Abstract
Abstract. According to Liu et al. (2014), borrowing, substituting and generating (BSG) are the main methods people used to acquire the discharge at ungauged stations. Two of the substitution (modelling and disaggregation) methods in combination with the borrowing idea are compared for simulating discharge for the Upper Salween and Mekong River Basin (USMRB). It is seen that with a simple borrowing/ disaggregating method, the Nash-Sutcliffe Efficiency (NSE) can reach 0.82. The similarity in the seasonal variation pattern is a more important requirement to identify if the two stations are to be considered as having hydrological similarity. From the experience obtained for the USMRB, an upstream station with shorter geographical distance may be more in hydrological similarity than a station in the far downstream. The NSE is quite low when borrowing occurs within the low altitude downstream region. The efficiency will be decreased when we borrow information from several stations which may be not in hydrological similarity.
Highlights
The Upper Salween and Mekong river basins (USMRB) are rich in water resources
The efficiency is compared by the Nash-Sutcliffe efficiency (NSE) and error of volume (EV)
Based on the BSG framework of the methodology for hydrological prediction for ungauged basins proposed by Liu et al (2014), possible methods for estimating water availability cross USMRB are researched
Summary
The Upper Salween and Mekong river basins (USMRB) are rich in water resources. Water availability is important for regional planning and water resources management. For data-scarce basins, Liu et al (2010, 2014) summarized a methodology for hydrological prediction, including the Borrow method (B), Substitute method (S) and Generation (G) method. Borrowing is obtaining hydrological information by transplanting measurements from a similar basin, extrapolating/interpolating the data from neighbouring catchments. Substituting is finding substitutes either from the ungauged basin or from donating area(s), and Generating is obtaining data via field or laboratory observations. Finding substitutes is classified further into S1, which is only within the ungauged basin by using fully process-based models without calibration; S2-1, which is from similar gauged basins by using established index/distribution; S2-2, which is from various gauged basins by using regression and/or process-based relationships between the climate/catchment features and hydrological signatures (CCH); and S3, which uses the information beyond the CCH relationship. Some new techniques in the combination of these methods are explored
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