Challenges & opportunities: water resources information systems

Slides:



Advertisements
Similar presentations
Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of.
Advertisements

MODIS image of central California Funding. The SSCZO is supported by NSFs Earth Sciences DivisionMore information:
The Color Colour of Snow and its Interpretation from Imaging Spectrometry.
MODIS satellite image of Sierra Nevada snowcover Big data and mountain water supplies Roger Bales SNRI, UC Merced & CITRIS.
Observatory design in the mountain west: scaling measurements & modeling in the San Joaquin Valley & Sierra Nevada Scope: Establish a “virtual” hydrologic.
Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez.
Monitoring the hydrologic cycle in the Sierra Nevada mountains.
CZO N-S transect of research catchments Main CZO site Wolverton Last Chance Sugar PineSugar Pine Gin FlatGin Flat MODIS ImageMODIS Image.
The Impacts of Climate Change on Portland’s Water Supply Richard Palmer and Margaret Hahn University of Washington Department of Civil and Environmental.
Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe.
Problem Description: Developing strategies for watershed management Problem Description: Developing strategies for watershed management Proposed Solution:
Outline Background, climatology & variability Role of snow in the global climate system Indicators of climate change Future projections & implications.
Developing Tools to Enable Water Resource Managers to Plan for & Adapt to Climate Change Amy Snover, PhD Climate Impacts Group University of Washington.
MODELING OF COLD SEASON PROCESSES Snow Ablation and Accumulation Frozen Ground Processes.
Applying Methods for Assessing the Costs and Benefits of CCA 2 nd Regional Training Agenda, 30 September – 4 October 2013 Priyanka Dissanayake- Regional.
WaterSmart, Reston, VA, August 1-2, 2011 Steve Markstrom and Lauren Hay National Research Program Denver, CO Jacob LaFontaine GA Water.
Southern Sierra CZO Funding. The instrument cluster and the CZO are supported by NSFs Earth Sciences Division. KREW is a program of the U.S. Forest Service.
Introduction to the Global Hydrologic Cycle and Water Budget, Part 1 Tamlin Pavelsky, Associate Professor of Global Hydrology Department of Geological.
Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of.
CZOs & water security: Western U.S. lessons & global implications NASA-MODIS satellite image Roger Bales Professor & Director Sierra Nevada Research Institute.
Observations and mechanisms of change in seasonally snow- covered mountain environments: Linking process to pattern Paul D. Brooks Hydrology and Water.
Recent advances in remote sensing in hydrology
Southern Sierra Critical Zone Observatory Activities from 2009 annual report Investigators: UCM: R. Bales (PI), M. Conklin, S. Hart, A. Behre UCB: J. Kirchner,
Retrieving Snowpack Properties From Land Surface Microwave Emissivities Based on Artificial Neural Network Techniques Narges Shahroudi William Rossow NOAA-CREST.
Reducing Canada's vulnerability to climate change - ESS J28 Earth Science for National Action on Climate Change Canada Water Accounts AET estimates for.
Agenda Project overview (brief) Modeling update (preliminary results) Next steps… Integration pathways.
CE 424 HYDROLOGY 1 Instructor: Dr. Saleh A. AlHassoun.
Snow Properties Relation to Runoff
MODSCAG fractional snow covered area (fSCA )for central and southern Sierra Nevada Spatial distribution of snow water equivalent across the central and.
Estimating the Spatial Distribution of Snow Water Equivalent and Snowmelt in Mountainous Watersheds of Semi-arid Regions Noah Molotch Department of Hydrology.
Engineering Hydrology (ECIV 4323)
Downscaling of forcing data Temperature, Shortwave (Solar) & Longwave (Thermal) CHARIS meeting, Dehra Dun, India, October 2014 Presented by: Karl Rittger.
Understanding hydrologic changes: application of the VIC model Vimal Mishra Assistant Professor Indian Institute of Technology (IIT), Gandhinagar
Spatial distribution of snow water equivalent across the central and southern Sierra Nevada Roger Bales, Robert Rice, Xiande Meng Sierra Nevada Research.
REASoN Multi-Resolution Snow Products for the Hydrologic Sciences University of California, Santa Barbara University of California, Merced Long term commitment.
INNOVATIVE SOLUTIONS for a safer, better world Capability of passive microwave and SNODAS SWE estimates for hydrologic predictions in selected U.S. watersheds.
Southern Sierra Critical Zone Observatory Integrating measurements for advances in hydrology & geochemistry A research platform for studying Earth surface.
1 National HIC/RH/HQ Meeting ● January 27, 2006 version: FOCUSFOCUS FOCUSFOCUS FOCUS FOCUSFOCUS FOCUSFOCUS FOCUSFOCUS FOCUSFOCUS FOCUSFOCUS FOCUSFOCUS.
Intellectual Merit: NSF supported researcher Roger Bales and colleagues have developed a prototype instrument cluster for the study of mountain hydrology,
Performance Comparison of an Energy- Budget and the Temperature Index-Based (Snow-17) Snow Models at SNOTEL Stations Fan Lei, Victor Koren 2, Fekadu Moreda.
Space-Time Series of MODIS Snow Cover Products
An advanced snow parameterization for the models of atmospheric circulation Ekaterina E. Machul’skaya¹, Vasily N. Lykosov ¹Hydrometeorological Centre of.
California’s climate. Sierra Nevada snow depth, April 13, 2005 April 1 snowpack was 3 rd largest in last 10 years cm snow Source:
Research design for hydrologic response to watershed treatments in the mixed conifer zone of California’s Sierra Nevada John Battles 1, Roger Bales 2,
Integrated measurements & modeling of Sierra Nevada water budgets UCM PI: Roger Bales LLNL Co-PI: Reed Maxwell.
Real-time Sierra Nevada water monitoring system Context & need Importance. Climate change introduces uncertainty into water forecasts that are based on.
Bias in April 1 forecasts (underforecast) for July-April unimpaired runoff for 15 Sierra Nevada basins was about 150% of average accumulation, i.e.
Introduction to the Global Hydrologic Cycle
Effect of forest management on water yields & other ecosystem services in Sierra Nevada forests UCB/UC Merced/UCANR project.
Embedded sensor network design for spatial snowcover Robert Rice1, Noah Molotch2, Roger C. Bales1 1Sierra Nevada Research Institute, University of California,
Broad, motivating science questions
Forests and Water in the Sierra Nevada
Precipitation-Runoff Modeling System (PRMS)
Quantitative vs. qualitative analysis of snowpack, snowmelt & runoff
Sierra Nevada Research Institute
Upper Rio Grande studies around 6 snow telemetry (SNOTEL) sites
Science issues Water balance/cycling in snowmelt dominated catchments
Hydrologic implications of 20th century warming in the western U.S.
Water team, watershed instrumentation
Measuring mountain water cycle at the basin scale
Statistical Applications of Physical Hydrologic Models and Satellite Snow Cover Observations to Seasonal Water Supply Forecasts Eric Rosenberg1, Qiuhong.
Kostas M. Andreadis1, Dennis P. Lettenmaier1
Snow is an important part of water supply in much of the world and the Western US. Objectives Describe how snow is quantified in terms of depth, density.
Hydrology CIVL341.
Hydrology CIVL341 Introduction
Forests, water & research in the Sierra Nevada
Engineering Hydrology (ECIV 4323)
Real-time Sierra Nevada water monitoring system
Hydrology CIVL341 Introduction
Engineering Hydrology (ECIV 4323)
Presentation transcript:

Challenges & opportunities: water resources information systems Roger Bales, Professor University of California, Merced CITRIS Corporate Sponsor Day May 2, 2006

Problem: how to modernize California’s water information systems Challenges: Widely dispersed decision making & growing, heterogeneous demand for information Decades old technology in use, with only modest, limited programs underway to upgrade systems Collect Store Search Retrieve Analyze Present Societal “pulls” (for this project)

Technological advances offer opportunities Incoming price signals Availability of satellite remote sensing information Development of inexpensive, low-power sensors & powerful, ground-based sensor networks Maturing of physically based, spatially distributed hydrologic models Technology “Push”: (CITRIS technology being used/developed etc)

Bridge the gaps with current practice Priority needs & opportunities Incoming price signals Integrate the science Bridge the gaps with current practice Technology “Push”: (CITRIS technology being used/developed etc)

Existing point measurements fail to sample spatial variability Snow water equivalent measurements Other examples precipitation soil moisture snow albedo vegetation properties evapotranspiration

Plot-scale controls on snow distribution snow depth, cm 106 94 82 70 58 112 100 88 76 64 snow depth, cm elevation solar radiation vegetation density slope wind exposure relations differ at small catchment vs. regional watershed scale (Molotch et al., 2004)

Snow vegetation / interactions open crown edge under canopy Valles Caldera, 2005 66 76 86 96 106 116 126 136 day of year snowpack depth, cm 20 40 60 80 100 120 140

Soil moisture follows same pattern as snow high low low high

Measuring mountain water cycle at the basin scale satellite remote sensing multiple instrument clusters in a basin flux tower Instrument cluster Flux tower or meteorological station Embedded sensor network Sap flow array Stream & groundwater sensors Data & communication hub

Embedded sensor network for mountain water cycle Sensors snow depth air temperature relative humidity solar radiation soil moisture soil temperature … Pod microcomputer data storage radio battery solar cell One node Mother pod signal/data to/from other nodes signal/data network data & control data logger & IP connection via phone, radio or direct signal/data to/from UC Merced

Pod & snow pinger at Gin flat sensor network

Mother pod, data logger & snow pinger at Gin flat embedded sensor network Snowcourse SNOTEL

Sierra Nevada fractional snow cover from satellite: 3/7/04 We want a time series of these.

Integration of satellite & ground-based measurement systems & modeling Some opportunities: snowcover extent & water equivalent soil moisture precipitation streamflow/runoff forecasting

Energy balance modeling scheme data cube incident solar air temp relative humidity wind speed SWE albedo SCA longwave t y x energy balance LSM vegetation topography soils Time SWE pixel by pixel SWE & SCA pixel by pixel runoff potential basin potential runoff Time

Scaling mountain water balance Blending measurements from multiple scales basin SWE precip radiation EB ground/RS topography ground soil moisture micromet bedrock soils remote sensing SCA albedo vegetation soil moisture snow distribution fluxes micromet plot/hillslope infiltration & recharge

Applications: snowmelt modeling, Marble Fork of the Kaweah River (Molotch et al., GRL, 2004) Melt flux = (Rnetmq + Tdar)SCA net radiation > 0 degree days > 0 snow covered area From Noah mq = energy to water depth conversion, 0.026 cm W-1 m2 day-1 ar = conversion parameter, based on wind, humidity, roughness

Magnitude of snowmelt: modeled – observed snow water equivalent SWE difference, cm AVIRIS albedo Tokopah basin, Sierra Nevada assumed albedo assumed w/ update Basin-average albedo estimated from remotely-sensed AVIRIS (Airborne Visible/Infrared Imaging Spectroradiometer) data specific to the catchment typically differed by 20% from albedo estimated using a common snow-age based empirical relation, as often used in climate or hydrologic models. Using the AVIRIS albedo estimates in a distributed snowmelt model that explicitly includes net solar radiation resulted in a much more accurate estimate of the timing and magnitude of snowmelt as compared to the same model with the empirical albedo. Model improvement was most significant in areas and at times where incident solar radiation was relatively high and temperatures low. Molotch, N. P., T. H. Painter, R. C. Bales, and J. Dozier, Incorporating remotely sensed snow albedo into spatially distributed snowmelt modeling, Geophysical Research Letters, 31, L03501 DOI:10.1029/2003GL019063, 2004.

Bridging the gaps & integrating the science – next steps Embedded sensor networks critical need for prototype deployments develop communications & systems for data integration Data & information systems address need for user-oriented integration of heterogeneous data for decision support applications develop digital watershed tools & technologies Economic impact of project (especially for the Corporate Sponsors)

Who benefits Benefit: enhance billions of dollars of decisions annually by reducing uncertainty & enabling efficient water management Partners: State/federal water managers Water information providers Regional/local water managers (irrigation, urban, hydropower) Research community Private sector Public policy or social impact of this CITRIS project