Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China
Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simul...
Ausführliche Beschreibung
Autor*in: |
Zhang, Yaling [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2021 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Übergeordnetes Werk: |
Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 36(2021), 1 vom: 24. Mai, Seite 33-49 |
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Übergeordnetes Werk: |
volume:36 ; year:2021 ; number:1 ; day:24 ; month:05 ; pages:33-49 |
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DOI / URN: |
10.1007/s00477-021-02037-6 |
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Katalog-ID: |
SPR045934630 |
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520 | |a Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. | ||
650 | 4 | |a Risk analysis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Copula model |7 (dpeaa)DE-He213 | |
650 | 4 | |a Markov process |7 (dpeaa)DE-He213 | |
650 | 4 | |a Effective precipitation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Crop water requirements |7 (dpeaa)DE-He213 | |
650 | 4 | |a The hilly area of southwest china |7 (dpeaa)DE-He213 | |
700 | 1 | |a Liang, Chuan |4 aut | |
700 | 1 | |a Zhao, Lu |0 (orcid)0000-0002-6196-4458 |4 aut | |
700 | 1 | |a Guan, Yunjie |4 aut | |
700 | 1 | |a Jiang, Shouzheng |4 aut | |
700 | 1 | |a Zhan, Cun |4 aut | |
700 | 1 | |a Du, Pu |4 aut | |
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10.1007/s00477-021-02037-6 doi (DE-627)SPR045934630 (SPR)s00477-021-02037-6-e DE-627 ger DE-627 rakwb eng Zhang, Yaling verfasserin aut Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. Risk analysis (dpeaa)DE-He213 Copula model (dpeaa)DE-He213 Markov process (dpeaa)DE-He213 Effective precipitation (dpeaa)DE-He213 Crop water requirements (dpeaa)DE-He213 The hilly area of southwest china (dpeaa)DE-He213 Liang, Chuan aut Zhao, Lu (orcid)0000-0002-6196-4458 aut Guan, Yunjie aut Jiang, Shouzheng aut Zhan, Cun aut Du, Pu aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2021), 1 vom: 24. Mai, Seite 33-49 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2021 number:1 day:24 month:05 pages:33-49 https://dx.doi.org/10.1007/s00477-021-02037-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4277 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 AR 36 2021 1 24 05 33-49 |
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10.1007/s00477-021-02037-6 doi (DE-627)SPR045934630 (SPR)s00477-021-02037-6-e DE-627 ger DE-627 rakwb eng Zhang, Yaling verfasserin aut Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. Risk analysis (dpeaa)DE-He213 Copula model (dpeaa)DE-He213 Markov process (dpeaa)DE-He213 Effective precipitation (dpeaa)DE-He213 Crop water requirements (dpeaa)DE-He213 The hilly area of southwest china (dpeaa)DE-He213 Liang, Chuan aut Zhao, Lu (orcid)0000-0002-6196-4458 aut Guan, Yunjie aut Jiang, Shouzheng aut Zhan, Cun aut Du, Pu aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2021), 1 vom: 24. Mai, Seite 33-49 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2021 number:1 day:24 month:05 pages:33-49 https://dx.doi.org/10.1007/s00477-021-02037-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4277 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 AR 36 2021 1 24 05 33-49 |
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10.1007/s00477-021-02037-6 doi (DE-627)SPR045934630 (SPR)s00477-021-02037-6-e DE-627 ger DE-627 rakwb eng Zhang, Yaling verfasserin aut Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. Risk analysis (dpeaa)DE-He213 Copula model (dpeaa)DE-He213 Markov process (dpeaa)DE-He213 Effective precipitation (dpeaa)DE-He213 Crop water requirements (dpeaa)DE-He213 The hilly area of southwest china (dpeaa)DE-He213 Liang, Chuan aut Zhao, Lu (orcid)0000-0002-6196-4458 aut Guan, Yunjie aut Jiang, Shouzheng aut Zhan, Cun aut Du, Pu aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2021), 1 vom: 24. Mai, Seite 33-49 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2021 number:1 day:24 month:05 pages:33-49 https://dx.doi.org/10.1007/s00477-021-02037-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4277 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 AR 36 2021 1 24 05 33-49 |
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10.1007/s00477-021-02037-6 doi (DE-627)SPR045934630 (SPR)s00477-021-02037-6-e DE-627 ger DE-627 rakwb eng Zhang, Yaling verfasserin aut Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. Risk analysis (dpeaa)DE-He213 Copula model (dpeaa)DE-He213 Markov process (dpeaa)DE-He213 Effective precipitation (dpeaa)DE-He213 Crop water requirements (dpeaa)DE-He213 The hilly area of southwest china (dpeaa)DE-He213 Liang, Chuan aut Zhao, Lu (orcid)0000-0002-6196-4458 aut Guan, Yunjie aut Jiang, Shouzheng aut Zhan, Cun aut Du, Pu aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2021), 1 vom: 24. Mai, Seite 33-49 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2021 number:1 day:24 month:05 pages:33-49 https://dx.doi.org/10.1007/s00477-021-02037-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4277 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 AR 36 2021 1 24 05 33-49 |
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10.1007/s00477-021-02037-6 doi (DE-627)SPR045934630 (SPR)s00477-021-02037-6-e DE-627 ger DE-627 rakwb eng Zhang, Yaling verfasserin aut Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. Risk analysis (dpeaa)DE-He213 Copula model (dpeaa)DE-He213 Markov process (dpeaa)DE-He213 Effective precipitation (dpeaa)DE-He213 Crop water requirements (dpeaa)DE-He213 The hilly area of southwest china (dpeaa)DE-He213 Liang, Chuan aut Zhao, Lu (orcid)0000-0002-6196-4458 aut Guan, Yunjie aut Jiang, Shouzheng aut Zhan, Cun aut Du, Pu aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 36(2021), 1 vom: 24. Mai, Seite 33-49 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:36 year:2021 number:1 day:24 month:05 pages:33-49 https://dx.doi.org/10.1007/s00477-021-02037-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4277 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 AR 36 2021 1 24 05 33-49 |
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Enthalten in Stochastic environmental research and risk assessment 36(2021), 1 vom: 24. Mai, Seite 33-49 volume:36 year:2021 number:1 day:24 month:05 pages:33-49 |
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Enthalten in Stochastic environmental research and risk assessment 36(2021), 1 vom: 24. Mai, Seite 33-49 volume:36 year:2021 number:1 day:24 month:05 pages:33-49 |
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Risk analysis Copula model Markov process Effective precipitation Crop water requirements The hilly area of southwest china |
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Zhang, Yaling @@aut@@ Liang, Chuan @@aut@@ Zhao, Lu @@aut@@ Guan, Yunjie @@aut@@ Jiang, Shouzheng @@aut@@ Zhan, Cun @@aut@@ Du, Pu @@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">SPR045934630</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509101302.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220110s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00477-021-02037-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR045934630</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00477-021-02037-6-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="100" ind1="1" ind2=" "><subfield code="a">Zhang, Yaling</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Copula model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Markov process</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Effective precipitation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop water requirements</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">The hilly area of southwest china</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Chuan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Lu</subfield><subfield code="0">(orcid)0000-0002-6196-4458</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guan, Yunjie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Shouzheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhan, Cun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Pu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Stochastic environmental research and risk assessment</subfield><subfield code="d">Berlin : Springer, 1987</subfield><subfield code="g">36(2021), 1 vom: 24. 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|
author |
Zhang, Yaling |
spellingShingle |
Zhang, Yaling misc Risk analysis misc Copula model misc Markov process misc Effective precipitation misc Crop water requirements misc The hilly area of southwest china Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China |
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Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China Risk analysis (dpeaa)DE-He213 Copula model (dpeaa)DE-He213 Markov process (dpeaa)DE-He213 Effective precipitation (dpeaa)DE-He213 Crop water requirements (dpeaa)DE-He213 The hilly area of southwest china (dpeaa)DE-He213 |
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misc Risk analysis misc Copula model misc Markov process misc Effective precipitation misc Crop water requirements misc The hilly area of southwest china |
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misc Risk analysis misc Copula model misc Markov process misc Effective precipitation misc Crop water requirements misc The hilly area of southwest china |
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Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China |
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Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China |
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Zhang, Yaling |
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Stochastic environmental research and risk assessment |
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Zhang, Yaling Liang, Chuan Zhao, Lu Guan, Yunjie Jiang, Shouzheng Zhan, Cun Du, Pu |
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title_sort |
risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest china |
title_auth |
Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China |
abstract |
Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
abstractGer |
Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
abstract_unstemmed |
Abstract The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
collection_details |
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container_issue |
1 |
title_short |
Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China |
url |
https://dx.doi.org/10.1007/s00477-021-02037-6 |
remote_bool |
true |
author2 |
Liang, Chuan Zhao, Lu Guan, Yunjie Jiang, Shouzheng Zhan, Cun Du, Pu |
author2Str |
Liang, Chuan Zhao, Lu Guan, Yunjie Jiang, Shouzheng Zhan, Cun Du, Pu |
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doi_str |
10.1007/s00477-021-02037-6 |
up_date |
2024-07-03T19:15:12.990Z |
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|
score |
7.4007177 |