Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area
- PDF / 2,169,921 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 63 Downloads / 171 Views
RESEARCH ARTICLE - HYDROLOGY
Improving ANN‑based streamflow estimation models for the Upper Indus Basin using satellite‑derived snow cover area Muhammad Hassan1 · Ishtiaq Hassan1 Received: 3 May 2020 / Accepted: 21 September 2020 © Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2020
Abstract The mountainous catchments often witness contrasting regimes and the limited available meteorological network creates uncertainty in both the hydrological data and developed models. To overcome this problem, remotely sensed data could be used in addition to on-ground observations for hydrological forecasting. The fusion of these two types of data gives a better picture and helps to generate adequate hydrological forecasting models. The study aims at the improvement of ANN-based streamflow estimation models by using an integrated data-set containing, the satellite-derived snow cover area (SCA) with on-ground flow observations. For this purpose, SCA of three sub catchments of Upper Indus Basin, namely Gilgit, Astore and Bunji coupled with their respective gauge discharges is used as model inputs. The weekly stream-flow models are developed for inflows at Besham Qila located just upstream of Tarbela dam. The data-set for modeling is prepared through normalizing all variables by scaling between 0 and 1. A mathematical tool, Gamma test is applied to fuse the inputs, and a best input combination is selected on the basis of minimum gamma value. A feed forward neural network trained via two layer Broyden Fletcher Goldfarb Shanno algorithm is used for model development. The models are evaluated on the basis of set of performance indicators, namely, Nash–Sutcliffe Efficiency, Root Mean Square Error, Variance and BIAS. A comparative assessment has also been made using these indicators for models developed, through data-set containing gauge discharges, only and the data-set fused with satellite-derived SCA. In particular, the current study concluded that the efficiency of ANNbased streamflow estimation models developed for mountainous catchments could be improved by integrating the SCA with the gauge discharges. Keywords Mountainous catchments · Upper Indus Basin · Snow cover area · Streamflow · Artificial neural network (ANN)
Introduction The runoff originating from the Hindukush–Karakoram–Himalaya (HKH) ranges, is mainly generated through melting of snow and glaciers (Fowler and Archer 2005; Hasson et al. 2017), and contributes up to 80% of mean annual flows of Upper Indus Basin (UIB) (Immerzeel et al. 2009; Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11600-020-00491-4) contains supplementary material, which is available to authorized users. * Muhammad Hassan [email protected] Ishtiaq Hassan [email protected] 1
Capital University of Science and Technology (CUST), Islamabad, Pakistan
Arfan et al. 2019). The glacier cover over HKH ranges is considered as one of the fastest retreating cover in the world. Prasad (2009) and Liu and Chen
Data Loading...
