Impacts of land use characterization in modeling hydrology and sediments for the Luxapallila Creek watershed, Alabama and Mississippi

被引:0
|
作者
Diaz-Ramirez, J. N. [1 ]
Alarcon, V. J.
Duan, Z. [3 ]
Tagert, M. L. [4 ]
McAnally, W. H. [1 ]
Martin, J. L. [1 ]
O'Hara, C. G. [2 ]
机构
[1] Mississippi State Univ, Dept Civil & Environm Engn, Mississippi State, MS 39762 USA
[2] Mississippi State Univ, GeoResources Inst, Mississippi State, MS 39762 USA
[3] CDM, New Orleans, LA USA
[4] Mississippi State Univ, Water Resources Res Inst, Mississippi State, MS 39762 USA
关键词
HSPE; hydrology; land use; sediments; watershed modeling;
D O I
暂无
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
The Hydrological Simulation Program - Fortran (HSPF), interfaced with the Better Assessment Science Integrating Point and Nonpoint (BASINS), was used to evaluate the impact of land use (as characterized by different land use/land cover (LU/LC) datasets) on hydrology and sediment components of the Luxapallila Creek watershed. The 1, 770 km(2) watershed is located in Alabama and Mississippi. Simulation of the watershed processes were tested at the hillslope and at the watershed outlet for the period between 1985 and 2003. Three LU/LC databases were used: the Geographic Information Retrieval and Analysis System (GIRAS), the Moderate Resolution Imaging Spectroradiometer land cover product (MODIS MOD12Q1), and the National Land Cover Dataset (NLCD). The two main land use categories revealed by the three LU/LC databases were forest and agricultural lands. Whereas forest cover mechanisms were the main source of water loss in hydrology simulation, agricultural land was the main source of sediment export in sediment modeling. Land use datasets Of coarser spatial resolution (MODIS and GIRAS) produced larger HSPF estimations for sediment fraction values than land use datasets identifying smaller percentages of those agricultural land cover classes (NLCD). Differences in agricultural land characterization among the land use datasets showed that sediment predictions were more sensitive than streamflow predictions to the scale and resolution of land use datasets. Choosing the right land use dataset will impact the modeling of sediments and, potentially, other water quality constituents that are related with agricultural activities.
引用
收藏
页码:139 / 151
页数:13
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