Exploration of sub-annual calibration schemes of hydrological models

This study compared hydrological model performances under different sub-annual calibration schemes using two conceptual models, IHACRES and HYMOD. In several publications regarding sub-annual calibration, the authors showed that such an approach generally performed better than the conventional whole period method. Hence, there are advantages in dividing the data into sub-annual periods for calibration. However, little attention has been paid to the issue of how to calibrate the non-continuous sub-annual period. Unlike the conventional calibration which assumes time-invariant parameters for the calibration period, the model parameters vary in sub-annual calibration. We have explored two sub-annual calibration schemes, serial calibration scheme (SCS) and parallel calibration scheme (PCS). We assume that the relationships between the rainfall and runoff could be different for each sub-annual period and consider intra-annual variations of the system. The models are then evaluated for a different validation period to avoid over-fitting and the optimal sub-annual calibration period is explored. Overall, we have found that PCS performed slightly better than SCS and the optimal calibration periods are seasonal and bimonthly for IHACRES and biannual for HYMOD. Since there are pros and cons in both SCS and PCS, we recommend choosing the method depending on the purpose of the model usage.</jats:p

Exploration of optimal time steps for daily precipitation bias correction:a case study using a single grid of RCM on the River Exe in southwest England

Bias correction is a necessary post-processing procedure in order to use Regional Climate Model (RCM) simulated local climate variables as the input data for hydrological models due to systematic errors of RCMs. Most of present bias correction methods adjust statistical properties between observed and simulated data based on a predefined duration (e.g., a month or a season). However, there is a lack of analysis about the optimal period for bias correction. This study has attempted to address the question whether there is an optimal number for bias correction groups (i.e., optimal bias correction period). To explore this optimal number we used a catchment in southwest England with the regional climate model HadRM3 precipitation data. The proposed methodology uses only one grid of RCM in the Exe catchment, one emission scenario (A1B) and one-member (Q0) among 11-members of HadRM3. We tried 13 different bias correction periods from 3-day to 360-day (i.e., the whole one year) correction using the quantile mapping method. After the bias correction a low pass filter is used to remove the high frequencies (i.e., noise) followed by estimating Akaike’s information criterion. For the case study catchment with the regional climate model HadRM3 precipitation, the results showed that about 8-day bias correction period is the best. We hope this preliminary study about the optimum number of bias correction period for daily RCM precipitation will stimulate more research activities to improve the methodology with different climatic conditions so that more experience and knowledge could be obtained. Future efforts on several unsolved problems have been suggested such as how strong the filter should be and the impact of the number of bias correction groups on river flow simulations

Hydrological modelling under climate change considering nonstationarity and seasonal effects

Traditional hydrological modelling assumes that the catchment does not change with time. However, due to changes of climate and catchment conditions, this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions. In this study IHACRES, a conceptual rainfall–runoff model, is applied to a catchment in southwest England. Long observation data (1961–2008) are used and seasonal calibration (only the summer) has been done since there are significant seasonal rainfall patterns. Initially, the calibration is based on changing the model parameters with time by adapting the parameters using the step forward and backward selection schemes. However, in the validation, both models do not work well. The problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. Therefore, a new scheme is explored. Only one parameter is selected for adjustment while the other parameters are set as the fixed and the regression of one optimised parameter is made not only against time but climate condition. The result shows that this nonstationary model works well both in the calibration and validation periods.</jats:p