Document Type : Research Article
Authors
Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
Abstract
Accurate precipitation forecasting is crucial for water resource management and preparedness for climatic events. In this regard, this research utilized five selected CMIP6 models to project the behavior of daily precipitation for stations within the Navrud watershed. The model data were downscaled using three bias correction methods (Local Intensity Scaling, Distribution Mapping, and Power Transformation) in the CMhyd software. During the evaluation of these methods’ accuracy based on statistical indices (R², RMSE, MAE, MSE, and NSE), the Local Intensity Scaling method demonstrated the best performance in reproducing the observed precipitation patterns. Finally, the MPI-ESM1-2-HR, MRI-ESM2-0, MIROC6, CNRM-CM6-1, and EC-Earth3-Veg models were chosen as the top-performing models and were employed in an ensemble model to predict precipitation for the next 30-year period (2025–2054) under SSP2-4.5 and SSP5-8.5 scenarios. The results indicated that the spatial pattern of precipitation in the Navrud watershed during the baseline period (1985–2014) decreased from east (Kharjagil station) to west (Nav station), and the ensemble model successfully simulated this spatial precipitation pattern. However, the ensemble model’s projection for the future period (2025–2054), while maintaining the overall precipitation distribution pattern, showed that according to the SSP2-4.5 scenario, precipitation at Khelian, Kharjgil, and Nav stations will decrease by 10.9, 9.3 and 5.5% respectively, and according to the SSP5-8.5 scenario, it will decrease by 8.3%, 8.01%, and 3.4% compared to the baseline period. Furthermore, drought periods are expected to intensify from May to October. Trend analysis using the modified Mann-Kendall test and Sen’s slope estimator indicated the absence of a statistically significant trend in the daily precipitation of the stations.
Introduction
Climate change, as one of the most significant global challenges, has widespread impacts on the environment, human livelihoods, and economic sustainability. This phenomenon can particularly lead to social and economic instability in regions whose economies depend on climate-sensitive sectors such as agriculture. Therefore, understanding past, present, and future climate trends, variability, and changes is essential for designing strategies to mitigate the adverse effects of this phenomenon. Among the various climate elements, precipitation, as a key hydro-climatic variable, plays a vital role in agricultural production and water resources management, directly influencing the social, economic, and environmental sustainability of regions. Climate change can cause significant fluctuations in precipitation patterns, resulting in floods, droughts, and landslides. These fluctuations also affect drinking water supply, groundwater and surface water resources, and agricultural productivity, posing challenges to water resources management. Accordingly, accurate and long-term precipitation projections form a fundamental basis for water resource planning, agricultural infrastructure design, natural disaster risk management, and the development of mitigation policies.
Material and Methods
In this study, daily precipitation data from three rain gauge stations Khalian, Kharejgil, and Nav were obtained from the Gilan Regional Water Company. To simulate future precipitation, five models from the CMIP6 ensemble, including MPI-ESM1-2-HR, MRI-ESM2-0, MIROC6, CNRM-CM6-1, and EC-Earth3-Veg, were selected from the ESGFDL database. Daily precipitation simulations for the historical period were then conducted using the outputs of the selected models and three bias-correction methods Local Intensity Scaling, Distribution Mapping, and Power Transformation implemented in the CMhyd software. After applying these methods, the optimal bias-correction technique and the best-performing models were identified based on evaluation metrics, including the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). Subsequently, to integrate the results of the selected models and reduce uncertainty, the Homadi model was applied using a rank-based approach and weighting of the top-performing models. Future precipitation projections for a 30-year period (2025–2054) were then generated based on the Homadi model outputs under two climate change scenarios: SSP2-4.5 and SSP5-8.5. To analyze the spatiotemporal variations in precipitation, mean monthly precipitation graphs for the studied stations were plotted, and annual precipitation totals were spatially mapped. Finally, the significance of precipitation trends during the baseline period and future horizon was assessed using the modified Mann Kendall test and Sen’s slope estimator.
Results and Discussion
In this study, precipitation simulation and projection for the Navrud watershed were carried out using five selected CMIP6 climate models and three bias-correction methods. To evaluate model performance, network-averaged statistical error metrics derived from station observations were employed. The results indicated that the Local Intensity Scaling bias-correction method outperformed the Power Transformation and Distribution Mapping approaches in terms of accuracy. Accordingly, the MPI-ESM1-2-HR, MRI-ESM2-0, MIROC6, CNRM-CM6-1, and EC-Earth3-Veg models, when corrected using the Local Intensity Scaling method, showed the highest consistency with observed data and were subsequently incorporated into the Homadi ensemble model based on their respective ranks and assigned weights for further analyses. Analysis of the spatial distribution of mean annual precipitation during the baseline period (1985–2014) revealed a decreasing gradient from east to west across the watershed, with the Kharejgil station identified as the wettest location and the Nav station as the driest. The Homadi model successfully reproduced this spatial pattern for the historical period with acceptable accuracy. Future precipitation projections for the period 2025–2054 under the SSP2-4.5 and SSP5-8.5 scenarios indicate that, although the spatial pattern of precipitation is largely preserved, the absolute values of annual precipitation are expected to decline at all stations. Notably, the reduction is projected to be slightly more pronounced under the intermediate scenario than under the pessimistic scenario. Such changes may have significant implications for regional water resources and intensify water stress in the study area. The analysis of monthly precipitation suggests a relative stability in the seasonal precipitation regime across all three stations. Autumn months, particularly October and November, continue to contribute the largest share of annual precipitation, whereas summer months—especially July and August remain the driest period of the year. Nevertheless, month-to-month variations differ depending on the climate scenario, and a relative increase in precipitation is projected for some colder months. The results of the modified Mann–Kendall test applied to daily precipitation at all three stations indicate that no statistically significant increasing or decreasing trends are evident in either the historical or future periods. The very small Sen’s slope values, Z-statistics close to zero, and high p-values suggest that precipitation variability is largely random and does not follow a distinct climatic trend. Overall, the findings confirm the effectiveness of the Homadi model in representing precipitation patterns and highlight the necessity of climate-adaptive planning to ensure sustainable water resource management in the Navrud watershed.
Conclusion
Analysis of climatic data for the Navrud watershed during the period 2005–2024 indicates that the region has experienced notable changes in precipitation, temperature, and soil moisture. The spatial pattern of precipitation reveals a concentration of rainfall in the eastern and northern parts of the watershed, with decreasing amounts toward the central and southwestern areas. Temporally, precipitation exhibited an increasing trend up to approximately 2020, followed by a marked decline in recent years. In contrast, temperature shows a consistent and sustained upward trend across the entire watershed, with an average increase of approximately 2.76 °C. This warming has been more pronounced in the eastern part of the basin, intensifying evapotranspiration processes and contributing to a reduction in soil moisture. Despite a relative increase in mean soil moisture, its interannual variability has been considerable, particularly during low-precipitation years. The De Martonne aridity index further characterizes the climate of the watershed as ranging from arid to semi-arid in the central and eastern regions, while indicating relatively more humid conditions in the northwestern part of the basin. The results of precipitation simulations using CMIP6 models demonstrate that the Local Intensity Scaling bias-correction method provides the best performance, and that the selected models are capable of accurately reproducing the spatial precipitation pattern of the baseline period. Projections for the future period (2025–2054) suggest that, although the overall precipitation pattern is preserved, a gradual decline in precipitation is expected at all stations, with a more pronounced reduction under the SSP2-4.5 scenario. The modified Mann–Kendall test further confirms the absence of statistically significant trends in daily precipitation. Overall, while the seasonal precipitation regime is largely maintained, reductions in summer precipitation and shifts in seasonal distribution may pose serious challenges for water resources management and climate change adaptation in the Navrud watershed.
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