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The Challenges of Flood Monitoring across Political Boundaries: Taking Stock of Emerging Opportunities and Moving Ahead

The Challenges of Flood Monitoring across Political Boundaries: Taking Stock of Emerging Opportunities and Moving Ahead. Faisal Hossain Department of Civil and Environmental Engineering Tennessee Technological University. The Story of the Niger River. 1. 4030 km long, 211,3200 km 2.

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The Challenges of Flood Monitoring across Political Boundaries: Taking Stock of Emerging Opportunities and Moving Ahead

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  1. The Challenges of Flood Monitoring across Political Boundaries: Taking Stock of Emerging Opportunities and Moving Ahead Faisal Hossain Department of Civil and Environmental Engineering Tennessee Technological University

  2. The Story of the Niger River 1. 4030 km long, 211,3200 km2 2. Flows through 5 countries 3. Drainage area comprised of 11 countries 4. Frequent river flooding induced by heavy rainfall Question: How does one monitor early the evolution of river flooding across political boundaries of 5 nations, 11 administrations and a diverse landscape? 5. Diverse climate, rainfall regime, soil conditions, topography = varying response of landscape to rainfall

  3. The Answer (Under Ordinary Circumstances) • Acquire all necessary data to set-up a hydrologic model • Static data parameters – topography, soil classification, river network, vegetation (seasonal) etc. • Dynamic data parameters – rainfall, runoff/streamflow and soil moisture Data Category A is relatively easy to derive from various databases (ex: Topography from Shuttle Radar Topography Mission - SRTM Elevation data at 90m resolution). Data Category B is the REAL CHALLENGE – space-time variability demands an accurate and continuous REAL-TIME data streaming system Especially – Rainfall: The main determinant of the make-up of flood

  4. NOAA CPC Summary of the Day 1987-1998 Distribution of surface stations is uneven and sparse in many area The Problems that Magnify the Challenge of Real-time Data Acquisition Problem 1: 11 national hydro-meteorological agencies with no agreement for real-time data exchange Transboundary problems and treaties on water resource sharing among nations is very well-documented and researched Problem 2: Sparse/inadequate network for data measurement in flood-prone regions But these treaties do not address REAL-TIME data sharing among nations Source: Aaron Wolf, Oregon State U

  5. The story continues… “A 1991 United Nations survey of hydrological monitoring networks showed "serious shortcomings" in sub-Saharan Africa, says Rodda. "Many stations are still there on paper," says Arthur Askew, director of hydrology and water resources at the World Meteorological Organization (WMO) in Geneva, "but in reality they don't exist." Even when they do, countries lack resources for maintenance. Zimbabwe has two vehicles for maintaining hydrological stations throughout the entire country, and Zambia just has one, says Rodda.” [Stokstad, E., Science, 285, 1199, 1999] Anectodal Evidence: “During the Mozambique floods in 2001, there were only 4 precipitation gauges reporting for the entire country” (from Dennis Lettenmaier, University of Washington)

  6. Niger River is not alone 214 International River Basins in 1979 UN Register 261 in 2002 (Updated) MAURITANIA C H I N A SENEGAL N E P A L BHUTAN MALI CHINA Ubiquitous in all 5 continents 145 countries are associated in IRBs Accounts for 40% of total land surface. > 50% of total surface flow I N D I A I N D I A Source: Aaron Wolf, Oregon State U MYANMAR VIETNAM LAO PDR BANGLADESH GUINEA THAILAND CAMBODIA

  7. State-of-the-Art on Operational Flood Monitoring Across Political Boundaries Many nations ‘locked’ in IRB – 30+ in High Flood Risk Zone Basin-wide flood monitoring range depends on knowledge of rainfall over upstream nations Real-time rainfall data across political boundaries not always available 15% of total death toll due to Natural Hazards is by river flooding. 250+ Billion dollar of damages annually Source: Dr. Aaron Wolf, Oregon State Univ.

  8. One solution – SpaceSatellite Rainfall(?) What we know–Satellites have potentialto overcome transboundary/institutional limitations Global Precipitation Measurement Mission in 2012 3-6 hours sampling 5X5 km - 10X10 kmresolution Coherent data –calibrated to unified system Greater accuracy expected than current products Global coverage!

  9. Other Emerging Opportunities • Two other planned hydrologic missions can potentially enhance the cause of IRBs– • HyDROS (Soil Moisture) and • WatER (Streamflow) WaTER HYDROS

  10. Some Countries where Satellite Rainfall Could Help (in principle)* * Hossain F.,and Katiyar N.(2006) EOS Transactions 87(5)

  11. However….. Recent work identified complex nature of satellite rainfall uncertainty and non-negligible implications at dynamic scales of surface hydrology (TTU-UConn collaboration)1,2 How effective is the use of satellite rainfall in large IRBs given its uncertainty? A critical assessment of satellite rainfall to understand the tradeoff (for current and future scenario) needed This Can Potentially Strengthen Community’s Argument for GPM’s importance to Society! Foundation exists for assessment of satellite rainfall data for a basin as a whole hydrologic unit BUT Transboundary limitation to modeling flow adds a new dimension to the whole problem 1 - Hossain, F. and E.N. Anagnostou (2005). Using a Multi-dimensional Satellite Rainfall Error Model to Characterize Uncertainty in Soil Moisture Fields Simulated by an Offline Land Surface Model. Geophys. Res. Lett.vol 32. 2. Hossain, F. and E.N. Anagnostou (2006). A Two Dimensional Satellite Rainfall Error Model. IEEE TGRS, vol 44(4). Zielinski, S. (2005). Earth observation programs may still be at risk, EOS Transactions, 86(43): 414.

  12. Science Questions • General Science Question • How realistic is the use of satellite rainfall in overcoming the transboundary limitations to flood monitoring? Will GPM improve Flood Monitoring? • Specific Questions • What specific IRBs, and downstream nations would benefit more than others from GPM? • What is the role played by climate and landscape? • Can we develop rules of thumb for application of satellite rainfall data in ungauged IRBs?

  13. What we need to move forward • A global ball-park (hydrologically-relevant) assessment of ALL flood-prone IRBs that can serve as a proxy to detailed investigation on a case by case basis. • Parsimonious and non-unique hydrologic modeling approach. (Too many IRBs So little time) • Hydro-political component – Need to mathematically (reasonably realistically) model political boundaries in IRBs. (No framework does that)

  14. Outline of Seminar (2nd half) • Development of a modeling approach as a way to initiate understanding of the impact of integrating satellite rainfall data for basin-wide flood monitoring across political boundaries. • Concept Demonstration: Mapping results globally. • Broader Research and Education Plan • A ‘proposed’ long-term agenda for moving forward in anticipation of future hydrologic space missions.

  15. The Hydrologic Modelling Approach Complexity dictated by the needs of (a) modularity (plug in and out process equations) (b) simplicity (requiring little time to set-up over IRBs (c) representing political boundaries in the hydrologic framework Geometric Configuration based on Open-Book Watershed (first proposed by Chow and Yen at Ilinois University (1969). Can handle irregular DEM (if necessary) Can handle higher order watersheds Solves explicitly on a grid volume basis Fully distributed (to study scales + optimal data integration) Parsimonious in modeling geometry, topography and surface hydrology Katiyar, N. and Hossain, F. 2006 Env. Mod. Soft. (In review)

  16. The Grid-based Process Descriptions Excess Rainfall: Calculated using simple mass balance (bucket) approach keeping track of soil moisture storage Overland Flow: Excess rainfall is routed as sheet flow from each grid along the steepest direction – Darcy-Weisbach or Manning’s Equation. River Flow: Lateral inflow from overland is routed using Manning’s Equation (normal depth equation) Assumption for regulated rivers – Dams do not act as control structures during flooding period (e.g. Farakka Barage in India on Ganges).

  17. Excess Rainfall Calculations s(t) = soil moisture storage p(t) = rainfall; qse(t)= surface flow qss(t)=subsurface flow ET and GW flow can be incorporated if needed

  18. Excess Rainfall Calculations If s(t)> Sf Sf = field capacity qss= 0 if s(t) < Sf L= grid length, β=ground slope, Φ = soil porosity, Ks= Sat. Conductivity if s(t) > Sb Sb= Soil storage capacity=Dφ D= depth to bedrock (depth of soil column) qse= 0 if s(t) < Sb Excess rainfall – Overland Routing – River Routing - Q After Jothityankoon et al., 2002, J. Hydrol.

  19. Excess Rainfall (i) θ L0 Overland Flow and River Flow V Darcy- Weisbach Eqn. Laminar Manning’s Eqn Turbulent After ‘Applied Hydrology’ Chow et al. 1988

  20. Summary of Inputs Static Input: Geophysical Parameters (Distributed): 1. Topography; 2. Soil type (Sf, porosity, Ks), 3. Effective Soil Depth; 4. River bed slope, 5. Channel hydraulic parameters; Minimum calibration – Data available from global databases Dynamic Input: Hydrometeorological Data (Distributed): 1. Rainfall; 2. Initial soil moisture field (and base flow). The Output (what you get for all this): Streamflow at any reach of the river, flow depth, inundation plain.

  21. The Hydro-political Component Basic Information on each riparian nation is needed For example: Senegal River Basin – Comparing the impact of assimilating basin wide satellite rainfall data Vs partially gauged rainfall on streamflow modeling accuracy for Senegal – How much can Senegal benefit? • Level of Idealization can be systematically reduced (for example): • Higher ordered watersheds. • 2. Use of in-situ DEM - political boundaries MAURITANIA Guinea SENEGAL 7% Mali 35% MALI Mauritania Mali 50% Senegal 8% Mauritania Senegal GUINEA

  22. Verification of the Model(The Moment of Truth for a novice Graduate Advisor!) Numerical Stability Test on an Openbook of 9X6 grids at 200 m resolution. Valley side slope = 0.01; river bed slope=0.001; Manning’s n =0.015 Error in Mass Balance Discharge Δx/Δt DURATION Physical Consistency

  23. Verification (Contd.) Time, mins

  24. Concept Demonstration Consider this scenario:Govt. of a downstream nation in an IRB wants to know (a hydrologically relevant ball-park estimate) of improvement that can be expected from the use of NASA’s current satellite rainfall data in improving basin-wide flood monitoring. NASA Satellite Rainfall Algorithm: IR-3B41RT, available real-time (hourly) on a best-effort basis, Calibrated to TRMM Microwave data Test Region: Oklahoma Mesonet (High Quality datasets for assessment) Upstream Downstream A Hypothetical Two-Nation IRB with Open-book configuration

  25. Demonstration (Contd.) Objective:To quantify ‘broadly’ the impact of IR-3B41RT data availability over upstream transboundary nations on downstream nation’s basin-wide streamflow prediction accuracy as a function of % of ungauged transboundary area. ASSUMPTIONS 1. Oklahoma Mesonet assumed a two-nation ‘IRB’ with a given % of upstream nation’s area as transboundary (i.e., ungauged). 2. Open-book configuration. An international river assumed 3. Downstream nation has access to in-situ rainfall data in real-time. 4. Hydrologic simulation of streamflow from basin-wide in-situ (WSR-88D Radar) rainfall data assumed benchmark.

  26. Model Set-up • 4 month simulation period (May – Aug, 2002) (Typical flooding period for many nations). • Daily time step, 0.25 degree simulation. • 6 months spin-up performed with NOAH-LSM to initialize soil moisture fields. • Geophysical parameters derived from Oklahoma Mesonet database, various maps, past work. • River bed slope =0.005; Valley slopes = 0.001, Effective soil depth = 0.5 m. • Silty loam soil, Ks=0.65 cm/hr, Porosity=0.5. • Assumed ‘river’ with rectangular section, 10 m depth, 100 m wide.

  27. Results IR-3B41RT suffers from overestimation overland – Needs Adjustment With Bias Adjustment

  28. Results (Contd.) Improvement Major Minor Negative Impact on predicting flow with 30% intraboundary rainfall data (70% transboundary – upstream nations) Impact of assimilating IR-3B41RT data over upstream nations on basin-wide streamflow prediction uncertainty for the downstream nation

  29. Speculations on IRBs Preliminary Speculation - Setting aside ALL assumptions Improvement Negligible Improvement

  30. More Intelligent Speculation Based on Koppen Climate Classification Source: Encyclopedia Britannica

  31. Speculation on IRBs (Contd.) Cfa & Cwa– Humid Subtropical; Bsh- Semi-arid GangesRiver– Bangladesh (+45%) ↑Yalu and Tomen Rivers – North Korea (+20%)↑LimpopoRiver – Mozambique (+35%)↑SenegalRiver – Senegal (+42%)↑La Plata River– Uruguay (+45%)↑

  32. The Next Road Ahead Our On-going effort needs to focus on: (1) Preparing accurate input database for running the modeling framework on major 35+ IRBs in Asia, Africa and South America. (1) Call the Presidents/Prime Ministers of these countries! (2) Communicating results to Community on Transboundary Water Resources Research to assess hydro-political implications. (1 and 2 has alignment with Dr. Aaron Wolf of OSU) (3) Liasing with the greater scientific community on WatER and GPM for synergy (alignment with Doug Alsdorf of Ohio State U, Dennis Lettenmaier of UW and NASA group of Bob Adler).

  33. The BIG PICTURE Broader Research Plan (on-going effort) • Emerging opportunities of flood monitoring across political boundaries using satellites.√ (2) a - Characterizing scale-dependent complexities of satellite rainfall error structure; Space-Time Stochastic Error Model Development –SREM2D b - Optimal integration of satellite rainfall data in ‘uncertain’ land surface models (e.g. NOAH and CLM) (3) Role hydrologic process complexity – identifying model complexity commensurate with satellite rainfall data uncertainty SREM2D

  34. Towards More Insightful Monte Carlo Methods of Uncertainty Assessment Two different models with similar deterministic simulation does not yield same derived distribution Hossain, F. 2006. ASCE J. Hydrol. Engg. (in review)

  35. BIG PICTURE (Contd.) Broader Educational Plan (Partnership with USGS) • The Advantages: • Modernizing Curriculum – making it more exciting to students. • Popularising your research at the senior and graduate level to identify interested candidates for a project. • Bringing a student up to speed for graduate school (Honors program, Senior Thesis, Summer Internships etc.). • Most importantly – the ‘fresh angle’ from the students – thinking outside the box – that I can not get by interacting with myself only. • Computer-assisted instruction scheme with a Graphical User Interface (GUI) to improve active learning in classroom of • The rainfall-runoff process (Modular Modeling System) • Stochastic Theory – its importance in modeling variability and observing implications – SREM2D Going up in Bloom’s Taxonomy Learning Pyramid: ASCE Body of Knowledge and ABET

  36. A Proposed Research Agenda (for moving ahead collectively) 1. Search for frameworks and metrics that actively promote feedback between hydrologists and satellite rainfall algorithm community. Hydrologists need to become active users of satellite remote sensing products. 2. Explore the role of hydrologic process controls on flood monitoring uncertainty. Traditional methods are less insightful. Need ‘wiser’ selection of models at ungauged basins compatible with uncertain data. 3. Explore the relative strengths and weaknesses of satellite-derived rainfall versus model (NWP) rainfall – Hydrologist needs to know which one to use when, where and why. 4. Explore synergy and coordination with communities on GPM, HyDROS and WatER; and operational flood forecasting agencies – for the ultimate Space-Borne Monitoring system ‘someday’. Example: 5 year MOU between TTU and IWM/FFWC of Bangladesh for unhindered exchange of data and testing schemes.

  37. Acknowledgements 1. All my hard working graduate students, especially Nitin Katiyar (and Amanda Harris and Preethi Raj). 2. The Water Center and CEE Dept. of Tennessee Tech for research support. 3. Aaron Wolf, collaborator and colleague, Oregon State of University. 4. Institute of Water Modeling, and Flood Forecasting Agency, Bangladesh. 5. Dennis Lettenmaier of UW and Doug Alsdorf of Ohio State U. for the Big Picture Value; Discussions and Synergism. 6. George Leavesley of USGS for educational partnership. 7. Christa Peters-Lidard and George Huffman of NASA for their continual critique of my work.

  38. Thank You! Questions? Map of the World if Politicians were Hydrologists

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