Evaluating the relation between meteorological drought and hydrological drought, and the precipitation distribution for drought classes and return periods over the upper Tigris River catchment

This study focuses on the assessment of successive drought relations by using the standardized precipitation index, standardized precipitation evapotranspiration index, and the streamflow drought index. The main goal is to propose lag times between drought events using factors like longitude and elevation and to construct maps that cover precipitation threshold values, and critical precipitation values corresponding to return periods using meteorological indices. For this purpose, monthly streamflow datasets of 42 stations and monthly meteorological datasets of 25 stations from 1972 to 2011 were used. Results indicate that mean elevations of the sub-catchments showed a decisive role in the amount of drought delay. The sub-catchments located in the low altitudes showed no delay in translation, whereas the sub-catchments located in the highly elevated regions showed 2-month delay in the monthly time scale. Moreover, the success of drought relations is more pronounced with temperature datasets, especially in the highly elevated regions for greater drought periods. In the second part, the spatial variation of the precipitation in defining the threshold values depicts that although there is some variety in the precipitation values for time scales less than 12 months, there is no visual difference between the two indices for yearly time scales. And, the mild and extreme droughts are obtained for yearly precipitation values of less than 628 and 427 mm, respectively. With calculations in return-period precipitation amounts, it is inferred that temperature is a strong dataset in defining precipitation values for return periods greater than 10 years and duration time less than 5 months. Since the findings in this study present physical and practical value, they can be key for stakeholders, policymakers, and end users in water allocation studies. Furthermore, it can be useful in ungauged points with missing data and therefore, if necessary, modification in crop patterns and changes in land use for specific areas can be done. And this study can be more beneficial by adding datasets covering climate change scenarios as future work.