SimIndus. Modeling, Simulation and Data Assimilation for Indus River Basin Management. Planning Commission, Islamabad April 8, 2011

Transcription

1 Modeling, Simulation and Data Assimilation for Indus River Basin Management SimIndus Group of Pakistani Researchers Presentation by: Dr Abubakr Muhammad (LUMS) Planning Commission, Islamabad April 8, 2011

2 The SimIndus Network Members Mr. Sarwar Nazir (NCP) [Coordinator] Dr. Abubakr Muhammad (LUMS) Dr. Amer Iqbal Bhatti (MAJU) Dr. Shoab Raza (PIEAS) Dr. Yousaf Shad (QAU) Dr. Adiqa Kiani (FUUAST) Mr. Zia-ud-Din (NDC) Mr. Muhammad Akhtar (NDC) Mr. Saifullah (NDC) Ms. Mariya Absar (LUMS/IIASA) Mr. Muhammad Asif (IIU) Mr. Muslim Shah (NDC) Mr. Muhammad Zeeshan (QAU)

3 NCP Water Connections

4 PIEAS Water Connections

5 MAJU Connections

6 QAU Water Connections

7 LUMS Water Group

8 LUMS Water Group

9 Lab for Cyber Physical Networks & Systems at LUMS-SSE

10 Motivation / Concerns Vulnerability sources Source. UNEP South Asia report, 2008 Annual canal diversions and sea escapage Flow reduction due to climate change 10

11 Motivation / Strengths Economically feasible hydro power potential. Crop yields despite drought.

12 SimIndus Agenda Questions What is the use of models/scientific data for users and citizens? [People] Do we need models to design new control Institutions? [Governance] What are the critical paths in maintenance, optimization and operation that can be solved by modeling? [SimIndus] Explorations What needs to be done? Who does what? What is being ignored by everyone?

13 SimIndus Interests (Summary) Focus on Modeling, simulation and data assimilation for Physical models Econometric models Operational models Regulatory methods End Product: Decision support systems at multiple level End User: Basin managers Scenario options Informed decisions backed by solid science and data

14 Need for Data Driven Models Current governance directions Participatory irrigation management Decision making at many levels. (IRSA, PIDA, WUA, FO ) Need decision support systems Water entitlements Being defined/debated at trans-boundary, national, provincial, regional, farm levels. Need scientific evidence to support legal claims, treaty/accord negotiation. Accountability Forecast and analysis for future and past scenarios. Conflict resolution requires scientific backup. Correctly interpret conflicting claims (e.g. on glacial melt) Entitlements Accountability Participation

15 A complex system of systems January 17,

16 Models, Data Assimilation and Usage Physical Models Sensors / Imaging Remote Sensing Discharges/diversion Water quality / Sediment Salinity Glacial melt Salt mobility Aquifer dynamics Delta ecosystems Siltation River morphology Evapotranspiration Econometric Models Data Assimilation Forecasts Agronomics Livestock /fishery Cropping Irrigation Set-points Operational Models IBMR, Flood Commission WAPDA sector planning Irrigation depts Feasibility studies Regulation Models Canal command Costing / Pricing Entitlements Cropping Patterns Groundwater balance

17 Model Repository (WB Picture) Types Planning Operations Multi-scale Daily Monthly Seasonal Long-term Source. World Bank, 2005

18 Need for Accurate Modeling of Very Complex Systems Water Salt Silt Courtesy. Asad Abidi, 2009 Bhutta, World Bank, 2005 Drainage Master Plan estimated that +34M tonnes of salt accumulated in root zone But measurements showed that salinity had stabilized, and was declining Better model predicts 3M tonnes/yr! January 17,

19 Need for Instrumentation / data Source. World Bank, 2005 You can not control what you can not measure. Need to combine accurate models with good data (assimilation). January 17, 2012 Source. Bastiaanssen,

20 Need to Settle Contradictory Claims Contradictory claims WB, ADB, DFID, No definite answers yet Challenges Sparse data Extreme topography Perfect case for model based data assimilation Courtesy. Mariya Absar, COMSTECH, 2010

21 Need to Settle Contradictory Claims Contradictory claims WB, ADB, DFID, No definite answers yet Challenges Sparse data Extreme topography Perfect case for model based data assimilation Courtesy. Mariya Absar, COMSTECH, 2010

22 Need for Preservation: e.g. Indus Delta Diverse bio-environment 200 species of fish 25,000 tonnes of shrimp/yr; 50% exported Reduced outflows have led to shrinking mangrove forests, declining fish stocks Fishing communities disappearing January 17, 2012 Need for flows into delta has been long recognized; written into Indus Waters Accord Need models to forecast and regulate 22

23 Existing Operational Models Indus Basin WB Study(1968) Indus Basin Model (IBM, 1982) Indus Basin Model Revised (IBMR, 1986) Indus Basin Model Revised-III (IBMR-III, 1992) Water resource database (2000-)

24 IBMR models Current Limitations Most physical models missing Flood impact Ground water dynamics Drainage Climate change Many econometric models missing 15 crops only Wheat centric

25 Existing Usage of Physical Models Practically non-existent Isolated reports and scientific papers Isolated research activities at centers of excellence / universities Little/no Instrumentation to test models Existing capacity in basic science/hydrogeology small No data assimilation Salt Water Silt

26 Existing Usage of Regulation Models Non-existent Critical for System engineering. e.g. canal command automation Conflict management Improvement in distribution efficiencies Real-time Farmer advice Demand driven water delivery Fair pricing Existing capacity for handling data or models in regulation practically zero

27 Existing Usage of Operational Models Practically non-existent Isolated reports and scientific papers Isolated research activities at centers of excellence / universities Little/no Instrumentation for basin management No data assimilation

28 Efforts within SimIndus Operational models Basin operation & management (QAU) Regulation models Canal command automation (LUMS) Physical models Aquifier dynamics (IAD PINSTECH) Work on glacier melt (PMD, IAD-PINSTECH) Econometric models Poverty, productivity and food security (FUUAST)

29 Case 1: Smart Water Grid (LUMS) Wireless connectivity Embedded controller Flow Measurements Gate control Demand driven delivery to farms

30 LUMS Smart Water Grid Model based systems engineering Increase of distribution efficiency Demand based delivery Improvement and enforcement of water rights

31 LUMS Smart Water Grid Control of nontechnical losses Detection of Leak or unauthorized takeoff Detection of unauthorized dumps System health monitoring Flood/breach security

32 Case 2: Stochastic Optimization of Two stage stochastic programming model. Stochastic models of river flows and rainfall. Can lead to improvements in IBMR. Indus Basin (QAU)

33 Case 2: Stochastic Optimization of Two stage stochastic programming model. Stochastic models of river flows and rainfall. Can lead to improvements in IBMR. Scenario based planning Indus Basin (QAU)

34 Case 2: Stochastic Optimization of Indus Basin (QAU)

35 Case 3: Water Resource Modeling (GCISC-Water Group) Glaciers in Pakistan cover about 13,680 sq.km. Melt water contribute 60% of flows from UIB. Glacier melt in Himalayas projected to increase within next 2-3 decades, followed by substantial decrease. GCISC findings Projected temperature rise in North Pak is higher than that in South. Projected precipitation change picture is not clear. Pakistan water resources picture remains unclear due to uncertain behavior of HKH glaciers. Source. Regional Conf on Climate Change Challenges for South Asia, Islamabad, 2009

36 Who would Benefit? End Users are decision makers Decision support system needed at all levels WAPDA, IRSA, SIRSA, Irrigation depts, PIDA bodies Critical for ensuring transparency and maximizing efficiency Enabler for setting up new legal structures and reform control institutions.

37 SimIndus Plans There is an urgent need for Consolidation Inclusion / feedback from potential users Interdisciplinary cross talk Expanding the scope to other important models Expansion of the research network Establishing connections with experts in policy, law and economics

38 The Way Forward.. Expand the Network Include water experts from PCRWR, UET (CE), AUF, IWMI, NIO, SUPARCO, GCISC.. Develop connections with international bodies like IIASA, ICTP, KAUST Field visits to local institutes Develop a position paper on national strategy for future research efforts.

39 References Asad Abidi, Indus Basin: Past, Present and Future. APSENA meeting, Univ of Illinois, Briscoe, Qamar, et al., Pakistan Country Water Resources Assistance Strategy-Water Economy: Running Dry, World Bank, Washington, 2005 Babel, Wahid, Freshwater under threat in South Asia, UNEP Report, Wim Bastiaanssen, Remote Sensing Applications on the Indus Basin, Indus Basin Workshop, Islamabad, Abubakr Muhammad, Hasan Nasir, Towards a Smart Water Grid in the Indus River Basin. LUMS Technical Report, Yousaf Shad Muhammad, Water Resources Management by Stochastic Optimization: A Case Study of Indus Basin Irrigation System, Mariya Absar, The Impact of Climate Change on the Galciers, Water Resources and Livelihood of Pakistan, COMSTECH, 2010.