{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/98848"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/98848","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Bridging the gap : comparative analysis of a gap filling X-band radar QPE algorithms and their implications for nowcasting and hydrological modeling","abstract":"Accurate Quantitative Precipitation Estimations (QPE) are foundational for hydrological modeling and proactive watershed management. This research examines how a single X-Band radar bridges the gap in QPE estimation and its implications for hydrological modeling and nowcasting. Initially, a comparison between X-band and S-band radars with Integrated Multi-satellitE Retrievals for GPM (IMERG) showed comparable performances within 80km, with both radars surpassing IMERG estimations. However, the X-band's efficacy decreased with range. To address this, the \"dynamic distance algorithm\" was introduced for X-band radar QPE. This method dynamically adjusts power function coefficients based on distance and significantly reduced the error compared to other advanced methodologies. When applied to the SWAT hydrological model in the Hinkson Creek Catchment, this algorithm outperformed both Multi-Radar Multi- Sensor (MRMS) QPE and rain gauge inputs for days of extreme discharge rates. Lastly, the effectiveness of six nowcasting models were explored with the MZZU radar. The LINDA nowcast system was identified as the most proficient but computationally intensive. Notably, SPROG and STEPS surpassed the extrapolation model in longer simulations. The research highlights the potential of the dynamic distance algorithm in enhancing hydrological modeling and underscores the pivotal role of select nowcasting models in weather forecasting.","abstract_html":"Accurate Quantitative Precipitation Estimations (QPE) are foundational for hydrological modeling and proactive watershed management. This research examines how a single X-Band radar bridges the gap in QPE estimation and its implications for hydrological modeling and nowcasting. Initially, a comparison between X-band and S-band radars with Integrated Multi-satellitE Retrievals for GPM (IMERG) showed comparable performances within 80km, with both radars surpassing IMERG estimations. However, the X-band's efficacy decreased with range. To address this, the "dynamic distance algorithm" was introduced for X-band radar QPE. This method dynamically adjusts power function coefficients based on distance and significantly reduced the error compared to other advanced methodologies. When applied to the SWAT hydrological model in the Hinkson Creek Catchment, this algorithm outperformed both Multi-Radar Multi- Sensor (MRMS) QPE and rain gauge inputs for days of extreme discharge rates. Lastly, the effectiveness of six nowcasting models were explored with the MZZU radar. The LINDA nowcast system was identified as the most proficient but computationally intensive. Notably, SPROG and STEPS surpassed the extrapolation model in longer simulations. The research highlights the potential of the dynamic distance algorithm in enhancing hydrological modeling and underscores the pivotal role of select nowcasting models in weather forecasting.","abstract_has_math":false,"creators":["Steward, Christopher"],"institution":"University of Missouri--Columbia","degree_name":"Ph. D.","degree_level":"Doctoral","degree_discipline":"Natural resources (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Fox, Neil"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T03:07:13Z","subjects":[],"languages":["eng","English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/98848"],"render_values":[{"text":"https://doi.org/10.32469/10355/98848","href":"https://doi.org/10.32469/10355/98848","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/98848","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Fox, Neil"]},{"key":"dc:creator","label":"Author","values":["Steward, Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-03-18T20:34:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-03-18T20:34:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Natural resources (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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Initially, a comparison between X-band and S-band radars with Integrated Multi-satellitE Retrievals for GPM (IMERG) showed comparable performances within 80km, with both radars surpassing IMERG estimations. However, the X-band's efficacy decreased with range. To address this, the \"dynamic distance algorithm\" was introduced for X-band radar QPE. This method dynamically adjusts power function coefficients based on distance and significantly reduced the error compared to other advanced methodologies. When applied to the SWAT hydrological model in the Hinkson Creek Catchment, this algorithm outperformed both Multi-Radar Multi- Sensor (MRMS) QPE and rain gauge inputs for days of extreme discharge rates. Lastly, the effectiveness of six nowcasting models were explored with the MZZU radar. The LINDA nowcast system was identified as the most proficient but computationally intensive. Notably, SPROG and STEPS surpassed the extrapolation model in longer simulations. The research highlights the potential of the dynamic distance algorithm in enhancing hydrological modeling and underscores the pivotal role of select nowcasting models in weather forecasting."]},{"key":"dc:title","label":"Title","values":["Bridging the gap : comparative analysis of a gap filling X-band radar QPE algorithms and their implications for nowcasting and hydrological modeling"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fox, Neil"],"dc:creator":["Steward, Christopher"],"dc:date.accessioned":["2024-03-18T20:34:30Z"],"dc:date.available":["2024-03-18T20:34:30Z"],"dc:date.issued":["2023"],"dc:description.abstract":["Accurate Quantitative Precipitation Estimations (QPE) are foundational for hydrological modeling and proactive watershed management. This research examines how a single X-Band radar bridges the gap in QPE estimation and its implications for hydrological modeling and nowcasting. Initially, a comparison between X-band and S-band radars with Integrated Multi-satellitE Retrievals for GPM (IMERG) showed comparable performances within 80km, with both radars surpassing IMERG estimations. However, the X-band's efficacy decreased with range. To address this, the \"dynamic distance algorithm\" was introduced for X-band radar QPE. This method dynamically adjusts power function coefficients based on distance and significantly reduced the error compared to other advanced methodologies. When applied to the SWAT hydrological model in the Hinkson Creek Catchment, this algorithm outperformed both Multi-Radar Multi- Sensor (MRMS) QPE and rain gauge inputs for days of extreme discharge rates. Lastly, the effectiveness of six nowcasting models were explored with the MZZU radar. The LINDA nowcast system was identified as the most proficient but computationally intensive. Notably, SPROG and STEPS surpassed the extrapolation model in longer simulations. The research highlights the potential of the dynamic distance algorithm in enhancing hydrological modeling and underscores the pivotal role of select nowcasting models in weather forecasting."],"dc:identifier.doi":["https://doi.org/10.32469/10355/98848"],"dc:identifier.uri":["https://hdl.handle.net/10355/98848"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["University of Missouri--Columbia"],"dc:title":["Bridging the gap : comparative analysis of a gap filling X-band radar QPE algorithms and their implications for nowcasting and hydrological modeling"],"dc:type":["Thesis"],"thesis:degree_discipline":["Natural resources (MU)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph. D."],"thesis:institution_name":["University of Missouri--Columbia"]},"updated_at":"2026-07-24T03:07:13Z"}