Abstract
Threshold level method is well-known for drought identification with the advantage of its simplicity. However, there is no criterion for classification of drought classes in this method. Therefore, a K index based on threshold level method was proposed and verified to perform well in drought assessment in the Luanhe River basin, China. Meteorological data and remote sensing data were used to calculate the net primary productivity (NPP) of the basin by the CASA model. Results showed that the model had a good performance in comparison with the downloaded yearly remote sensing NPP and monthly PsnNet. The NPP in the basin tended to increase slowly during the year of 2000–2010, and the NPP in the downstream was generally larger than upstream. In three selected representative drought years, drought reduced NPP by about 15–25%, and during the growth season, drought may reduce NPP by about 4.6%, 2.7%, and 1.9% in July, August, and September, respectively, due to reduced precipitation. NPP on grasslands and agricultural land were more susceptible to drought than forests. The result of gray incidence analysis showed that the effects of drought on NPP had a certain delay with about 5 months.
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References
Chaves MM (1991) Effects of water deficits on carbon assimilation. J Exp Bot 42:1–16
Cheng M, Wang RR, Xue HX et al (2012) Effects of drought on ecosystem net primary production in northwestern China. J Arid Land Res Environ 26(6):1–7
Field CB, Randerson JT, Malmstrom CM (1995) Global net primary production: combining ecology and remote sensing. Remote Sens Environ 51:74–88
Hannaford J, Lloyd-Hughes B, Keef C, Parry S, Prudhomme C (2011) Examining the large-scale spatial coherence of European drought using regional indicators of precipitation and streamflow deficit. Hydrol Process 25:1146–1162
Hao YH, Wang W, Wang GQ et al (2009) Effects of climate change and human activities on the karstic springs in Northern China: a case study of the Liulin Springs. Acta Pedol Sin 83(1):138–144
Hao YH, Zhao JJ, Li HM et al (2012) Karst hydrological processes and grey system model. J Am Water Resour Assoc 48(4):656–666
Hu CH, Zhao LX, Wang YX et al (2016) Analysis of the relationship between the meteorological, agriculture and hydrological drought. Meteorol Environ Sci 39(4):1–6
Huang L, He B, Chen AF et al (2016) Drought dominates the interannual variability in global terrestrial net primary production by controlling semi-arid ecosystems. Sci Rep 6:24639
Lei TJ, Wu JJ, Li XH et al (2015) A new framework for evaluating the impacts of drought on net primary productivity of grassland. Sci Total Environ 536:136–172
Liu HG, Tang XL, Zhou GY et al (2007) Spatial and temporal patterns of net primary productivity in the duration of 1981-2000 in Guangdong, China. Acta Ecol Sin 27(10):4065–4074
Lloret F, Siscart D, Dalmases C (2004) Canopy recovery after drought dieback in holm-oak Mediterranean forests of Catalonia (NE Spain). Glob Chang Biol 10:2092–2099
Loon AV (2013) On the propagation of drought. How climate and catchment characteristics influence hydrological drought development and recovery. Wur Wageningen University, Wageningen
Palmer, W.C., 1965. Meteorological drought. Research paper 45, U.S. Dept. of Commerce, 58 pp.
Palta JA, Nobel PS (1989a) Root respiration for Agave desert: influence of temperature, water status, and root age on daily patterns. J Exp Bot 40:181–186
Palta JA, Nobel PS (1989b) Influences of water status, temperature, and root age on daily patterns of root respiration for two cactus species. Ann Bot 63:651–662
Piao SL, Fang JY, Guo QH (2001a) Terrestrial net primary production and its spatio-temporal patterns in China during 1982-1999. Acta Sci Nat Univ Pekin 37(4):563–569
Piao SL, Fang JY, Guo QH (2001b) Application of CASA model to the estimation of Chinese terrestrial net primary productivity. Acta Phytoecol Sin 25(5):603–608
Potter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA (1993) Terrestrial ecosystem production A process model based on global satellite and surface data. Glob Biogeochem Cycles 7(4):811–841
Prudhomme C, Parry S, Hannaford J, Clark DB, Hagemann S, Voss F (2011) How well do largescale models reproduce regional hydrological extremes in Europe? J Hydrometeorol 12:1181–1204
Qiu JY (2016) The temporal-spatial variation of NPP in Zhongwei-Shapotou nature reserve based on CASA model. Lanzhou University, Lanzhou
Running SW, Gower ST (1991) FOREST BGC, a general model of forest ecosystem processes for regional applications.II. Dynamic carbon allocation and nitrogen budgets. Tree Physiol 9:147–160
Shi XL (2013) Study on distributed hydrological simulation and drought evaluation method in Luanhe River basin based on SWAT model. University of Chinese Academy of Sciences, Beijing
Sousa SIV, Martins FG, Pereira MC, Alvim-Ferraz MCM (2006) Prediction of ozone concentrations in Oporto city with statistical approaches. Chemosphere 64(7):1141–1149
Sun BF, Zhao H, Wang XK (2016) Effects of drought on net primary productivity: roles of temperature, drought intensity, and duration. Chin Geogr Sci 26(2):270–282
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718
Wang JY, Li AN, Jin HA (2015) A review on research advances in estimation models for net primary production of vegetation in wetlands. Wetl Sci 13(5):636–644
Xia XA, Wang PC, Chen HB et al (2006) Analysis of downwelling surface solar radiation in China from National Centers for Environmental Prediction reanalysis, satellite estimates, and surface observations. J Geophys Res-Atmos 111:D09103
Xiao X, Zhang QY, Scott S et al (2005) Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sens Environ 94(1):105–122
Yu D, Shao H, Shi P et al (2009) How does the conversion of land cover to urban use affect net primary productivity? A case study in Shenzhen city, China. Agric For Meteorol 149(11):2054–2060
Zhang X, Xia J, Jia SF (2005) Water security of drought period and its risk assessment. J Hydraul Eng 36(9):1138–1142
Zhao F (2015) Remote sensing estimation and validation of net primary production in the Xilingol grassland based on CASA model. Chinese Academy of Agricultural Sciences, Beijing
Zhao MS, Running SW (2010) Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329:940–943
Zhao MS, Heinsch FA, Nemani RR et al (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ 95(2):164–176
Zhou SH (2015) Research on the streamflow change and the distinction of hydrological drought in Luanhe River basin. Tianjin University, Tianjin
Zhu WQ, Pan YZ, Long ZH et al (2005) Estimating net primary productivity of terrestrial vegetation based on GIS and RS: a case study in Inner Mongolia, China. J Remote Sens 3:300–307
Zhu WQ, Pan YZ, Zhang JS (2007) Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing. J Plant Ecol 31(3):413–424
Funding
This work was supported by the National Natural Science Foundation of China (No. 51479130) and State Key Laboratory of Hydraulic Engineering Simulation and Safety Foundation (No. HESS1405).
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Li, J., Zhou, K. & Chen, F. Drought severity classification based on threshold level method and drought effects on NPP. Theor Appl Climatol 142, 675–686 (2020). https://doi.org/10.1007/s00704-020-03348-4
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DOI: https://doi.org/10.1007/s00704-020-03348-4