Improved identification and monitoring of meteorological, agricultural, and hydrological droughts using the modified nonstationary drought indices in the Yellow River Basin of China
- 1College of Water Resources and Architectural Engineering at Northwest Agriculture and Forestry University, Northwest A&F University, Yangling, Shaanxi, 712100, PR China (niubenwazg@nwafu.edu.cn)
- 2NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia (de.li.liu@dpi.nsw.gov.au)
- 3Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia (de.li.liu@dpi.nsw.gov.au)
- 4Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity/College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, PR China (xhjiang@nwu.edu.cn)
- 5State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi, 712100, PR China (nercwsi@vip.sina.com)
- 6College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang, 832003, PR China (mikeyork@163.com)
The nonstationarity exists in the drought indicator series, making drought monitoring and assessment more complex and accurate. Our study aimed to better identify and monitor meteorological, agricultural, and hydrological droughts in the Yellow River Basin (YRB) of China. We constructed the temporal-variate nonstationary standardized drought indices for precipitation (NSPI), evapotranspiration (NSPEI), soil moisture (NSSMI), and runoff (NSRI) based on nine Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Further, we investigated the performance of NSPI/NSPEI/NSSMI/NSRI by comparing with the historical drought events in YRB. The results show that: (1) The probability distribution function (PDF), smoothing function (SF), and degree of freedom (DF) had a significant influence on the fitting goodness of the GAMLSS model. Additionally, PDF was the most important factor to determine the fitting goodness, while the appropriate SF and DF improved the fitting effects of the model. (2) The stationary GAMLSS model mainly was applied to lower-timescale series, and the nonstationary GAMLSS models were more suitable for the higher-timescale series. (3) The NSPI and NSPEI were more sensitive to identifying meteorological drought, but NSPEI was more accurate in identifying drought intensity and duration. The agricultural drought identified by NSSMI agreed well with the historical drought events but had a poor response to mild and moderate drought. (4) Form NSRI, hydrological drought in the tributaries of the YRB was affected by meteorological drought, and the intensity of hydrological drought was heavier than that in the mainstream of the YRB. In conclusion, the nonstationary drought indices could identify drought events more accurately and provide valuable information for drought-resistant work in the YRB.
How to cite: Niu, B., Li, Y., Liu, D. L., Wei, K., Jiang, X., Yao, N., Feng, H., Yu, Q., He, J., Yang, Z., Jiang, Y., Yang, G., and Lin, H.: Improved identification and monitoring of meteorological, agricultural, and hydrological droughts using the modified nonstationary drought indices in the Yellow River Basin of China, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2483, https://doi.org/10.5194/egusphere-egu23-2483, 2023.
