Template-Type: ReDIF-Article 1.0
Author-Name: Gabriela Barančíková
Author-Workplace-Name: Prešov Regional Station, Soil Science and Conservation Research Institute in Bratislava, Prešov, Slovakia
Author-Name: Jarmila Makovníková
Author-Workplace-Name: Banská Bystrica Regional Station, Soil Science and Conservation Research Institute in Bratislava, Banská Bystrica Slovakia
Author-Name: Rastislav Skalský
Author-Workplace-Name: Department of Soil Science and Survey, Soil Science and Conservation Research Institute in Bratislava, Bratislava, Slovakia
Author-Name: Zuzana Tarasovičová
Author-Workplace-Name: Department of Soil Science and Survey, Soil Science and Conservation Research Institute in Bratislava, Bratislava, Slovakia
Author-Name: Martina Nováková
Author-Workplace-Name: Department of Remote Sensing and Informatics, Soil Science and Conservation Research Institute in Bratislava, Bratislava, Slovakia
Author-Name: Ján Halás
Author-Workplace-Name: Prešov Regional Station, Soil Science and Conservation Research Institute in Bratislava, Prešov, Slovakia
Author-Name: Monika Gutteková
Author-Workplace-Name: Prešov Regional Station, Soil Science and Conservation Research Institute in Bratislava, Prešov, Slovakia
Author-Name: Štefan Koco
Author-Workplace-Name: Prešov Regional Station, Soil Science and Conservation Research Institute in Bratislava, Prešov, Slovakia
Title: Simulation of soil organic carbon changes in Slovak arable land and their environmental aspects
Abstract: One of the key goals of the Thematic Strategy for Soil Protection is to maintain and improve soil organic carbon (SOC) stocks. A decline of SOC stocks is politically perceived as a serious threat to soil quality and functions. A suitable tool for acquiring the information on SOC stock changes is modelling. The RothC-26.3 model was applied for long-term modelling (1970-2007) of the SOC stock in the topsoil of croplands of Slovakia. Simulation results show a gradual increase in the SOC stock in the first phase of modelling (1970-1995) mainly due to higher carbon input in the soil. A significant linear correlation (r = 0.4**, n = 275) was found between carbon input and the final simulation of SOC stock. A close relationship between the SOC stock and soil production potential index representing the official basis for soil quality assessment in Slovakia was also determined and a polynomial relationship was found which describes the relation at the 95% confidence level. We have concluded from the results that balanced or positive changes in the SOC stock dynamics that are important for sustainable use of soils could be influenced positively or negatively in Slovakia by political decisions concerning the soil management. Moreover, the soil production potential index can be used as soil quality information support for such decision-making.
Keywords: agricultural management, long-term simulation, RothC model, soil organic carbon, soil quality
Journal: Soil and Water Research
Pages: 45-51
Volume: 7
Issue: 2
Year: 2012
DOI: 10.17221/38/2011-SWR
File-URL: http://swr.agriculturejournals.cz/doi/10.17221/38/2011-SWR.html
File-Format: text/html
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Handle: RePEc:caa:jnlswr:v:7:y:2012:i:2:id:38-2011-SWR

Template-Type: ReDIF-Article 1.0
Author-Name: Pooyan RAHIMY
Author-Workplace-Name: Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, Netherlands
Title: Effects of soil depth spatial variation on runoff simulation, using the Limburg Soil Erosion Model (LISEM), a case study in Faucon catchment, France
Abstract: Soil depth is an important parameter for models of surface runoff. Commonly used models require not only accurate estimates of the parameter but also its realistic spatial distribution. The objective of this study was to use terrain and environmental variables to map soil depth, comparing different spatial prediction methods by their effect on simulated runoff hydrographs. The study area is called Faucon, and it is located in the southeast of the French Alps. An additive linear model of "land cover class" and "overland flow distance to channel network" predicted the soil depth in the best way. Regression kriging (RK) used in this model gave better accuracy than ordinary kriging (OK). The soil depth maps, including conditional simulations, were exported to the hydrologic model of LISEM, where three synthetic rainfall scenarios were used. The hydrographs produced by RK and OK were significantly different only at rainfalls of low intensity or short duration.
Keywords: conditional simulation, Faucon, hydrograph, kriging, LISEM, soil depth
Journal: Soil and Water Research
Pages: 52-63
Volume: 7
Issue: 2
Year: 2012
DOI: 10.17221/25/2011-SWR
File-URL: http://swr.agriculturejournals.cz/doi/10.17221/25/2011-SWR.html
File-Format: text/html
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Handle: RePEc:caa:jnlswr:v:7:y:2012:i:2:id:25-2011-SWR

Template-Type: ReDIF-Article 1.0
Author-Name: Kazimierz BANASIK
Author-Workplace-Name: Department of River Engineering, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences - SGGW, Warsaw, Poland
Author-Name: Leszek HEJDUK
Author-Workplace-Name: Department of River Engineering, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences - SGGW, Warsaw, Poland
Title: Long-term changes in runoff from a small agricultural catchment
Abstract: River runoff is an important indicator of environmental changes, which usually include climate and/or land use changes, and is also the basis of catchment water management. This study presents results of monitoring and analysis of 48-year precipitation and runoff from a small agricultural catchment located in central Poland. No land use changes in that period have been reported. Mean monthly distributions of precipitation and runoff for the long-term period showed that July was the wettest month in respect of precipitation and a drier one in respect of runoff, averaging 12.9% and 5.2% of their annual values, respectively. To evaluate the trend of three annual hydrometeorological parameters, i.e. precipitation, runoff and runoff coefficient, the Mann-Kendall test was applied. It indicated no trend in respect of precipitation, and decreasing trends of runoff and runoff coefficient at a 95% level of significance. Linear approximation of the annual runoff values indicated a decrease in runoff of ca. 1.2 mm per year for the analysed period. A few other functions were also used for better approximation of runoff data.
Keywords: climate change, Poland, runoff variability, small watershed, trend analysis
Journal: Soil and Water Research
Pages: 64-72
Volume: 7
Issue: 2
Year: 2012
DOI: 10.17221/40/2011-SWR
File-URL: http://swr.agriculturejournals.cz/doi/10.17221/40/2011-SWR.html
File-Format: text/html
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Handle: RePEc:caa:jnlswr:v:7:y:2012:i:2:id:40-2011-SWR

Template-Type: ReDIF-Article 1.0
Author-Name: Sayed Farhad MOUSAVI
Author-Workplace-Name: Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran
Author-Name: Mohammad Javad AMIRI
Author-Workplace-Name: Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran
Title: Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system
Abstract: High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R2 = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R2 = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.
Keywords: Adaptive Neural-Based Fuzzy Inference System, nitrate pollution, water quality parameters
Journal: Soil and Water Research
Pages: 73-83
Volume: 7
Issue: 2
Year: 2012
DOI: 10.17221/46/2010-SWR
File-URL: http://swr.agriculturejournals.cz/doi/10.17221/46/2010-SWR.html
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Handle: RePEc:caa:jnlswr:v:7:y:2012:i:2:id:46-2010-SWR