Abstract
This study evaluates the choice of the meteorological data set in the simulation of the streamflow of a Mexican basin, in the bias correction of climate simulations, and in the climate change impact on hydrological indicators. The selected meteorological data sets come from stations, two interpolated data sets and one reanalysis data set. The climate simulations were taken from the five-member ensemble from the second generation Canadian Earth System Model (CanESM2) under two representative concentration pathways (RCPs), for a reference period (1981–2000) and two future periods (2041–2060 and 2081–2100). The selected lumped hydrological model is GR4J, which is a daily lumped four-parameter rainfall-runoff model. Firstly, the results show that GR4J can be calibrated and validated with the meteorological data sets to simulate daily streamflow; however, the hydrological model leads to different hydrological responses for the basin. Secondly, the bias correction procedure obtains a similar relative climate change signal for the variables, but the magnitude of the signal strongly varies with the source of meteorological data. Finally, the climate change impact on hydrological indicators also varies depending on the meteorological data source, thus, for the overall mean flow, this uncertainty is greater than the uncertainty related to the natural variability. On the other hand, mixed results were found for high flows. All in all, the selection of meteorological data source should be taken into account in the evaluation of climate change impact on water resources.
Highlights
Evaluation of the climate change impact on water resources is an important issue in hydrology science, as the expected changes in the precipitation and temperature patterns would affect the availability of water for the population as well as the ecosystems
The case of HF10 2081–2100 representative concentration pathways (RCPs) 4.5 is outstanding, as the null hypothesis is not rejected with any pair combination. These results show that for overall mean flow (OMF), the climate change signal (CCS) obtained from processed gridded data and the reanalysis is different from the CCS obtained through station data
The uncertainty related to the choice of meteorological data was evaluated in the calibration and validation of a lumped hydrological model (GR4J), in the correction of climate simulations, and in the estimation of the climate change impact on hydrological indicators
Summary
Evaluation of the climate change impact on water resources is an important issue in hydrology science, as the expected changes in the precipitation and temperature patterns would affect the availability of water for the population as well as the ecosystems. It is expected that the climate change impact on the hydrological regimes would lead to changes in average runoffs. Arnell et al [1] argue that runoffs would increase in high latitudes, but they would decrease in other regions, such as Central and South America. The evaluation of the impact of climate change on water resources requires high-quality meteorological records in order to calibrate and validate the hydrological models, and to bias-correct climate simulations. The Intergovernmental Panel on Climate Change (IPCC) argues that historical records in many regions are poor, especially for those more vulnerable to
Sign-in/Register to view highlights & summary 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.
