The most significant factor in human vulnerability to climate is the impact of climate on water resources and ecosystems. The ability to make precise predictions about this detrimental risk is challenging, especially when human populations, land use change, and other factors are also affecting water futures. Theme III focuses on developing a unified water prediction system and ecosystem services using remote sensing of earth systems. This work aligns with the NOAA’s “Water Prediction” research priority. Unified water prediction and modeling methodologies will be created to aid the development of operational products and services. The Theme includes climate-informed water and ecosystems modeling, socio-economic impacts and vulnerability assessments. The work also supports NOAA’s mission to provide improved protection of life and property from natural hazards, and for a better understanding of the total environment.
Project I. Hydrologic extremes – A systematic risk assessment of droughts and floods using in-situ and remote sensing products
The discussion on climate adaptation and mitigation invariably comes to recognize that many of the potential impacts on society are felt through changes in the regional water resources. These changes may manifest as change in the intermittency or frequency and intensity of rainfall events, alteration or extension of the dominant wet/warm/cold season(s), or shift in the inter-annual frequency and persistence of wet and drought years. Such extremes can have adverse impacts on the natural ecosystem, society and the economy of the region. It is important to explore strategies for adaptation to natural hazards and to manage the potentially impacted sectors. In this work, we will address this significant area, exploring the modeling, and prediction of floods and droughts, and how these may affect interlinked human activities at multiple scales of cities and river basins.
Task 1: Demand sensitive drought risk assessment for the continental United States
In this task, we will develop Drought Indices that consider variability in climate and changing demands to accurately represent the duration and severity of drought both within season and across seasons and years. We will validate this newly developed drought index with current SMOS and future SMAP soil moisture data. Satellite based soil moisture anomalies will be used in conjunction with NDVI to provide current conditions of vegetation health. Finally, we will explore the potential for drought predictability at lead times of 0-6 months using multi-model seasonal ensemble forecasts and climate predictors; use them to inform decision making at the sector level for near-term adaptation and risk hedging.
Task 2: Flood risk assessment using in-situ and remote sensing data products
Activities under this task will include development of high-resolution urban flood guidance and hazard warnings (uFFG) system by incorporating hydro-meteorological data from the NY-uHMT (New York Urban Hydrometeorology Test Bed). This will help identify and isolate areas where land development has altered the runoff characteristics of the New York City Area. Results from these activities will not only be useful in planning for storm emergencies but will also allow for improved designs of infrastructure. In addition, river peak flow and volume estimates will be derived for USGS streamflow gauges in the Northeast USA using observed stage height as a threshold. Hydrologic models will be used wherever flow observations are not available. Moreover, integrating observations into models for future/proposed NWS operational forecast system for Puerto Rico will be used to improve flash flood guidance for Puerto Rico. Together this forms a key step towards improving regional resiliency against the wide range of impacts incurred by extreme storm event related flooding.
Project II. Water resources assessment
The ET and Snow data products will address critical challenges in water cycle observations, will improve understanding of the vulnerability of agricultural systems to inter-annual variability in weather, and better represent human-environment interactions, including feedbacks on regional climate.
Task 1: Automated System for Evapotranspiration Mapping
This Task will provide automated mapping of ET at regional and national scales using remote sensing data and global climate grids. The developed system using SEBAL algorithm will be tested against both in-situ ET data from eddy flux correlation towers and other ET algorithms (MOD16, ALEXI, DisALEXI) in irrigated regions of the western United States. Evaporative stress index anomalies will be quantified using thermal band imagery from geostationary satellites.
Task 2: Development and validation of snow data product
We will utilize the previously developed Global Multi-Sensor Automated Snow and Ice Mapping System, modify it and apply to historical satellite data to generate a long-term dataset of daily global maps of snow and ice extent. The spatial resolution of the maps will be 4 km. The primary intent is to develop new and improved snow and ice cover climatology and a corresponding daily dataset covering the period from 1998 to 2016 for use in climate modeling and in particular within the joint NOAA and NASA Global Precipitation Mission (GPM) project.
The second part of the task consists of complementing the derived high-resolution snow extent maps with snow depth and snow water equivalent (SWE) data. Information on the snow depth and SWE will be derived from observations of SSM/I, SSMIS and SMSR2 sensors. Snow depth and SWE retrievals will be made only at locations identified with snow through the snow cover maps. We will compare our SWE and snow depth retrievals with operational MIRS data. As a result of this activity we expect to produce an improved global snow depth and SWE climatology and a corresponding daily high spatial resolution dataset.
Project III. Synoptic and seasonal monitoring of the earth system
Phenology is the study of cyclic and seasonal phenomena of Earth’s biosphere and the associated relationship to climate. Commonly referring to vegetation in a larger context, such cyclic processes also embrace ocean and cryosphere phenomena. This project aims to develop a global scale multi-year phenology dataset, derived from multiple satellite remote sensing datasets characterizing land surface, ocean, and cryosphere cyclic phenomena, providing a comprehensive characterization of phenology measures, and supporting investigation of trans-domain phenomena and teleconnections associated with regional feedbacks and global climate processes. Specific focus is on arctic and dryland landscapes that have increasingly been recognized for their role in the global carbon cycle.
A key component of this project is flux measurements from a network of 12 eddy covariance tower sites from Barrow, Alaska to Southern California, covering Arctic, chaparral and coastal sage scrub, coastal marine and desert systems. Observational capabilities are further augmented by using observations from UAS platforms for atmosphere, land and ocean observations and satellite product validation.
Task 1: Phenology
Seasonal vegetation and land surface processes dynamics significantly impact carbon, energy and water cycles and weather (surface energy balance, transpiration vapor fluxes). To elucidate unique and complimentary information associated with surface processes and vegetation phenology, we will combine radar backscatter and microwave emissivity measurements (e.g. ASCAT, SSM/I, AMSR2, SMAP, WindSAT, GMI) that are sensitive to both vegetation structure and water status, with satellite optical-IR datasets (e.g., MODIS and AVHRR, GOES-R). Emissivity estimates will be retrieved for more than 35 years. The developed emissivity products will be utilized to explore the state of the surface in terms of freeze and thaw cycles. Since diurnal passive microwave information is routinely accessible, we will enhance current IR-based land surface temperature product that are not normally available under cloudy conditions.
In addition, arctic and dryland landscapes have increasingly been recognized for their role in the global carbon cycle. Improving the representation of plant and landscape phenology in remote sensing analyses and ecosystem models, especially for these two extreme and relatively poorly studied environments, is an urgent priority. This study will synthesize plant and landscape phenological studies collected in the northern Chihuahuan Desert and in several tundra landscapes in northern Alaska to i) develop datasets suitable for validation of models and remote sensing products; ii) determine the climatic drivers of phenology in these ecosystems, and iii) assess the utility of NOAA/Landsat satellites to discern phenological trends in these two extreme ecosystems. Phenological data will be registered with the National Phenology Network (NPN) to which NOAA is partnered and analyses will use a range of NOAA climate data, remote sensing platforms and model output for analysis and scaling. Products will be compared to indicators of phenological activity produced by the NPN. Such products are poised to be included in next-generation ecosystem and climate models.
Task 2. Land-Atmosphere Fluxes
NOAA has extensive air monitoring stations around the world. These stations measure a number of gases, including CO2 and CH4. One goal of NOAA is to use seasonal and interannual variation in atmospheric CO2 and CH4 concentrations to estimate surface fluxes (ocean and land) of these gases. CREST will augment these NOAA capabilities with flux measurements from a network of 12 eddy covariance tower sites from Barrow, Alaska to Southern California, covering Arctic, chaparral and coastal sage scrub, coastal marine and desert systems. The tower measurements can be compared to inversion modeling of atmospheric concentrations and winds (e.g. WRF STILT) to give increased confidence in the validity of estimates of surface fluxes calculated from eddy covariance measurement and from inversions of atmospheric measurements. This will complement ecosystem-level evapotranspiration and phenology analyses.
Task3. Development of sensors for UAS platforms for Environmental Intelligence and Satellite Product Validation (crosscutting and collaborative with NCAS).
The use of unmanned systems (aerial and water surface) is gaining traction within NOAA, as it provides opportunities for more localized and higher resolution observations. These systems can then be used to complement satellite observations, and for calibration/validation of satellite sensors in combination with ancillary sensors.