COMPARING SPATIAL INTERPOLATION METHODS FOR CMIP5 MONTHLY PRECIPITATION AT CATCHMENT SCALE (Record no. 19574)
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230628160617.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230628b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 21281 |
| Author | Md Monowar Hossain |
| 245 ## - TITLE STATEMENT | |
| Title | COMPARING SPATIAL INTERPOLATION METHODS FOR CMIP5 MONTHLY PRECIPITATION AT CATCHMENT SCALE |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol,41(2),Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Roorkee |
| Name of publisher, distributor, etc. | Indian Water Resources Society |
| Year | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 28-34p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Use of Regional Climate Models (RCMs) is prevalent in downscaling the large scale climate information from the General Circulation<br/>Models (GCMs) to local scale. But it is computationally intensive and requires application of a numerical weather prediction model. For<br/>more straightforward computation, spatial interpolation are commonly used to re-gridding the GCM data to local scales. There are many<br/>interpolation methods available, but mostly they are chosen randomly, especially for GCM data. This study compared eight interpolation<br/>methods (linear, bi-linear, nearest neighbour, distance weighted average, inverse distance weighted average, first-order conservative,<br/>second-order conservative and bi-cubic interpolation) for re-gridding of CMIP5 decadal experimental data to a catchment scale. For this,<br/>CMIP5 decadal precipitation data from three GCMs were collected and subset for Australia and then re-gridded to 0.05 degree spatial<br/>resolution matching with the observed gridded data. The re-gridded data were subset for Brisbane catchment in Queensland, Australia and a<br/>number of skill tests (root mean squared error, mean absolute error, correlation coefficient, Pearson correlation, Kendal’s tau correlation<br/>and index of agreement) were conducted for a selected observed point to check the performances of different interpolation methods.<br/>Additionally, temporal skills were computed over the entire catchment and compared. Based on the skill tests over the study area, the second-<br/>order conservative (SOC) method was found to be an appropriate choice for interpolating the gridded dataset |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4621 |
| Topical term or geographic name entry element | Civil Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 21282 |
| Co-Author | Garg, Nikhil |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0970-6984 |
| Place, publisher, and date of publication | Roorkee Indian Institute of Technology Roorkee |
| Title | Journal of indian water resource society |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://iwrs.org.in/journal/apr2021/5apr.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 28/06/2023 | 2023-0946 | 28/06/2023 | 28/06/2023 | Articles Abstract Database |