Towards the evaluation of regional ecosystem integrity using NDVI, brightness temperature and surface heterogeneity.

Maintaining ecological integrity is globally acknowledged as a strategic goal, yet there is no consensus on a practical and widely usable methodology to assess it. This study proposes a comprehensive approach to quantify regional ecosystem integrity based on FAIR data, obtained using satellite remote sensing and image analysis. Three variables are central to this approach: normalized difference vegetation index (NDVI), at-satellite brightness temperature (BT) and vegetation surface heterogeneity (HG), corresponding to ecosystem integrity indicators exergy capture, biotic water flows and abiotic heterogeneity. The indicators are assessed across the vegetation period and a representative Regional Index of Ecological Integrity (RIEI) is proposed to express the integrity of two case study areas and representative land use types. The proposed approach proved powerful in representing the anthropogenic and autopoietic gradient within study regions in high detail. Arable lands and urban areas ranked lowest, while dense forests and wetlands highest, agriculture being the most significant factor reducing regional integrity. Areas with conservation significance ranked either having the highest integrity, when dense vegetation was present, and mediocre or even low in case of e.g., sand dunes, marches and rock formations. Limitations of the method comprise: insufficient representation of biodiversity, sensitivity to cloud cover and demanding in-situ validation. The approach can be scaled from global to local level, adapted to various remote sensing techniques and complemented by a diversity of data (e.g., ecosystem services, geomorphological, climatic) to provide deeper understanding of landscape ecosystem integrity.

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction.

Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).

Phenological shifts in lake stratification under climate change.

One of the most important physical characteristics driving lifecycle events in lakes is stratification. Already subtle variations in the timing of stratification onset and break-up (phenology) are known to have major ecological effects, mainly by determining the availability of light, nutrients, carbon and oxygen to organisms. Despite its ecological importance, historic and future global changes in stratification phenology are unknown. Here, we used a lake-climate model ensemble and long-term observational data, to investigate changes in lake stratification phenology across the Northern Hemisphere from 1901 to 2099. Under the high-greenhouse-gas-emission scenario, stratification will begin 22.0 ± 7.0 days earlier and end 11.3 ± 4.7 days later by the end of this century. It is very likely that this 33.3 ± 11.7 day prolongation in stratification will accelerate lake deoxygenation with subsequent effects on nutrient mineralization and phosphorus release from lake sediments. Further misalignment of lifecycle events, with possible irreversible changes for lake ecosystems, is also likely.