The analysis and protection of watersheds, along with spring water resources, depend on the accurate identification of catchments. Building on previous research that correlated spring hydrographs with high-resolution, satellite-based Global Precipitation Measurement – Integrated Multi-satellitE Retrievals for GPM (GPM-IMERG) data, I improve the speed and accuracy of catchment identification and hydrodynamic characterization via an enhanced Empirically Constrained Hydrologic Operation (ECHO) algorithm. This research (1) establishes optimal parameter inputs for the algorithm to enable reliable identification of source point-locations, (2) removes human in-the-loop processing, and thus reduce potential operator bias, and (3) explores the potential for using a limited dataset of precipitation proxies for delineation and geolocation. The algorithm is validated within a semi-controlled environment using IMERG and United States Geologic Survey (USGS) precipitation gauge data that has a known location. It is benchmarked against statistical approaches: Cross-Correlation, Pearson Correlation, Spearman Correlation, Total Accumulation, and Binary Frequency. The results demonstrate success of the ECHO algorithm in controlled geolocation when the gauge location is withheld while establishing higher accuracy over the alternative statistical approaches. This enhanced ECHO method holds implications for water resource protection, groundwater exploration, and introduces novel applications for rapidly delineating traditional watersheds, springsheds, and transboundary aquifers. This is particularly useful in scenarios where standard, time-consuming dye tracing tests might be impractical, difficult, or impossible to mount.
","abstract_html":"<p>The analysis and protection of watersheds, along with spring water resources, depend on the accurate identification of catchments. Building on previous research that correlated spring hydrographs with high-resolution, satellite-based Global Precipitation Measurement – Integrated Multi-satellitE Retrievals for GPM (GPM-IMERG) data, I improve the speed and accuracy of catchment identification and hydrodynamic characterization via an enhanced Empirically Constrained Hydrologic Operation (ECHO) algorithm. This research (1) establishes optimal parameter inputs for the algorithm to enable reliable identification of source point-locations, (2) removes human in-the-loop processing, and thus reduce potential operator bias, and (3) explores the potential for using a limited dataset of precipitation proxies for delineation and geolocation. The algorithm is validated within a semi-controlled environment using IMERG and United States Geologic Survey (USGS) precipitation gauge data that has a known location. It is benchmarked against statistical approaches: Cross-Correlation, Pearson Correlation, Spearman Correlation, Total Accumulation, and Binary Frequency. The results demonstrate success of the ECHO algorithm in controlled geolocation when the gauge location is withheld while establishing higher accuracy over the alternative statistical approaches. This enhanced ECHO method holds implications for water resource protection, groundwater exploration, and introduces novel applications for rapidly delineating traditional watersheds, springsheds, and transboundary aquifers. This is particularly useful in scenarios where standard, time-consuming dye tracing tests might be impractical, difficult, or impossible to mount.</p>","abstract_has_math":false,"creators":["Longenecker, Jake M"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Earth and Atmospheric Sciences","degree_department":null,"school":null,"contributors":["Steven Kidder","Reza Khanbilvardi","Robert Walter"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T01:57:59Z","subjects":["Groundwater Exploration","Hydrograph Identification","Springshed Modeling","Watershed Protection","Remote Sensing","Water Resource Management"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/1118","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Steven Kidder","Reza Khanbilvardi","Robert Walter"]},{"key":"dc:creator","label":"Author","values":["Longenecker, Jake M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-10T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Earth and Atmospheric Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Groundwater Exploration","Hydrograph Identification","Springshed Modeling","Watershed Protection","Remote Sensing","Water Resource Management"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/1118"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The analysis and protection of watersheds, along with spring water resources, depend on the accurate identification of catchments. Building on previous research that correlated spring hydrographs with high-resolution, satellite-based Global Precipitation Measurement – Integrated Multi-satellitE Retrievals for GPM (GPM-IMERG) data, I improve the speed and accuracy of catchment identification and hydrodynamic characterization via an enhanced Empirically Constrained Hydrologic Operation (ECHO) algorithm. This research (1) establishes optimal parameter inputs for the algorithm to enable reliable identification of source point-locations, (2) removes human in-the-loop processing, and thus reduce potential operator bias, and (3) explores the potential for using a limited dataset of precipitation proxies for delineation and geolocation. The algorithm is validated within a semi-controlled environment using IMERG and United States Geologic Survey (USGS) precipitation gauge data that has a known location. It is benchmarked against statistical approaches: Cross-Correlation, Pearson Correlation, Spearman Correlation, Total Accumulation, and Binary Frequency. The results demonstrate success of the ECHO algorithm in controlled geolocation when the gauge location is withheld while establishing higher accuracy over the alternative statistical approaches. This enhanced ECHO method holds implications for water resource protection, groundwater exploration, and introduces novel applications for rapidly delineating traditional watersheds, springsheds, and transboundary aquifers. This is particularly useful in scenarios where standard, time-consuming dye tracing tests might be impractical, difficult, or impossible to mount.
"]},{"key":"dc:title","label":"Title","values":["Advancing the Accuracy of Watershed Analysis Across Diverse Hydrometeorological and Geologic Regimes via Classification and Analysis of GPM-IMERG Products"]}]}],"canonical_facts":{"dc:contributor":["Steven Kidder","Reza Khanbilvardi","Robert Walter"],"dc:creator":["Longenecker, Jake M"],"dc:date.available":["2023-08-10T07:00:00Z"],"dc:description.abstract":["The analysis and protection of watersheds, along with spring water resources, depend on the accurate identification of catchments. Building on previous research that correlated spring hydrographs with high-resolution, satellite-based Global Precipitation Measurement – Integrated Multi-satellitE Retrievals for GPM (GPM-IMERG) data, I improve the speed and accuracy of catchment identification and hydrodynamic characterization via an enhanced Empirically Constrained Hydrologic Operation (ECHO) algorithm. This research (1) establishes optimal parameter inputs for the algorithm to enable reliable identification of source point-locations, (2) removes human in-the-loop processing, and thus reduce potential operator bias, and (3) explores the potential for using a limited dataset of precipitation proxies for delineation and geolocation. The algorithm is validated within a semi-controlled environment using IMERG and United States Geologic Survey (USGS) precipitation gauge data that has a known location. It is benchmarked against statistical approaches: Cross-Correlation, Pearson Correlation, Spearman Correlation, Total Accumulation, and Binary Frequency. The results demonstrate success of the ECHO algorithm in controlled geolocation when the gauge location is withheld while establishing higher accuracy over the alternative statistical approaches. This enhanced ECHO method holds implications for water resource protection, groundwater exploration, and introduces novel applications for rapidly delineating traditional watersheds, springsheds, and transboundary aquifers. This is particularly useful in scenarios where standard, time-consuming dye tracing tests might be impractical, difficult, or impossible to mount.
"],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/1118"],"dc:subject":["Groundwater Exploration","Hydrograph Identification","Springshed Modeling","Watershed Protection","Remote Sensing","Water Resource Management"],"dc:title":["Advancing the Accuracy of Watershed Analysis Across Diverse Hydrometeorological and Geologic Regimes via Classification and Analysis of GPM-IMERG Products"],"thesis:degree_discipline":["Earth and Atmospheric Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:57:59Z"}