Conference Agenda

In the context of global climate change, drought, flood and wetland vegetation change caused by hydrometeorological change have caused great variations to the food, water, soil resources and ecological environment on which human beings depend for survival. The hydrometeorological characteristics in middle and lower Yangtze River Basin have significant spatiotemporal heterogeneity. A series of in-depth study of the hydrometeorological changes in middle and lower Yangtze River Basin and its impact on wetland vegetation were conducted based on satellite remote sensing data, meteorological observation data, hydrological observation data, and statistical yearbook data.

The 2030 SDGs identify water (SDG 6), as well as vegetation/land use (SDG15) as keys parameters for providing the economic, social, and environmental well-being of the present and future generations. Satellite data can be powerful tools to reach these objectives or at least provide valuable indicators at different scales. ICEYE and Radarsat data have been exploited in synergy with Sentinel2 to insure the monitoring of water extent. Whereas IceSat and Sentinel3, Sentinel6 altimetric data have been exploited to monitor water bodies’ altitude. This acquired knowledge is crucial for the exploitation of SWOT products which first delivery will occur in summer 2023.

Water quality assessment, a pillar for SGD, requests to develop and validate processing protocols for multiple sensor systems. New advances have been done for the cCO2 estimation based on model using Sentinel-3-derived lake environmental variables and field data. Works done over sixteen lakes on the middle and lower reaches of the Yangtze basins shown that CO2 concentrations were low in the summer and autumn but high in the winter and spring with dramatic variations. The annual mean CO2 concentrations of lakes revealed that about 28% of the lakes acted as weak atmospheric CO2 sinks while the rest were sources. CO2 concentrations decreased with increasing eutrophication and decreasing lake size. With this problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it’s not clear how lake transitions occur at regional and global scales. To answer this point, a long-term monitoring of 22 lakes of the Yangtze watershed have been carried out, exploiting a novel innovative and efficient three steps algorithm that can distinguish, aquatic vegetation (AV), floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). The AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic system is still ongoing in the middle Yangtze watershed. In addition, over large lakes (>500 km2), including the Taihu and Chaohu, daily MR satellite observations over 20 years was explored by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics at global scale.

The deep neural network obtained excellent performance in the temperature, precipitation, and gross primary production (GPP) models, far exceeding other ensemble models and single models, with monthly model average correlation coefficients of 0.99, 0.82, and 0.926 respectively. Based on the previous monthly scale ensemble dataset, it was found that under three SPP scenarios in the future (2021-2100), the high-latitude regions of the Northern Hemisphere will warm significantly, while the spatiotemporal distribution pattern of precipitation shows no significant difference. Under different scenario models, GPP changes show obvious spatiotemporal heterogeneity, among which the area of GPP extremely significant increase and decrease under SSP5-8.5 scenario accounts for 37.6% and 34.6% respectively, which is much larger than other scenario models. On the other hand, compared with the SSP1-2.6 scenario, GPP under the SSP5-8.5 scenario is more sensitive to seasonal changes in precipitation and temperature.

Soil moisture is of great importance in assessing the potential impacts of climate changes on energy and water cycles, and they are key indicators of drought assessment. The history and future drought conditions were studied. The applicability of ESA CCI soil moisture data in Yangtze River Basin was verified, and a concept of lag time was proposed to quantify the hysteresis between soil moisture and meteorological elements, such as precipitation, temperature and evapotranspiration, under different climatic conditions and timescales. A novel Comprehensive Agricultural Drought Index (CADI) was then constructed to reflect the feedback of time lag effects in drought assessment. The CADI was able to effectively monitor the annual and seasonal variations and spatial pattern of agricultural drought, particularly better identify summer droughts, from which the crop phenology related agriculture drought monitoring can benefit.

In 2020, Poyang Lake suffered the most serious flood hazard since the 21st century, which presents the characteristics of sharp shift from drought to flood. A multi-criteria model combining the analytic hierarchy process and Entropy weight method (AHP-Entropy) was proposed to assess the long and short flood risk. Validation of the flood risk assessment results shows that the flood risk assessment model has a great consistency with Sentinel-1 SAR data, which indicated that the presented flood risk model is reliable. The introduction of the differential correlation measure (∆Corr) characterizes the strength of water or energy limitations and finds that ∆Corr can detect the response of surface water and energy to short-term surface processes. Through the analysis of ∆Corr, the global critical soil moisture (CSM) content for China was calculated to be 0.16 m3m-3, which is slightly biased towards the dry end and helps to improve the expression of soil moisture stress in the model.

Hydrology is a critical environmental condition for the evolution of wetland ecosystems. The hydrological influences on wetland cover distribution and transition in a large complex lake-floodplain system, Poyang Lake were then investigated. The statistical results of annual inundation conditions for different wetland cover types indicated that vegetation communities were preferential to hydrological environments with shorter annual inundation than water and mudflats, and different vegetation communities were distributed in areas with considerable variations in annual inundation, which suggested a substantial hydrological influence on the distribution of wetland cover in Poyang Lake.

The main objective of the project is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a synergic and innovative way to integrate RS data from NRSCC and ESA into terrestrial system models for better quantifying the water cycle at the watershed/regional scale. The objective will be achieved through the following sub-objectives: i) Retrieval of key water cycle variables from multi-source RS data (WP1); ii) Development of real time RS LDAS to integrate RS data into terrestrial system models (WP2); iii) Calibration/validation of terrestrial system models using RS retrievals of key water cycle variables (WP3); iv) Parameter estimations for terrestrial system models based on the LDAS (WP3); v) Closing and quantifying the water cycle at the watershed/regional scale based on the LDAS (WP4).

Two LDASs have been developed in the project, one for the silk road endorheic river basins (LDAS_Silk) and the other for EUROCORDEX-domain (LDAS_EU). LDAS_Silk was developed based on the watershed system model and a common software for nonlinear and non-Gaussian land data assimilation (ComDA). LDAS_EU was developed based on the Terrestrial System Modeling Platform (TSMP) and Parallel Data Assimilation Framework (PDAF). Multi-source RS data, from visible to thermal infrared and microwave, have been applied to retrieve key ecohydrological variables, such as evapotranspiration (ET), snow coverage area (SCA), snow water equivalent (SWE), snow depth (SD), soil moisture (SM), lake and glacier extents, irrigation, and vegetation density and structure. The data have been applied as forcing, calibration and validation data, and for assimilation into the two LDASs.

Through a 3-year research, the project team has made significant progress in the retrieval of multiple ecohydrological variables, development of the LDAS, and application of the data product to ecohydrological processes analysis and prediction. This presentation will report the progress of the project in the past three years, focusing on the collaboration of the Sino-Euro sides, including young scientists training, co-investigation of LDAS, and co-attending conferences.