Evaluation of hydrological parameters and sediment dynamics in the Borçka Dam watershed using the SWAT model

Author:

Yıldırımer Saim1ORCID,Özalp Mehmet2ORCID

Affiliation:

1. KARABÜK ÜNİVERSİTESİ, ORMAN FAKÜLTESİ, ORMAN MÜHENDİSLİĞİ BÖLÜMÜ, HAVZA YÖNETİMİ ANABİLİM DALI

2. ARTVİN ÇORUH ÜNİVERSİTESİ, ORMAN FAKÜLTESİ, ORMAN MÜHENDİSLİĞİ BÖLÜMÜ, HAVZA YÖNETİMİ ANABİLİM DALI

Abstract

Various human-originating interventions and/or activities have been playing the major role for substantially impacting natural flow regime, water quality, and sediment transport amounts of running waters (streams, creeks etc.) in a negative way. While many studies using in-field measurements of such impacts have proven these changes, applying modeling methods in order to assess such effects are still improving. This study used the SWAT model to assess annual changes in water regime, quality, and sediment yield for Murgul, Hatila, Fabrika, and Godrahav Creeks based on field measurements. The model estimated the highest annual surface flow at Murgul Creek (2.41 m3/s) and the lowest at Fabrika Creek (0.19 m3/s). Sediment yields were 61855 t/yr at Murgul, 29826 t/yr at Hatila, 3165 t/yr at Fabrika, and 7835 t/yr at Godrahav. The model also provided reliable predictions for most sub-creeks, with R2 values between 0.85 and 0.91 and NSE values between 0.72 and 0.84. For run-off, Hatila, Fabrika, and Godrahav showed high reliability with R2 and NSE values around 0.85 and 0.80, respectively, while Murgul had lower scores (R2: 0.53, NSE: 0.22). Sediment yield was reliable in Hatila and Fabrika with R2 around 0.82, but less so in Godrahav and Murgul, with NSE values showing significant variability. Water quality predictions for NO3 were acceptable across all creeks, with R2 values around 0.82 and varied NSE values, indicating generally reliable outcomes. However, the model predicted less favorable outcomes for Murgul Creek due to significant human-induced alterations. While the SWAT model was generally promising, the study emphasizes the need for detailed, long-term data to improve prediction accuracy.

Publisher

Artvin Coruh Universitesi Orman Fakultesi Dergisi

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