
Revision notes for “Potential application of hydrological ensemble prediction in forecasting flood and its components over the Yarlung Zangbo River Basin, China” (hess-2018-179) Dear Editor and referees, 5 Thanks a lot for your great efforts to review this manuscript and give very valuable comments. We agree with your suggestions which will be of great help to improve the quality of our manuscript. Here we have addressed the comments from you and the detailed description is attached in this document. 10 Best regards, Li Liu, Yue-Ping Xu,Suli Pan, Zhixu Bai To Referee #2 1. Page 9 line 10: the out-performance during evaluation period could also be related to the shorter length of the timespan. 15 Response: Thanks for your suggestion. We have added this reason in the revised manuscript. “Generally speaking, the model performance during evaluation is more satisfactory than that during calibration. It is probably caused by the existence of considerable extraordinary flood events during the calibration period. The relatively shorter timespan in evaluation is also one of the reasons.” 20 2. Page 9 line 15: please add some references to support the opinion. Response: We have added some previous studies to justify the opinion. Please see Page 9, Line 15-17: “We also guess that within downstream regions the hydrological process becomes too complicated due to human activities to be simulated by models (Li et al., 2013; Liu et al., 2014).” Li, F., Xu, Z., Feng, Y., Liu, M. and Liu, W.: Changes of land cover in the Yarlung Tsangpo River basin from 1985 to 2005, 25 Environ. Earth Sci., 68, 181-188, ,2013. Liu, Z., Yao, Z., Huang, H., Wu, S., and Liu, G.: Land use and climate changes and their impacts on runoff in the Yarlung Zangbo river basin, China, Land Degrad. Dev., 25, 203-215, doi:10.1002/ldr.1159, 2014. 3. Page 9 line 31: “for the output snow depth from VIC is actually the sum of snow and glacier/ice” is redundant, delete it. 30 Response: We have deleted this sentence in the revised manuscript. 4. Page 13 line 31: change “compared” into “comparing”. 1 Response: We have changed “compared” with “comparing”. 5. Page 14 line 10: since you mentioned other two methods calculating the glacier melt, and meanwhile in line 13 stated “Overly complicated methods probably bring out more uncertainties….more observations are available with the development 5 of technologies in the future, more elaborate separation method is expected” what’s the observed input for these two models, are they overly complicated and with more uncertainty? If not, please modify the deduction since it cannot be reached either based on your study or the studies you referred to. In the discussion section, please add some references on the similar studies using the proxy method and make a comparison with this study. 10 Response: Thank you for your suggestion. (1) What’s the observed input for these two models, are they overly complicated and with more uncertainty? If not, please modify the deduction since it cannot be reached either based on your study or the studies you referred to. The simple degree-day glacier algorithm requires only the temperature data and the distribution of glacier in study area. The significant defect of this method is without considering water balance. The energy balance method requires incoming longwave 15 radiation, emitted longwave radiation, outgoing longwave radiation and other data to calculate related glacier surface-energy balance and mass balance. If all those data are calculated from air temperature rather than observation, the sparse distributed meteorological network is highly likely to bring in additional uncertainties. The deduction here is somewhat deviated from our conclusion, and we have modified the related part based on our study. (2) In the discussion section, please add some references on the similar studies using the proxy method and make a comparison 20 with this study. The practice using proxy as observations is common when observed streamflow is absent. Similar studies can be found in Arnal et al. (2018) and Harrigan et al. (2018). We have added the relevant studies in the discussion section. “For streamflow components forecast, the biggest challenge is the absence of data series of in-situ streamflow components. Therefore, in this study the simulation driven by observed forcing becomes an alternative to act as proxy and thus the error 25 stemming from hydrological model is avoided. This is a common practice when observation is absent (Arnal et al., 2018; Harrigan et al., 2018).” Arnal, L., Cloke, H.L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B. and Pappenberger, F.,: Skilful seasonal forecasts of streamflow over Europe? Hydrol. Earth Syst. Sci., 22(4), 2057-2072, 2018. Harrigan, S., Prudhomme, C., Parry, S., Smith, K. and Tanguy, M.: Benchmarking ensemble streamflow prediction skill in the 30 UK, Hydrol. Earth Syst. Sci., 22(3), 2023-2039, 2018. Page 18, line 25: please add the year for the reference. Response: We have added the year for the reference. 2 To Referee #3 1. How the “ability” of ensemble predictions to forecast can be defined? It could be expected that some weather projections predict observed flood peak and other not. At which point the hypothesis can be falsified? What is a level of CRPS, CRPSS or MAE at which authors would agree that ensemble predictions are unable to forecast a flood peak? In my opinion it always is 5 possible to insist that the approach is “able” - better or worse. The problem should be rather analysed in terms of suitability, where the ensemble predictions are compared with other methods. Then it would be possible quantifying the performance of different methods using i.e. CRPSS. Such an approach can be found i.e. in Pappenberger et al. (2005) who analyzed ensemble along with deterministic predictions. Response: Thank you very much. 10 (1) We agree that it is always difficult to interpret CRPS, CRPSS and MAE. However, it is still the most commonly used metrics to quantify ensemble forecasts. We have read the recommended study by Pappenberger et al. (2005). It is a good example to analyze ensemble with deterministic predictions, but, in our opinion, it is not suitable for our study. Firstly, in the study of Pappenberger et al. (2005) (hereinafter referred as “Pappenberger’ study”), only one case study is used, and thus the plot of hydrograph against lead time is feasible. In our study, we choose more than ten case studies for each station, making it 15 redundant to adopt similar plots. Secondly, the deterministic predictions in Pappenberger’ study consisted of 6 ensembles, and it is convenient to graph all the hydrographs, but in our study the S-simulation prediction is still composed by 51 ensembles. (2) We totally agree to classify the problem as suitability, and we have revised the expression in the manuscript. The CRPSS has been used to quantify the model performance in original paper as shown in Fig. 6 and Fig. 10-13. “The two purposes of this study are therefore to investigate the suitability of HEPS in forecasting flood volume and its 20 components over cold and mountainous area, and the impact of an ensemble of selected pareto optimal solutions on model simulation and forecasting compared to a single parameter set.” 2. Hydrograph separation is probably the weakest part of the study. According to the text, glaciers are important part of the basin system. However, the applied model does not account for this component. In Page 5, line 16-18 authors assume that it 25 can be described as snow accumulation. This is a very rough assumption and its limitations can be seen in Fig 3 (authors are aware of this fact, page 8, lines 25-26), where flows, probably shaped by glacier outflow, are not explained. What is the point analysing forecasted streamflow components section 4.3), if the essential element of the base flow is missing? The conclusion to this remark could be the sentence from the Discussion section, referring to the forecasted components (Page 14, lines 18- 19): 30 “Therefore, in this study the simulation driven by observations becomes an alternative to act as proxy although it is difficult to determine whether such proxy is believable or not.” If it is hard to determine that we can believe the method or not, it is not a scientific approach. 3 Response: It is our mistake to unclearly describe the glacier part. Actually, the glacier only takes up about 2% of the area in the YZR (Zhang et al., 2013), and of streamflow less than 10% (Chen et al., 2017). The underestimation of baseflow in Fig.3 is not, to a great degree, caused by lack of glacier modeling. For the low flow period is the time when the glacier should be accumulated, the absence of glacier is supposed to overestimate baseflows. We have compared our results with a previous 5 study (Zhang et al., 2013, Fig.3(e), the study period is somewhat different, from 1961 to 1999 in that study), similar underestimation exits in low flow period, even though a glacier is embedded in the hydrological model. The authors thought the errors were mainly from the observed inputs. However, how much the inputs might be underestimated for the YZR is actually unknown. In this study, one of the reasons for this phenomenon is that the objective functions used to calibrate hydrological model emphasize more on high flows. The underestimation is, in the meanwhile, caused by the errors of 10 meteorological measurements in the study area, which has been documented by previous studies like Tong et al. (2014) and Zhang et al.
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