Drought severity classification based on threshold level method and drought effects on NPP
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ORIGINAL PAPER
Drought severity classification based on threshold level method and drought effects on NPP Jianzhu Li 1 & Keke Zhou 1 & Fulong Chen 2 Received: 4 December 2018 / Accepted: 27 July 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
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. Keywords Drought . Threshold level method . Net primary productivity (NPP) . CASA model
1 Introduction Net primary productivity (NPP) refers to the amount of organic matter accumulated per unit area and unit time of vegetation, which is an important parameter in the terrestrial ecological process and reflects the vegetation production capacity (Zhu et al. 2007). Moreover, changes in NPP can be an indicator of climate change, land use, resource development, and other issues directly or indirectly. Therefore, the calculation and analysis of the NPP of ecosystems have always been a hot spot, which are of great significance for land use planning and sustainable ecosystem management. NPP data can be obtained in two ways: measured data and estimated by models. The measured NPP is usually conducted in a spot, which could not meet the need on a large scale such as basin or national scale. With the development of remote
* Fulong Chen [email protected] 1
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
2
College of Water Conservancy and Architectural Engineering, Shihezi University, Shihezi 832000, China
sensing technology, large-scale remote sensing data can effectively solve this problem. There are mainly Miami model and CASA (Carnegie Ames Stanford Approach) model to estimate NPP. The Miami model only uses the temperature and precipitation data to estimate NPP, for which the data is convenient to acquire, but the physiological and ecological processes of vegetation are ignored, and the estimated NPP by Mia
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