{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:33:57Z","timestamp":1780367637132,"version":"3.54.1"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,2,2]],"date-time":"2015-02-02T00:00:00Z","timestamp":1422835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The generation of reliable information for improving the understanding of hydroelectric reservoir dynamics is fundamental for guiding decision-makers to implement best management practices. In this way, we assessed the performance of different interpolation algorithms to map the bathymetry of the Tucuru\u00ed hydroelectric reservoir, located in the Brazilian Amazon, as an aid to manage and operate Amazonian reservoirs. We evaluated three different deterministic and one geostatistical algorithms. The performance of the algorithms was assessed through cross-validation and Monte Carlo Simulation. Finally, operational information was derived from the bathymetric grid with the best performance. The results showed that all interpolation methods were able to map important bathymetric features. The best performance was obtained with the geostatistical method (RMSE = 0.92 m). The information derived from the bathymetric map (e.g., the level-area and level-volume diagram and the three-dimensional grid) will allow for optimization of operational monitoring of the Tucuru\u00ed hydroelectric reservoir as well as the development of three-dimensional modeling studies.<\/jats:p>","DOI":"10.3390\/ijgi4010220","type":"journal-article","created":{"date-parts":[[2015,2,2]],"date-time":"2015-02-02T10:27:36Z","timestamp":1422872856000},"page":"220-235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Assessment of Spatial Interpolation Methods to Map the Bathymetry of an Amazonian Hydroelectric Reservoir to Aid in Decision Making for Water Management"],"prefix":"10.3390","volume":"4","author":[{"given":"Marcelo","family":"Curtarelli","sequence":"first","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos,  12227-010 S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaquim","family":"Le\u00e3o","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos,  12227-010 S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6328-0001","authenticated-orcid":false,"given":"Igor","family":"Ogashawara","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos,  12227-010 S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o","family":"Lorenzzetti","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos,  12227-010 S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Stech","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos,  12227-010 S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1146\/annurev.fl.14.010182.001101","article-title":"Dynamics of lakes, reservoirs, and cooling ponds","volume":"14","author":"Imberger","year":"1982","journal-title":"Ann. 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