Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods

Abstract

Ecological water diversion projects (EWDP) are an effective management tool for restoring oasis ecosystems in arid regions. Given the potential for drier climatic conditions in arid regions in the future, it is essential to develop water diversion strategies that can adapt to climate change and reduce the risk of oasis ecosystem degradation. Here, this study used a Bayesian optimization-based long- and short-term memory (BO-LSTM) model to determine the optimal amount of water diversion needed to maintain healthy growth of oasis vegetation under future climate change scenarios in the Qingtu Oasis, which is a typical downstream oasis of inland rivers restored by EWDP in China. The results showed that the BO-LSTM model effectively captured the response of oasis vegetation to changes in water inundation areas and drought stress with low computational cost and high accuracy. The study revealed that regional vegetation became more vulnerable than previously thought when extreme drought and a drying trend were taken into account. It was found that if the amount of water entering the oasis ranges from 10 to 15 million m3, there will be a decline in the growth of oasis vegetation as indicated by the normalized difference vegetation index (NDVI). Even if current levels of water diversion (20 million m3) are maintained, oasis vegetation may still face growth decline due to meteorological drought. The optimal amount of water diversion was determined to be 25 million m3, resulting in a 21.7% increase in NDVI regardless of drought events. This study represents an innovative approach as it couples EWDP, climate change, and oasis vegetation dynamics based on deep learning models, which unveils divergent responses of oasis vegetation to climate change and EWDP and verifies a non-linear relationship between water diversion amounts and ecological benefits generated.