In this study, a statistical modeling approach was developed to monitor climate change over time in the Kokcha River Basin, located in northeastern Afghanistan. Monthly meteorological data from 1979 to 2023 were analyzed using ARIMA and Seasonal ARIMA (SARIMA) models to identify trends and make future projections. The initial phase involved stationarity testing of temperature and precipitation time series, confirming the presence of significant trends. Regression analyses were conducted for 1980–2009 and 2012–2023. Results indicate a noticeable recent increase in average temperature, surpassing 6 °C, and heightened variability in precipitation patterns. The correlation between temperature and precipitation has become more moderate over time. Specifically, the Pearson correlation coefficient declined from −0.5913 (1980–2009), indicating a strong negative relationship, to −0.1593 (2012–2023), which is statistically insignificant. Furthermore, the slope of the regression model decreased from −44.66 to −3.04, suggesting a reduced sensitivity of precipitation to temperature fluctuations, likely due to increasingly complex atmospheric processes. Forecasts generated by SARIMA models for precipitation demonstrated high accuracy, supported by low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. Projections extending to 2030 suggest a continued rise in temperatures and increased unpredictability in precipitation events. These findings highlight the growing climate risk in the Kokcha River Basin and underscore the urgent need for proactive water resource management. Statistical forecasting methods employed in data-scarce and climate-sensitive regions, such as Afghanistan, can inform policy development and adaptation strategies in similarly vulnerable contexts.
- Open Access
- Article
Forecasting Hydroclimatic Variables in the Kokcha River Basin Afghanistan: A Time Series Modeling Approach
Author Information
Received: 16 Dec 2025 | Revised: 19 Apr 2026 | Accepted: 28 Apr 2026 | Published: 12 May 2026
Abstract
Keywords
ARIMA & SARIMA | time series analysis | climate change | Kokcha River Basin | water resources
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