concept, using water residence time (hydraulic aspect) and time-specific N-uptake by microorganisms (biological aspect). Short time related processes of mobilization and immobilization are neglected, because they are of minor importance for the derivation of measures on the regional scale.

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Section: Resultsmentioning

confidence: 99%

“…according to the Water Framework Directive. All information about the quantification of nutrient input within the catchment areas considering its sources and pathways is described in detail in Halbfaß et al (2010) and Gebel et al (2010). …”

Section: Introductionmentioning

confidence: 99%

Modelling of long term nitrogen retention in surface waters

2010

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“…For this seepage water amounts were estimated from climate and soil data. Model calculations are conducted according to the assumptions of the model "Stoffbilanz" (Gebel et al, 2010). Results and Discussion Table 2 shows that the prediction of N fertilizer demand highly influenced N fertilizer rates.…”

Section: Methodsmentioning

confidence: 99%

Organic matter fractions and N mineralization in vegetable‐cropped sandy soils

Jegajeevagan

Sleutel

Ameloot

et al. 2013

Soil Use and Management

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Soil organic nitrogen mineralization rates and possible predictors thereof were investigated for vegetable‐growing soils in Belgium. Soil organic matter (SOM) was fractionated into sand (> 53 μm) and silt+clay (< 53 μm) fractions. The latter fraction was further separated into 6%NaOCl‐oxidation labile (6%NaOCl‐ox) and resistant N and C and subsequently into 10%HF‐extractable (mineral bound) and resistant (recalcitrant) N and C. The N mineralization turnover rate (% of soil N/year) correlated with several of the investigated N or C fractions and stepwise linear regression confirmed that the 6%NaOCl‐ox N was the best predictor. However, the small R² (0.42) of the regression model suggests that soil parameters other than the soil fractions isolated here would be required to explain the significant residual variation in N mineralization rate. A next step could be to look for alternative SOM fractionations capable of isolating bioavailable N. However, it would appear that the observed relationships between N fractions and N mineralization may not be causal but indirect. The number of vegetable crops per rotation did not influence N mineralization, but it did influence 6%NaOCl‐ox N, probably as an effect of differences in crop residues returned and organic manure supply. However, the nature of this relation between management, SOM quality and N mineralization is not clear. Explanation of correlations between N mineralization and presumed bioavailable N fractions, like the 6%NaOCl‐ox N, requires further mechanistic elucidation of the N mineralization process.

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“…The web GIS-based software STOFFBI-LANZ (Gebel et al 2010(Gebel et al , 2012a(Gebel et al , 2012b was used to calculate runoff, sediment and nutrient input in the whole catchment area (variable grid size from 125 × 125 m up to 500 × 500 m), as well as in the subcatchment Sheyuchuan (fixed grid size of 50 × 50 m). The approach is suitable for modeling on regional scale and requires a minimum of parameters to run the model.…”

Section: Methodology and Technologymentioning

confidence: 99%

Web GIS-based simulation of water fluxes in the Miyun catchment area

Gebel¹,

Meißner²,

Halbfass³

et al. 2014

iForest

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Change of land use and agricultural management, combined with severe droughts during the last two decades, are the main reasons for a strong decrease of water quality and quantity in the Miyun reservoir (China). Due to semi-arid monsoon climate, episodic water fluxes have to be considered adequately in an integrated water resources management in the total catchment area. To get insight into runoff generation processes, sediment and nutrient source areas and transfer dynamics, a monitoring approach was established in the Miyun catchment area spanning from plot measurements with weighable gravitation lysimeters and in situ measurements to a subcatchment scale of approx. 10 km 2 . One of these subcatchments (Sheyuchuan) was selected to adapt the methodologies to the regional runoff pattern using the web GIS-based modeling tool STOFFBILANZ. Calibration of the daily runoff modeling is based on the lysimeter observations from Shixia station and runoff observations in Sheyuchuan subcatchment for the reference year 2011. Despite the fact that the climatic water balance in the region is negative, the precipitation event on 24 July 2011 with a rainfall of 107 mm caused a significant leachate at the lysimeter. The daily modeling for the lysimeter core by the FAO-ETc-adj approach showed a similar deep percolation and a similar distribution of evapotranspiration for the year considered. In the next step, the calibrated model was applied to the subcatchment Sheyuchuan to compare the results of direct runoff and groundwater runoff (deep percolation) with the runoff observations at the weir. Deep percolation was extremely small (1.7 mm yr -1 ), and direct runoff was 26.9 mm yr -1 in 2011. In comparison to the monitoring results at the weir, a disagreement in the quantity of runoff was found between the observation on the one hand and the simulation on the other hand, which can be explained by water storage in reservoirs for irrigation purposes. Additionally, the water fluxes in the total catchment area were simulated at meso-scale, obtaining a good correlation with observed values.Keywords: Bottom-up, Direct Runoff, Lysimeter, Meso-Scale, Miyun, Model, Percolation a grid with variable cell size (125 × 125 m up to 500 × 500 m; approx. 366 000 grid cells) by using a QUADTREE procedure (Fig. 1). Cropland as well as residential areas got the highest resolution in order to optimize the indication of critical source areas.The major part of the Miyun catchment area is mountainous with steep inclinations with elevation ranging from 60 to 2200 m a.s.l. Annual average precipitation in the sub-humid continental monsoon climate is lower than 500 mm yr -1 and occurs mainly in summer, whereas winter and spring are extremely dry (Chen et al. 2010). Precipitation increases from east to west, annual average temperature increases from south-east to north-west, ranging from 6.3 to 10.9 °C. Land cover is dominated by rocky mountain forest (54.8%) with a smooth transition to shrubbery, heath and dry grassland (31.5%). Cropland (10.7%) can be foun...

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Modelling of nitrogen turnover and leaching in Saxony

Cited by 11 publications

References 10 publications

Modelling of long term nitrogen retention in surface waters

Modelling of long term nitrogen retention in surface waters

Organic matter fractions and N mineralization in vegetable‐cropped sandy soils

Web GIS-based simulation of water fluxes in the Miyun catchment area

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