Spatial interpolation techniques play an important
role in hydrology as many point observations need to be interpolated to create
continuous surfaces. Despite the availability of several tools and methods for
interpolating data, <a>not all of them work consistently
for hydrologic applications</a><a>. One of the techniques,
Laplace Equation, which is used in hydrology for creating flownets, has rarely
been used for interpolating hydrology data</a>. The objective of this study is
to examine the efficiency of Laplace formulation (LF) in interpolating hydrologic
data and compare it wih other widely used methods such as the inverse distance
weighting (IDW), natural neighbor, and kriging. Comparison is performed
quantitatively for using root mean square error (RMSE), visually for creating
reasonable surfaces and computationally for ease of operation and speed. Data
related to surface elevation, river bathymetry, precipitation, temperature, and
soil moisture data are used for different areas in the United States. RMSE
results show that LF performs better than IDW and is comparable to other
methods for accuracy. LF is easy to use
as it requires fewer input parameters compared to IDW and Kriging.
Computationally, LF is comparable to other methods in terms of speed when the
datasets are not large. Overall, LF offers an robust alternative to existing
methods for interpolating various hydrology data. Further work is required to
improve its computational efficiency with large data size and find out the
effects of different cell size.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14597550 |
| Date | 14 May 2021 |
| Creators | Tianle Xu (10802667) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/INTERPOLATING_HYDROLOGIC_DATA_USING_LAPLACE_FORMULATION/14597550 |
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