Paper Accepted by Water Resources Management

The following paper about reference evapotranspiration projection under climate change has been accepted for publication by Water Resources Management.

Maqsood, J., A. A. Farooque, F. Abbas, T. Esau, X. Wang, B. Acharya, and H. Afzaal. Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios. Water Resources Management, accepted on October 5, 2021.

More details will come soon once the paper is published.

Paper Published in Remote Sensing

Title: Long-Term Projection of Water Cycle Changes over China Using RegCM

Journal: Remote Sensing

DOI: https://doi.org/10.3390/rs13193832

Abstract: The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.