Warming trends of perialpine lakes from homogenised time series of historical satellite and in-situ data

Lake Surface Water Temperature in high altitude lakes in the Pyrenees: Combining satellite with monitoring data to assess recent trends

Lake Surface Water Temperature (LSWT) influences critical bio-geological processes in lake ecosystems, and there is growing evidence of rising LSWT over recent decades worldwide and future shifts in thermal patterns are expected to be a major consequence of global warming. At a regional scale, assessing recent trends and anticipating impacts requires data from a number of lakes, but long term in situ monitoring programs are scarce, particularly in mountain areas. In this work, we propose the combined use of satellite-derived temperature with in situ data for a five-year period (2017-2022) from 5 small (<0.5km2) high altitude (1880-2680 masl) Pyrenean lakes. The comparison of in situ and satellite-derived data in a common period (2017-2022) during the summer season showed a notably high (r = 0.94, p < 0.01) correlation coefficient, indicative of a robust relationship between the two data sources. The root mean square errors ranged from 1.8 °C to 3.9 °C, while the mean absolute errors ranged from 1.6 °C to 3.6 °C. We applied the obtained in situ-satellite eq. (2017-2022) to Landsat 5, 7 and 8/9 data since 1985 to reconstruct the summer surface temperature of the five studied lakes with in situ data and to four additional lakes with no in situ monitoring data. Reconstructed LSWT for the 1985-2022 showed an upward trend in all lakes. Moreover, paleolimnological reconstructions based on sediment cores studies demonstrate large changes in the last decades in organic carbon accumulation, sediment fluxes and bioproductivity in the Pyrenean lakes. Our research represents the first comprehensive investigation conducted on high mountain lakes in the Pyrenees that compares field monitoring data with satellite-derived temperature records. The results demonstrate the reliability of satellite-derived LSWT for surface temperatures in small lakes, and provide a tool to improve the LSWT in lakes with no monitoring surveys.

Slow response of surface water temperature to fast atmospheric variability reveals mixing heterogeneity in a deep lake

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.

Interaction between temperature and nutrients: How does the phytoplankton community cope with climate change?

Climate change and increasing nutrient concentrations are two major threats to lake ecosystems. Furthermore, warming is exacerbating the symptoms of eutrophication in freshwaters. Facing both environmental challenges simultaneously is more urgent than ever to preserve and recover water quality and protect the remaining biodiversity. Here, we used long-term observational data to investigate the phytoplankton response to the interaction between temperature and nutrient variations in a deep mesotrophic subalpine lake (L. Iseo, Italy). Despite the existing management programs, we observed a deterioration of the physicochemical status of the lake between 1993 and 2021 in the water column. The average rate of temperature increase was 0.02 °C y-1 across the studied period and accelerated after the last complete mixing events (2005 and 2006), particularly during the last decade (0.08 °C y-1). Water warming caused severe impacts on nutrient cycling, reflected by the overall increase in nutrient concentrations. The direct effect of warming was of great importance for phytoplankton, leading to an increase in total biovolume and a loss of diversity and promoting the dominance of Cyanobacteria. Nutrient variations, especially TN and the DSi:TP ratio, considerably structured the community composition. The modification of physicochemical parameters caused by the last complete mixing events led to a remarkable, long-term taxonomical and functional reorganization of the phytoplankton community. This study illustrates that phytoplankton in deep subalpine lakes will experience severe changes in the upcoming years, and that complete mixing events may constitute a threshold for community transformation. Our results stress the importance of using powerful interpretative models with multifaceted long-term datasets to disentangle the pathways by which temperature and nutrients could regulate the phytoplankton community. Our conclusions will help to integrate climate change into mitigation strategies to preserve ecosystem structure and functions.

Analysis of Climate Change Effects on Surface Temperature in Central-Italy Lakes Using Satellite Data Time-Series

Evaluation of the impact of climate change on water bodies has been one of the most discussed open issues of recent years. The exploitation of satellite data for the monitoring of water surface temperatures, combined with ground measurements where available, has already been shown in several previous studies, but these studies mainly focused on large lakes around the world. In this work the water surface temperature characterization during the last few decades of two small–medium Italian lakes, Lake Bracciano and Lake Martignano, using satellite data is addressed. The study also takes advantage of the last space-borne platforms, such as Sentinel-3. Long time series of clear sky conditions and atmospherically calibrated (using a simplified Planck’s Law-based algorithm) images were processed in order to derive the lakes surface temperature trends from 1984 to 2019. The results show an overall increase in water surface temperatures which is more evident on the smallest and shallowest of the two test sites. In particular, it was observed that, since the year 2000, the surface temperature of both lakes has risen by about 0.106 °C/year on average, which doubles the rate that can be retrieved by considering the whole period 1984–2019 (0.053 °C/year on average).

Monitoring Lakes Surface Water Velocity with SAR: A Feasibility Study on Lake Garda, Italy

The SAR Doppler frequencies are directly related to the motion of the scatterers in the illuminated area and have already been used in marine applications to monitor moving water surfaces. Here we investigate the possibility of retrieving surface water velocity from SAR Doppler analysis in medium-size lakes. ENVISAT images of the test site (Lake Garda) are processed and the Doppler Centroid Anomaly technique is adopted. The resulting surface velocity maps are compared with the outputs of a hydrodynamic model specifically validated for the case study. Thermal images from MODIS Terra are used in support of the modeling results. The surface velocity retrieved from SAR is found to overestimate the numerical results and the existence of a bias is investigated. In marine applications, such bias is traditionally removed through Geophysical Model Functions (GMFs) by ascribing it to a fully developed wind waves spectrum. We found that such an assumption is not supported in our case study, due to the small-scale variations of topography and wind. The role of wind intensity and duration on the results from SAR is evaluated, and the inclusion of lake bathymetry and the SAR backscatter gradient is recommended for the future development of GMFs suitable for lake environments.

Detecting Climate Driven Changes in Chlorophyll-a in Deep Subalpine Lakes Using Long Term Satellite Data

Climate change has increased the temperature and altered the mixing regime of high-value lakes in the subalpine region of Northern Italy. Remote sensing of chlorophyll-a can help provide a time series to allow an assessment of the ecological implications of this. Non-parametric multiplicative regression (NPMR) was used to visualize and understand the changes that have occurred between 2003–2018 in Lakes Garda, Como, Iseo, and Maggiore. In all four deep subalpine lakes, there has been a disruption from a traditional pattern of a significant spring chlorophyll-a peak followed by a clear water phase and summer/autumn peaks. This was replaced after 2010–2012, with lower spring peaks and a tendency for annual maxima to occur in summer. There was a tendency for this switch to be interspersed by a two-year period of low chlorophyll-a. Variables that were significant in NPMR included time, air temperature, total phosphorus, winter temperature, and winter values for the North Atlantic Oscillation. The change from spring to summer chlorophyll-a maxima, relatively sudden in an ecological context, could be interpreted as a regime shift. The cause was probably cascading effects from increased winter temperatures, reduced winter mixing, and altered nutrient dynamics. Future trends will depend on climate change and inter-decadal climate drivers.

Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach

Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000-2018 across all the investigated lakes. Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement. All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of -0.038 °C/y during the period of 2000-2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000-2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.

The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake

Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at different temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly offers an unprecedented resource in the research and management of aquatic resources.

Effects of Habitat Partitioning on the Distribution of Bacterioplankton in Deep Lakes

In deep lakes, many investigations highlighted the existence of exclusive groups of bacteria adapted to deep oxygenated and hypoxic and anoxic hypolimnia. Nevertheless, the extent of bacterial strain diversity has been much less scrutinized. This aspect is essential for an unbiased estimation of genetic variation, biodiversity, and population structure, which are essential for studying important research questions such as biogeographical patterns, temporal and spatial variability and the environmental factors affecting this variability. This study investigated the bacterioplankton community in the epilimnetic layers and in the oxygenated and hypoxic/anoxic hypolimnia of five large and deep lakes located at the southern border of the Alps using high throughput sequencing (HTS) analyses (16S rDNA) and identification of amplicon sequence variants (ASVs) resolving reads differing by as little as one nucleotide. The study sites, which included two oligomictic (Garda and Como) and three meromictic lakes (Iseo, Lugano, and Idro) with maximum depths spanning from 124 to 410 m, were chosen among large lakes to represent an oxic-hypoxic gradient. The analyses showed the existence of several unique ASVs in the three layers of the five lakes. In the case of cyanobacteria, this confirmed previous analyses made at the level of strains or based on oligotyping methods. As expected, the communities in the hypoxic/anoxic monimolimnia showed a strong differentiation from the oxygenated layer, with the exclusive presence in single lakes of several unique ASVs. In the meromictic lakes, results supported the hypothesis that the formation of isolated monimolimnia sustained the development of highly diversified bacterial communities through ecological selection, leading to the establishment of distinctive biodiversity zones. The genera identified in these layers are well-known to activate a wide range of redox reactions at low O₂ conditions. As inferred from 16S rDNA data, the highly diversified and coupled processes sustained by the monimolimnetic microbiota are essential ecosystem services that enhance mineralization of organic matter and formation of reduced compounds, and also abatement of undesirable greenhouse gasses.

Groundwater and surface water quality characterization through positive matrix factorization combined with GIS approach

This study aims at testing the effectiveness of Positive Matrix Factorization in characterizing groundwater and surface water quality, in terms of identifying main hydrochemical features and processes (natural and anthropogenic) that govern them. This method is applied in a hydro-system featured by a strong interrelation between groundwater and surface water and highly impacted by agricultural activities. Therefore, a holistic approach considering groundwater together with the surface water bodies, consisting in lake, several rivers and springs, was used. Multivariate statistical analysis, in particular Factor Analysis, has been proved to be effective in elaborating and interpreting water quality data highlighting the information carried within them, but it presents some limitations: it does not consider data uncertainty and it groups variables which are correlated positively and negatively. Moreover, in some cases the resulting factors are not clearly interpretable, describing each one various overlapping features/processes. Here, Positive Matrix Factorization is applied to groundwater and surface water quality data, and the results are compared to those obtained through a Factor Analysis in terms of both factor profiles and their spatial distribution through a GIS approach. Results of isotopes analysis are used to validate PMF output and support interpretation. Positive Matrix Factorization allows to consider data uncertainty and the solution respects two positivity constraints, based on the concept of chemical mass balance, which leads to a more environmentally interpretable solution. Results show that Positive Matrix Factorization identifies five different factors reflecting main features and natural and anthropogenic processes affecting the study area: 1) surface water used for irrigation, 2) groundwater subjected to reducing processes at advanced stages, 3) groundwater subjected to reducing processes at early stages, 4) groundwater residence time and 5) the effects of the agricultural land use on both groundwater and surface water. Positive Matrix Factorization leads to a more detailed understanding of the studied system as compared to Factor Analysis which identifies only three factors with overlapping information. Based on the results of this study, Positive Matrix Factorization could be a useful technique to perform groundwater and surface water quality characterization and to reach a deeper understanding of the phenomena that govern water chemistry.

A low-cost, autonomous mobile platform for limnological investigations, supported by high-resolution mesoscale airborne imagery

Two complementary measurement systems-built upon an autonomous floating craft and a tethered balloon-for lake research and monitoring are presented. The autonomous vehicle was assembled on a catamaran for stability, and is capable of handling a variety of instrumentation for in situ and near-surface measurements. The catamaran hulls, each equipped with a small electric motor, support rigid decks for arranging equipment. An electric generator provides full autonomy for about 8 h. The modular power supply and instrumentation data management systems are housed in two boxes, which enable rapid setup. Due to legal restrictions in Switzerland (where the craft is routinely used), the platform must be observed from an accompanying boat while in operation. Nevertheless, the control system permits fully autonomous operation, with motion controlled by speed settings and waypoints, as well as obstacle detection. On-board instrumentation is connected to a central hub for data storage, with real-time monitoring of measurements from the accompanying boat. Measurements from the floating platform are complemented by mesoscale imaging from an instrument package attached to a He-filled balloon. The aerial package records thermal and RGB imagery, and transmits it in real-time to a ground station. The balloon can be tethered to the autonomous catamaran or to the accompanying boat. Missions can be modified according to imagery and/or catamaran measurements. Illustrative results showing the surface thermal variations of Lake Geneva demonstrate the versatility of the combined floating platform/balloon imagery system setup for limnological investigations.

Plankton dynamics across the freshwater, transitional and marine research sites of the LTER-Italy Network. Patterns, fluctuations, drivers

A first synoptic and trans-domain overview of plankton dynamics was conducted across the aquatic sites belonging to the Italian Long-Term Ecological Research Network (LTER-Italy). Based on published studies, checked and complemented with unpublished information, we investigated phytoplankton and zooplankton annual dynamics and long-term changes across domains: from the large subalpine lakes to mountain lakes and artificial lakes, from lagoons to marine coastal ecosystems. This study permitted identifying common and unique environmental drivers and ecological functional processes controlling seasonal and long-term temporal course. The most relevant patterns of plankton seasonal succession were revealed, showing that the driving factors were nutrient availability, stratification regime, and freshwater inflow. Phytoplankton and mesozooplankton displayed a wide interannual variability at most sites. Unidirectional or linear long-term trends were rarely detected but all sites were impacted across the years by at least one, but in many case several major stressor(s): nutrient inputs, meteo-climatic variability at the local and regional scale, and direct human activities at specific sites. Different climatic and anthropic forcings frequently co-occurred, whereby the responses of plankton communities were the result of this environmental complexity. Overall, the LTER investigations are providing an unparalleled framework of knowledge to evaluate changes in the aquatic pelagic systems and management options.