On the use of distributed hydrologic model for filling large gaps at different parts of the streamflow data

Ergün E., Demirel M. C.

Engineering Science and Technology, an International Journal, vol.37, 2023 (SCI-Expanded) identifier


© 2022 Karabuk UniversityComplete streamflow data is indispensable for water resources engineers to design, plan and operate the structures on rivers. To reveal statistically meaningful results, there should be sufficient length of observations with no missing data. However, for different reasons, e.g. failure of gauge instrument and weather conditions during manual recording, there can be missing parts in the measurements. In this study, we assessed the effectiveness of using a distributed hydrologic model in combination with remotely sensed LAI data to complete one year data gap for two different basins i.e. Moselle Basin and Konya Closed Basin (KCB). Cochem gauge from Moselle Basin and D16A100 gauge from KCB are used to show the effect of data quality and length on the results. Further, the effect of gap location is analyzed using randomly selected one-year-gap from the beginning, middle or end of the discharge time series since there have been already statistical gap filling methods developed for small gaps spread over the data. Nash–Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE) and SPAtial Efficiency (SPAEF) are used to evaluate the hydrologic model (mHM) performance in gap-filling. The results indicate that mHM can simulate streamflow dynamics in both basins (KGE above 0.88) during calibration period using continuous meteorological forcings. Further, having good quality forcing and adequate length of calibration are shown to be the key of successful gap filling either one-year long or shorter but frequent gaps. The results also show that mHM predicts the missing data in Cochem (Moselle) better than those data from gauge Küçükmuhsine (KCB). This seems to be not only due to the good quality and long data of Cochem but also rainfed flow regime in Moselle is easier to predict as compared to intermittent rivers.