Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

Achenafi Teklay; Yihun T Dile; Dereje H Asfaw; Haimanote K Bayabil; Kibruyesfa Sisay

doi:10.1016/j.heliyon.2019.e02469

Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

Heliyon. 2019 Sep 18;5(9):e02469. doi: 10.1016/j.heliyon.2019.e02469. eCollection 2019 Sep.

Authors

Achenafi Teklay¹, Yihun T Dile², Dereje H Asfaw³, Haimanote K Bayabil⁴, Kibruyesfa Sisay⁵

Affiliations

¹ Ethiopian Institute of Water Resources, Department of Water Resources Engineering and Management, Addis Ababa University, Addis Ababa, Ethiopia.
² Spatial Science Laboratory, Ecosystem Science and Management Department, Texas A & M University, College Station, TX, 77801, USA.
³ Addis Ababa Institute of Science and Technology, School of Environmental and Civil Engineering, Addis Ababa University, Addis Ababa, Ethiopia.
⁴ Agricultural and Biological Engineering, Tropical Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Homestead, FL, 33031, USA.
⁵ Ethiopian Environment and Forest Research Institute, Addis Ababa, Ethiopia.

Abstract

The Weather Research and Forecasting (WRF) model is one of the regional climate models for dynamically downscaling climate variables at finer spatial and temporal scales. The objective of this study was to evaluate the performance of WRF model for simulating temperature and rainfall over Lake Tana basin in Ethiopia. The WRF model was configured for six experimental setups using three land surface models (LSMs): Noah, RUC and TD; and two land use datasets: USGS and updated New Land Use (NLU). The performances of WRF configurations were assessed by comparing simulated and observed data from March to August 2015. The result showed that temperature and rainfall simulations were sensitive to LSM and land use data choice. The combination of NLU with RUC and TD produced very small cold bias (0.27 °C) and warm bias (0.20 °C) for 2m maximum temperature (Tmax) and 2m minimum temperature (Tmin), respectively. WRF model with RUC and NLU captured well the observed spatial and temporal variability of Tmax, while TD and NLU for Tmin. Moreover, rainfall simulation was better with NLU; especially NLU and Noah configuration produced the smallest mean bias (2.39 mm/day) and root mean square error (6.6 mm/day). All the WRF experiments overestimated light and heavy rainfall events. Overall, findings showed that the application of updated land use data substantially improved the WRF model performance in simulating temperature and rainfall. The study would provide valuable support for identifying suitable LSM and land use data that can accurately predict the climate variables in the Blue Nile basin.

Keywords: Agricultural engineering; Agricultural water management; Agriculture; Atmospheric science; Climatology; Dynamical downscaling; Environmental impact assessment; Environmental management; Environmental science; Experiment; Simulations; USGS; Updated land use.