Ninad Bhagwat, Xiaobing Zhou, Raja Nagisetty, Liping Jiang, Glenn Shaw, Martha Apple, Jeremy Clotfelter
{"title":"Snowmelt runoff model (SRM) for regulated watersheds with regulation-correction","authors":"Ninad Bhagwat, Xiaobing Zhou, Raja Nagisetty, Liping Jiang, Glenn Shaw, Martha Apple, Jeremy Clotfelter","doi":"10.1007/s13201-025-02579-y","DOIUrl":null,"url":null,"abstract":"<div><p>We expanded the Snowmelt Runoff Model (SRM) to simulate streamflow in regulated watersheds, resulting in a modified framework termed the Expanded SRM (E-SRM) that integrates multi-year automated batch processing, nested iterators, and a seasonal divider algorithm for streamflow simulation. A parsimonious regulation-correction approach was developed that conceptually divides the watershed into a pristine upstream “daughter” subwatershed and a larger, regulated “mother” watershed. Hydrological parameter transferability was assumed between the daughter and mother watersheds. We applied the E-SRM to the Morony watershed in Montana, USA (~ 59,400 km<sup>2</sup>; elevation range: 860–3418 m). The area was subdivided into the Morony and Canyon Ferry watersheds, with the latter treated as a pristine basin for calibration. Following calibration at Canyon Ferry, regulation-correction was applied using streamflow from the Canyon Ferry, Hauser, and Holter dams. Validation was conducted over the entire Morony watershed. Three methodological scenarios were evaluated: (1) 21-year calibration and 21-year validation; (2) 11-year calibration and 21-year validation; and (3) calibration using odd years with validation on odd and even years. In all scenarios, comparison between observed and regulation-corrected streamflow showed improved performance across multiple model assessment metrics. These included both absolute (e.g. Nash–Sutcliffe Efficiency: from − 0.16 to 0.74, − 0.43 to 0.59, − 0.16 to 0.67; Kling–Gupta Efficiency) and relative (e.g. Root-Mean-Square Error, Normalized Root-Mean-Square Error, Mean Absolute Error, and Volume Difference) indicators, highlighting the significant impact of flow regulation on SRM performance. The E-SRM framework offers new opportunities for research and practical application in water resource management.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"15 8","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-025-02579-y.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Water Science","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s13201-025-02579-y","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 0
Abstract
We expanded the Snowmelt Runoff Model (SRM) to simulate streamflow in regulated watersheds, resulting in a modified framework termed the Expanded SRM (E-SRM) that integrates multi-year automated batch processing, nested iterators, and a seasonal divider algorithm for streamflow simulation. A parsimonious regulation-correction approach was developed that conceptually divides the watershed into a pristine upstream “daughter” subwatershed and a larger, regulated “mother” watershed. Hydrological parameter transferability was assumed between the daughter and mother watersheds. We applied the E-SRM to the Morony watershed in Montana, USA (~ 59,400 km2; elevation range: 860–3418 m). The area was subdivided into the Morony and Canyon Ferry watersheds, with the latter treated as a pristine basin for calibration. Following calibration at Canyon Ferry, regulation-correction was applied using streamflow from the Canyon Ferry, Hauser, and Holter dams. Validation was conducted over the entire Morony watershed. Three methodological scenarios were evaluated: (1) 21-year calibration and 21-year validation; (2) 11-year calibration and 21-year validation; and (3) calibration using odd years with validation on odd and even years. In all scenarios, comparison between observed and regulation-corrected streamflow showed improved performance across multiple model assessment metrics. These included both absolute (e.g. Nash–Sutcliffe Efficiency: from − 0.16 to 0.74, − 0.43 to 0.59, − 0.16 to 0.67; Kling–Gupta Efficiency) and relative (e.g. Root-Mean-Square Error, Normalized Root-Mean-Square Error, Mean Absolute Error, and Volume Difference) indicators, highlighting the significant impact of flow regulation on SRM performance. The E-SRM framework offers new opportunities for research and practical application in water resource management.