Evaluation of the effects of drought indicators on water poverty (Case study: Gorgan region)

Background and Objective:

Water resources are the common aspect of the goals and challenges of sustainable development, the lack of which is one of the big multidimensional problems of the current century and is one of the main reasons for positive and negative developments in the world. Therefore, Water Poverty Index (WPI) is one of the indices defined for this purpose. This index shows the effect of the combination of effective factors on the scarcity and stress of water resources. It provides the conditions for prioritizing and developing management versions in different regions. To determine water scarcity and poverty in each region, attention should be paid to the conditions of water resources in the studied region, the ability to calculate the index and the existence of information and data in the studied region, as well as the selection of selected criteria and components in that region. This study uses the water poverty index to investigate the shortage and tension of water resources. Its relationship with univariate drought indices based on precipitation, including SPI and ZSI, and variable indices based on precipitation and evapotranspiration including SPEI and RDI were searched for for its influence on drought.



Materials and Methods

The study area in this research is Hashem-Abad meteorological station in Gorgan region. The statistical period for calculating the water poverty index based on the data available in the study area was considered 13 years (2003-2015). The water poverty index in this research is calculated based on five main components, which include the resource (groundwater loss), meteorological (temperature and precipitation), consumption (water need), capacity (river discharge), and environmental (salinity). Each of the components must be weighted to calculate the water poverty index. For this purpose, the AHP hierarchical technique was used. First, a questionnaire was prepared, and the components were scored based on the opinion of regional water experts and university professors, and then, using Expert Choice software, the weight of the main components of the water poverty index was determined, and finally, the WPI index for the study area in this research was also estimated. Then, in the next step, drought indices SPI, SPEI, RDI, and ZSI were calculated in 6-month and 12-month time windows. To calculate the drought indices, the precipitation and temperature data at the Hashem-Abad meteorological station for a period of 30 years (1990-2019) were considered, which were sorted monthly. The coding necessary to calculate the SPI and SPEI indices in time windows 6 and 12 months was done by R programming and statistical software. Also, the EXCELL software calculated two indicators, RDI and ZSI. Finally, the relationship between drought indices and water poverty index was searched based on simple one-to-multivariate correlations.



Results and Discussion

The water poverty index’s results showed that the resources and environment component had the highest value in 2009 and 2010 and the lowest value in 2010 and 2016, respectively. Concerning meteorological, capacity, and consumption components, the highest values were in 2010, 2004, and 2009, respectively, and the lowest in 2010, 2016, and 2016, respectively. Questionnaire analysis of WPI index components with AHP showed that resources and environment components had the highest and lowest weight with values of 0.354 and 0.041, respectively. But multiplying these weights by their related components found that the components of consumption, environment, resources, meteorology, and capacity had the greatest effect in calculating water poverty index. The range of WPI changes during the years (2004-2016) varies from 26 to 82, so in 2014, one of the driest years, the region was in the poorest state of water resources. The year 2008 had the best conditions. Considering the average WPI of about 55, out of the 13 years studied, the WPI was lower than the average in 8 years. In the next step, there was no possibility of non-linear modeling due to the lack of data. Therefore, simple one-to-multivariate correlations were used. The results of these correlations showed that using the multivariate linear regression method by considering the drought index in a 12-month time window along with two 6-month time windows related to the first and second half of the year increases their correlation with the water poverty index. Examining the effect of the time window considered for the drought index in relation to the water poverty index shows that the 12-month time window has a higher correlation than the six-month time window. Also, among the six-month time windows, in the SPEI index, the first six months of the year, which includes the spring and summer seasons, had a higher correlation with the water poverty index. Correlation results between drought indices and WPI showed that the annual time interval is more suitable than the 6-month one. Among the four indices studied, the SPEI index with R2=0.90 had the highest correlation, while the ZSI index with R2=0.81 had the lowest correlation with WPI.



Conclusion

Based on the results of the components of the water poverty index in this research, it was observed that the consumption component in Gorgan region had the biggest role in the WPI estimation, so water conservation can greatly contribute to solving water poverty. Due to the high volume of water consumption in the agricultural sector, some measures should be taken to manage water consumption and choose the appropriate cultivation patterns. The high correlation of WPI with drought indices, especially the SPEI variable index, makes the importance of creating a drought monitoring and forecasting system more tangible. Global warming and climate change in the future, which this region is not exempt from, can make the problems of water poverty and lack of water more severe in this region.