Wood, EricInternal validati<strong>on</strong> <strong>of</strong> a distributed hydrologicalmodel through land surface temperature fromremote sensingCorbari, Chiara 1 ; Ravazzani, Giovanni 1 ; Masser<strong>on</strong>i, Daniele 1 ;Mancini, Marco 1 ; Wood, Eric 21. Politecnico di Milano, Milano, Italy2. Princet<strong>on</strong> University, Princet<strong>on</strong>, NJ, USAThis presentati<strong>on</strong> presents a validati<strong>on</strong> <strong>of</strong> a distributedenergy-water balance model through <strong>the</strong> c<strong>on</strong>straints <strong>on</strong> aninternal model variable, <strong>the</strong> pixel-scale equilibriumtemperature, using remote sensing data <strong>of</strong> land surfacetemperature. The pixels span different test scales fromagricultural fields to <strong>the</strong> river basin. The model algorithmsolves <strong>the</strong> system <strong>of</strong> energy and mass balances in term <strong>of</strong> <strong>the</strong>equilibrium pixel temperature or representative equilibriumtemperature (RET) that governs <strong>the</strong> fluxes <strong>of</strong> energy andmass over <strong>the</strong> basin domain. This equilibrium surfacetemperature is compared to land surface temperature (LST)as retrieved from operati<strong>on</strong>al remote sensing data atdifferent spatial and temporal resoluti<strong>on</strong>s. The LST is acritical model state variable and remote sensing LST thatcan be effectively used, in combinati<strong>on</strong> with energy and massbalance modeling, to m<strong>on</strong>itor latent and sensible heatfluxes. The fluxes regulated from this equilibriumtemperature are also compared and c<strong>on</strong>trolled from thosecomputed from local eddy covariance tower data. Adiscussi<strong>on</strong> <strong>on</strong> <strong>the</strong> representativeness <strong>of</strong> satellite, eddycovariance and energy water balance fluxes is made, showingscale c<strong>on</strong>gruence am<strong>on</strong>g <strong>the</strong>se. A number <strong>of</strong> case studieshave been carried out ranging from agricultural districtareas to river basins using data from operati<strong>on</strong>al satellitesensors and specific airborne flight. The case studies includea maize field in Landriano (Italy), <strong>the</strong> agricultural district <strong>of</strong>Barrax (Spain) and <strong>the</strong> Upper Po river basin (Italy). Thepresent approach c<strong>on</strong>tributes to <strong>the</strong> research directi<strong>on</strong>highlighted 30 years ago from Jim Dooge, who encouraged<strong>the</strong> scientific modeling community to analyze <strong>the</strong> behaviour<strong>of</strong> <strong>the</strong> model internal state variable (e.g. soil moisture and itsproxy) in additi<strong>on</strong> to <strong>the</strong> traditi<strong>on</strong>al external fluxes (e.g.discharge) to obtain better understanding <strong>of</strong> hydrologicprocess and model analysis. We think that <strong>the</strong> use <strong>of</strong> LSTand RET c<strong>on</strong>cepts from energy water balance modeling is ac<strong>on</strong>tributi<strong>on</strong> in this directi<strong>on</strong> and is synergistic to effortsd<strong>on</strong>e in remote sensing microwave regarding soil moisture.evaporati<strong>on</strong> at <strong>the</strong> earth surface. Soil moisture also has astr<strong>on</strong>g effect <strong>on</strong> surface energy exchange. Thus soil moisturetrends may have a great impact <strong>on</strong> climate change over land.Likewise, soil moisture is clearly important for <strong>the</strong>hydrologic applicati<strong>on</strong>s such as flood and droughtm<strong>on</strong>itoring, wea<strong>the</strong>r forecast, water management andagricultural plant growth. There has been a range <strong>of</strong>spaceborne remote sensing sensors deployed during <strong>the</strong> lasttwo decades to retrieve near surface (~ 0 - 2 cm) soilmoisture. The retrieval techniques to derive satellite basedsoil moisture include ei<strong>the</strong>r physically based algorithms(such as radiative transfer equati<strong>on</strong>s) or various indirectstatistical algorithms. Multiple efforts have been devoted in<strong>the</strong> community to retrieve <strong>the</strong> global near surface soilmoisture c<strong>on</strong>tent from <strong>the</strong> Advanced Microwave ScanningRadiometer (AMSR-E) 10.7 GHz measurements using sometype <strong>of</strong> Radiative Transfer Models (RTM). We find that amajor challenge for using a physically based RTM to retrievesurface soil moisture is to determine <strong>the</strong> suitable values <strong>of</strong>RTM parameters such as <strong>the</strong> surface soil and vegetati<strong>on</strong>properties, because <strong>the</strong> default values <strong>of</strong> <strong>the</strong>se parametersderived from standard approaches, e.g., soil texture or landcover lookup table, <strong>of</strong>ten do not reflect <strong>the</strong> reality and resultin wr<strong>on</strong>g or <strong>of</strong>f-physical-limit soil moisture retrievals. In thisstudy, we calibrate three major static surface parameters, <strong>the</strong>fracti<strong>on</strong> <strong>of</strong> vegetati<strong>on</strong> coverage, roughness height and sandfracti<strong>on</strong>, in <strong>the</strong> Land Surface Microwave Emissi<strong>on</strong> Model(LSMEM), such that <strong>the</strong> surface emissivity predicti<strong>on</strong>s from<strong>the</strong> model climatologically match with <strong>the</strong> AMSR-Emeasurements. The calibrati<strong>on</strong> produces robust parameters,i.e., <strong>the</strong> parameter values remain stable with l<strong>on</strong>ger trainingperiods. Additi<strong>on</strong>ally, to improve <strong>the</strong> vegetati<strong>on</strong> thicknessparameter, which is dynamic in space and time and difficultto calibrate, we implement a new scheme developed atUniversity <strong>of</strong> M<strong>on</strong>tana to calculate <strong>the</strong> vegetati<strong>on</strong> opticaldepth. The new calibrati<strong>on</strong> technique al<strong>on</strong>g with <strong>the</strong>updated parameterizati<strong>on</strong>s in LSMEM produces a reliablesoil moisture estimate from AMSR-E for <strong>the</strong> period <strong>of</strong> 2002to 2011 with higher accuracy. The above calibrati<strong>on</strong>technique will be applied to o<strong>the</strong>r potential past (e.g.Tropical Rainfall Measuring Missi<strong>on</strong> Microwave Imager(TMI); Scanning Multi-channel Microwave Radiometer(SMMR)) and current (e.g. Soil Moisture and Ocean Salinity(SMOS)) sensors to create a c<strong>on</strong>sistent global soil moistureclimatic data record.Wood, EricCreating Global Soil Moisture Data Record FromAMSR-E Through Calibrati<strong>on</strong> <strong>of</strong> a RadiativeTransfer ModelPan, Ming 1 ; Sahoo, Alok 1 ; Wood, Eric 11. Dept <strong>of</strong> CEE, Princet<strong>on</strong> University, Princet<strong>on</strong>, NJ, USASoil moisture is a critical element for both global waterand energy budgets. Soil moisture c<strong>on</strong>trols <strong>the</strong>redistributi<strong>on</strong> <strong>of</strong> rainfall into infiltrati<strong>on</strong>, surface run<strong>of</strong>f and152
Wood, Eric F.Challenges in Developing L<strong>on</strong>g-term Climate DataRecords for <strong>the</strong> Terrestrial Water and Energy CyclesINVITEDWood, Eric F. 1 ; Pan, Ming 1 ; Sahoo, Alok K. 1 ; Troy, Tara J. 3 ;Vinukollu, Raghuveer K. 2 ; Sheffield, Justin 11. Dept Civil & Envir<strong>on</strong>mental Engineering, Princet<strong>on</strong>University, Princet<strong>on</strong>, NJ, USA2. Swiss Re, Arm<strong>on</strong>k, NY, USA3. Columbia Univsersity, New York, NY, USAComprehensive documentati<strong>on</strong> <strong>of</strong> <strong>the</strong> terrestrial watercycle at <strong>the</strong> global scale and its evoluti<strong>on</strong> over time isfundamental to understanding Earth’s climate system andassessing <strong>the</strong> impacts due to climate change. Suchdocumentati<strong>on</strong> is also needed to characterize <strong>the</strong> memories,pathways and feedbacks between key water, energy andbiogeochemical cycles. With such enhanced understanding,<strong>the</strong>re is <strong>the</strong> potential for research programs to resolveoverarching scientific goals to document <strong>the</strong> energy andwater cycles. GEWEX’s l<strong>on</strong>g-term scientific goal is to obtaina quantitative descripti<strong>on</strong> <strong>of</strong> wea<strong>the</strong>r-scale variati<strong>on</strong>s in <strong>the</strong>global energy and water cycles over a period <strong>of</strong> at least 20years, which will provide <strong>the</strong> needed scientific basis forunderstanding climate variability and change. Such l<strong>on</strong>gtermdata sets have been referred to as Earth System DataRecords (ESDRs) by NASA’s MEaSUREs program, ClimateData Records (CDRs) by NOAA’s Nati<strong>on</strong>al Climatic DataCenter and <strong>the</strong> European Organizati<strong>on</strong> for <strong>the</strong> Exploitati<strong>on</strong><strong>of</strong> Meteorological Satellites (EUMETSAT). In developingglobal-scale climate data records, satellite-based observati<strong>on</strong>s<strong>of</strong>fer global c<strong>on</strong>sistency that can fill spatial-temporal gaps inground-based data collecti<strong>on</strong>. Observati<strong>on</strong>s from satellitemissi<strong>on</strong>s over <strong>the</strong> last two decades are already providingimportant observati<strong>on</strong>s that are being used to developCDRs. In this presentati<strong>on</strong> <strong>the</strong> underlying challenges will bediscussed in using remote sensing based variables fordeveloping l<strong>on</strong>g-term CDRs for <strong>the</strong> terrestrial water andenergy budget variables. These challenges include temporalc<strong>on</strong>sistency am<strong>on</strong>g sensors and algorithms <strong>on</strong> differentsatellites, uncertainty in retrieval algorithms, and lack <strong>of</strong>budget closure when using <strong>the</strong> independently estimatedterms. Recent results show that using multiple remotesensing estimates, merged with in-situ and model estimatesand applying budget closure c<strong>on</strong>straints can lead toc<strong>on</strong>sistent l<strong>on</strong>g-term CDRs. These CDRs will be used toassess variability and trends in regi<strong>on</strong>al and global water andenergy budgets.Worden, JohnC<strong>on</strong>straints <strong>on</strong> High Latitude Moisture Fluxes andC<strong>on</strong>tinental Recycling Using Satellite and AircraftMeasurements <strong>of</strong> Water Vapor IsotopesWorden, John 1 ; Lee, Jung-Eun 1 ; Cherry, Jessie 2 ; Risi, Camille 4 ;Frankenberg, Christian 1 ; Welker, Jeff 2 ; Cable, Jessie 2 ; No<strong>on</strong>e,David 31. JPL / California Institute <strong>of</strong> Tech<strong>on</strong>ology, Pasadena, CA,USA2. University <strong>of</strong> Alaska Fairbanks, Fairbanks, AK, USA3. University <strong>of</strong> Colorado, Boulder, CO, USA4. Le Laboratoire de Météorologie Dynamique, Paris, FranceChanges in <strong>the</strong> water cycle at high latitudes couldsubstantially change <strong>the</strong> global energy balance due to severalpositive and negative feedbacks. For example, <strong>the</strong> decrease in<strong>the</strong> arctic ice cap can have a positive feedback resulting from<strong>the</strong> decreased albedo and increased water vapor in <strong>the</strong>atmosphere. The change in cloud cover can be ei<strong>the</strong>r apositive or negative feedback. If <strong>the</strong> frequency <strong>of</strong> snowfallincreases as a result <strong>of</strong> increased moisture, it would be anegative feedback because fresh snow has higher albedo thanold snow or bare ground. Characterizing <strong>the</strong> distributi<strong>on</strong> <strong>of</strong><strong>the</strong> moisture sources, rainfall, c<strong>on</strong>tinental recycling and <strong>the</strong>processes c<strong>on</strong>trolling cloud distributi<strong>on</strong>s are <strong>the</strong>n critical forunderstanding how changes in <strong>the</strong> polar sea ice and snowdistributi<strong>on</strong>s will affect future climate. Measurements <strong>of</strong>water isotopes can place c<strong>on</strong>straints <strong>on</strong> <strong>the</strong> distributi<strong>on</strong> <strong>of</strong><strong>the</strong>se processes because local and transported water vaporhave different isotope signals . In this poster we use newmeasurements <strong>of</strong> water vapor isotopes from <strong>the</strong> Aura TESand JAXA GOSAT satellites, in situ measurements fromground and aircraft, and <strong>the</strong> LMDz model to examine <strong>the</strong>distributi<strong>on</strong> <strong>of</strong> moisture sources at high latitudes and toestimate <strong>the</strong> amount <strong>of</strong> c<strong>on</strong>tinental recycling.Xaver, AngelikaRecent progress <strong>on</strong> <strong>the</strong> Internati<strong>on</strong>al Soil MoistureNetworkXaver, Angelika 1 ; Gruber, Alexander 1 ; Hegyiova, Alena 1 ;Dorigo, Wouter A. 1 ; Drusch, Matthias 21. Institute <strong>of</strong> Photogrammetry & <strong>Remote</strong> <strong>Sensing</strong>, ViennaUniversity <strong>of</strong> Technology, Vienna, Austria2. ESTEC, European Space Agency, Noordwijk, Ne<strong>the</strong>rlandsFor <strong>the</strong> calibrati<strong>on</strong> and validati<strong>on</strong> <strong>of</strong> satellite- and landsurface model based soil moisture estimates in situ soilmoisture measurements are indispensable. Although acouple <strong>of</strong> meteorological networks measuring soil moistureexist, <strong>on</strong> a global and l<strong>on</strong>g-term scale, ground-basedobservati<strong>on</strong>s are few. In additi<strong>on</strong>, measurements fromdifferent networks are performed in quite different ways,resulting in significant disparities, e.g., with respect to soilmoisture units, measurement depths, and sampling rates.This has been <strong>the</strong> reas<strong>on</strong> for initiating <strong>the</strong> Internati<strong>on</strong>al SoilMoisture Network (ISMN;http://www.ipf.tuwien.ac.at/insitu/), a centralized data153
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Alfieri, Joseph G.The Factors Influ
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accuracy of snow derivation from si
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climate and land surface unaccounte
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producing CGF snow cover products.
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performance of the AWRA-L model for
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Euphorbia heterandena, and Echinops
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California Institute of Technology
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