The robustness of conceptual rainfall-runoff modelling under climate variability – A review

Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. I...
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Autor*in:	Ji, Hong Kang [verfasserIn] Mirzaei, Majid [verfasserIn] Lai, Sai Hin [verfasserIn] Dehghani, Adnan [verfasserIn] Dehghani, Amin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review

Übergeordnetes Werk:	Enthalten in: Journal of hydrology - Amsterdam [u.a.] : Elsevier, 1963, 621
Übergeordnetes Werk:	volume:621

DOI / URN:	10.1016/j.jhydrol.2023.129666

Katalog-ID:	ELV010021272

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520			\|a Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making.
650		4	\|a Conceptual rainfall–runoff modelling
650		4	\|a Model robustness
650		4	\|a Model transferability
650		4	\|a Split-sample tests
650		4	\|a Climate variability
650		4	\|a Review
700	1		\|a Mirzaei, Majid \|e verfasserin \|0 (orcid)0000-0003-1452-9004 \|4 aut
700	1		\|a Lai, Sai Hin \|e verfasserin \|4 aut
700	1		\|a Dehghani, Adnan \|e verfasserin \|4 aut
700	1		\|a Dehghani, Amin \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.jhydrol.2023.129666 doi (DE-627)ELV010021272 (ELSEVIER)S0022-1694(23)00608-X DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Ji, Hong Kang verfasserin (orcid)0000-0002-2698-4373 aut The robustness of conceptual rainfall-runoff modelling under climate variability – A review 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making. Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review Mirzaei, Majid verfasserin (orcid)0000-0003-1452-9004 aut Lai, Sai Hin verfasserin aut Dehghani, Adnan verfasserin aut Dehghani, Amin verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
spelling	10.1016/j.jhydrol.2023.129666 doi (DE-627)ELV010021272 (ELSEVIER)S0022-1694(23)00608-X DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Ji, Hong Kang verfasserin (orcid)0000-0002-2698-4373 aut The robustness of conceptual rainfall-runoff modelling under climate variability – A review 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making. Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review Mirzaei, Majid verfasserin (orcid)0000-0003-1452-9004 aut Lai, Sai Hin verfasserin aut Dehghani, Adnan verfasserin aut Dehghani, Amin verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
allfields_unstemmed	10.1016/j.jhydrol.2023.129666 doi (DE-627)ELV010021272 (ELSEVIER)S0022-1694(23)00608-X DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Ji, Hong Kang verfasserin (orcid)0000-0002-2698-4373 aut The robustness of conceptual rainfall-runoff modelling under climate variability – A review 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making. Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review Mirzaei, Majid verfasserin (orcid)0000-0003-1452-9004 aut Lai, Sai Hin verfasserin aut Dehghani, Adnan verfasserin aut Dehghani, Amin verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
allfieldsGer	10.1016/j.jhydrol.2023.129666 doi (DE-627)ELV010021272 (ELSEVIER)S0022-1694(23)00608-X DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Ji, Hong Kang verfasserin (orcid)0000-0002-2698-4373 aut The robustness of conceptual rainfall-runoff modelling under climate variability – A review 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making. Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review Mirzaei, Majid verfasserin (orcid)0000-0003-1452-9004 aut Lai, Sai Hin verfasserin aut Dehghani, Adnan verfasserin aut Dehghani, Amin verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
allfieldsSound	10.1016/j.jhydrol.2023.129666 doi (DE-627)ELV010021272 (ELSEVIER)S0022-1694(23)00608-X DE-627 ger DE-627 rda eng 690 VZ 38.85 bkl Ji, Hong Kang verfasserin (orcid)0000-0002-2698-4373 aut The robustness of conceptual rainfall-runoff modelling under climate variability – A review 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making. Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review Mirzaei, Majid verfasserin (orcid)0000-0003-1452-9004 aut Lai, Sai Hin verfasserin aut Dehghani, Adnan verfasserin aut Dehghani, Amin verfasserin aut Enthalten in Journal of hydrology Amsterdam [u.a.] : Elsevier, 1963 621 Online-Ressource (DE-627)268761817 (DE-600)1473173-3 (DE-576)077610628 1879-2707 nnns volume:621 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.85 Hydrologie: Allgemeines VZ AR 621
language	English
source	Enthalten in Journal of hydrology 621 volume:621
sourceStr	Enthalten in Journal of hydrology 621 volume:621
format_phy_str_mv	Article
bklname	Hydrologie: Allgemeines
institution	findex.gbv.de
topic_facet	Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review
dewey-raw	690
isfreeaccess_bool	false
container_title	Journal of hydrology
authorswithroles_txt_mv	Ji, Hong Kang @@aut@@ Mirzaei, Majid @@aut@@ Lai, Sai Hin @@aut@@ Dehghani, Adnan @@aut@@ Dehghani, Amin @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	268761817
dewey-sort	3690
id	ELV010021272
language_de	englisch
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author	Ji, Hong Kang
spellingShingle	Ji, Hong Kang ddc 690 bkl 38.85 misc Conceptual rainfall–runoff modelling misc Model robustness misc Model transferability misc Split-sample tests misc Climate variability misc Review The robustness of conceptual rainfall-runoff modelling under climate variability – A review
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topic_title	690 VZ 38.85 bkl The robustness of conceptual rainfall-runoff modelling under climate variability – A review Conceptual rainfall–runoff modelling Model robustness Model transferability Split-sample tests Climate variability Review
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title	The robustness of conceptual rainfall-runoff modelling under climate variability – A review
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title_full	The robustness of conceptual rainfall-runoff modelling under climate variability – A review
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title_sort	the robustness of conceptual rainfall-runoff modelling under climate variability – a review
title_auth	The robustness of conceptual rainfall-runoff modelling under climate variability – A review
abstract	Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making.
abstractGer	Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making.
abstract_unstemmed	Conceptual rainfall-runoff (CRR) models are widely used tools in climate change impact studies. However, the assumption that hydroclimate variables are stationary is no longer justifiable when input forcing is significantly different from the hydro-climatological conditions used in model building. It is particularly important to identify and discard such modelling that are unsuitable for future prediction in a calibration/evaluation strategy. Previous literatures have thoroughly investigated the implications of climate change on catchments around the world, but a few studies have systematically assessed the transferability of CRR models. In this paper, the transferability of CRR models in a climate variability context is reviewed. First, the development of the data split methods for examining parameter dependence on climate and the associated objective function with model robustness metrics are presented. Second, by comparing the outcomes collectively, both the robustness assessment of the classic differential split-sample test (DSST) and its variants, such as the large-sample generalized split-sample test (GSST), and linkages between model transferability with non-stationary climate and with catchment characteristics are explored. Among others, we answer the following questions: (1) Under which climatic constraints can models empirically be transferred? (2) Are models more difficult to transfer in catchments with certain characteristics? A set of model transferability criteria that explicitly consider potential failure scenarios at different steps in a data splitting approach is established. Thus a strategy to diagnose model transferability is proposed for routinely assessing the prediction ability of CRR models under various climate conditions, particularly when results are used to inform adaptation decision-making.
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title_short	The robustness of conceptual rainfall-runoff modelling under climate variability – A review
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up_date	2024-07-06T16:32:53.597Z
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