Received: 01 Mar 2018 – Discussion started: 16 Apr 2018
Abstract. In drought years, it is important to have an estimate or prediction of the probability that a water shortage risk will occur to enable risk mitigation. This study developed an improved logistic probability prediction model for water shortage risk in situations when there is insufficient data. First, information flow was applied to select water shortage risk factors. Then, the logistic regression model was used to describe the relation between water shortage risk and its factors, and an alternative method of parameter estimation (maximum entropy estimation) was proposed in situations where insufficient data was available. Water shortage risk probabilities in Beijing were predicted under different inflow scenarios by using the model. There were two main findings of the study. (1) The water shortage risk probability was predicted to be very high in 2020, although this was not the case in some high inflow conditions. (2) After using the transferred and reclaimed water, the water shortage risk probability declined under all inflow conditions (59.1% on average), but the water shortage risk probability was still high in some low inflow conditions.
How to cite. Qian, L., Zhang, R., Bai, C., Wang, Y., and Wang, H.: An improved logistic probability prediction model for water shortage risk in situations with insufficient data, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2018-56, 2018.
The statistical data about risk and its factors are insufficient in China. For this reason, we proposed an improved logistic regression model for predicting water shortage risk probability when data is insufficient. The risk probability prediction model for water shortage risk was constructed and tested based on the data from 1979 to 2012. It was concluded that the risk prediction model was applicable. Risks in 2020 were evaluated under different scenarios of inflow conditions.
The statistical data about risk and its factors are insufficient in China. For this reason, we...