國立虎尾科技大學 |

Numerical simulations of sand dune morphodynamics and their application to the Mu Us Dune Field, China.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Numerical simulations of sand dune morphodynamics and their application to the Mu Us Dune Field, China./
作者:	Ma, Fei.
面頁冊數:	1 online resource (128 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
標題:	Physical geography. -
電子資源:	click for full text (PQDT)
ISBN:	9780355521320

Numerical simulations of sand dune morphodynamics and their application to the Mu Us Dune Field, China.
Ma, Fei.

Numerical simulations of sand dune morphodynamics and their application to the Mu Us Dune Field, China. - 1 online resource (128 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.

Includes bibliographical references

Semi-arid landscapes are heavily influenced by climate change and human activities. Sand dunes in the Mu Us region, China experienced rapid stabilization during the past few decades, which is postulated to be a response to recent climate change (Xu et al., 2015) and land use change (Mason et al., 2008). With the information extracted from high-resolution Google Earth imagery and ASTER GDEM data and machine-learning models, I evaluated the relative importance of multiple environmental and anthropogenic variables for the recent changes in dune activity and vegetation cover. Spatial variation in climate variables contributed little, while elevation, human activities and vegetation spatial distribution played significant roles in both dune migration rate and vegetation cover change rate. To further visualize dune morphodynamic response to external forcings over time, I modified a Cellular Automaton (CA) model (Werner, 1995) and fit it to the Mu Us dune field by tuning model parameters (downwind transport jump unit, slab thickness, deposition probability, and erosion probability) . The model produced realistic barchans dune forms with only wind processes included, while parabolic dune forms were simulated with a combination of wind and anchoring vegetation. Through adjusting erosion probability (p e), the model is capable of testing various wind and vegetation growth scenarios. With a linearly declining pe, the vegetation integrated CA model was used to simulate the increase in precipitation in the late 1990s, and it produced stabilized parabolic dune forms that closely resemble those observed in the field. The successful simulation of realistic dune form changes also proves the CA model to be an effective research and educational tool for exploring interactions among climate, geomorphology and vegetation and predicting future trends of dune activity.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355521320Subjects--Topical Terms:

784466
Physical geography.
Index Terms--Genre/Form:

554714
Electronic books.

Numerical simulations of sand dune morphodynamics and their application to the Mu Us Dune Field, China.
LDR:03099ntm a2200337K 4500 001 913139
005 20180614071649.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355521320
035 $a (MiAaPQ)AAI10687694
035 $a (MiAaPQ)wisc:14993
035 $a AAI10687694
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Ma, Fei. $3 1185826
245 1 0 $a Numerical simulations of sand dune morphodynamics and their application to the Mu Us Dune Field, China.
264 0 $c 2017
300 $a 1 online resource (128 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Joseph A. Mason.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
504 $a Includes bibliographical references
520 $a Semi-arid landscapes are heavily influenced by climate change and human activities. Sand dunes in the Mu Us region, China experienced rapid stabilization during the past few decades, which is postulated to be a response to recent climate change (Xu et al., 2015) and land use change (Mason et al., 2008). With the information extracted from high-resolution Google Earth imagery and ASTER GDEM data and machine-learning models, I evaluated the relative importance of multiple environmental and anthropogenic variables for the recent changes in dune activity and vegetation cover. Spatial variation in climate variables contributed little, while elevation, human activities and vegetation spatial distribution played significant roles in both dune migration rate and vegetation cover change rate. To further visualize dune morphodynamic response to external forcings over time, I modified a Cellular Automaton (CA) model (Werner, 1995) and fit it to the Mu Us dune field by tuning model parameters (downwind transport jump unit, slab thickness, deposition probability, and erosion probability) . The model produced realistic barchans dune forms with only wind processes included, while parabolic dune forms were simulated with a combination of wind and anchoring vegetation. Through adjusting erosion probability (p e), the model is capable of testing various wind and vegetation growth scenarios. With a linearly declining pe, the vegetation integrated CA model was used to simulate the increase in precipitation in the late 1990s, and it produced stabilized parabolic dune forms that closely resemble those observed in the field. The successful simulation of realistic dune form changes also proves the CA model to be an effective research and educational tool for exploring interactions among climate, geomorphology and vegetation and predicting future trends of dune activity.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Physical geography. $3 784466
650 4 $a Geomorphology. $3 566098
650 4 $a Geology. $3 670379
655 7 $a Electronic books. $2 local $3 554714
690 $a 0368
690 $a 0484
690 $a 0372
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Wisconsin - Madison. $b Geography. $3 1185827
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10687694 $z click for full text (PQDT)