{"doi":"10.5194/piahs-373-87-2016","title":"Spatial variability of the parameters of a semi-distributed hydrological model","abstract":"<jats:p>Abstract. Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).\n                    </jats:p>","journal":"Proceedings of the International Association of Hydrological Sciences","year":2016,"id":673969,"datarank":0.5495342469194471,"base_score":3.6635616461296463,"endowment":3.6635616461296463,"self_citation_contribution":0.5495342469194471,"citation_network_contribution":0.0,"self_endowment_contribution":0.5495342469194471,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":38,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1760910,"name":"Guillaume Thirel","orcid":"0000-0002-1444-1830","position":1,"is_corresponding":false},{"id":1760911,"name":"Vazken Andréassian","orcid":"0000-0001-7124-9303","position":2,"is_corresponding":false},{"id":1760912,"name":"Charles Perrin","orcid":null,"position":3,"is_corresponding":false},{"id":1760913,"name":"Maria-Helena Ramos","orcid":"0000-0003-1133-4164","position":4,"is_corresponding":false},{"id":1760909,"name":"Alban de Lavenne","orcid":"0000-0002-9448-3490","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Spatial variability of the parameters of a semi-distributed hydrological model","abstract":"<jats:p>Abstract. Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).\n                    </jats:p>","is_dataset_classified":null,"base_score":3.6635616461296463,"endowment":3.6635616461296463,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W2377703416","authors":[],"funders":[],"total_grants":0,"fwci":2.0532,"citation_percentile":0.86402888,"influential_citations":0,"citation_trend":[{"year":2017,"count":2},{"year":2018,"count":2},{"year":2019,"count":5},{"year":2020,"count":2},{"year":2021,"count":6},{"year":2022,"count":6},{"year":2023,"count":9},{"year":2024,"count":3},{"year":2025,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.proc-iahs.net/373/87/2016/piahs-373-87-2016.pdf","host_type":"journal"},{"url":"https://www.proc-iahs.net/373/87/2016/piahs-373-87-2016.pdf","host_type":"publisher"},{"url":"https://piahs.copernicus.org/articles/373/87/2016/piahs-373-87-2016.pdf","host_type":"publisher"},{"url":"https://doi.org/10.5194/piahs-373-87-2016","host_type":"journal"},{"url":"https://hal.science/hal-01342122","host_type":"repository"},{"url":"https://doaj.org/article/5b3404c100384823b52b62807364073f","host_type":"repository"}],"fields_of_study":["Hydrology and Watershed Management Studies","Flood Risk Assessment and Management","Hydrology and Drought Analysis"],"mesh_terms":[],"keywords":["Identifiability","Calibration","Streamflow","Parameter space","Distributed element model","Estimation theory","Closure (psychology)","Water balance","Model parameter","Computer science","Identification (biology)","Hydrology (agriculture)","Hydrological modelling","Flow (mathematics)","Environmental science","Mathematics","Data mining","Statistics","Geology","Algorithm","Geography","Ecology","Geotechnical engineering","Machine learning"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Clean water and sanitation"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-16T15:04:15.625459Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}