Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale at- mospheric Weather Research and Forecasting...Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale at- mospheric Weather Research and Forecasting （WRF） model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin （184,319 km2）. The method is tested during three winter seasons （2012-2015）. The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent （SWE） and snow-covered areas （SCA）. The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data （ATBD-MOD 10） has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements （snow survey）.展开更多
Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but t...Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide suscept-ibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.展开更多
Soil organic carbon(SOC) has primary importance in terms of soil physics, soil fertility and even of climate change control. One hundred soil samples were taken from an intensively cultivated Cambisol to quantify SOC ...Soil organic carbon(SOC) has primary importance in terms of soil physics, soil fertility and even of climate change control. One hundred soil samples were taken from an intensively cultivated Cambisol to quantify SOC redistribution triggered by soil erosion under a subhumid climate, by the simultaneous application of diffuse reflectance(240–1 900 nm) and traditional physico-chemical methods.The representative sample points were collected from the solum along the slopes at the depth of 20–300 cm with a mean SOC content of 12 g kg^(-1). Hierarchical cluster analyses were performed based on the determined SOC results. The spatial pattern of the groups created were similar, and even though the classifications were not the same, diffuse reflectance had proven to be a suitable method for soil/sediment classification even within a given arable field. Both organic and inorganic carbon distributions were found to be a proper tool for estimations of past soil erosion processes. The SOC enrichment was found on two sedimentary spots with different geomorphological positions. Soil organic matter composition also differed between the two spots due to selective deposition of the delivered organic matter. The components with low-molecular-weight reached the bottom of the slope where they could leach into the profile, while the more polymerised organic matter compositions were delivered and deposited even before on a higher segment of the slope in an aggregated form. This spatial difference appeared below the uppermost tilled soil layer as well, referring the lower efficiency of conventional ploughing tillage in soil spatial homogenisation.展开更多
文摘Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale at- mospheric Weather Research and Forecasting （WRF） model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin （184,319 km2）. The method is tested during three winter seasons （2012-2015）. The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent （SWE） and snow-covered areas （SCA）. The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data （ATBD-MOD 10） has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements （snow survey）.
基金The study was supported by theÚNKP-17-2 New National Excellence Program of the Ministry of Human Capacities,Hungary[grant number ELTE/12421/65(2017)]This research was partly supported by the Thematic Excellence Programme,Industry and Digitization Subprogramme,NRDI Office[grant number ED_18-1-2019-0030].
文摘Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide suscept-ibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.
基金funded by the Hungarian Foundation(OTKA)(No.PD-100929)supported by the KutatóKari Kiválósági Támogatás-Research Centre of Excellence-11476-3/2016/FEKUTsupported by the János Bolyai Research Fellowship by the Hungarian Academy of Sciences
文摘Soil organic carbon(SOC) has primary importance in terms of soil physics, soil fertility and even of climate change control. One hundred soil samples were taken from an intensively cultivated Cambisol to quantify SOC redistribution triggered by soil erosion under a subhumid climate, by the simultaneous application of diffuse reflectance(240–1 900 nm) and traditional physico-chemical methods.The representative sample points were collected from the solum along the slopes at the depth of 20–300 cm with a mean SOC content of 12 g kg^(-1). Hierarchical cluster analyses were performed based on the determined SOC results. The spatial pattern of the groups created were similar, and even though the classifications were not the same, diffuse reflectance had proven to be a suitable method for soil/sediment classification even within a given arable field. Both organic and inorganic carbon distributions were found to be a proper tool for estimations of past soil erosion processes. The SOC enrichment was found on two sedimentary spots with different geomorphological positions. Soil organic matter composition also differed between the two spots due to selective deposition of the delivered organic matter. The components with low-molecular-weight reached the bottom of the slope where they could leach into the profile, while the more polymerised organic matter compositions were delivered and deposited even before on a higher segment of the slope in an aggregated form. This spatial difference appeared below the uppermost tilled soil layer as well, referring the lower efficiency of conventional ploughing tillage in soil spatial homogenisation.
