Abstract
Abstract. Physically based distributed hydrological models are widely used for hydrological simulations in various environments. As with conceptual models, they are limited in data-sparse basins by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from fragmentary historical records of previously existing gauging stations or from temporary gauging during field surveys, which might be of value for model calibration. However, unlike lumped conceptual models, such an approach has not been explored sufficiently for physically based distributed models. This study explored how the use of limited continuous daily streamflow data might support the application of a physically based distributed model in data-sparse basins. The influence of the length of the observation period on the calibration of the widely applied soil and water assessment tool model was evaluated in four Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations of the four basins, respectively. To ensure the differences in the model simulations solely come from differences in the calibration data, the generalized likelihood uncertainty analysis scheme was employed for the automatic calibration and uncertainty analysis. In the four basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e., performances similar to the benchmark calibrations were achieved. The models of the wet Jinjiang and Donghe basins could be effectively calibrated using a shorter data record (1 month), compared with the dry Heihe and upstream Yalongjiang basins (6 months). Even though the four basins are very different, when using 1-year or 6-month (covering a whole dry season or rainy season) data, the results show that data from wet seasons and wet years are generally more reliable than data from dry seasons and dry years, especially for the two dry basins. The results demonstrated that this idea could be a promising approach to the problem of calibration of physically based distributed hydrological models in data-sparse basins, and findings from the discussion in this study are valuable for assessing the effectiveness of short-period data for model calibration in real-world applications.
Highlights
Flood and droughts are the two most prevalent natural disasters, considered to have affected 140 million people annually, on average, between 2005 and 2014 (United Nations Offices for Disaster Risk Reduction, 2016)
Ten commonly calibrated soil and water assessment tool (SWAT) parameters from the literature were selected for the automatic calibration using generalized likelihood uncertainty estimation (GLUE), and their prior ranges were set based on the recommendation from SWAT-CUP
Our suggestion is in line with those made by Yapo et al (1996) and Melsen et al (2014): data from wetter periods are preferred for model calibration. These findings indicate that, to know the “wetness level” of the short-period data, i.e., the records were observed in a wet year or dry year and in a rainy season or dry season, may be helpful to judge whether good simulation could be derived from calibration using a certain short period of observations
Summary
Flood and droughts are the two most prevalent natural disasters, considered to have affected 140 million people annually, on average, between 2005 and 2014 (United Nations Offices for Disaster Risk Reduction, 2016). Many parameters of hydrological models are conceptual without explicit physical mean-. Values of parameters with explicit physical meaning can be measured, the scale of measurement and model simulation is different, which makes it difficult to apply measured values to hydrological models directly. These measurements require intensive field surveys, which are not available in most studies. Parameters of such model are usually obtained from model calibration based on streamflow data. Because of resource constraints (e.g., financial and human resources), there has been a general decline in the networks designed to monitor streamflow (Wohl et al, 2012), especially in developing countries, which has become a major obstacle to the applications of hydrological models in basins where streamflow data are sparse (Hrachowitz et al, 2013)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
