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Distributed hydrological modeling using soil depth estimated from landscape variables derived with enhanced terrain analysis.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
書名/作者:	Distributed hydrological modeling using soil depth estimated from landscape variables derived with enhanced terrain analysis.
作者:	Tesfa, Teklu K.
面頁冊數:	222 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-05, Section: B, page: 2916.
Contained By:	Dissertation Abstracts International71-05B.
標題:	Hydrology.
標題:	Geomorphology.
標題:	Water Resource Management.
ISBN:	9781109723717
摘要、提要註:	The spatial patterns of land surface and subsurface characteristics determine the spatial heterogeneity of hydrological processes. Soil depth is one of these characteristics and an important input parameter required by distributed hydrological models that explicitly represent spatial heterogeneity. Soil is related to topography and land cover due to the role played by topography and vegetation in affecting soil-forming processes. The research described in this dissertation addressed the development of statistical models that predict the soil depth pattern over the landscape; derivation of new topographic variables evaluated using both serial and parallel algorithms; and evaluation of the impacts of detailed soil depth representation on simulations of stream flow and soil moisture. The dissertation is comprised of three papers.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3398731

Distributed hydrological modeling using soil depth estimated from landscape variables derived with enhanced terrain analysis.
Tesfa, Teklu K.

Distributed hydrological modeling using soil depth estimated from landscape variables derived with enhanced terrain analysis. - 222 p.

Source: Dissertation Abstracts International, Volume: 71-05, Section: B, page: 2916.

Thesis (Ph.D.)--Utah State University, 2010.

The spatial patterns of land surface and subsurface characteristics determine the spatial heterogeneity of hydrological processes. Soil depth is one of these characteristics and an important input parameter required by distributed hydrological models that explicitly represent spatial heterogeneity. Soil is related to topography and land cover due to the role played by topography and vegetation in affecting soil-forming processes. The research described in this dissertation addressed the development of statistical models that predict the soil depth pattern over the landscape; derivation of new topographic variables evaluated using both serial and parallel algorithms; and evaluation of the impacts of detailed soil depth representation on simulations of stream flow and soil moisture. The dissertation is comprised of three papers.

ISBN: 9781109723717Subjects--Topical Terms:

217916
Hydrology.

Distributed hydrological modeling using soil depth estimated from landscape variables derived with enhanced terrain analysis.
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100 1 $a Tesfa, Teklu K. $3 423311
245 1 0 $a Distributed hydrological modeling using soil depth estimated from landscape variables derived with enhanced terrain analysis.
300 $a 222 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-05, Section: B, page: 2916.
500 $a Adviser: David G. Tarboton.
502 $a Thesis (Ph.D.)--Utah State University, 2010.
520 $a The spatial patterns of land surface and subsurface characteristics determine the spatial heterogeneity of hydrological processes. Soil depth is one of these characteristics and an important input parameter required by distributed hydrological models that explicitly represent spatial heterogeneity. Soil is related to topography and land cover due to the role played by topography and vegetation in affecting soil-forming processes. The research described in this dissertation addressed the development of statistical models that predict the soil depth pattern over the landscape; derivation of new topographic variables evaluated using both serial and parallel algorithms; and evaluation of the impacts of detailed soil depth representation on simulations of stream flow and soil moisture. The dissertation is comprised of three papers.
520 $a In paper 1, statistical models were developed to predict soil depth pattern over the watershed based on topographic and land cover variables. Soil depth was surveyed at locations selected to represent the topographic and land cover variation at the Dry Creek Experimental Watershed, near Boise, Idaho. Explanatory variables were derived from a digital elevation model and remote sensing imagery for regression to the field data. Generalized Additive and Random Forests models were developed to predict soil depth over the watershed. The models were able to explain about 50% of the soil depth spatial variation, which is an important improvement over the soil depth extracted from the SSURGO national soil database.
520 $a In paper 2, definitions of the new topographic variables derived in the effort to model soil depth, and serial and Message Passing Interface parallel implementations of the algorithms for their evaluation are presented. The parallel algorithms enhanced the processing speed of large digital elevation models as compared to the serial recursive algorithms initially developed.
520 $a In paper 3, the impact of spatially explicit soil depth information on simulations of stream flow and soil moisture as compared to soil depth derived from the SSURGO soil database has been evaluated. The Distributed Hydrology Vegetation Soil Model was applied using automated parameter optimization technique with all input parameters the same except soil depth. Stream flow was less impacted by the detailed soil depth information, while simulation of soil moisture was slightly improved due to the detailed representation of soil depth.
590 $a School code: 0241.
650 4 $a Hydrology. $3 217916
650 4 $a Geomorphology. $3 404672
650 4 $a Water Resource Management. $3 423292
690 $a 0388
690 $a 0484
690 $a 0595
710 2 $a Utah State University. $b Civil and Environmental. $3 422940
773 0 $t Dissertation Abstracts International $g 71-05B.
790 1 0 $a Tarboton, David G., $e advisor
790 1 0 $a Chandler, David G. $e committee member
790 1 0 $a McKee, Mac $e committee member
790 1 0 $a Boettinger, Janis L. $e committee member
790 1 0 $a Cutler, Adele $e committee member
790 1 0 $a McNamara, James P. $e committee member
790 $a 0241
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3398731