Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China

Abstract

Abstract. In recent year, floods becomes a serious issue in the Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide an early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River (YZR) basin, China, has not been investigated. This study adopts the variable infiltration capacity (VIC) model to forecast the annual maximum floods and annual first floods in the YZR based on precipitation and the maximum and minimum temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). N simulations are proposed to account for parameter uncertainty in VIC. Results show that when trade-offs between multiple objectives are significant, N simulations are recommended for better simulation and forecasting. This is why better results are obtained for the Nugesha and Yangcun stations. Our ensemble flood forecasting system can skillfully predict the maximum floods with a lead time of more than 10 d and can predict about 7 d ahead for meltwater-related components. The accuracy of forecasts for the first floods is inferior, with a lead time of only 5 d. The base-flow components for the first floods are insensitive to lead time, except at the Nuxia station, whilst for the maximum floods an obvious deterioration in performance with lead time can be recognized. The meltwater-induced surface runoff is the most poorly captured component by the forecast system, and the well-predicted rainfall-related components are the major contributor to good performance. The performance in 7 d accumulated flood volumes is better than the peak flows.

A new fractal-theory-based criterion for hydrological model calibration

“…Apart from criteria that are used to calculate model errors over the entire test period, there are also many criteria that focus on a certain period of interest. For example, the criteria mentioned above are calculated over flood periods (Liu et al, 2017(Liu et al, , 2019 or dry periods (Demirel et al, 2013). There have also been studies that calibrated hydrological models via hydrological components other than the streamflow, such as evapotranspiration (Pan et al, 2017), soil moisture (Gao et al, 2015), the snow water equivalent, and even glacier melt (Liu et al, 2019).…”

“…For example, the criteria mentioned above are calculated over flood periods (Liu et al, 2017(Liu et al, , 2019 or dry periods (Demirel et al, 2013). There have also been studies that calibrated hydrological models via hydrological components other than the streamflow, such as evapotranspiration (Pan et al, 2017), soil moisture (Gao et al, 2015), the snow water equivalent, and even glacier melt (Liu et al, 2019). Another approach that is applied to improve the performance of models is the use of hydrological signatures (Shafii and Tolson, 2015).…”

“…While the Hurst exponent calculated with rescaled range analysis is more widely used, the Hausdorff dimension can easily be extended to multifractal analysis and has prospective applications in hydrology (Bai et al, 2019;Zhou et al, 2014). The fractality of a time series is generally considered to reflect its self-affinity, periodicity, long-term memory and irregularity (Bai et al, 2019;Hurst, 1951;Mandelbrot, 2004). Self-affinity is a feature of a fractal whose pieces are scaled by different amounts in the x and y directions, and the fractal dimensions represent the selfaffinity of the time series.…”

How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau?

“…Simulating in those data‐sparse basins remains a challenge and should be careful. Previous studies (Zhang et al ., 2013; Tong et al ., 2014a; Liu et al ., 2019) have proved that streamflow simulation in the TP is usually subject to underestimation. Therefore, seeking more accurate meteorological proxy to complement insufficient in situ measurements is crucial in this region.…”

“…This region is also one of the most sensitive areas to global climate change due to the particularly cold and high‐elevation environment (Liu and Chen, 2000; Yao et al ., 2012). It is found that in the past decades the TP has undergone prominent changes in hydrological processes as a result of global warming (Wang et al ., 2014; Wang et al ., 2017; Liu et al ., 2019), making it urgent to establish accurate modelling system to track and predict those changes.…”