CORRELATIONS BETWEEN TOPOGRAPHIC WETNESS INDEX AND SOIL MOISTURE IN THE PANNONIAN REGION OF CROATIA

被引:2
作者
Hestera, Hrvoje [1 ]
Plantak, Mladen [2 ]
Gernhardt, Dalibor [1 ]
机构
[1] Croatian Def Acad, Ilica 256b, HR-10000 Zagreb, Croatia
[2] Odjel Zastite Voda Prirode & Okolisa, Elektroprojekt dd, Gradevinsko arhitektonski biro, Alexandera von Humboldta 4, Zagreb 1000, Croatia
来源
GEOGRAPHIA TECHNICA | 2024年 / 19卷 / 01期
关键词
Topographic Wetness Index; Soil wetness; Loess; Digital Elevation Model; Raster cell resolution; DRAINAGE NETWORKS; TRAFFICABILITY; CATCHMENT; MODEL; AREA;
D O I
10.21163/GT_2024.191.07
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The study investigates the relationship between variations of topographic wetness indices, resampled digital elevation model (DEM) resolutions and soil moisture in Croatia ' s Pannonian region during all seasons. It delves into the spatial distribution of soil moisture, vital for various sectors including agriculture, forestry, ecology, and military. Utilizing topographic wetness index (TWI), the research focuses on resampling high -resolution DEM into coarser resolutions and flow -routing methods concerning in-situ soil moisture measurements. It encompasses the Kaznica River catchment area, characterized by diverse topography and soil types, including fine-grained automorphic and hydromorphic clays. The study extensively conducts field measurements, evaluating soil moisture at various depths (1, 15, 30, and 45cm) across all seasons, cross-referenced with meteorological data. Through an examination of six runoff algorithms and six spatial resolutions, it concludes that the optimal TWI resolution for correlation with soil moisture is 150m at a 30cm soil depth. However, it stresses the necessity of calibrations for precise soil depth, cell resolutions and season. The shallowest soil depth has the lowest correlation coefficients in all periods, while the highest coefficients were achieved in period with the highest soil moisture values. Recognizing the complexity of factors influencing soil moisture, it recommends the integration of additional data sources like remote sensing, other geomorphological indexes, and detailed land cover analysis to enhance the accuracy of predictions.
引用
收藏
页码:89 / 102
页数:14
相关论文
共 52 条
[1]   Evaluating digital terrain indices for soil wetness mapping - a Swedish case study [J].
Agren, A. M. ;
Lidberg, W. ;
Stromgren, M. ;
Ogilvie, J. ;
Arp, P. A. .
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2014, 18 (09) :3623-3634
[2]  
[Anonymous], 2013, User Manual for the ML3 ThetaProbe (ML3-UM-1.0)
[3]  
Bauer J., 1985, Landschaftsgenese und Landschaftsokologie, V10, P1
[4]  
Beven K.J., 1979, Hydrol. Sci. Bull., V24, P43, DOI [DOI 10.1080/02626667909491834, 10.1080/02626667909491834]
[5]  
Bognar A., 1978, Croatian geographical bulletin-Geografski glasnik, V40, P21
[6]   Evaluating topographic wetness indices across central New York agricultural landscapes [J].
Buchanan, B. P. ;
Fleming, M. ;
Schneider, R. L. ;
Richards, B. K. ;
Archibald, J. ;
Qiu, Z. ;
Walter, M. T. .
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2014, 18 (08) :3279-3299
[7]   TOPOGRAPHIC CONTROLS OF SOIL-MOISTURE DISTRIBUTIONS [J].
BURT, TP ;
BUTCHER, DP .
JOURNAL OF SOIL SCIENCE, 1985, 36 (03) :469-486
[8]  
Dasch J., 2020, Next-Generation NATO Reference Mobility Model (NG-NRMM)
[9]  
DAvello T., 2016, Best Practices for Processing Raster Data in Soil Survey Applications, P11
[10]  
Drzavna geodetska uprava, 2004, CRONO GIP, P1