An Investigation of Summertime Inland Water Body Temperatures in California and Nevada (USA): Recent Trends and Future Projections Nathan C. Healey1, Simon J. Hook1, Sebastiano Piccolroaz2, Marco Toffolon2, Robert Radocinski1 NASA Jet Propulsion Laboratory, California Institute of Technology, 2. University of Trento European Geosciences Union General Assembly 2016 – Abstract EGU2016-11248 – X4.26 Figure 4. Comparison between air temperature and observed and simulated water temperature for the case of UC Davis (Air) and NASA buoy (Water). calibration validation Abstract Results Table 3. Monthly RMSE obtained in calibration and in validation against NASA buoy water temperature considering different sources of observed air temperature. Summertime Temperatures and Recent Trends Inland water body temperature has been identified as an ideal indicator of potential climate change. Understanding inland water body temperature trends is important for forecasting impacts to limnological, biological, and hydrological resources. Many inland water bodies are situated in remote locations with incomplete data records of in-situ monitoring or lack in-situ observations altogether. Thus, the utilization of satellite data is essential for understanding the behavior of global inland water body temperatures. Part of this research provides an analysis of summertime (July-September) temperature trends in the largest California/Nevada (USA) inland water bodies between 1991 and 2015. We examine satellite temperature retrievals from ATSR (ATSR-1, ATSR-2, AATSR), MODIS (Terra and Aqua), and VIIRS sensors. Our findings indicate that inland water body temperatures in the western United States were rapidly warming between 1991 and 2009, but since then trends have been decreasing. This research also includes implementation of a model called air2water to predict future inland water body surface temperature through the sole input of air temperature. Using projections from CMIP5-CCSM4 output, our model indicates that Lake Tahoe is expected to experience an increase of roughly 3 °C by 2100. Air Temperature Data Source RMSE [°C] Cal Val NASA buoy 0.45 0.60 UC Davis 0.53 0.67 SNOTEL 0.56 0.71 CRU TS3.21 n.a. CMIP5-CCSM4 0.58 Figure 2. Summertime (July-August) surface temperature trends (AVHRR NOAA-18, ATSR-1, ATSR-2, AATSR, MODIS aqua, MODIS terra, VIIRS) for six inland water bodies in California and Nevada (USA). a b Average RMSE for calibration and validation: 0.54°C and 0.66°C, respectively. Model Prediction Under Climate Change Scenarios NASA UC Davis SNOTEL CRU CMIP5-CCSM4 Air temperature datasets: Study Sites c d b a RCP 4.5 Air Temperature [oC] Water Temperature [oC] The water temperature/air temperature relationship is approximately linear. e f LST prediction is the same irrespective of the source of air temperature data adopted (i.e., notice that the slope of the water temperature/air temperature linear fit is the same in all cases, Figure 5). This results is certainly not trivial, even with models of higher complexity. By 2100, Lake Tahoe is expected to experience an increase of summertime surface temperature of ~3 ºC. d c RCP 8.5 Air Temperature [oC] Water Temperature [oC] Figure 1. Map of the six inland water bodies in California and Nevada (USA) in this study. Materials and Methods Table 1. Processing criteria for satellite temperature analysis. Satellite Data: Sensors analyzed: AVHRR NOAA 18 ATSR-1, ATSR-2, AATSR MODIS Aqua and Terra VIIRS Time of Day Nighttime Sensor Zenith Angle < 45o Range to Target Coordinates < 1.0 km Standard Deviation (3x3 Pixel Array) < 0.5 K LOWESS Smoothing Factor 0.2 LOWESS Iterations 3 Cloud Mask Sensor Specific Model Performance: Long-term Trends at Lake Tahoe air2water [Piccolroaz et al., 2013] is a simple lumped model that allows for estimating Lake Surface Temperature (LST) using air temperature as the only meteorological forcing. air2water is derived from the volume-integrated heat equation applied to the upper (well-mixed) layer of the lake: 𝜌 𝑐 𝑝 𝑉 𝑠 𝑑 𝑇 𝑤 𝑑𝑡 =𝐴 H 𝑛𝑒𝑡 Figure 3. Main heat exchange affecting the surface layer. 𝑇 𝑠 = 𝑎 𝑜 + 𝑎 1 𝑇 11 + 𝑎 2 𝑇 11 − 𝑇 12 + 𝑎 3 𝑇 11 − 𝑇 12 1−𝑠𝑒𝑐 𝛳 𝒂 𝒐 , 𝒂 𝟏 , 𝒂 𝟐, 𝑎𝑛𝑑 𝒂 𝟑 - split-window coefficients (lake specific) 𝑻 𝟏𝟏 and 𝑻 𝟏𝟐 - 11 and 12 μm brightness temperatures, respectively 𝜭 - sensor view angle [Hulley et al., 2011] Inland Waterbody Surface Temperature v1.0 Algorithm Figure 5. Relationships between reconstructed air temperature and modeled lake surface temperature in the four seasons for two climate change scenarios: (a,b) RCP 4.5, and (c,d) RCP 8.5, and time series plots of future projections for the two different RCP scenarios.. 𝝆 is water density 𝒄 𝒑 is the specific heat capacity 𝑽 𝒔 is the surface volume of water involved in the heat exchange with the atmosphere (time dependent) 𝑻 𝒘 is the LST 𝒕 is time 𝑨 is the surface area of the lake 𝑯 𝒏𝒆𝒕 is the net heat flux into the upper water volume (accounting for the main fluxes entering and exiting 𝑽 𝒔 : short- and long-wave radiation, sensible, and latent heat fluxes). Conclusions In-situ Data: All inland water bodies in this study show summertime warming, but recently trends have flattened or even led to cooling. air2water is a robust tool to predict lake surface temperature when only air temperature is available. Given different air temperature datasets the outputs in projection are always approximately the same. Future work will analyze the application of the air2water model for climate change prediction in other lakes for which in-situ data are available. The expected thermal response of lakes will be investigated in details. Table 2. Details of data utilized in modeling water temperature with air2water at Lake Tahoe Data set Data type Height/Depth and Resolution Time interval Frequency Water temperature NASA buoy in situ water surface/skin - point location 1999 - present 5-minute UC Davis buoy water surface/~1 m depth - point location 1967 - present Bi-weekly Air temperature ~3 m height - point location 2002 - present UC Davis meteorological station Daily SNOTEL meteorological station ~170 m height - point location 1989 - present Climate Research Group (CRU) TS3.21 Gridded Observations Equivalent height ~2 m - 0.5o x 0.5o 1950 - 2005 Monthly CMIP5-CCSM4 Gridded GCM Equivalent height ~2 m - 1.0o x 1.0o 2006 - 2100 References and Acknowledgements air2water calibration is performed by minimizing the Root Mean Square Error (RMSE) between simulations and observations, using water temperature from NASA buoys, and all available datasets of air temperature. Hulley, G. C., S. J. Hook, and P. Schneider (2011), Optimized split-window coefficients for deriving surface temperatures from inland water bodies, Remote Sens. Environ., 115, 3758-3769. Piccolroaz, S., Toffolon, M., Majone, B. (2013), A simple lumped model to convert air temperature into surface water temperature in lakes. Hydrol. Earth Syst. Sci., 17, 3323–3338. The research described on this poster was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. The authors would also like to acknowledge the National Climate Assessment.. © 2016. All rights reserved.