Kun Zhang

Welcome to my personal website.

I'm currently working as a Postdoctoral Fellow at the Department of Mathematics joint with the School of Biological Science at The University of Hong Kong. Prior to that, I worked at the Institute of Tibetan Plateau Research, Chinese Academy of Sciences as a special research assistant on eco-hydrology and remote sensing application in endorheic river basins.

My research starts from modeling global evapotranspiration and soil water dynamics, focusing on the terrestrial water cycle and its interactions with climate change. The Bayesian approaches have usually been used in my projects to explore the sensitivity and optimization of model parameters. Based on land surface model and combining multi-source observations of remote sensing and in situ flux sites, I am trying to understand the response of the water cycle and vegetation to climate change, particularly under extreme events.

Contact

• Email: kunzh (at) hku.hk


• Address: 2N-08, Kadoorie Biological Science Building, Pokfulam Road

Terrestrial Evapotranspiration (ET) Modelling

Model parameters are significant factors influencing the model performance. Based on the current commonly used process-based remote sensing evapotranspiration models, we identified and analyzed the key parameter functions under different plant functional types using the eddy covariance (EC) observations across the globe, and gave a more suitable set of optimized parameters using a data-model fusion algorithm under the Bayesian framework (Zhang et al., 2017 ). Moreover, multiscale verification was expanded from the plot scale to the global scale, and the global ET estimates were compared with the mainstream ET products (Zhang et al., 2019 ). In addition, A physically-based ecohydrological model, called Simple Terrestrial Hydrosphere (SiTHv1), was developed and updated (SiTHv2) to estimate the terrestrial ET and ET-related variables based on the groundwater-soil-plant-atmosphere continuum. The major feature in SiTH model is to adjust the allocation of potential plant transpiration to different soil layers combined with root distribution and soil water conditions, which can assure the adaptability of plant growth (Zhu et al., 2019 ; Zhang et al., 2022 ).

Relevant codes are available at GitHub for PT-JPL url and MOD16 url. The terrestrial ET data set based on SiTHv2 (daily, 0.1° globally) can be achieved from the TPDC url.

Estimation of Global Irrigation Water Use

Quantification of the global irrigation water use (IWU) is crucial to understanding the anthropogenic disturbance of the natural hydrological cycle and optimal agricultural water management. However, it is challenging to obtain time series data with the conventional survey-based approach, while the current satellite-based IWU estimations are subject to data gaps and the model structure. Hence, we propose a comprehensive framework to couple the different processes associated with irrigation and integrate multiple satellite observations to estimate the global IWU (Zhang et al., 2022 ). The ensemble IWU estimate demonstrates an improved performance when compared to the IWU obtained from individual satellite observations. Large amounts of IWU are apparent in India, China, the US, Europe, and Pakistan, making up over 70 percent of the global IWU. A general underestimation of IWU is found both in this work and previous studies, due to the coarse resolution and asynchronism of the various satellite products, the changes in irrigated areas, and the deficiency in detecting irrigation events under the case of saturated soil moisture. Nevertheless, the proposed framework has showed advantages in integrating multiple precipitation and soil moisture data to address the uncertainties in estimating global IWU, and is a new attempt to use satellite to monitor global IWU. Generated data set is available at the TPDC url.

Extreme climate events, drivers and plant response

In the context of global warming, extreme climate events occur frequently. Heatwave is one of the climate extremes characterized by an abnormally high temperature near the Earth's surface, which has a substantial influence on ecosystem and human health. More than 166,000 people died worldwide due to heatwaves from 1998 to 2017, while the heatwave occurrences are projected to be more intense. Severe and sustained hot temperatures are usually tightly coupled with drought events, due to the enhanced atmospheric moisture demand and accelerated water loss in the soil. With reduced humidity, heatwaves can easily raise the risk of wildfires, which can be devastating to the natural environment and agriculture. Recently, I have been working on heatwave study to identify global heatwave episodes during the past several decades utilizing several data sources, such as ground-based, remote sensing, and reanalysis data. The establishment of heatwave information with high spatial and temporal resolution will be beneficial for explore its interaction with the climate variability and vegetation feedback over time series.