پژوهشنامه مدیریت حوزه آبخیز (Oct 2024)
Local Analysis of Drought and Climate Change Projection in Future Periods under the CMIP6 Model (Case Study: Mazandaran Province)
Abstract
Extended Abstract Background: Climate changes can significantly affect socioeconomic activities and quality of life, especially in countries that are currently facing water tensions. Climate models play a key role in assessing the impact of climate change and developing adaptation and resilience strategies. Considering the importance of food security and then water security in resilience against climate change, as well as the significant contribution of Mazandaran province in the production of agricultural products and food supply of the country, it is very important to examine the drought situation of this province and its climate change process. In this study, therefore, the wet and dry durations in the 20-year base period of Mazandaran province were evaluated using the standardized standard precipitation index. Then, projections of temperature and precipitation at the local scale were made in future periods using the five global circulation models (GCM) available in phase 6 of the climate output project (CMIP6) under three scenarios SSP2.6, SSP4.5, and SSP8.5. Methods: In this research, six meteorological stations, viz. Ramsar, Noshahr, Siyabisheh, Babolsar, and Qarakhil, were selected due to the coverage of the most statistical years and suitable spatial distribution in the region. Time series of precipitation and daily maximum/minimum temperatures were collected for six selected stations in the region with a base statistical period of 20 years (January 1999 to December 2018). After ensuring the quality of the data, the trend of their changes was analyzed using Mann-Kendall and age slope tests. Standard precipitation index values were calculated and evaluated in different intervals. Finally, large-scale data from five general circulation models (ACCESS-CM2, CanESM2, CNRM-CM-6-1, MRI-ESM2-0, and NESM3) were downscaled by the LARS-WG6 climate generator. Thus, predictions of seasonal and annual changes for Tmax, Tmin, and precipitation in two future periods (2040-2060 and 2080-2100) were made using the average of selected GCMs. Results: As a result of the statistical analysis of the above data, minimum and maximum temperatures increased and precipitation decreased during the standard period, but no significant trend was found at the 0.05 level. The analysis also shows that the state's worst droughts occurred in 2007, 2009, late 2011, early 2012, and 2018, with SPI values below-1.0 at stations. The wettest years in the region are 2004-2006 and 2017. The frequency of wet periods is higher than dry periods for all seasons in the region. In the microscale aspect, the results confirmed the ability of the LARS-WG6 model to simulate temperature more accurately than local precipitation, with more precipitation errors in wet seasons. Among these results, the lowest value of the correlation coefficient (0.941) was obtained for maximum and minimum monthly temperatures, which means that the squared error value is between 1.05 and 3.82°C. The largest differences between modeled precipitation and observations occurred during the rainy season when GCMs underestimated precipitation. The analysis of future climate changes revealed that all five GCMs indicated a continued increase in temperature in the study area. However, differences in the magnitude of signal changes were observed in different GCMs and SSPs. These predicted temperature changes are significant and reliable because all models agree on the direction of temperature change across the province. Overall, the increase in average Tmax and Tmin is significant in SSP8.5 compared to SSP4.5 because of no reduction in greenhouse gas emissions. Thus, the largest mean changes in the SSP8.5 scenario for 2050 and 2090 at the provincial level for maximum temperature are increases of 2.64 and 4.72 °C during spring, and the largest mean changes in minimum temperature were calculated to rise to 2.97 and 4.83 °C during autumn. Future changes in precipitation proved to be more complex and unpredictable than temperature. The largest incremental changes in local precipitation in 2090 under the SSP4.5 (40.5%) and SSP8.5 (51.9%)scenarios were shown by the Ramsar station. In the study area, it is projected 38.86% and 43.95% on average in the feature period (2040-2060) and 45.11% and 65.94% in the feature period (2080-2100) under SSP4.5 and SSP8.5. Thus, the results of this study show that the shift toward wetter seasons at the provincial level in the future will cause more precipitation in the western part of the province. Conclusion: Examining the drought situation in the base period shows the occurrence of periods with near-normal rainfall in longer intervals, and Sari and Qarakhail stations report more drought than stations in the west of the province. All GCM forecasting models presented the same results in significant warming trends in this province. For precipitation, however, it is suggested to investigate other models in this regard due to the sensitivity of the issue and the uncertainties involved in the issue. Considering the effect of changes in temperature parameters and precipitation on water resources and floods in the region, it is necessary to adopt suitable management strategies for the future to be resilient against climate change.
