Satellite observations of lake surface state to improve weather forecasting in a lake-rich region

The thermal and dynamic properties of water bodies are important factors affecting the structure of the atmospheric boundary layer which stores and transports energy and mass. The storage and heat transfer of lakes play an essential role in energy and water exchanges with the atmosphere. At high latitudes, the effects of lake ice on climate mostly occur at the local/regional scale, with the degree of influence dependent on the magnitude, timing, location, duration of ice cover and the size of the water body. Ground-based lake temperature and ice observations have been used to investigate the role of lakes in the weather and climate, and the response of lakes to climate. However, in the last two to three decades, it has been observed that the number of ground-based observations, lake ice in particular, has been decreasing dramatically in several countries across the northern hemisphere. In this context, remotely sensed earth observations represent a practical tool in support of the scientific and operational modeling communities, permitting to monitor Lake Surface Water Temperature (LSWT) and ice cover. Data assimilation methods have been used widely to solve the initial value problem in numerical weather prediction (NWP) models. There is a variety of users and applications of space-borne observations in NWP systems; however, not much attention has been paid on the assimilation of remotely-sensed LSWT data in pre-operational NWP environments for improvement of the weather forecast using the optimal interpolation method. This thesis aimed to demonstrate how retrieved remotely-sensed LSWT observations can improve the representation of lake-atmosphere interactions in NWP models. More specifically, LSWT observations were used to improve the representation of lake surface state in the High Resolution Limited Area Model (HIRLAM), a three-dimensional numerical weather prediction (NWP) model. To attain this goal, satellite-derived LSWT observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Along-Track Scanning Radiometer (AATSR) sensors onboard the Terra/Aqua and ENVISAT satellites, respectively, were first evaluated against in-situ measurements collected by the Finnish Environment Institute (SYKE) for a selection of large to medium-size lakes during the open-water season. Results show a good agreement between MODIS and in-situ measurements from 22 Finnish lakes, with a mean bias of -1.13 ˚C determined over five open water seasons (2007-2011). Evaluation of MODIS during an overlapping period (2007-2009) with the AATSR-L2 product currently distributed by the European Space Agency (ESA) shows a mean bias error of -0.93 ˚C for MODIS and a warm mean bias of 1.08 ˚C for AATSR-L2. Two additional LSWT retrieval algorithms were applied to produce more accurate AATSR products. The algorithms use ESA’s AATSR-L1B brightness temperature product to generate new L2 products: one based on Key et al. (1997) and the other on Prata (2002) with a finer resolution water mask than used in the creation of the AATSR-L2 product distributed by ESA. The accuracies of LSWT retrievals are improved with the Key and Prata algorithms with biases of 0.78 ˚C and -0.11 ˚C, respectively, compared to the original AATSR-L2 product (3.18 ˚C). The impact of remotely-sensed LSWT observations in the analysis of lake surface state of HIRLAM forecasting system was then investigated. Data assimilation experiments were performed with the HIRLAM model. Selected thermal remote-sensing LSWT observations provided by MODIS and AATSR sensors were included into the assimilation. The domain of the experiments, which focused on two winters (2010-2011 and 2011-2012), covered northern Europe. Validation of the resulting objective analyses against independent observations demonstrated that the description of the lake surface state can be improved by the introduction of space-borne LSWT observations, compared to the result of pure prognostic parameterizations or assimilation of the available limited number of in-situ lake temperature observations. Further development of the data assimilation methods and solving of several practical issues were found to be necessary in order to fully benefit from the space-borne observations of lake surface state for the improvement of the operational weather forecast. Lastly, the lake-specific autocorrelation function based on LSWT remotely sensed observations was approximated in HIRLAM. A new autocorrelation function of lake pairs was approximated and compared against the original function utilized in current version of HIRLAM to investigate potential improvements demonstrated through HIRLAM sensitivity experiments. The autocorrelation function is calculated based on distance and lake depth differences for each lake pairs. Results show that large lakes are more sensitive to the impact of the autocorrelation. These results also suggest that the high concentrated observations can improve the enhanced result; however, ground-based observations of LSWT are barely available for NWP applications. Overall, results from this thesis clearly demonstrate the benefits of assimilating space-borne LSWT observations into a weather forecasting system such as HIRLAM, and that comprehensive assimilation of LSWT observations can improve NWP results

Global trends in timing and rates of chlorophyll-a increase in cold-temperate and temperate lakes

Associated publication: https://doi.org/10.5194/essd-14-5139-2022Lakes are key ecosystems within the global biogeosphere. However, the environmental controls on the biological productivity of lakes – including surface temperature, ice phenology, nutrient loads, and mixing regime – are increasingly altered by climate warming and land-use changes. To better characterize global trends in lake productivity, we assembled a dataset on chlorophyll-a concentrations as well as associated water quality parameters and surface solar radiation for temperate and cold-temperate lakes experiencing seasonal ice cover. We developed a method to identify periods of rapid net increase of in situ chlorophyll-a concentrations from time series data and applied it to data collected between 1964 and 2019 across 343 lakes located north of 40◦ . The data show that the spring chlorophyll-a increase periods have been occurring earlier in the year, potentially extending the growing season and increasing the annual productivity of northern lakes. The dataset on chlorophyll-a increase rates and timing can be used to analyze trends and patterns in lake productivity across the northern hemisphere or at smaller, regional scales. We illustrate some trends extracted from the dataset and encourage other researchers to use the open dataset for their own research questions. The PCI dataset and additional data files can be openly accessed at the Federated Research Data Repository at https://doi.org/10.20383/102.0488 (Adams et al., 2021).Global Water Futures (GWF), Lake Futures project || Canada First Research Excellence Fund (CFREF

Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model, link to supplementary data

A global constant value of the extinction coefficient (Kd) is usually specified in lake models to parameterize water clarity. This study aimed to improve the performance of the 1-D freshwater lake (FLake) model using satellite-derived Kd for Lake Erie. The CoastColour algorithm was applied to MERIS satellite imagery to estimate Kd. The constant (0.2/m) and satellite-derived Kd values as well as radiation fluxes and meteorological station observations were then used to run FLake for a meteorological station on Lake Erie. Results improved compared to using the constant Kd value (0.2/m). No significant improvement was found in FLake-simulated lake surface water temperature (LSWT) when Kd variations in time were considered using a monthly average. Therefore, results suggest that a time independent, lake-specific, and constant satellite-derived Kd value can reproduce LSWT with sufficient accuracy for the Lake Erie station. A sensitivity analysis was also performed to assess the impact of various Kd values on the simulation outputs. Results show that FLake is sensitive to variations in Kd to estimate the thermal structure of Lake Erie. Dark waters result in warmer spring and colder fall temperatures compared to clear waters. Dark waters always produce colder mean water column temperature (MWCT) and lake bottom water temperature (LBWT), shallower mixed layer depth (MLD), longer ice cover duration, and thicker ice. The sensitivity of FLake to Kd variations was more pronounced in the simulation of MWCT, LBWT, and MLD. The model was particularly sensitive to Kd values below 0.5/m. This is the first study to assess the value of integrating Kd from the satellite-based CoastColour algorithm into the FLake model. Satellite-derived Kd is found to be a useful input parameter for simulations with FLake and possibly other lake models, and it has potential for applicability to other lakes where Kd is not commonly measured

Impact of partly ice-free Lake Ladoga on temperature and cloudiness in an anticyclonic winter situation – a case study using a limited area model

At the end of January 2012, a low-level cloud from partly ice-free Lake Ladoga caused very variable 2-m temperatures in Eastern Finland. The sensitivity of the High Resolution Limited Area Model (HIRLAM) to the lake surface conditions was tested in this winter anticyclonic situation. The lake appeared to be (incorrectly) totally covered by ice when the lake surface was described with its climatology. Both parametrisation of the lake surface state by using a lake model integrated to the NWP system and objective analysis based on satellite observations independently resulted in a correct description of the partly ice-free Lake Ladoga. In these cases, HIRLAM model forecasts were able to predict cloud formation and its movement as well as 2-m temperature variations in a realistic way. Three main conclusions were drawn. First, HIRLAM could predict the effect of Lake Ladoga on local weather, when the lake surface state was known. Second, the current parametrisation methods of air–surface interactions led to a reliable result in conditions where the different physical processes (local surface processes, radiation and turbulence) were not strong, but their combined effect was important. Third, these results encourage work for a better description of the lake surface state in NWP models by fully utilising satellite observations, combined with advanced lake parametrisation and data assimilation methods

Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

Shallow lakes, with depths less than <i>ca</i>. 3.5–4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component of the cryosphere. The duration of the ice season has major implications for the regional and local climate, as well as for the physical and biogeochemical processes of the lakes. With day and night observations in all weather conditions, synthetic aperture radar (SAR) sensors provide year-round acquisitions. Monitoring the evolution of radar backscatter (σ°) is useful for detecting the timing of the beginning and end of the ice season. Analysis of the temporal evolution of C-band σ° from Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR, with a combined frequency of acquisitions from two to five days, was employed to evaluate the potential of SAR to detect the timing of key lake-ice events. SAR observations from 2005 to 2011 were compared to outputs of the Canadian Lake Ice Model (CLIMo). Model simulations fall within similar ranges with those of the SAR observations, with a mean difference between SAR observations and model simulations of only one day for water-clear-of-ice (WCI) from 2006 to 2010. For freeze onset (FO), larger mean differences were observed. SAR analysis shows that the mean FO date for these shallow coastal lakes is 30 September and the mean WCI date is 5 July. Results reveal that greater variability existed in the mean FO date (up to 26 days) than in that of melt onset (MO) (up to 12 days) and in that of WCI (6 days). Additionally, this study also identifies limitations and provides recommendations for future work using C-band SAR for monitoring the lake- ice phenology of shallow Arctic lakes

Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

Shallow lakes, with depths less than ca. 3.5–4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component of the cryosphere. The duration of the ice season has major implications for the regional and local climate, as well as for the physical and biogeochemical processes of the lakes. With day and night observations in all weather conditions, synthetic aperture radar (SAR) sensors provide year-round acquisitions. Monitoring the evolution of radar backscatter (σ°) is useful for detecting the timing of the beginning and end of the ice season. Analysis of the temporal evolution of C-band σ° from Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR, with a combined frequency of acquisitions from two to five days, was employed to evaluate the potential of SAR to detect the timing of key lake-ice events. SAR observations from 2005 to 2011 were compared to outputs of the Canadian Lake Ice Model (CLIMo). Model simulations fall within similar ranges with those of the SAR observations, with a mean difference between SAR observations and model simulations of only one day for water-clear-of-ice (WCI) from 2006 to 2010. For freeze onset (FO), larger mean differences were observed. SAR analysis shows that the mean FO date for these shallow coastal lakes is 30 September and the mean WCI date is 5 July. Results reveal that greater variability existed in the mean FO date (up to 26 days) than in that of melt onset (MO) (up to 12 days) and in that of WCI (6 days). Additionally, this study also identifies limitations and provides recommendations for future work using C-band SAR for monitoring the lake- ice phenology of shallow Arctic lakes

Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

Developing accurate groundwater vulnerability maps is important for the sustainable management of groundwater resources. In this research, resampling methods [e.g., Bootstrap Aggregating (BA) and Disjoint Aggregating (DA)] are combined with machine learning (ML) models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Random Forest (RF), to improve the GALDIT groundwater vulnerability mapping framework that considers Groundwater occurrence (G) (i.e., aquifer type), Aquifer hydraulic conductivity (A), depth to groundwater Level (L), Distance from the seashore (D), Impact of existing seawater intrusion status (I), and aquifer Thickness (T). The proposed approach overcomes the subjectivity of the weights and ratings given to the six variables in the GALDIT framework (via the ML methods) and helps address the small dataset issue (via resampling methods) common to groundwater vulnerability predictive mapping. Considering the Shabestar Plain aquifer, situated in the northeast of Lake Urmia (Iran), the predicted vulnerability indices from GALDIT were adjusted using total dissolved solid (TDS, an indicator of drinking water quality) concentrations, and were modeled by the ML models. Pearson’s correlation coefficient (r) and distance correlation (DC) between the predicted vulnerability indices and TDS were used to validate the models. Using a validation set, the GALDIT framework (r = 0.447 and DC = 0.511) was compared against the best performing standalone (XGBoost-GALDIT, r = 0.613, DC = 0.647) and coupled resampling (BA-XGBoost-GALDIT, r = 0.659, DC = 0.699 and DA-RF-GALDIT, r = 0.616, DC = 0.662) ML models, revealing that the proposed framework significantly increases r and DC metrics. In general, the BA resampling method led to better performing ML models than DA. However, in all cases, it was found that integrating resampling methods and ML models are promising tools to improve the accuracy of GALDIT vulnerability models