Contribution of the satellite-data driven snow routine to a karst hydrological model

Çallı S. S., Çallı K. Ö., Yılmaz M. T., Çelik M.

JOURNAL OF HYDROLOGY, cilt.607, ss.1-17, 2022 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 607
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.jhydrol.2022.127511
  • Dergi Adı: JOURNAL OF HYDROLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-17
  • Ankara Üniversitesi Adresli: Evet

Özet

Snow recharge is an important dominant hydrological process in the high altitude mountainous karstic aquifer

systems. In general, widely used karst-dedicated hydrological models (e.g., KarstMod, Varkarst) do not include a

snow routine in the model structure to avoid increasing the number of model parameters while representing the

complex hydrological process. As a result, recharge process is not represented well, which questions the optimality

of the results that can be obtained under available datasets. This study presents a novel pre-processing

method –called SCA routine– to compensate for the missing snow routine in karst models. The proposed preprocessing

method is driven by temperature, precipitation, and satellite-based snow observation datasets. The

method classifies the precipitation input into three physical phases (rain, snow, and mixed) based on the temperature

datasets to distribute each phase over the catchment using satellite-driven Snow-Covered Area (SCA)

products. By the proposed method, the spring discharge simulations are regulated well in time and magnitude.

To examine the added utility of the SCA routine, the SCA-included simulations are compared to the model

performances with no routine and the classical Degree-Day method as a benchmark. To test the efficiency of our

proposed method, we used a karst hydrological model (KarstMod) to simulate the karst spring discharge in a

well-observed semi-arid snow-dominated karstic aquifer (Central Taurus, Turkey). Our results confirmed that the

KarstMod model coupled by SCA routine ensures better model performance with a value of NSE = 0.784 than

those of the classical Degree-day method (NSE = 0.760) and the model with no routine (NSE = 0.306), thus

providing a physically more realistic parameter set.