Land Surface Hydrology Research Group
Research
ESTIMATING THE WATER CYCLE FROM SPACE
Our research for this falls under several projects:
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.
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
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 conjuction 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 conjuction 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:
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.
A hydrologic ensemble seasonal forecast system over the Eastern U.S.
...
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.
