تأثیر سناریوهای اقلیمی IPCC-CMIP6 بر رواناب حوضه با رویکردهای ANN و رگرسیون چندگانه | ||
| منابع آب و تغییر اقلیم | ||
| مقاله 6، دوره 1، شماره 4، دی 1404، صفحه 55-68 اصل مقاله (1.52 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22091/wrcc.2025.14715.1026 | ||
| نویسندگان | ||
| مهدیه کلهری1؛ عماد کهریزی* 2؛ گلناز امیری3؛ مژگان یاراحمدی4 | ||
| 1گروه مهندسی عمران، دانشگاه قم، ایران | ||
| 2دکتری، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران | ||
| 3دانشجوی دکتری، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه زنجان، زنجان، ایران. | ||
| 4کارشناس ارشد، دانشکده مهندسی منابع طبیعی، دانشکده احیای مناطق خشک و کوهستانی، دانشگاه تهران، تهران، ایران. | ||
| چکیده | ||
| تغییرات اقلیمی بهعنوان یکی از چالشهای اساسی قرن حاضر، موجب دگرگونی در چرخههای هیدرولوژیکی در مقیاسهای جهانی و منطقهای شده و پیامدهای آن بهویژه در مناطق خشک و نیمهخشک آشکارتر است. هدف این پژوهش، ارزیابی تغییرات آتی رواناب در حوضه آبریز خرمآباد با استفاده از دادههای بهروز اقلیمی و مدلهای دادهمحور میباشد. بدین منظور، دادههای گزارش ششم ارزیابی هیئت بیندولتی تغییر اقلیم (IPCC) تحت سناریوهای اجتماعی–اقتصادی مشترک SSP1-2.6 (خوشبینانه) و SSP5-8.5 (بدبینانه) برای دورههای زمانی 2069-2040 و 2099-2070 مورد استفاده قرار گرفت. ریزمقیاسنمایی متغیرهای اقلیمی با بهرهگیری از روش عامل تغییر انجام شد. برای شبیهسازی رواناب آینده، دو مدل دادهمحور شامل شبکه عصبی مصنوعی (ANN) و رگرسیون چندگانه (MR) توسعه داده شد. نتایج نشان دادند که مدلهای CMIP6 در شبیهسازی دمای دوره تاریخی عملکرد بهتری نسبت به بارش دارند که بیانگر عدمقطعیت بیشتر در پیشبینی بارش است. همچنین تحلیل دادههای ریزمقیاسنماییشده حاکی از کاهش بارش و افزایش دمای متوسط بلندمدت ماهانه در هر دو سناریوی اقلیمی میباشد. در بخش مدلسازی آبشناسی، ANN در مقایسه با مدل MR، دقت بالاتری در شبیهسازی رواناب و بازنمایی روابط غیرخطی میان متغیرهای اقلیمی از خود نشان داد. پیشبینیهای حاصل از مدل ANN کاهش قابلتوجه رواناب را در تمامی دورهها و سناریوهای آینده نشان میدهند که بیانگر افزایش تنش آبی و ضرورت برنامهریزی هدفمند برای مدیریت پایدار منابع آب در حوضه مورد مطالعه است. | ||
| کلیدواژهها | ||
| منابع آب؛ سناریوهای SSP؛ شبیهسازی رواناب؛ شبکه عصبی مصنوعی؛ رگرسیون چندگانه | ||
| عنوان مقاله [English] | ||
| Impact of IPCC CMIP6 Climate Scenarios on Basin Runoff Using ANN and Multiple Regression Approaches | ||
| نویسندگان [English] | ||
| Mahdiyeh Kalhori1؛ Emad Kahrizi2؛ Golnaz Amiri3؛ Mozhgan Yarahmadi4 | ||
| 1Department of Civil Engineering, University of Qom, Iran | ||
| 2PhD, Department of Civil Engineering, Faculty of Engineering and Technology, University of Qom, Qom, Iran | ||
| 3Ph.D. Student, Department of Civil Engineering, Faculty of Engineering, Zanjan University, Iran. | ||
| 4M.Sc. of Watershed Management, Faculty of Natural Resources, University of Tehran, Iran. E-mail: myarah-madi1128@gmail.com | ||
| چکیده [English] | ||
| Abstract Objective: The objective of this study is to assess future runoff variations in the Khorramabad basin using up-to-date climate data and data-driven modeling approaches. Method: To this end, outputs from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) under the Shared Socioeconomic Pathways SSP1-2.6 (optimistic) and SSP5-8.5 (pessimistic) were employed for the future periods 2040–2069 and 2070–2099. Climate variables were downscaled using the change factor method. To simulate future runoff, two data-driven models, namely Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR), were developed. Results: The evaluation of climate models indicated that CMIP6 models perform more reliably in simulating historical temperature than precipitation, highlighting the higher uncertainty associated with precipitation projections. Furthermore, analysis of the downscaled data revealed a decreasing trend in precipitation and an increasing trend in long-term monthly mean temperature under both climate scenarios. In the hydrological modeling framework, the ANN model outperformed the multiple linear regression model in terms of runoff simulation accuracy and its ability to capture complex nonlinear relationships among climatic variables. Conclusion: Runoff projections derived from the ANN model indicate a substantial decline in runoff across all future periods and scenarios, suggesting intensified water stress and underscoring the urgent need for targeted planning and adaptive strategies to ensure sustainable water resources management in the studied watershed. English Extended Abstract Introduction Climate change is a principal driver of hydrological alteration worldwide, and semi-arid catchments are especially vulnerable because reductions in precipitation and increases in temperature translate directly into heightened water‑stress. The present study quantifies the projected evolution of surface runoff in the Khorramabad River Basin (4250 km², Lurestan Province, Iran) by coupling the latest IPCC-AR6 climate information with data-driven hydrological models. The primary objectives were to (i) evaluate the performance of the newest CMIP6 general-circulation models (GCMs) for the basin, (ii) downscale the GCM outputs to the basin scale, and (iii) assess the ability of a non-linear Artificial Neural Network (ANN) and a multiple linear regression (MR) model to simulate future runoff under the SSP1-2.6 (optimistic) and SSP5-8.5 (pessimistic) pathways. Method Historical climate series (1971-2000) for precipitation and air temperature were compiled from local stations and used as the reference for model evaluation. Fourteen CMIP6 GCMs were extracted for the historical period and for two future windows – mid-century (2040-2069) and end‑century (2070‑2099) – under SSP1‑2.6 and SSP5‑8.5. A simple change‑factor (bias‑correction) approach was applied to translate the coarse-resolution GCM fields to the basin scale; the precipitation factor was defined as the ratio of future to baseline mean monthly precipitation, while the temperature factor was the difference between future and baseline mean monthly temperature. Model skill was assessed using RMSE, NSE, Kling-Gupta Efficiency (KGE) and the coefficient of determination (R²). IPSL‑CM6A‑LR emerged as the most skillful GCM for precipitation (RMSE ≈ 49 mm yr⁻¹, NSE ≈ 0.82, KGE ≈ 0.86) and ACCESS‑CM2 for temperature (RMSE ≈ 0.83 °C, NSE ≈ 0.99). For hydrological simulation, two data-driven models were constructed: Multiple Linear Regression (MR): A linear equation linking monthly runoff (Q) to precipitation (P) and temperature (T). Artificial Neural Network (ANN): A feed-forward network with a 3‑10‑1 architecture (three inputs, ten hidden neurons, one output). The observed dataset was partitioned into training (70 %), validation (15 %), and testing (15 %) subsets. ANN training employed the Levenberg-Marquardt algorithm, and model performance was evaluated with the same statistical metrics as above. Results Downscaled CMIP6 projections indicate a consistent decline in mean annual precipitation of 5–9 % and a rise in mean monthly temperature of up to ~5 °C by the 2070-2099 horizon, with the magnitude of change markedly larger under the high‑emission SSP5‑8.5 scenario. The ANN consistently outperformed the MR model across all evaluation periods and metrics. In the independent test set the ANN achieved NSE = 0.35, RMSE ≈ 12 % of the observed runoff range, KGE > 0.60 and a correlation coefficient r ≈ 0.71, whereas the MR model produced NSE values close to zero and substantially larger RMSE, reflecting its inability to capture the non‑linear precipitation‑runoff relationship. Using the ANN, future runoff is projected to decrease by 15-30 % relative to the historic period across both scenarios, with the greatest reductions occurring under SSP5-8.5. The runoff decline mirrors the simulated precipitation deficits and the temperature-driven increase in evapotranspiration, signalling a pronounced intensification of water scarcity in the Khorramabad basin. Conclusion Integrating up‑to‑date CMIP6 climate projections with an ANN-based hydrological framework provides a robust, basin-specific assessment of future water availability in a semi‑arid environment. The analysis reveals a clear trajectory toward reduced surface‑water resources, especially under the pessimistic SSP5‑8.5 pathway, and underscores the urgency of implementing adaptive water‑management strategies such as improved irrigation efficiency, optimized allocation policies, the development of flood‑and‑drought mitigation infrastructure, and early‑warning systems. Future work should explore hybrid physics‑data models and multi-scenario ensembles to better resolve extreme flow events and to further reduce predictive uncertainty. | ||
| کلیدواژهها [English] | ||
| Water Resources, SSP Scenario, Runoff Simulation, Artificial Neural Network, Multiple Regression | ||
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