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...
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Autor*in:	Zhang, Yaling [verfasserIn] Liang, Chuan Zhao, Lu Guan, Yunjie Jiang, Shouzheng Zhan, Cun Du, Pu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Risk analysis Copula model Markov process Effective precipitation Crop water requirements The hilly area of southwest china

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 36(2021), 1 vom: 24. Mai, Seite 33-49
Übergeordnetes Werk:	volume:36 ; year:2021 ; number:1 ; day:24 ; month:05 ; pages:33-49

Links:	Volltext

DOI / URN:	10.1007/s00477-021-02037-6

Katalog-ID:	SPR045934630

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245	1	0	\|a Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China
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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.
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650		4	\|a Effective precipitation \|7 (dpeaa)DE-He213
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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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912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
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allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	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
sourceStr	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
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Risk analysis Copula model Markov process Effective precipitation Crop water requirements The hilly area of southwest china
isfreeaccess_bool	false
container_title	Stochastic environmental research and risk assessment
authorswithroles_txt_mv	Zhang, Yaling @@aut@@ Liang, Chuan @@aut@@ Zhao, Lu @@aut@@ Guan, Yunjie @@aut@@ Jiang, Shouzheng @@aut@@ Zhan, Cun @@aut@@ Du, Pu @@aut@@
publishDateDaySort_date	2021-05-24T00:00:00Z
hierarchy_top_id	27160235X
id	SPR045934630
language_de	englisch
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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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topic_title	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
topic	misc Risk analysis misc Copula model misc Markov process misc Effective precipitation misc Crop water requirements misc The hilly area of southwest china
topic_unstemmed	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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title	Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China
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title_full	Risk analysis of natural water resources scarcity based on a stochastic simulation model in the hilly area of southwest China
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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
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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
ppnlink	27160235X
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doi_str	10.1007/s00477-021-02037-6
up_date	2024-07-03T19:15:12.990Z
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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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