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DCCEEW_Geospatial - Average Relative Nutrient Loss Rate Due to Water Erosion for Total Nitrogen, Total Phosphorus and Soil Organic Carbon
This dataset represents the average of the relative nutrient loss rates due to water erosion for the three nutrients total nitrogen, total phosphorus and soil organic carbon. The dataset is masked to cropping and grazing lands. The units are percentage/year. Relative nutrient loss is calculated as the annual loss of nutrient from the top 5 cm of soil relative to the total stock of each nutrient in the full depth of the soil profile. Annual erosion rate data are from Teng et al. (2016) and soil nutrient data are from the Soil and Landscape Grid of Australia. For a full description of the methods used to generate this datset see McKenzie et al. (2017).For raster data download follow link: Hillslope Erosion download To present the average relative nutrient loss rate data in Figure 4.5 in McKenzie et al. (2017), the data were divided into seven classes using percentiles as the class breaks. That is, 20 % of the grid cells fell into each of the first four classes, 10 % of the grid cells into the fifth class, and 5 % into each of the sixth and seventh classes. The actual average nutrient loss rate values which represent those class breaks are listed below:0-20th percentile: < 0.003 %/y20-40th percentile: 0.003 - 0.005 %/y40-60th percentile: 0.005 - 0.009 %/y60-80th percentile: 0.009 - 0.019 %/y80-90th percentile: 0.019 - 0.045 %/y90-95th percentile: 0.045 - 0.098 %/y95-100th percentile: > 0.098 %/yNOTE: The associated dataset is available on request to geospatial@dcceew.gov.au
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DCCEEW_Geospatial - Hillslope Erosion AvgNutrLossRate pct.tif
공공데이터포털
This dataset represents the average of the relative nutrient loss rates due to water erosion for the three nutrients total nitrogen, total phosphorus and soil organic carbon. The dataset is masked to cropping and grazing lands. The units are percentage/year. Relative nutrient loss is calculated as the annual loss of nutrient from the top 5 cm of soil relative to the total stock of each nutrient in the full depth of the soil profile. Annual erosion rate data are from Teng et al. (2016) and soil nutrient data are from the Soil and Landscape Grid of Australia. For a full description of the methods used to generate this datset see McKenzie et al. (2017).To present the average relative nutrient loss rate data in Figure 4.5 in McKenzie et al. (2017), the data were divided into seven classes using percentiles as the class breaks. That is, 20 % of the grid cells fell into each of the first four classes, 10 % of the grid cells into the fifth class, and 5 % into each of the sixth and seventh classes. The actual average nutrient loss rate values which represent those class breaks are listed below:0-20th percentile: < 0.003 %/y20-40th percentile: 0.003 - 0.005 %/y40-60th percentile: 0.005 - 0.009 %/y60-80th percentile: 0.009 - 0.019 %/y80-90th percentile: 0.019 - 0.045 %/y90-95th percentile: 0.045 - 0.098 %/y95-100th percentile: > 0.098 %/y
Average annual soil loss and sediment yield by National Land Cover Dataset land cover class for the conterminous US
공공데이터포털
This data set gives estimates of erosion and sediment yield based on our study for the conterminous US by land cover class. The data correspond to Fig 4 in the manuscript. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).
Average annual soil loss and sediment yield avoided for each National Land Cover Dataset land cover class for the conterminous US
공공데이터포털
This data set gives estimates of erosion and sediment yield avoided due to the presence of natural land cover. These estimates are given for each land cover class and summarized for the conterminous US. The data correspond to Fig 6 in the manuscript. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).
Average Annual Soil Loss and Sediment Yield Calculated for All 12-Digit HUCs in the Conterminous US
공공데이터포털
This data set contains estimated average annual soil loss (Mg ha-1 yr-1) and estimated average annual sediment yield (Mg ha-1 yr-1) aggregated by HUC-12 watersheds for the conterminous US. This data set corresponds to Fig 3 in the manuscript. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).
Comparison of Soil Loss Estimates Derived using the Revised Universal Soil Loss Equation with those Derived by the Iowa State University's Daily Erosion Project for 12-digit HUCs.
공공데이터포털
This data contains a comparison between the soil loss values we calculated using RUSLE and those produced by the Iowa State University's Daily Erosion Project (DEP). The comparison is done for almost 5,000 12-digit HUC's in Iowa, and parts of Minnesota, Nebraska, Kansas, and Missouri. The DEP uses the Water Erosion Prediction Project (WEPP) hillslope model with high temporal resolution, Next-Generation Weather 200 RADAR (NEXRAD) precipitation, and crop specific parameters such as C and P factors obtained from the confidential NRI database The comparison between RUSLE and DEP was made for HUC-12s with greater than 75% agricultural land cover. This threshold was used because DEP only models agricultural erosion, while our RUSLE-derived HUC-12 estimates include all land cover types. This data set corresponds to Fig 2 in the manuscript. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).
Comparison of Soil Loss Estimates Derived using the Revised Universal Soil Loss Equation with those Derived by the Iowa State University's Daily Erosion Project for 12-digit HUCs.
공공데이터포털
This data contains a comparison between the soil loss values we calculated using RUSLE and those produced by the Iowa State University's Daily Erosion Project (DEP). The comparison is done for almost 5,000 12-digit HUC's in Iowa, and parts of Minnesota, Nebraska, Kansas, and Missouri. The DEP uses the Water Erosion Prediction Project (WEPP) hillslope model with high temporal resolution, Next-Generation Weather 200 RADAR (NEXRAD) precipitation, and crop specific parameters such as C and P factors obtained from the confidential NRI database The comparison between RUSLE and DEP was made for HUC-12s with greater than 75% agricultural land cover. This threshold was used because DEP only models agricultural erosion, while our RUSLE-derived HUC-12 estimates include all land cover types. This data set corresponds to Fig 2 in the manuscript. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).
Data for Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016
공공데이터포털
These are the soil quality data for each county (listed by fips code) for each scenario. This dataset is associated with the following publication: Zhang, X., T. Lark, C. Clark, Y. Yuan, and S. LeDuc. Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 16: 1-14, (2021).
Data for Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016
공공데이터포털
These are the soil quality data for each county (listed by fips code) for each scenario. This dataset is associated with the following publication: Zhang, X., T. Lark, C. Clark, Y. Yuan, and S. LeDuc. Grassland-to-cropland conversion increased soil, nutrient, and carbon losses in the US Midwest between 2008 and 2016. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 16: 1-14, (2021).
DCCEEW_Geospatial - Soil acidification risk
공공데이터포털
An index of the risk of soil acidification based only on soil characteristics and current land use has been calculated using the following indicators:Lime requirements (LR, 5 classes) – calculated using the current pH and buffering capacity (see Figure 2.4).The likely NAAR – estimated using the classes defined by the provisional NAAR ranking (see Table 2.3) and mapped using the Australian Land Use and Management Classification (Table 2.4) (5 classes ) (see Figure 2.5) This spatial layer is the result of combining these two indices according to Table 2.4.Soil acidification is likely to be a problem in areas with a high risk ranking. This is useful for framing priorities for interventions but the map provides no information on the effectiveness of current land management. This important consideration is much harder to determine. Risk of Acidification values: 1 = Low, 2 = Medium, 3 = High risk