Simulation and Evaluation of Hydrothermal Conditions in Crop Growth Period: A Case Study of Highland Barley in the Qinghai-Tibet Plateau
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Models and Data
2.3. Simulation of Transition Probability
2.4. Simulation of Precipitation
2.5. Simulation of Temperature and Solar Radiation
2.6. Experimental Protocol
3. Results
3.1. Analysis of Precipitation Transition Probability
3.2. Analysis of Temperature Simulations in the Growth Period
3.3. Analysis of Simulation Radiation Results in the Growth Period
3.4. Analysis of Precipitation Results in the Growth Period
3.5. Analysis of Extreme Weather Conditions during the Growth Period
4. Conclusions and Perspectives
4.1. Analysis of Precipitation Transition Probability
4.2. Potential Applications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wallis, T.W.R.; Griffiths, J.F. An assessment of the weather generator (WXGEN) used in the erosion/productivity impact cal-culator (EPIC). Agric. For. Meteorol. 1995, 73, 115–133. [Google Scholar] [CrossRef]
- Erosion/Productivity Impact Calculator, Model Documentation. Available online: http://agrilife.org/epicapex/files/2015/05/EpicModelDocumentation.pdf (accessed on 12 March 2022).
- Generating Climatic Data for a Water Erosion Prediction Model. Available online: https://agris.fao.org/agris-search/search.do?recordID=US8843444 (accessed on 12 March 2022).
- Baffault, C.; Nearing, M.A.; Nicks, A.D. Impact of CLIGEN parameters on WEPP-predicted average annual soil loss. Trans. ASAE 1995, 39, 447–457. [Google Scholar] [CrossRef]
- Khoi, D.N.; Suetsugi, T. Uncertainty in climate change impacts on streamflow in Be River Catchment, Vietnam. Water Environ. J. 2012, 26, 530–539. [Google Scholar] [CrossRef]
- Zhang, X.J.; Lin, S.J.; Ma, C. Design rainstorm estimation of Qiantang River Basin under HadCM3 model. Hydrology 2013, 33, 21–26. [Google Scholar]
- Wang, X.B.; Wang, S.Q.; Chen, J.H. Simulation of production potential of Chinese super hybrid rice in Meng-India-Myanmar region. Acta Geogr. Sin. 2018, 73, 2184–2197. [Google Scholar]
- Zhou, Y.T.; Gao, Y.; Liu, F.G.; Wang, J.A. Application of weather generator in disaster risk assessment. J. Catastrophology 2022, 37. [Google Scholar]
- Gabriel, K.R.; Neumann, J.A. Markov chain model for daily rainfall occurrence in Tel Aviv Israel. Q. J. R. Meteorol. Soc. 1962, 88, 90–95. [Google Scholar] [CrossRef]
- Jones, P.D.; Reid, P.A. Assessing future changes in extreme precipitation over Britain using regional climate model integra-tions. Int. J. Climatol. A J. R. Meteorol. Soc. 2001, 21, 1337–1356. [Google Scholar] [CrossRef]
- Bruhn, J.A.; Fry, W.E.; Fick, G.W. Simulation of Daily Weather Data Using Theoretical Probability Distributions. J. Appl. Meteorol. 1980, 19, 1029–1036. [Google Scholar] [CrossRef] [Green Version]
- USDA-Water Erosion Prediction Project Hillslope Profile and Watershed Model Documentation. Available online: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/wepp-model-documentation/ (accessed on 12 March 2022).
- Liu, J.; Williams, J.R.; Wang, X.; Yang, H. Using MODAWEC to generate daily weather data for the EPIC model. Environ. Model. Softw. 2009, 24, 655–664. [Google Scholar] [CrossRef]
- Semenov, M.A.; Barrow, E.M. Use of a stochastic weather generator in the development of climate change scenarios. Clim. Chang. 1997, 35, 397–414. [Google Scholar] [CrossRef]
- Zubaidi, S.L.; Kot, P.; Hashim, K.; Alkhaddar, R.; Abdellatif, M.; Muhsin, Y.R. Using LARS –WG model for prediction of temperature in Columbia City, USA. IOP conference series. Mater. Sci. Eng. 2019, 584, 12026. [Google Scholar]
- Mahmood, R.; Jia, S. Observed and simulated hydro-climatic data for the lake Chad basin, Africa. Data Brief. 2019, 25, 104043. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Wang, S.; Chen, J.; Cui, H.; Wu, Y.; Ravindranath, N.H.; Rahman, A. Simulating potential yields of Chinese super hybrid rice in Bangladesh, India and Myanmar with EPIC model. J. Geogr. Sci. 2018, 28, 1020–1036. [Google Scholar] [CrossRef] [Green Version]
- Ding, Y.G.; Zhang, J.L.; Jiang, Z.H. Simulation experiment of extreme precipitation based on Multi-state Markov chain model. Acta Meteorol. Sin. 2009, 67, 20–27. [Google Scholar]
- Zhang, G.H. Adaptability of CLIGEN weather Generator in the Yellow River Basin. J. Soil Water Conserv. 2004, 18, 175–178. [Google Scholar]
- Ma, X.G.; Shen, Z.R. Visual programming of random weather generator and its future application in agroecology. Sci. Agric. Sin. 2002, 35, 1473–1478. [Google Scholar]
- Liao, Y.M.; Chen, D.L.; Gao, G.; Xie, Y. Impacts of Climate Changes on Parameters of a Weather Generator for Daily Precipi-tation in China. Acta Geogr. Sin. 2009, 64, 871–878. [Google Scholar]
- Teng, Y.Q.; Ma, W.J. Risk assessment of rice planting based on generalized linear model. Math. Pract. Cogn. 2019, 49, 1–17. [Google Scholar]
- Wang, Y.L.; He, L.Y. Research and application of crop growth simulation models. J. Huazhong Agric. Univ. 2005, 5, 107–113. [Google Scholar]
- Lu, X.H. Simulation of Climate Change on Conservation Tillage of Winter Wheat in Western Region of Henan Province. Available online: https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CDFD&dbname=CDFD0911&filename=2010147433.nh&uniplatform=NZKPT&v=Xh3ydTXgkezun1sWf3a-9I5i9mMRevCGshaOomgFPMsRMag31EeIRF83BHeDfznE (accessed on 16 September 2010).
- Zhang, Y.; Ren, X.C.; Zhao, Y.; Cao, H.; Feng, H. Impact and simulation of climate change on water consumption and yield of winter-wheat in Guanzhong Region. Agric. Res. Arid. Areas 2016, 34, 220–228. [Google Scholar]
- Yan, Y.J.; Xu, J.X.; Xiao, H. Summer Maize Net Irrigation Water Requirement to Climate Change in Henan Province in China during 2021−2050. Clim. Chang. Res. 2017, 13, 138–148. [Google Scholar]
- Zou, W.; He, W.Z.; Li, D.Y.; He, L.X.; Xie, S.Q. Presence Problem of Highland Barley Production and the Way of Increasing Yield in Yunnan Plateau. Chin. Agric. Sci. Bull. 2003, 6, 71–72. [Google Scholar]
- Ma, W.D.; Su, P.; Jia, W.; Liu, F.G.; Wang, J.A. Advances in the Research on the Exposure of Highland Barley Natural Disasters on the Background of Climate Change. J. Catastrophology 2020, 35, 215–221. [Google Scholar]
- Wang, R.; Jiang, Y.; Zhang, A.Y.; Gao, Y.; Wang, J.A. Review on Crop Exposure of Natural Disasters. J. Catastrophology 2019, 2, 215–221. [Google Scholar]
- Xu, X.D.; Dong, L.L.; Zhao, Y.; Wang, Y.J. Effect of the Asian Water Tower over the Qinghai-Tibet Plateau and the characteristics of atmospheric water circulation. Chin. Sci. Bull. 2019, 64, 2830–2841. (In Chinese) [Google Scholar] [CrossRef] [Green Version]
- Li, J.J.; Wen, S.X.; Zhang, Q.S.; Wang, F.B.; Zheng, B.X.; Li, B.Y. Discussion on the age, amplitude and form of the uplift of the Qinghai Tibet Plateau. Sci. China 1979, 6, 608–616. [Google Scholar]
- Ma, W.D.; Jia, W.; Su, P.; Feng, X.Y.; Liu, F.G.; Wang, J.A. Mapping Highland Barley on the Qinghai–Tibet Combing Land-sat OLI Data and Object-Oriented Classification Method. Land 2021, 10, 1022. [Google Scholar] [CrossRef]
- Liao, Y.M.; Zhang, Q.; Chen, D.L. Precipitation simulation in China With a weather Generator. Acta Geogr. Sin. 2004, 59, 417–426. [Google Scholar]
- National Intelligent Agricultural Meteorological data—Main meteorological indicators in Menyuan County, Menyuan County Meteorological Bureau. 2020.
- Shi, W.L.; Yang, Q.K.; Mu, W.H. Applicability test of CLIGEN in Loess Plateau. Sci. Soil Water Conserv.-Tion 2006, 2, 18–23. [Google Scholar]
- Zhang, H.; Huang, G.H.; Wang, D.; Zhang, X. Uncertainty assessment of climate change impacts on the hydrology of small prairie wet-lands. J. Hydrol. 2011, 396, 94–103. [Google Scholar] [CrossRef] [Green Version]
- Hayhoe, H. Relationship between weather variables in observed and WXGEN generated data series. Agric. For. Meteorol. 1998, 90, 203–214. [Google Scholar] [CrossRef]
- Zhang, L.X.; Chen, X.L.; Xin, X.G. Short commentary on CMIP6 Scenario Model Intercomparison Project (ScenarioMIP). Clim. Chang. Res. 2019, 15, 519–525. [Google Scholar]
Growth Period | Month | Indexes |
---|---|---|
Sowing-emergence period | April | Temperature: Min 1 °C; Max 35 °C |
Tillering period | May | Temperature: Min 3 °C; Max 21 ℃ |
Jointing-booting period | Jun | Temperature: Min 3 °C; Max 21 °C; Precipitation: not less than 70 mm |
Booting-heading period | July | Temperature: Min 10 °C; Max 32 °C; Precipitation: not less than 70 mm |
Filling-maturation period | August and September | Temperature: Min 10 °C; Max 25 °C; Precipitation: not less than 70 mm |
Test Index | Test Method | Number of Passing Test Stations | Proportion (%) | Failed to Pass |
---|---|---|---|---|
Monthly minimum temperature | K-S Test | 49 | 96 | Pulan, Shangri-La |
F-Test | 50 | 98 | Pulan | |
T-Test | 51 | 100 | - | |
Monthly maximum temperature | K-S Test | 49 | 96 | Pulan, Shangri-La |
F-Test | 51 | 100 | - | |
T-Test | 50 | 98 | Shangri-La |
Test Index | Slope | R2 | BIAS | RMSE |
---|---|---|---|---|
Annual average temperature | 1.086 | 0.993 | −0.018 | 0.686 (°C) |
Test Index | Test Method | Number of Passing Test Stations | Proportion (%) | Failed to Pass |
---|---|---|---|---|
Monthly solar radiation | K-S Test | 46 | 90 | Pulan, Shangri-La, Xiaozaohuo, Shiquanhe, Gongshan |
F-Test | 50 | 98 | Shangri-La | |
T-Test | 49 | 96 | Xiaozaohuo, Shi-quanhe |
Test Index | Slope | R2 | BIAS | RMSE |
---|---|---|---|---|
Annual average solar radiation | 1.112 | 0.928 | 0.047 | 1.65 (MJ/m2) |
Test Index | Test Method | Number of Passing Test Stations | Proportion (%) | Failed to Pass |
---|---|---|---|---|
Monthly precipitation | K-S Test | 48 | 94 | Pulan, Shiquanhe, Shangri-La |
F-Test | 50 | 98 | Shiquanhe | |
T-Test | 51 | 100 | - |
Test Index | Slope | R2 | BIAS | RMSE |
---|---|---|---|---|
Annual average number of wet days | 1.069 | 0.994 | −0.067 | 8.374 (d) |
Annual average precipitatio-n | 1.007 | 0.999 | −0.006 | 8.040 (mm) |
Annual average heavy rain days | 0.949 | 0.954 | 0.182 | 1.023 (d) |
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Zhou, Y.; Ma, W.; Liu, F.; Wang, J. Simulation and Evaluation of Hydrothermal Conditions in Crop Growth Period: A Case Study of Highland Barley in the Qinghai-Tibet Plateau. Sustainability 2022, 14, 5932. https://doi.org/10.3390/su14105932
Zhou Y, Ma W, Liu F, Wang J. Simulation and Evaluation of Hydrothermal Conditions in Crop Growth Period: A Case Study of Highland Barley in the Qinghai-Tibet Plateau. Sustainability. 2022; 14(10):5932. https://doi.org/10.3390/su14105932
Chicago/Turabian StyleZhou, Yuantao, Weidong Ma, Fenggui Liu, and Jing’ai Wang. 2022. "Simulation and Evaluation of Hydrothermal Conditions in Crop Growth Period: A Case Study of Highland Barley in the Qinghai-Tibet Plateau" Sustainability 14, no. 10: 5932. https://doi.org/10.3390/su14105932