Journal
WATER RESOURCES MANAGEMENT
Volume 29, Issue 15, Pages 5631-5647Publisher
SPRINGER
DOI: 10.1007/s11269-015-1138-0
Keywords
Drought; Standardized precipitation index; Time-dependent standardized precipitation index; Non-stationarity; GAMLSS
Categories
Funding
- National Natural Science Foundation of China [51479130]
Ask authors/readers for more resources
In a rapidly changing environment, a greater concern about the establishment and improvement of drought indices is expected. The main goal of this study is to develop and apply a time-dependent Standardized Precipitation Index (SPIt) that takes account of the possible non-stationary behaviors in precipitation records. Summer precipitation observations (1959 similar to 2011) from 21 raingauge stations in the Luanhe River basin are fitted with non-stationary Gamma distributions respectively by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). The temporal variability of the distribution's parameter (related to the mean) is flexibly described by an optimized polynomial function. Based on the non-stationary distribution, the SPIt is calculated and then employed to assess the spatio-temporal characteristics of summer drought in the basin. Results of the non-stationary modeling indicate an overall decreasing trend in the summer precipitation during 1959 similar to 2011, and especially a significant decrease in the period of 2000 to 2011. The SPIt is found to be more robust and reliable compared with the traditional Standardized Precipitation Index (SPI). Moreover, remarkable difference is observed between the historical drought assessments of SPIt and SPI in the Luanhe River basin, implying that the non-stationarity of hydrological time series cannot be ignored in drought analyses and forecasts. The proposed SPIt method can be a feasible alternative for drought monitoring under non-stationary conditions, intended to provide a valuable reference for further studies.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available