Terrestrial Hydrology Research Group

Princeton University

Research

ESTIMATING THE WATER CYCLE FROM SPACE

Our research for this falls under several projects:

  • Regional Terrestrial Evaporation Estimation Based on Satellite Data
  • Use of International Polar Year data to improve attribution of long-term hydrologic changes in Arctic Eurasian land areas
  • Establishing a Global Soil Moisture Earth System Data Record
  • Development of a Global Water Cycle Modeling and Assimilation System
  • Estimating Continental-Scale Water Balances Through Modeling and Assimilation of EOS Terra and Aqua Data
  • Understanding hydrologic sensitivity and land-atmosphere coupling through space-based remote sensing
  • Regional Terrestrial Evaporation Estimation based on Satellite Data

    Climate variables measured from space are important for us to understand the role of the terrestrial hydrosphere-biosphere in Earth's climate system. Such measurements are also needed to further our understanding of the global climate and its variability, both spatially and temporally, and our understanding of the coupling between the terrestrial and atmospheric branches of the hydrologic cycle and how this coupling may influence climate variability and predictability. One of the most important climate variables is surface latent heating (or evapotranspiration) because surface latent heating links the water and energy cycles, links the water and carbon cycle through vegetation transpiration, couples the land and atmosphere, and operates over quick (diurnal) and slow (seasonal) time scales. Currently space-based measurements of surface evapotranspiration (or latent heating) are unavailable, even though the observational basis is available from the MODIS sensors on TERRA and AQUA. Providing Regional Terrestrial Evaporation estimation based on satellite data is the focus of this project.


    The overall objective of this on-going project is to provide a MODIS-based regional to continental-scale evapotranspiration data product to the land surface climate community. The evapotranspiration algorithm will be based mainly on the Surface Energy Balance System (SEBS) model. To achieve this goal, the evapotranspiration models will be evaluated thoroughly at local (site) scale before satellite data is incorporated to obtain the estimates of evapotranspiration at larger spatial scales. To derive a global estimation of evapotranspiration, surface meteorology and radiation will be obtained from the Global Land Data Assimilation System (GLDAS) hosted at NASA/GSFC or from some other satellite sensors. This project is in conjunction with Dr. Bob Su at the International Institute for Geo-Information Science and Earth Observation (ITC) in the Netherlands.


    Development of a Global Water Cycle Modeling and Assimilation System

    The motivating science and applications research question for this research work is:


    “Can global remote sensing products, from current and anticipated space sensors, provide the basis for scientific water cycle studies and water resources applications (e.g. flood and drought monitoring), particularly in those parts of the world where in-situ networks are too sparse to support more traditional methods of hydrologic prediction?”


    In this research work, we are developing a global water cycle modeling and assimilation system testbed that can provide a “seamless suite of water cycle information”. By seamless suite, we mean the merging of hydrologic data and model predictions across sensors, models and scales; from in-situ networks to satellite platforms, from land surface models, to weather prediction models, to decision support models.


    Achieving this vision of a “seamless suite of hydrologic information” is necessary for NASA to fulfill its promise of providing global data that can be used for global water cycle scientific studies, for international initiatives such as the Global Earth Observation System of Systems (GEOSS) and for applications such as global flood and drought monitoring.


    The specific goals of this project are:

    1) to develop the system framework for the seamless merging of data and predictions across sensors and models

    2) construct a prototype test-bed for the system

    3) demonstrate that the test-bed can be used to assess current and proposed (i.e. future ‘decadal survey’ and NPOESS) satellite sensors in providing sufficient skill needed for scientific water cycle studies and water resources applications


    Estimating Continental-Scale Water Balances Through Modeling and Assimilation of EOS Terra and Aqua Data

    Central to understanding the role of the terrestrial hydrosphere-biosphere in the Earth’s climate system is the measurement of land surface hydrologic variables from space. Observations taken from EOS platforms, when augmented by other experimental and operational satellite, allow for the estimation of variables such as precipitation, soil moisture, snow properties, evapotranspiration and surface water. These measurements in turn allow direct or indirect estimation of most terms in regional scale water balances. Remotely sensed estimates of these variables are needed to address NASA Water and Energy Cycle Sponsored Research (NEWS) second-tier research questions related to variability of Earth’s climate system, detection of climate trends, acceleration of the hydrologic cycle, and climate response to disturbance. Unfortunately, neither direct observations nor remotely sensed estimates of the hydrologic fluxes and state variables alone are sufficient to provide closure to the water balance due to sampling and retrieval errors, suggesting that data assimilation that combines these data with the land surface modeling is needed. Relevant to the above scientific problem, this project tries to address the following question:


    "How can in-situ and satellite data be combined with the land surface model predictions, using data assimilation techniques, to produce improved, coherent merged products that are space-time continuous over the land areas of the globe?"


    This research work recognizes that additional research is required to evaluate and demonstrate how, and to what extent, remote sensing data can be utilized in land surface modeling. This leads to three subsequent science questions that are primarily being studied in this project:

    1) What is the best data assimilation procedure for combining satellite data with in-situ and land surface modeling, given the spatial, temporal and physical characteristics of the remote sensing, in-situ and model variables.

    2) How much improvement is provided by the assimilation of satellite data into land surface modeling, over its use alone or with land surface modeling alone, for the estimating of the terrestrial water cycle components? Can we estimate the information content of the remotely sensed observations?

    3) To what extent can remote sensing observations, assimilated into land surface models, be used to improve estimates of the terrestrial water balance at regional to continental scales and to help better represent the variability of water cycle components in space and time?


    The above first question focuses on an evaluation of data assimilation methods that will help NEWS prediction capabilities; the second focuses on the remote sensing data and their characteristics for the assimilation into land surface modeling; and the third evaluates the potential improvements to terrestrial water cycle components and water cycle variability.


    An evaluation of approaches and potential benefits of assimilating satellite remote sensing into land surface models will contribute to the NEWS second ties research question “How can predictions of climate variability and change be improved?” And “How are precipitation, evaporation and the cycling of water changing?” by carrying out a comprehensive and quantitative evaluation of how to assimilate satellite data and subsequent improvements in estimates of terrestrial water cycle variables (soil moisture, snow properties, runoff and evaporation).


    Understanding hydrologic sensitivity and land-atmosphere coupling through space-based remote sensing

    The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report states with greater than ninety percent certainty that recent perturbations to precipitation and temperature regimes are attributable to anthropogenic greenhouse gas emissions. Understanding how projected climate change manifests itself in the terrestrial water cycle is an unresolved research question. Of particular importance is understanding the sensitivity of the terrestrial hydrologic cycle to climate change, including the strength of land-atmospheric coupling that is of fundamental importance from weather scales (days) to climate scales (seasonally). Further, understanding hydrologic sensitivity to climate change is directly relevant to water resource availability and drought assessment such that understanding hydrologic sensitivity to climate change should be viewed as a grand challenge for hydrology.


    The ability to evaluate hydrologic sensitivity and land-atmospheric coupling through remote sensing has vastly improved through advancements in the technology, maturation and data availability from numerous satellite missions (i.e. AVHRR, GOES, NASA Aqua, NASA Terra, NASA TRMM). NASA has made available an unprecedented record of remote sensing products with efficient temporal (greater than once-daily) and spatial coverage (near-global coverage at resolutions from 2.5deg and finer) covering periods of several years. This project is motivated by the following overarching science question:


    "Can remote sensing observations be used to evaluate hydrologic sensitivity to climate change, including the strength of land-atmospheric coupling?"


    The project is developed around the following activities:

    a) Developing a framework through which to understand land-atmosphere coupling and sensitivity through remote sensing

    b) Developing (and acquiring) the remote sensing data sets to be used in that analysis

    c) Carrying out diagnostic analyses on the remote sensing derived hydrologic sensitivity measures and land-atmospheric coupling strengths derived over the 25-year satellite record for selected regions


    The results will help evaluate the impact of recent and potential climate change on regions showing stronger or weaker hydrologic sensitivity and coupling. Project tasks include the creation of two new warm-season remote sensing datasets: soil moisture and evapotranspiration, for the periods of 1983-87 and 2002-06. This effort directly supports NASA’s objective to expand and accelerate the realization of economic and societal benefits from Earth Science, information and technology.


    GLOBAL WATER CYCLE STUDY

    Our research for this falls under several projects:

  • Developing consistent Earth System Data Records for the global terrestrial water cycle
  • Development and diagnostic analysis of a multi-decadal global evaporation product for NEWS
  • Establishing a Global Soil Moisture Earth System Data Record
  • Developing consistent Earth System Data Records for the global terrestrial water cycle

    We are developing consistent, long-term Earth System Data Records (ESDRs) for the major components (storages and fluxes) of the terrestrial water cycle at a spatial resolution of 0.5 degrees (lat-long) and for the period 1950 to near-present. The resulting ESDRs are intended to provide a consistent basis for estimating the mean state and variability of the land surface water cycle at the spatial scale of the major global river basins. The ESDRs are being developed for a) surface meteorology (precipitation, air temperature, humidity and wind), b) surface downward radiation (solar and longwave) and c) derived and/or assimilated fluxes and storages such as surface soil moisture storage, total basin water storage, snow water equivalent, storage in large lakes, reservoirs, and wetlands, evapotranspiration, and surface runoff. We are constructing data records for all variables back to 1950, recognizing that the post-satellite data will be of higher quality than pre-satellite (a reasonable compromise given the need for long-term records to define interannual and interdecadal variability of key water cycle variables). A distinguishing feature of this project is the inclusion of two variables that reflect the massive effects of anthropogenic manipulation of the terresrtial water cycle, specifically reservoir storage, and irrigation water use.


    The ESDRs merge satellite derived products with predictions of the same variables by LSMs driven by merged satellite and in situ forcing data sets (most notably precipitation), with the constraint that the merged products will close the surface water budget. The primary land surface forcing variable, precipitation, will be formed by merging model (reanalysis) and in situ data with satellite-based precipitation products such as TRMM, GPCP, and CMORPH. Derived products include surface soil moisture (from TRMM, AMSR-E, SMMR, SSM/I passive microwave and ERS microwave scatterometers), snow extent (from MODIS and AVHRR), evapotranspiration (model- derived using ISCCP radiation forcings from geostationary and LEO satellites), and runoff (from LSM predictions and in-situ measurements).


    This project is in conjunction with Dr. Dennis Lettenmaier (University of Washington), Dr. Paul Houser (George Mason U.), Dr. Christian Kummerow (Colorado State U.) and Dr. Rachel Pinker (U. Maryland, College Park).


    Development and diagnostic analysis of a multi-decadal global evaporation product for NEWS

    This research work is focusing on developing a multi-decadal global evaporation product. Documenting the global water and energy cycle through observations is fundamental to achieve the goals of GEWEX and NASA’s Earth Science Research Strategy to obtain a quantitative description of the variations in the global energy and water cycle. Such documentation is needed to enable NASA and its supported NEWS investigators to acquire enhanced knowledge of Earth’s climate, including characterizing the memories, pathways and feedbacks between key water, energy and biogeochemical cycles. Amongst the various climate cycle variables, surface evapotranspiration (ET) or latent heat flux is often considered the climate linchpin variable because it plays a central role in the water, energy and carbon cycles, and is common to all three. It is unique in providing the link between the energy and water budgets at the land surface; the link between the terrestrial water and carbon cycle through vegetation transpiration, plays a central role in coupling the land and ocean surfaces to the atmosphere, and operates over fast (diurnal) and slow (seasonal) time scales. Much of our understanding of the complex feedback mechanisms between the Earth surface and the surrounding atmosphere is focused on quantifying this process, as well as to determine the biological environment and its water use efficiency. The GEWEX Radiation Panel (GRP), in collaboration with the GEWEX Land Surface Study (GLASS), has launched an activity called LandFlux with the goal of fostering the needed capabilities to produce and diagnose a global, multidecadal surface turbulent flux data product. The GRP has already supported the development of an ocean surface heat flux activity (SeaFlux). A LandFlux product, coordinated with the SeaFlux product would contribute to the efforts of the NASA NEWS team in their collective effort to further our understanding of the global energy and water cycles.


    Establishing a Global Soil Moisture Earth System Data Record

    In this project, we are developing and refining the algorithms required for the production and provision of a consistent long-term (1979 to current), global, 1/2 degree, daily, surface Soil Moisture earth system Data Record (SMDR), including uncertainties. This product is composed of two product elements: The primary product is based entirely on retrievals from microwave sensors resulting in a validated surface (~0.05m deep) soil moisture focused on croplands and grasslands. These spatial fields of surface soil moisture is retrieved from microwave brightness temperatures measured from the Scanning Multichannel Microwave Radiometer (SMMR) at 6.63 and 10.69 GHz, the TRMM Microwave Imager (TMI) at 10.65 GHz and from the NASA/Aqua Advanced Microwave Scanning Radiometer (AMSR-E) at 6.7 and 10.65 GHz. The secondary product is based on the assimilation of microwave radiance from these microwave sensors into a multi-model ensemble, which will result in a root zone (~1m deep) soil moisture data record. These model-integrated products provide a pathway for incorporation into coupled earth system prediction models, to improve climate and weather predictions or can be analyzed to provide a better understanding and representation of land-atmosphere processes. A third major activity of this research work is an extensive calibration and validation activity to refine the currently being developed SMDRs, and to specify their uncertainty. It is well established that remotely-sensed and modeled soil moisture exhibit significant differences. We will therefore attempt to use all available in-situ observations to develop operators that can convert between retrieved, in-situ, and modeled soil moisture. This SMDR will provide an important baseline and algorithm that can be extended with the future NPOESS Conical Scanning Microwave Imager/Sounder (CMIS) and ESA Soil Moisture and Ocean Salinity (SMOS) sensors. Following these steps, we will fully embrace NASA's "missions to measurements" concept by including both EOS and NPOESS sensors in a single integrated soil moisture data record useful for addressing a range of science questions.


    This project is in conjunction with Dr. J. Shukla (Institute of Global Environment and Society/Center for Research on Environment and Water).


    UNDERSTANDING THE TERRESTRIAL WATER AND ENERGY BUDGETS AND THEIR CHANGES IN HIGH LATITUDES

    Our research for this falls under three projects:

  • Understanding the mean, variability, and trends in the water and energy budgets across Northern Eurasia
  • Understanding Change in the Climate and Hydrology of the Arctic Land Region: Synthesizing the Results of the ARCSS Fresh Water Initiative Projects
  • Use of International Polar Year data to improve attribution of long-term hydrologic changes in Arctic Eurasian land areas
  • Understanding the mean, variability, and trends in the water and energy budgets across Northern Eurasia

    Northern Eurasia represents a large continental land area undergoing potentially dramatic, and irreversible, ecosystem change. Much of this region is either boreal forest or tundra, both of which are fragile ecosystems. Climate simulations predict that temperature changes associated with global warming will be most severe in high latitudes, due in large part to radiation feedbacks between the earth and atmosphere, making these areas potential indicators of global climate change. There is mounting evidence of recent warming in the Arctic, including thinning and a decrease in the maximum extent of sea ice, permafrost warming, and the northward migration of the tree-line. In addition to environmental stresses, a vastly different paradigm for resource extraction has occurred since the demise of the Soviet Union. In combination, these stresses result in an overall poor state of knowledge about the terrestrial water and energy budget across northern Eurasia, particularly their variability, trends, and teleconnections to global climate.


    The overarching science question we will be addressing during this project is "How have changes in climate, landcover and water management in northern Eurasia over the last half-century affected the land surface hydrology and flood frequency, and what are the impacts at region to continental scales?" To address this question, we will use land surface modeling, with satellite-based landcover data and in-situ data, to assess the impact on the water and energy fluxes across Northern Eurasia. Changes in the surface fluxes could be caused by various processes, such as climate variability and change, landscape variability and change, and anthropogenic effects. Diagnostic studies will be conducted to better understand the coupling of land surface hydrologic processes to atmosphereic processes over a range of spatial and temporal scales. Additional experiments will assess the impacts on the water and energy cycle from future changes in climate, land cover, and water maangement. This project is in conjunction with Dr. Laura Bowling at Purdue University and collaborators from three Russian institutions.


    Understanding Change in the Climate and Hydrology of the Arctic Land Region: Synthesizing the Results of the ARCSS Fresh Water Initiative Projects

    In 2002, the NSF funded a group of Freshwater Initiative projects (FWI), most of which have recently completed. This project intends to utilize results from the FWI projects and to incorporate the results in a pan-arctic hydrologic synthesis activity. The goal is twofold-- first to detect and attribute observed change in the pan arctic hydrologic cycle and to uncover feedbacks and changes in the Arctic climate system associated with observed land surface changes. Throughout, use will be made of integrated atmospheric and land surface data sets that will support the use of models (both uncoupled land surface models and coupled land-atmosphere-ocean models). The overarching science question to be addressed is: How do changes in arctic land processes affect the climate of the region, what are the implications of these changes for the arctic hydrologic cycle (including coupling and feedbacks with the atmosphere), and what are the impacts of changes in the arctic freshwater system on global climate? This project seeks to attribute which hydrologic processes have contributed to observed change. It requires a careful synthesis design built around a strategy of uncoupled, partially coupled, and fully coupled land-atmosphere models at the regional (pan-arctic) scale. This project also is documented to what extent the observed changes in the Arctic terrestrial hydrologic cycle are due to imported change from other regions and to what extend the observed terrestrial changes are exported to the atmosphere and ocean systems. This project is in conjunction with Dr. John Cassano (University of Colorado Boulder), Dr. Dennis Lettenmaier (University of Washington), and Dr. Charles Vorosmarty (City University of New York).


    Use of International Polar Year data to improve attribution of long-term hydrologic changes in Arctic Eurasian land areas

    Major hydrologic changes have been observed over much of the Eurasian Arctic over the last half century. These include widespread increases in permafrost active layer depth, changes in the extent of lakes and wetlands, and increased river discharge, especially in winter. A number of causes have been suggested for these trends, including thawing of permafrost, construction of dams, land cover change, and melting of land ice. In most cases, coincident changes in precipitation have not been observed.

    These changes motivate the overarching science question for this project: "To what extent are changes in permafrost characteristics, as compared with seasonal patterns of snow cover extent, reservoir storage, and land cover change responsible for observed changes in the discharge of major Arctic rivers over the second half of the Twentieth Century?" We intend to address this question through intensive use of IPY permafrost data to be collected as part of IPY project #125, Thermal state of permafrost, with which we will coordinate closely. We will use land surface modeling, incorporating new in situ and remote sensing data sets to be acquired as part of IPY activities, as well as recent advances in representation of the hydrologic effects of permafrost, improved modeling of snow cover dynamics over large areas, land cover change, and water management. We will further utilize our land surface model in cooperation with Vladimir Romanovsky to help identify portions of the pan-arctic domain where additional permafrost borehole measurements will be most useful. Through representation of these potential change agents in long term (50-75 year) simulations of streamflow for the major Eurasian Arctic river basins and their largest tributaries, and comparison with observed streamflow records, we expect to understand the relative effects of these various changes, as well as the direct effects (e.g., through earlier onset of spring snowmelt) of climate change over the last 50-75 years. This project is in conjunction with Dr. Dennis Lettenmaier (University of Washington) and Dr. Pavel Groisman (National Climate Data Center).


    HYDROLOGIC PREDICTABILITY

    Our current research for this falls under one project:

  • Assessment of the Predictive Skill of GPM-Era Precipitation Estimates for Hydrologic Applications
  • Development of an Experimental National Hydrologic Prediction System
  • Ensemble Hydrologic Forecasts over the Southeast USA in support of the NIDIS project
  • The influence of land surface state initialization of seasonal forecasts skill in the NCEP NOAH-LSM Climate Forecast System
  • Assessment of the Predictive Skill of GPM-Era Precipitation Estimates for Hydrologic Applications

    It is expected that spaceborne precipitation estimates during the era of the Global Precipitation Measurement (GPM) mission (available at a resolution of approximately three hours in time and ten kilometers in space) will be useful for a wide range of hydrological applications. This expectation is particularly high for regions where traditional hydrometeorological networks are sparse, such as in Africa and much of Asia. The extent to which GPM-era precipitation estimates will be skillful and useful, however, is unresolved, and a better determination thereof is the primary focus of this research project. Secondary objectives include a refinement of the error characterization of spaceborne precipitation estimates and exploration of ways to improve the predictive skills of spaceborne precipitation estimates by means of integrating them with other remotely sensed information like surface soil moisture and surface temperature.


    A framework for systematic evaluation of the predictive skill of GPM-era precipitation estimates are being developed and applied to hydrologic applications ranging from event-based river flow forecasting for basins of approximately 10,000 km2 and larger to regional-to-continental scale water cycle variables such as soil moisture and water budget studies. The predictive skill of a variety of precipitation products for forecasts of streamflow and soil moisture content are being objectively assessed relative to a reference standard. This project uses the available precipitation records based on surface rain gauge and radar data, and infrared, passive and active microwave information observations from spaceborne sensors.


    This project is going to yield a quantitative assessment of the predictive skill of GPM-era precipitation estimates and how that may vary with geographical region, seasons, and space and time scales of the hydrologic application. The outcome of this research is not only central to NASA's GPM mission, but also of great interest to the broader hydrometeorology and climate research and operational communities.


    Development of an Experimental National Hydrologic Prediction System

    We have previously developed hydrologic and drought nowcast and forecast methods based on the North American Land Data Assimilation System (NLDAS). We have also created testbeds for advanced seasonal hydrologic forecast methods that utilize NOAA climate forecasts in real-time. This current project integrates previously developed forecast methods and products into an experimental national hydrologic prediction system through the following tasks:


    1) Integrate existing UW and Princeton hydrologic forecast and drought recovery systems to produce seamless national hydrologic nowcasts and forecasts at a common spatial resolution

    2) Expand the unified system to include multiple land schemes

    3) Implement improved methods of parameter transfer from catchment-scale model implementation to the gridded national system

    4) Develop an ensemble seasonal reservoir storage capability for large reservoirs

    5) Implement methods for hydrologic forecast error estimation and forecast verification

    6) Develop methods of assimilating streamflow observations to overcome uncertainties in the first month(s) of seasonal hydrological forecasts

    7) Develop GFS-based near-term hydrologic forecast capability to improve month-1 outlooks and provide a seamless hydrologic forecast suite bridging weather and climate time scales


    This project is in conjunction with Dr. Dennis Lettenmaier (University of Washington).


    Ensemble Hydrologic Forecasts over the Southeast USA in support of the NIDIS project

    This project uses the Climate Forecast System Reanalysis and Reforecast System (CFSRR) to improve drought prediction with a focus on the selected basins over the Southeast. Most tests are being done using the current CFS operational system. The next version of the CFS (CFSRR) will be available in 2011. The final tests will be done using the new forecast system. The objectives of this project are:


    1) Downscale and bias correct the CFS forecasts over the Southeast (SE) based on the Bayesian method

    2) Develop hydrologic forecasts of soil moisture and river flow over the SE based on the Princeton system

    3) Implement and test the system at NCEP/EMC

    4) Develop drought indices (standardized precipitation index (SPI), soil moisture percentile, and standardized runoff index (SRI)) over the SE based on the forecasts

    5) Develop a web page to establish early drought warning system for the SE

    6) Design products to provide water supply information to SERFC and the NIDIS user community over the Southeast

    7) Work closely with the Southeast River Forecast Center (SERFC) and OHD develop applications


    The influence of land surface state initialization of seasonal forecasts skill in the NCEP NOAH-LSM Climate Forecast System

    A fundamental challenge for seasonal forecasting is to determine the extent to which climate can be predicted on timescales of weeks to seasons, and to provide such forecasts, and their quantified uncertainties, that are useful to decision making. Current operational seasonal forecasting at NCEP initialize the forecast period using fields of sea surface temperature (SST), on the assumption that the atmosphere responds in predictable ways to SST. Research over the past decade has demonstrated that land state initialization (especially surface soil moisture) can increase skill in summer season precipitation forecast skill. A recent GEWEX/GLACE intercomparison study found that this coupling was weak in the current version of the NCEP Climate Forecast System (CFS), but since CFS will be upgraded with the state-of-the-art Noah land surface model, there is hope that its predictability will improve. For this project we use the already upgraded Global Forecast System (GFS), because it has the same atmospheric parameterization as CFS and the Noah LSM. It is run with specified SSTs, which is the mode used in the land predictability experiments of this project.


    Thus the central science and focus of this project is "To what extent does accurate land surface state initialization contribute to seasonal climate forecast skill in the NCEP Noah-LSM Climate Forecast System (CFS-Noah), and can this skill be realized operationally? The goals of the project are a deeper understanding of the role land plays in seasonal forecast skill and how this role may vary geographically and seasonally around the globe, the development of a strategy to capture this operationally, and an assessment of how improvements in seasonal climate forecasts translates into improvements in the seasonal forecasts of hydrologic variables like streamflow, soil moisture or drought.


    PROCESS UNDERSTANDING

    Our current research for this falls under one project:

  • Identifying Mechanisms of US Drought Initiation, Persistence and Recovery using Observations, Reanalysis and Climate Model Data
  • Optimal Dynamic Predictions of Semi-Arid Land Cover Change and Implications for Ecosystem Goods and Services
  • Identifying Mechanisms of US Drought Initiation, Persistence and Recovery using Observations, Reanalysis and Climate Model Data

    This research project addresses the following research question:


    “What are the mechanisms for initiation, maintenance and recovery of large scale drought in the US and do seasonal forecasts models (specifically NCEP CFS) represent well these mechanisms?” This question consists of three sub questions: a) What are the atmospheric and land surface precursors to drought? b) What are the mechanisms that can be verified that prolong drought and c) What are the processes that lead to drought recovery and do seasonal prediction models replicate these?


    Our approach is based on considerations of the large scale atmospheric-land water budgets and the synthesis of existing datasets, and consists of the following tasks:

    1) Evaluate drought occurrence and their space-time evolution using observation driven simulations of 20th century land hydrology

    2) Evaluate land-atmosphere water budgets and moisture sources for US regions from observational, reanalysis and remote sensing derived datasets

    3) Identify/classify the precursors to drought in terms of the land-atmosphere water budgets and how they diverge from their climatological mean and the likelihood of drought initiation given atmospheric moisture sources and soil moisture anomalies

    4) Investigate mechanisms for drought maintenance by using analytical models of recycling to explore the role of local and remote reinforcement of drought

    5) Identify conditions for drought recovery. Concurrent analysis of drought events and moisture advection will be used to identify what conditions are necessary to recover from drought

    6) Assessment of predictive tools. Using the previously identified relationships we will evaluate how well seasonal hindcasts of the NCEP Climate Forecast System are able to replicate the mechanisms for drought initiation, maintenance and recovery


    Optimal Dynamic Predictions of Semi-Arid Land Cover Change and Implications for Ecosystem Goods and Services

    Savannas play an important role in the cycling of carbon and water and a central role in the socioeconomic health of many regions, such as much of Africa, where grazing and subsistence forestry are prime elements of the economy. These ecosystems are sensitive to changes in climatic forcing and land use (e.g. grazing and fire) and are readily observed by remote sensing platforms. Anticipated changes in the inter-annual variability of rainfall and continued increases in population in these regions put the global and regional services of the African savanna in jeopardy and, furthermore, raise questions about their future role in the climate system.


    In this project, we are developing a system that optimally merges remote sensing data, a hydrological model, and a vegetation dynamics model to provide projections of land cover change over space and time. Projections are forced by a combination of variability and change in climate and land use pressures. This project tries to address the following questions:

    a) How can past land use impacts to savanna structure be objectively unraveled from climate impacts?

    b) What is the predictive skill added by augmenting traditional in-situ climate observing system (precipitation and surface meteorology) forcing with NASA remote sensing products (e.g., TRMM precip and AMSR-E, surface temperature from MODIS, near-surface meteorology from AIRS)?

    c) Given that the space-time structure of errors in the precipitation and the land use intensity inputs to the model are quite unique from one another, then to what extent can satellite NDVI data be used to optimally update model predictions of grass and tree cover, and partition the deduced model errors between climate and land use forcing?

    d) How may future climate variability feed through the African savanna system to affect its structure and goods and services, given a range of climate projections?

    e) How best can a remote sensing driven prediction system provide useful information for drought assessment and land management decision support?


    The project’s approach is to merge remote sensing products with the VIC hydrological model and the savanna vegetation dynamics model to study land use pressures and droughts for a 2.4 million km2 region from Kenya through Botswana. We perform a retrospective analysis with a 50 year gridded forcing data set in order to examine how past droughts and land use pressures have interacted to impact the grass-tree composition of the landscape and how this in turn affected the biomass production for use by the local populations. We use a 20 year NDVI record to test the vegetation dynamics model and a several year data set of passive microwave measurements to retrieve soil moisture for testing the VIC model. Finally, the models are driven with seasonal precipitation forecast fields for making forecasts of land cover change and its impact on goods and services. The model is also intended for use as a decision making tool to design land use restrictions for sustainable use of the savannas in the face of predicted decreases in mean rainfall and increases in its interannual variability.


    This project is in conjunction with Dr. John D. Albertson (Duke U.), Dr. H. H. (Hank) Shugart (U. Virginia) and Dr. Silvia Ferrari (Duke U.).


    DATA ASSIMILATION OF SATELLITE-BASED OBSERVATIONS INTO LAND SURFACE MODELS

    Central to understanding the role of the terrestrial hydrosphere-biosphere in Earth's climate system is the measurement of land surface hydrologic variables from space. Observations taken from EOS platforms, when augmented by other experimental and operational satellites, allow for the estimation of variables such as precipitation, soil moisture, snow properties, evapotranspiration and surface water. These measurements in turn allow direct or indirect estimation of most terms in regional scale water balances. Remotely sensed estimates of these variables are needed to address research questions related to variability of Earth's climate system, detection of climate trends, acceleration of the hydrologic cycle, and climate response to disturbance. Unfortunately, neither direct observations nor remotely sensed estimates of hydrologic fluxes and state variables alone are sufficient to provide closure to the water balance due to sampling and retrieval errors, suggesting that data assimilation that combines these data with land surface modeling is needed. The overall scientific question is how in-situ and satellite data can be combined with land surface model predictions, using data assimilation techniques, to produce improved, coherent merged products that are space-time continuous over the land areas of the globe.


    Additionally, research is required to evaluate and demonstrate how, and to what extent, remote sensing data can be utilized in land surface modeling. This leads to three subsequent science questions: 1) What is the best data assimilation procedure for combining satellite data with in-situ and land surface modeling, given the spatial, temporal and physical characteristics of the remote sensing, in-situ and modeled variables. 2) How much improvement is provided by the assimilation of satellite data into land surface modeling, over its use alone or with land surface modeling alone, for the estimating of the terrestrial water cycle components. Can we estimate the information content of the remotely sensed observations? 3) To what extent can remote sensing observations, assimilated into land surface models, be used to improve estimates of the terrestrial water balance at regional to continental scales and to help better represent the variability of water cycle components in space and time.

    Recently completed projects

    NORTH AMERICAN DROUGHT IN THE 21ST CENTURY

    The central science question to be addressed by this project is: What is the susceptibility of the continental U.S. to drought over the next century, and what role is anthropogenic warming likely to play in U.S. drought susceptibility? Among U.S. natural hazards, drought is the most costly. The 1988 drought alone, in 2002 dollars, cost almost $62B, making it by far the most costly natural disaster. A number of studies over the last decade have suggested that, as a result of greenhouse warming, the interior of the northern hemisphere continents will become more susceptible to droughts over the next century. Given economic sensitivities to drought, understanding the nature of drought risk sensitivity to climate change is a question that we believe is a key national concern.


    A key shortcoming of past studies of future climate and drought is that the land surface representations used in climate models, in general, have not been able to produce realistic land surface hydrologic conditions. Furthermore, past studies that have evaluated the potential for future drought have not considered the possible role of vegetation change. We will use the Community Climate System Model (CCSM), in conjunction with a modified version of the Community Land Model (CLM) that will incorporate a more realistic representation of land surface hydrology, to evaluate the susceptibility of the U.S. to drought over the next century. In so doing, we will address the following subsidiary questions:


    a) What have been the space-time signatures of 20th century drought on precipitation, soil moisture, and streamflow, and how might those change in the 21st century?

    b) What is the role of climate-vegetation feedbacks in exacerbating or ameliorating North American drought severity and intensity?

    c) Can useful estimates of drought recovery probabilities be developed based on off-line and or coupled ensemble climate prediction methods?

    SOIL MOISTURE MEMORY AND SEASONAL PRECIPITATION PREDICTABILITY IN THE GAPP DOMAIN

    One of the research priority areas in the GEWEX Americans Prediction Project (GAPP) science plan is to determine the extent of land surface memory processes and its contribution to precipitation predictability at seasonal to interannual timescales. This project is directly responsive to this GAPP research priority. A central element of the research is to determine the predictability of warm season precipitation over the continental U.S. (CONUS) using a number of modeling approaches and data. These include (i) observationally-forced North American Land Data Assimilation System (NLDAS) land surface model-based output and observed precipitation; (ii) the new NCEP regional reanalysis model output, and, if logistically feasible, (iii) equivalent studies using other models (e.g. the GFDL high resolution regional model, NCEP regional spectral model (RSM) or the NASA NSIP model). The first approach with the NLDAS model and observations will provide a baseline from which the coupled modeling predictability results can be compared.


    The project will use statistical methods such as Empirical Orthogonal Teleconnection (EOT) analysis to search for the teleconnection patterns between soil moisture and precipitation anomalies over the CONUS. Combinations of soil moisture and precipitation fields, with different time lags, will be chosen for the analysis. This analysis will also be done with daily, weekly and monthly averaged fields. The application of the analysis to the NCEP regional reanalysis output is critical in understanding the feedback and predictability processes in the model, and if the model can be used successfully for seasonal predictions. The expected results will reveal critical regions where, and periods when, soil moisture anomalies contribute significantly to precipitation predictability through moisture recycling and other mechanisms. We will also study the atmospheric moisture fluxes and transport in the regional analysis so as to gain insights into the physical, climatic mechanisms influencing the teleconnection patterns.


    The expected results will have important applications in seasonal prediction, and will provide insights on how initial land states provided by a land data assimilation system to the regional NCEP models will contribute to the predictability of precipitation at seasonal to interannual timescales. It will also draw our attention to regions and periods that are critical in drought forecasting and monitoring. The processes study on moisture cycling will be important in developing and improving land surface and boundary layer parameterizations to better represent these processes.

    LAND SURFACE MODELING STUDIES IN SUPPORT OF AQUA AMSR-E VALIDATION

    Understanding the role of the terrestrial hydrosphere-biosphere in Earth's climate system has been identified as a principal focus of the NASA Earth Science Enterprise (ESE) Global Water and Energy Cycle (GWEC) program. The cycling of water drives energy exchanges between the atmosphere, ocean and land, which in-turn drive the Earth's climate, and associated variability. Measurements of spatially distributed soil moisture and snow properties from space using the AMSR-E instrument on NASA's Earth Observing System (EOS) satellite Aqua, represent a valuable resource for investigations into the influence of surface hydrology on climate.


    In this project, we will provide modeling support to the AMSR-E validation activities through a combination of process-based hydrological modeling and the simulation of the AMSR-E measurements, including the AMSR-E antenna pattern, orbital characteristics, and gridded products. A central premise of this project is that hydrological modeling can help bridge the gap between the small-scale in-situ field observations and the AMSR-E 60 km footprint, and between the short-term field experiments and continuous AMSR-E measurements.


    Project objectives include: (1) Assessing the accuracy of AMSR-E level-2 and -3 gridded data products, given the surface heterogeneity within the sampling footprint. (2) Identifying relationships between the small-scale ground validation and the AMSR-E gridded product, and determining how these relationships vary with geographic region and season. And (3) Evaluating whether the modeling and AMSR-E antenna simulations aid in the effective design of validation studies, and in error estimates for the AMSR-E data products.

    Iceberg over Labrador sea.