Hejiang Cai

Bio: Hejiang Cai is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Teleconnection & Normalized Difference Vegetation Index. The author has an hindex of 1, co-authored 2 publications receiving 4 citations. Previous affiliations of Hejiang Cai include National University of Singapore.

Evolution characteristics of potential evapotranspiration over the Three-River Headwaters Region

Abstract: This study investigates the evolution characteristics of potential evapotranspiration (PET) over the Three-River Headwaters Region (TRHR) based on observations at 14 stations during 1960–2014. Firs...

Toward improved lumped groundwater level predictions at catchment scale: mutual integration of water balance mechanism and deep learning method

Abstract: • A novel hybrid model for simulating groundwater level was developed. • The hybrid model integrated water balance equations with deep learning algorithm. • The proposed model presented the superiority and powerful simulation ability. • The automatic parameterizing ability enhanced the model for cross-region simulation. Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.

Comprehensive evaluation of vegetation responses to meteorological drought from both linear and nonlinear perspectives

Abstract: The frequent occurrence of drought events in recent years has caused significant changes in plant biodiversity. Understanding vegetation dynamics and their responses to climate change is of great significance to reveal the behaviour mechanism of terrestrial ecosystems. In this study, NDVI and SIF were used to evaluate the dynamic changes of vegetation in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought in the PRB was evaluated from both linear and nonlinear perspectives, and the difference of vegetation response to meteorological drought in different land types was revealed. Cross wavelet analysis was used to explore the teleconnection factors (e.g., large-scale climate patterns and solar activity) that may affect the relationship between meteorological drought and vegetation dynamics. The results show that 1) from 2001 to 2019, the vegetation cover and photosynthetic capacity of the PRB both showed increasing trends, with changing rates of 0.055/10a and 0.036/10a, respectively; 2) compared with NDVI, the relationship between SIF and meteorological drought was closer; 3) the vegetation response time (VRT) obtained based on NDVI was mainly 4–5 months, which was slightly longer than that based on SIF (mainly 3–4 months); 4) the VRT of woody vegetation (mainly 3–4 months) was longer than that of herbaceous vegetation (mainly 4–5 months); and 5) vegetation had significant positive correlations with the El Niño Southern Oscillation (ENSO) and sunspots but a significant negative correlation with the Pacific Decadal Oscillation (PDO). Compared with sunspots, the ENSO and the PDO were more closely related to the response relationship between meteorological drought and vegetation. The outcomes of this study can help reveal the relationship between vegetation dynamics and climate change under the background of global warming and provide a new perspective for studying the relationship between drought and vegetation.

Impacts of Climate Change and Land Use/Cover Change on Regional Hydrological Processes: Case of the Guangdong-Hong Kong-Macao Greater Bay Area

Abstract: Climate change and land use/cover change (LUCC) have been widely recognized as the main driving forces that can affect regional hydrological processes, and quantitative assessment of their impacts is of great importance for the sustainable development of regional ecosystems, land use planning and water resources management. This study investigates the impacts of climate change and LUCC on variables such as streamflow (SF), soil moisture (SM) and evapotranspiration (ET) in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by using Soil and Water Assessment Tools (SWAT) model under different scenarios during 1979–2018. The results show that the simulation performances were overall good, with Nash-Sutcliffe Efficiency Coefficient (NSE) and coefficient of determination (R2) greater than 0.80 for the monthly-scale SF calibration and validation. According to the results of trend and change point tests of meteorological series, the baseline period (1979–1997) and the interference period (1998–2018) were determined. Interestingly, other land use types were basically converted to urban land, leading to a rapid urbanization in the GBA. Compared with the SF values of the eight estuaries of the Pearl River Basin in the baseline period, both climate change and LUCC has led to the decrease in the SF values in the interference period, and the combined effect of climate change and LUCC was slightly greater than their individual effect. Overall, climate change and LUCC both have important impacts on regional hydrological processes in the GBA.

A New Perspective on Drought Propagation: Causality

Abstract: The essence of propagation from meteorological to hydrological drought is the cause-effect relationship between precipitation and runoff. This study challenged the reliability of applying linear or non-linear correlation (i.e., closeness/similarity, a non-directional scalar) to study drought propagation (i.e., causality, a directional vector). Meanwhile, in the field of hydrometeorology, causality analysis is burgeoning in model simulations, but still rare in analyzing the observations. Therefore, this study aims to provide a new perspective on drought propagation (i.e., causality) using convergent cross mapping (CCM) based on pure observations. Compared with the results in previous studies, the effectiveness of applying causality analysis in drought propagation study was proven, indicating that causality analysis would be more powerful than correlation analysis, especially for detecting drought propagation direction.