Evaluation of PERSIANN family remote sensing precipitation products for snowmelt runoff estimation in a mountainous basin

Uysal G., Sorman A. U.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, cilt.66, sa.12, ss.1790-1807, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 12
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/02626667.2021.1954651
  • Dergi Adı: HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, PASCAL, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, Geobase, INSPEC, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1790-1807
  • Anahtar Kelimeler: satellite-based precipitation, mountainous basin, snow, hydrological modelling, GAUGE OBSERVATIONS, PASSIVE MICROWAVE, EASTERN PART, SATELLITE, MODEL, RADAR, COVER, VERIFICATION, SIMULATIONS, PREDICTION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Estimating snowmelt runoff in mountainous basins is a challenging task due to limited precipitation measurements. Satellite-based Precipitation Products (SBPs) are readily available, but still suffer from large errors in cold climate regions. This study aims to evaluate and propose post-bias corrections for uncorrected Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and pre-bias-corrected PERSIANN-Climate Data Record (PERSIANN-CDR) daily SBPs using enriched observed data in the upper Euphrates River Basin, Turkey. SBPs are also employed in a daily multilayer perceptron (MLP) model to quantify the impact on runoff. PERSIANN-CDR outperforms PERSIANN in terms of correlation and detection, but biases substantially increase in PERSIANN-CDR in the snow accumulation season. The MLP has been trained and validated using observed precipitation data with 0.86 and 0.83 Nash-Sutcliffe efficiency (NSE), respectively. Applying post-bias corrections by a modified multiplicative linear scaling method improves runoff estimation with NSE values increasing from 0.48 to 0.61 and 0.38 to 0.68 for PERSIANN and PERSIANN-CDR, respectively.