Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach
Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zandmoghaddam, Shahin [verfasserIn] Nazemi, Ali [verfasserIn] Hassanzadeh, Elmira [verfasserIn] Hatami, Shadi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 33(2019), 10 vom: 25. Juli, Seite 3579-3594 |
|---|---|
| Übergeordnetes Werk: |
volume:33 ; year:2019 ; number:10 ; day:25 ; month:07 ; pages:3579-3594 |
| Links: |
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| DOI / URN: |
10.1007/s11269-019-02319-3 |
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| Katalog-ID: |
SPR018399274 |
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| 520 | |a Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. | ||
| 650 | 4 | |a Regional water resource systems |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Local system dynamics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Emulation approach |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Data-driven modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sensitivity analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Oldman River basin |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nazemi, Ali |e verfasserin |4 aut | |
| 700 | 1 | |a Hassanzadeh, Elmira |e verfasserin |4 aut | |
| 700 | 1 | |a Hatami, Shadi |e verfasserin |4 aut | |
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10.1007/s11269-019-02319-3 doi (DE-627)SPR018399274 (SPR)s11269-019-02319-3-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Zandmoghaddam, Shahin verfasserin aut Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. Regional water resource systems (dpeaa)DE-He213 Local system dynamics (dpeaa)DE-He213 Emulation approach (dpeaa)DE-He213 Data-driven modeling (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Oldman River basin (dpeaa)DE-He213 Nazemi, Ali verfasserin aut Hassanzadeh, Elmira verfasserin aut Hatami, Shadi verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 10 vom: 25. Juli, Seite 3579-3594 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:10 day:25 month:07 pages:3579-3594 https://dx.doi.org/10.1007/s11269-019-02319-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 10 25 07 3579-3594 |
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10.1007/s11269-019-02319-3 doi (DE-627)SPR018399274 (SPR)s11269-019-02319-3-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Zandmoghaddam, Shahin verfasserin aut Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. Regional water resource systems (dpeaa)DE-He213 Local system dynamics (dpeaa)DE-He213 Emulation approach (dpeaa)DE-He213 Data-driven modeling (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Oldman River basin (dpeaa)DE-He213 Nazemi, Ali verfasserin aut Hassanzadeh, Elmira verfasserin aut Hatami, Shadi verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 10 vom: 25. Juli, Seite 3579-3594 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:10 day:25 month:07 pages:3579-3594 https://dx.doi.org/10.1007/s11269-019-02319-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 10 25 07 3579-3594 |
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10.1007/s11269-019-02319-3 doi (DE-627)SPR018399274 (SPR)s11269-019-02319-3-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Zandmoghaddam, Shahin verfasserin aut Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. Regional water resource systems (dpeaa)DE-He213 Local system dynamics (dpeaa)DE-He213 Emulation approach (dpeaa)DE-He213 Data-driven modeling (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Oldman River basin (dpeaa)DE-He213 Nazemi, Ali verfasserin aut Hassanzadeh, Elmira verfasserin aut Hatami, Shadi verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 10 vom: 25. Juli, Seite 3579-3594 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:10 day:25 month:07 pages:3579-3594 https://dx.doi.org/10.1007/s11269-019-02319-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 10 25 07 3579-3594 |
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10.1007/s11269-019-02319-3 doi (DE-627)SPR018399274 (SPR)s11269-019-02319-3-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Zandmoghaddam, Shahin verfasserin aut Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. Regional water resource systems (dpeaa)DE-He213 Local system dynamics (dpeaa)DE-He213 Emulation approach (dpeaa)DE-He213 Data-driven modeling (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Oldman River basin (dpeaa)DE-He213 Nazemi, Ali verfasserin aut Hassanzadeh, Elmira verfasserin aut Hatami, Shadi verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 10 vom: 25. Juli, Seite 3579-3594 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:10 day:25 month:07 pages:3579-3594 https://dx.doi.org/10.1007/s11269-019-02319-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 10 25 07 3579-3594 |
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10.1007/s11269-019-02319-3 doi (DE-627)SPR018399274 (SPR)s11269-019-02319-3-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Zandmoghaddam, Shahin verfasserin aut Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. Regional water resource systems (dpeaa)DE-He213 Local system dynamics (dpeaa)DE-He213 Emulation approach (dpeaa)DE-He213 Data-driven modeling (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Oldman River basin (dpeaa)DE-He213 Nazemi, Ali verfasserin aut Hassanzadeh, Elmira verfasserin aut Hatami, Shadi verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 10 vom: 25. Juli, Seite 3579-3594 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:10 day:25 month:07 pages:3579-3594 https://dx.doi.org/10.1007/s11269-019-02319-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 10 25 07 3579-3594 |
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Zandmoghaddam, Shahin @@aut@@ Nazemi, Ali @@aut@@ Hassanzadeh, Elmira @@aut@@ Hatami, Shadi @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018399274</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111061000.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11269-019-02319-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018399274</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11269-019-02319-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="a">630</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.33</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zandmoghaddam, Shahin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. 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Zandmoghaddam, Shahin |
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Zandmoghaddam, Shahin ddc 550 bkl 43.33 misc Regional water resource systems misc Local system dynamics misc Emulation approach misc Data-driven modeling misc Sensitivity analysis misc Oldman River basin Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach |
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550 630 ASE 43.33 bkl Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach Regional water resource systems (dpeaa)DE-He213 Local system dynamics (dpeaa)DE-He213 Emulation approach (dpeaa)DE-He213 Data-driven modeling (dpeaa)DE-He213 Sensitivity analysis (dpeaa)DE-He213 Oldman River basin (dpeaa)DE-He213 |
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ddc 550 bkl 43.33 misc Regional water resource systems misc Local system dynamics misc Emulation approach misc Data-driven modeling misc Sensitivity analysis misc Oldman River basin |
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ddc 550 bkl 43.33 misc Regional water resource systems misc Local system dynamics misc Emulation approach misc Data-driven modeling misc Sensitivity analysis misc Oldman River basin |
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Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach |
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Zandmoghaddam, Shahin Nazemi, Ali Hassanzadeh, Elmira Hatami, Shadi |
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representing local dynamics of water resource systems through a data-driven emulation approach |
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Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach |
| abstract |
Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. |
| abstractGer |
Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. |
| abstract_unstemmed |
Abstract Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the interactions between water availability, water demands and their natural and anthropogenic drivers across a range of spatial and temporal scales. Despite significant progresses, system models often undergo various level of simplifications. For instance, several variables are represented within models as prescribed values; and therefore, their links with their natural and anthropogenic drives are not represented. Here we propose a data-driven emulation approach to represent the local dynamics of water resource systems through advising a set of interconnected functional mappings that not only learn and replicate input-output relationships of an existing model, but also link the prescribed variables to their corresponding natural and anthropogenic drivers. To demonstrate the practical utility of the suggested methodology, we consider representing the local dynamics at the Oldman Reservoir, which is a critical infrastructure for effective regional water resource management in southern Alberta, Canada. Using a rigorous setup/falsification procedure, we develop a set of alternative emulators to describe the local dynamics of irrigation demand and withdrawals along with reservoir release and evaporation. The non-falsified emulators are then used to address the impact of changing climate on the local irrigation deficit. Our analysis shows that local irrigation deficit is more sensitive to changes in local temperature than those of local precipitation. In addition, the rate of change in irrigation deficit is much more significant under a unit degree of warming than a unit degree of cooling. Such local understandings are not attainable by the existing operational model. |
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| container_issue |
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| title_short |
Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach |
| url |
https://dx.doi.org/10.1007/s11269-019-02319-3 |
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| author2 |
Nazemi, Ali Hassanzadeh, Elmira Hatami, Shadi |
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Nazemi, Ali Hassanzadeh, Elmira Hatami, Shadi |
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| doi_str |
10.1007/s11269-019-02319-3 |
| up_date |
2024-07-03T19:24:44.276Z |
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| score |
7.4007044 |