Introduction
In the present study, to monitor droughts, the RCP4.5 scenario of the CanESM2 model of the Fifth
IPCC Report and the most appropriate distribution functions of drought indicators were used to assess
the current climate change and drought conditions in the present and future. Since the drought in an
area can be affected by various climatic parameters, in this study, in addition to using SPI as a practical
index, the important SSI index was also used to assess drought.
Materials and Methods
In the present study, the following steps were performed to monitor, evaluate, and inform the occurrence
of droughts in Tehran province.
1. Quality control of precipitation and water flow parameters during the period 1986-2018
2. Prediction of these parameters during the period 2020-2050 based on daily output data of CanESM2
model under the RCP4.5 scenario using SDSM model
3.Selecting the most appropriate distribution function with time series for both SPI and SSI index
4.Drought detection and simulation using SPI and SSI drought characteristics during the next period
(2050-2050).
Results and Discussion
The results of predicting the time series of precipitation and water flow using the DSM model
In evaluating this model, two RMSE and MSE criteria were used, the results are given in Table 1
According to the results of Table 1, all eight stations had acceptable errors and it can be claimed that
the SDSM model is more successful in predicting precipitation than Water flow.
Selecting the most appropriate cumulative distribution functions
Tables (2) and (3) show the ranking results of the studied functions for precipitation and forecasted data
of meteorological and hydrometric stations.
According to the results of Table 1, all eight stations had acceptable errors and it can be claimed that
the SDSM model is more successful in predicting precipitation than Water flow.
Selecting the most appropriate cumulative distribution functions
Tables (2) and (3) show the ranking results of the studied functions for precipitation and forecasted data
of meteorological and hydrometric stations.
As shown in Table (2), at Mehrabad, Nimrud, and Ahar stations, the Fatigue life function was selected,
and at the Latian station, the Wibble function was selected as the best cumulative distribution function.
Using the Kolmogorov Smirnov test and according to the P-Value, Normal distribution function shows
a better fit for Firoozkooh and Jajroud stations The Weibull function also shows the best fit for the
Namrod station, and the Fatigue life function shows the most suitable fit for the Latin station.
Matching SSI and SPI drought indicators for the next period
The results show that due to the use of distribution functions, the drought situation has had similar
results based on two indicators with two different quantities. This means that the use of proposed
distribution functions has greatly reduced the percentage of predictive error
Conclusion
The results for future showed that Sharifabad station has the highest drought index (-2.74) based on SSI,
and according to SPI, the highest drought index (-2.17) is for Latian station. It should be noted that the
matching of the two indicators at Namroud and Latian stations was also studied and the results showed
that the difference in the numerical values of these two quantities did not fit well for a 5 year period.