Comparison of Land Use Area Estimates from Three Different Data Sources for the Upper Mississippi River Basin

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS). The comparison was performed for 1992 versions of the datasets because that was the only consistent year available among all three data sources. The results show that differences in land use area estimates increased as comparisons shifted from the entire UMRB to smaller 4- and 8-digit watershed regions (as expected). However, the area estimates for the major land use categories remained generally consistent among all three data sets across each level of spatial comparison. Differences in specific crop and grass/forage land use categories were magnified with increasing refinement of the spatial unit of comparison, especially for close-grown crops, pasture, and alfalfa/hayland. The NLCD close-grown crop area estimates appear very weak relative to the NRI and HUMUS, and the lack of specific crop land use estimates limits its viability for UMRB agricultural-based modeling scenarios. However, the NLCD is a key source of non-agricultural land use data for HUMUS and supplemental wetland land use area estimates for the NRI. We conclude that comparisons between more recent versions of the data sets (i.e., 1997 NRI, 1997 or 2002 Agricultural Census, and 2001 NLCD) would not result in significant additional insights and that the 1997 NRI is a viable land use data source for current CARD UMRB water quality modeling studies. However, adoption of other land use data such as USDA-NASS remote sensing data should be investigated

Structural, electronic and magnetic properties of some early vs late transition dimetallaborane clusters - A theoretical investigation

International audienceThe strength of DFT methods in analyzing the electronic and magnetic properties of a series of dimetallaboranes of varied stoichiometry and architectural core, namely M2B3, M2B4 and M2B5 with both early- and late-transition metals is demonstrated. In particular, the observed 1H and 11B chemical shifts of most of the studied compounds are reproduced with a good accuracy of a few ppm at the DFT-GIAO BP86/TZ2P/SC level for the compounds with first-row transition metal elements and at the B3LYP/TZ2P/SO level for those with second- and third-row transition metal elements. This allows structural applications in elucidating the number and the location of bridging hydrogen atoms in experimentally poorly characterized metallaboranes such as (Cp*Cr)2B4H

Ensemble 3D CNN and U-Net-based brain tumour classification with MKKMC segmentation

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer.   This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers

Comparison of Land Use Area Estimates from Three Different Data Sources for the Upper Mississippi River Basin

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS). The comparison was performed for 1992 versions of the datasets because that was the only consistent year available among all three data sources. The results show that differences in land use area estimates increased as comparisons shifted from the entire UMRB to smaller 4- and 8-digit watershed regions (as expected). However, the area estimates for the major land use categories remained generally consistent among all three data sets across each level of spatial comparison. Differences in specific crop and grass/forage land use categories were magnified with increasing refinement of the spatial unit of comparison, especially for close-grown crops, pasture, and alfalfa/hayland. The NLCD close-grown crop area estimates appear very weak relative to the NRI and HUMUS, and the lack of specific crop land use estimates limits its viability for UMRB agricultural-based modeling scenarios. However, the NLCD is a key source of non-agricultural land use data for HUMUS and supplemental wetland land use area estimates for the NRI. We conclude that comparisons between more recent versions of the data sets (i.e., 1997 NRI, 1997 or 2002 Agricultural Census, and 2001 NLCD) would not result in significant additional insights and that the 1997 NRI is a viable land use data source for current CARD UMRB water quality modeling studies. However, adoption of other land use data such as USDA-NASS remote sensing data should be investigated.</p

Comparison of Land Use Area Estimates from Three Different Data Sources for the Upper Mississippi River Basin

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS). The comparison was performed for 1992 versions of the datasets because that was the only consistent year available among all three data sources. The results show that differences in land use area estimates increased as comparisons shifted from the entire UMRB to smaller 4- and 8-digit watershed regions (as expected). However, the area estimates for the major land use categories remained generally consistent among all three data sets across each level of spatial comparison. Differences in specific crop and grass/forage land use categories were magnified with increasing refinement of the spatial unit of comparison, especially for close-grown crops, pasture, and alfalfa/hayland. The NLCD close-grown crop area estimates appear very weak relative to the NRI and HUMUS, and the lack of specific crop land use estimates limits its viability for UMRB agricultural-based modeling scenarios. However, the NLCD is a key source of non-agricultural land use data for HUMUS and supplemental wetland land use area estimates for the NRI. We conclude that comparisons between more recent versions of the data sets (i.e., 1997 NRI, 1997 or 2002 Agricultural Census, and 2001 NLCD) would not result in significant additional insights and that the 1997 NRI is a viable land use data source for current CARD UMRB water quality modeling studies. However, adoption of other land use data such as USDA-NASS remote sensing data should be investigated.agricultural land, cropland, HUMUS, land use area estimates, NLCD, non-agricultural land, NRI, UMRB, water quality modeling.

Some Challenges in Hydrologic Model Calibration for Large-Scale Studies: A Case Study of SWAT Model Application to Mississippi-Atchafalaya River Basin

This study is a part of the Conservation Effects Assessment Project (CEAP) aimed to quantify the environmental and economic benefits of conservation practices implemented in the cultivated cropland throughout the United States. The Soil and Water Assessment Tool (SWAT) model under the Hydrologic United Modeling of the United States (HUMUS) framework was used in the study. An automated flow calibration procedure was developed and used to calibrate runoff for each 8-digit watershed (within 20% of calibration target) and the partitioning of runoff into surface and sub-surface flow components (within 10% of calibration target). Streamflow was validated at selected gauging stations along major rivers within the river basin with a target R2 of &gt;0.6 and Nash and Sutcliffe Efficiency of &gt;0.5. The study area covered the entire Mississippi and Atchafalaya River Basin (MARB). Based on the results obtained, our analysis pointed out multiple challenges to calibration such as: (1) availability of good quality data, (2) accounting for multiple reservoirs within a sub-watershed, (3) inadequate accounting of elevation and slopes in mountainous regions, (4) poor representation of carrying capacity of channels, (5) inadequate capturing of the irrigation return flows, (6) inadequate representation of vegetative cover, and (7) poor representation of water abstractions (both surface and groundwater). Additional outstanding challenges to large-scale hydrologic model calibration were the coarse spatial scale of soils, land cover, and topography

SWAT: Model use, calibration, and validation

SWAT (Soil and Water Assessment Tool) is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. Several calibration techniques have been developed for SWAT, including manual calibration procedures and automated procedures using the shuffled complex evolution method and other common methods. In addition, SWAT-CUP was recently developed and provides a decision-making framework that incorporates a semi-automated approach (SUFI2) using both manual and automated calibration and incorporating sensitivity and uncertainty analysis. In SWAT-CUP, users can manually adjust parameters and ranges iteratively between autocalibration runs. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. The user interaction or manual component of the SWAT-CUP calibration forces the user to obtain a better understanding of the overall hydrologic processes (e.g., baseflow ratios, ET, sediment sources and sinks, crop yields, and nutrient balances) and of parameter sensitivity. It is important for future calibration developments to spatially account for hydrologic processes; improve model run time efficiency; include the impact of uncertainty in the conceptual model, model parameters, and measured variables used in calibration; and assist users in checking for model errors. When calibrating a physically based model like SWAT, it is important to remember that all model input parameters must be kept within a realistic uncertainty range and that no automatic procedure can substitute for actual physical knowledge of the watershed