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1. DEVELOP AN ENSEMBLE OF THE TERRESTRIAL WATER BALANCE FROM UNIQUE COMBINATIONS OF REMOTE SENSING (RS) PRODUCTS. A PROCEDURE WILL BE APPLIED TO CONSTRAIN MULTIPLE COMBINATIONS OF SATELLITE PRODUCTS TOWARDS OVERALL WATER BALANCE CLOSURE AT A MONTHLY TIME STEP OVER MAJOR GLOBAL RIVER BASINS. RESIDUAL ERRORS ARE REDISTRIBUTED AMONG WATER BALANCE COMPONENTS. AN EXPERIMENT TO ATTRIBUTE RESIDUAL ERRORS TO OBSERVABLE GEOPHYSICAL AND CLIMATIC FEATURES WILL EXPLORE TRANSFERABILITY OF WATER BALANCE CLOSURE TO UNGAUGED AREAS IN A SPATIALLY DISTRIBUTED MANNER. THIS GLOBAL SATELLITE-BASED ENSEMBLE WILL BE A FIRST-OF-ITS-KIND ESTIMATE OF THE WATER CYCLE USEFUL TO UPDATE PREVIOUS ESTIMATES OF GLOBAL FRESHWATER AVAILABILITY. 2. EVALUATE IMPROVEMENTS IN PERFORMANCE IN NASA LIS DATA ASSIMILATION WHEN ASSIMILATING REESEN. ASSIMILATING THE CONSTRAINED WATER BALANCE COMPONENTS FROM REESEN IS EXPECTED TO OVERCOME ERRORS FROM THE PREVIOUS ASSIMILATION OF RAW UNCONSTRAINED WATER BALANCE COMPONENTS AND PROVIDE KEY IMPROVEMENTS THROUGH INTER-COMPONENT WATER BALANCE CONSISTENCY. ESTIMATES OF STREAMFLOW AND EVAPOTRANSPIRATION WILL BE EVALUATED AGAINST REFERENCE DATA SETS TO QUANTIFY POTENTIAL IMPROVEMENTS AND THE SYSTEM WILL BE APPLIED TO SIMULATE A SET OF RECENT INTERNATIONAL DROUGHTS AS CASE STUDIES. 3. CREATE PROBABILISTIC EXPRESSIONS OF THE FLUCTUATIONS IN THE WATER CYCLE AND QUANTIFY THE SIGNATURE OF HUMAN ACTIVITIES. REESEN PROVIDES A NOVEL AND IMPLICIT OBSERVATION OF THE IMPACT OF HUMAN ACTIVITIES ON WATER CYCLE VARIABILITY. SINCE EXISTING ESTIMATES OF GLOBAL FRESHWATER ARE BASED ON MODELS THAT LACK ACCURATE REPRESENTATION OF HUMAN ACTIVITIES DIFFERENCING REESEN WITH THESE AS WELL AS WITH REFERENCE 'UNMANAGED' OBSERVATIONAL DATASETS WILL OFFER NEW INSIGHTS INTO THE ROLE OF HUMAN ACTIVITIES ON THE WATER CYCLE. THESE COMPARISONS WILL FURTHER PROVIDE QUALITY-CONTROL/QUALITY-ASSURANCE FOR REESEN AS WELL AS TO IDENTIFY THE MOST IMPORTANT AREAS WHERE MODELS MAY BE IMPROVED.

$269,754FY2020National Aeronautics and Space AdministrationNASA

The Regents Of The University Of Colorado

Investigators

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1. DEVELOP AN ENSEMBLE OF THE TERRESTRIAL WATER BALANCE FROM UNIQUE COMBINATIONS OF REMOTE SENSING (RS) PRODUCTS. A PROCEDURE WILL BE APPLIED TO CONSTRAIN MULTIPLE COMBINATIONS OF SATELLITE PRODUCTS TOWARDS OVERALL WATER BALANCE CLOSURE AT A MONTHLY TIME STEP OVER MAJOR GLOBAL RIVER BASINS. RESIDUAL ERRORS ARE REDISTRIBUTED AMONG WATER BALANCE COMPONENTS. AN EXPERIMENT TO ATTRIBUTE RESIDUAL ERRORS TO OBSERVABLE GEOPHYSICAL AND CLIMATIC FEATURES WILL EXPLORE TRANSFERABILITY OF WATER BALANCE CLOSURE TO UNGAUGED AREAS IN A SPATIALLY DISTRIBUTED MANNER. THIS GLOBAL SATELLITE-BASED ENSEMBLE WILL BE A FIRST-OF-ITS-KIND ESTIMATE OF THE WATER CYCLE USEFUL TO UPDATE PREVIOUS ESTIMATES OF GLOBAL FRESHWATER AVAILABILITY. 2. EVALUATE IMPROVEMENTS IN PERFORMANCE IN NASA LIS DATA ASSIMILATION WHEN ASSIMILATING REESEN. ASSIMILATING THE CONSTRAINED WATER BALANCE COMPONENTS FROM REESEN IS EXPECTED TO OVERCOME ERRORS FROM THE PREVIOUS ASSIMILATION OF RAW UNCONSTRAINED WATER BALANCE COMPONENTS AND PROVIDE KEY IMPROVEMENTS THROUGH INTER-COMPONENT WATER BALANCE CONSISTENCY. ESTIMATES OF STREAMFLOW AND EVAPOTRANSPIRATION WILL BE EVALUATED AGAINST REFERENCE DATA SETS TO QUANTIFY POTENTIAL IMPROVEMENTS AND THE SYSTEM WILL BE APPLIED TO SIMULATE A SET OF RECENT INTERNATIONAL DROUGHTS AS CASE STUDIES. 3. CREATE PROBABILISTIC EXPRESSIONS OF THE FLUCTUATIONS IN THE WATER CYCLE AND QUANTIFY THE SIGNATURE OF HUMAN ACTIVITIES. REESEN PROVIDES A NOVEL AND IMPLICIT OBSERVATION OF THE IMPACT OF HUMAN ACTIVITIES ON WATER CYCLE VARIABILITY. SINCE EXISTING ESTIMATES OF GLOBAL FRESHWATER ARE BASED ON MODELS THAT LACK ACCURATE REPRESENTATION OF HUMAN ACTIVITIES DIFFERENCING REESEN WITH THESE AS WELL AS WITH REFERENCE 'UNMANAGED' OBSERVATIONAL DATASETS WILL OFFER NEW INSIGHTS INTO THE ROLE OF HUMAN ACTIVITIES ON THE WATER CYCLE. THESE COMPARISONS WILL FURTHER PROVIDE QUALITY-CONTROL/QUALITY-ASSURANCE FOR REESEN AS WELL AS TO IDENTIFY THE MOST IMPORTANT AREAS WHERE MODELS MAY BE IMPROVED. · GrantIndex