S p = z-(z b +h)

1. Computational Data Structure Conceptual diagram of tRIBS [Vivoni, E. et al. 2002] Vegetation uptake RUNOFF RIVER NETWORK SOIL AQUIFER Dara Entekhabi (P.I.) & Aldrich Castillo (Grad. Student) – MIT, Civil & Environmental Engg. Lloyd Chua Hock Chye– Nanyang Technological University Fabio Castelli – University of Florence Integrated Modeling of Surface and Sub-surface Hydrology 1. INTRODUCTION 3. MODELS CONSIDERED 4. GOALS 5. PROPOSED METHODOLOGY Improve modeling of the unsaturated zone and its dynamic interface with the saturated zone (perhaps adding a 3-D groundwater module) Major advances in… Upon further evaluation, one of the following fully-distributed, physically-based hydrologic models will be selected for this research: Preliminary Systems Analysis and Requirements Analysis GIS • Alluvial Areas • Mountainous Areas TIN-based Real-time Integrated Basin Simulator (tRIBS) Identification of Study Area Model Development or Improvement WgMax = Sp(ne-Cc) WgMax = Sp(ne-Cc) computing Sp= z-(zb+h) z Conceptual Ks Ks Wc Wg Wc Wg Triangle Edge Voronoi cell Data Collection Analytical (n , Kf) Literature & Databases Numerical Wg Wg Remote Sensing variable h Model Implementation zb Field Measurements Node Calibration Data Pre-processing and Quality Control Simulation weather forecasting NASA SMAP Project Improve modeling of the groundwater-surface water interaction remote sensing Forecasting MM5 Precipitation Forecast surface water Analysis of Results • Developed at Parsons Lab, MIT • Triangulated Irregular Network (TIN) mesh provides variable spatial resolution with only 5-10% of the computational nodes of a DEM without loss of terrain attribute information • Static inputs: topography, soils, land use/cover • Dynamic inputs: rainfall, thermodynamic forcings • Continuous simulation during storm and inter-storm periods • Complete water/energy cycle (surface-subsurface coupling) • Multiple capabilities (forecasting, estimation, synthesis) … mean improvements in hydrologic modeling capabilities. H z BUT these advances are not yet (fully) utilized by current hydrologic models and modeling systems. Wc Wg KsH/H0 6. EXPECTED RESULTS AND APPLICATIONS groundwater Wg variable h (n , Kf) • Unlike traditional lumped hydrologic models, physically-based distributed hydrologic models can: • Utilize high resolution data sets • Account spatial variability of basin properties and meteorological forcings • Enable hydrologic predictions at ungauged interior points zb Model Outputs • Stream flow • Soil moisture • Fluxes • Infiltration • Runon/Runoff • Groundwater interflow • ET (mass and energy) • Concentration of contaminants in groundwater, stormwater, and surface water Coupled with a 2-D finite-difference solver of Dupuit free-surface groundwater flow MOdello Bilancio Idrologico DIstributo e Continuo (MOBIDIC) • Goals of the U.S. National Weather Service Distributed Hydrologic Model Intercomparison Project (DMIP): • Identify and help develop models that best utilize NEXRAD and other spatial data sets to improve RFC–scale river simulations • Help guide distributed hydrologic modeling research, science, and applications Continue the development of a complete contaminant fate and transport model integrated with the distributed hydrologic model (initial efforts will focus on a conservative chemical tracer) Expected Applications Data assimilation, such as the Ensemble Kalman Filter, can be used to combine noisy observations with uncertain model estimates. • Stream flow nowcasting (real-time) and forecasting • Risk assessment and management • Warning or emergency response system (e.g. floods, landslides) • Development planning (e.g. change in land use, water resources management) • Human health studies • Extreme event analysis • Agricultural productivity improvement • Groundwater remediation • Watershed analysis or sustainability studies 2. PROBLEM DESCRIPTION • Components: • Contaminants Map: Production/Buildup, Decay/Treatment • Mass Balance and Kinetics • Transport Model: Advection-Diffusion The Hydrologic Cycle • A complex system of multiple interdependent processes • With spatial and temporal variability • Involves the movement of mass and energy Conceptual diagram of MOBIDIC [Castelli, F., 2008] Add an ensemble data assimilation capability • Developed at the University of Florence by F. Castelli and team • Soil-Vegetation-Atmosphere Transfer (SVAT) Hydrology: Coupled water and energy balance with heat diffusion into the soil • Hydraulics: • Muskingum flood routing • Multi-layer 2-D aquifers • Explicit groundwater exchanges • Running operational applications • Regional flood forecasting • Flood risk mapping • Sustainability study of water resources and practices • Ongoing development • Refinement of groundwater module • Assimilation of satellite land temperature data • Transport of pollutants Real-time assimilation of remote sensing and surface data (river stages, gw levels, etc.) 7. REFERENCES [Vivoni, E. et al. 2002] Input Control Land Atmosphere Soil Moisture Temperature Radiation Temperature Humidity Windspeed Castelli, F. (2008), Integrated water resources management and flood control with hydrologic modeling: from distributed parameters to distributed results, Slide Presentation, NUS, Singapore. Flores, A. (2008), Hillslope-scale soil moisture estimation with a physically-based ecohydrology model and L-band microwave remote sensing observations from space, Dissertation, MIT, Cambridge, MA. Ivanov, V. et al. (2004), ‘Catchment hydrologic response with a fully distributed triangulated irregular network model’, Water Resour. Res., 40, W11102. Vivoni, E. (2002), TIN and similarity in landscape processes, Conference Proceeding, AGU Fall Meeting. Soil Moisture Dynamics River Network • An inverse problem • Requires a 3-D model esp. for the unsaturated zone • Wetting front dynamics is spatially distributed Discharge Precipitation Distributed Hydrologic Model Output Apply and test the model in a case study (study area is still to be determined) [Vivoni, E. et al. 2002]