Hydro-economic modeling for more efficient water resources management

Transcription

1 SUSTAINABLE, JUST & PRODUCTIVE WATER RESOURCES DEVELOPMENT IN WESTERN NEPAL UNDER CURRENT & FUTURE CONDITIONS (DIGO JAL BIKAS DJB) Hydro-economic modeling for more efficient water resources management March Jeuland Duke University 1 st August 2017

2 Overview 1. Brief overview of hydro-economic modeling What is it and why is it useful? Traditional approach Brief overview of more recent conceptual advances 2. Examples 3. Basic overview of model structure for Western Nepal 2

3 What is hydro-economic modeling? Hydroeconomic models represent regional scale hydrologic, engineering, environmental and economic aspects of water resources systems within a coherent framework (Harou et al., 2009) Solution-oriented tools for discovering strategies to advance efficiency in water use Shift away from quantity-based, or physical, targets Reliance on a single metric to guide decisions economic value (rather than multiple objectives) 3

4 Why is hydro-economic modeling useful? Can answer questions about the value of new development (which hydropower, irrigation projects generate highest value, relative to costs?) Can be used to improve basin coordination (should dam storage be increased during some periods to better allow dry season irrigation?) Can help identify tradeoffs between uses (do we lose irrigation potential if we increase hydropower production?) Can help expand solution set (can changes in water management solve shortages?) Can help quantify economic losses or gains due to changes in other factors (how does climate or land use change affect economic outcomes?) 4

5 Common applications of these models From a recent review (Bekchanov et al. 2017), much work on: Economic sensitivities (to climate change, value of energy, etc.) Tradeoffs between uses, especially irrigation vs. hydropower Water efficiency investments (drip irrigation, lining of canals, etc.) Water pricing (which makes water use more efficient) Water trade (where users can trade water rights to those willing to pay) Relatively less on: Ecosystem values Complex feedbacks (water-energy-food nexus) 5

6 Our ambition: Better capturing this complexity Environmental System Food/Land Use System - Preparing land - Growing crops - Raising livestock - Harvesting ONproduce - Drying, processing - Storing food products - Transport, distribution - Preparing food Energy System - Extracting resources - Harnessing hydro, wind, solar, biomass energy - Generating and transmitting electricity - Production, refinement and distribution of transport fuels - Storing, buffering Hydropower, power plant cooling, extraction, (bio)fuels Water pumping, delivery, water treatment, energy for desalination Water System - Manage renewable surfaceand groundwater resources - Distribute water supply for human consumption - Collect sewage - Treat wastewater to protect human and ecological health - Transfer between basins - Desalination 6

7 Traditional hydro-economic approach Optimization model that helps planner choose best infrastructure (e.g. where, how big) Economics enter the objective function, which is in $ Typical objectives include maximizing the value of market outputs and/or minimizing penalty functions for undesirable impacts (like floods) Reliance on historical flows (stationarity) Exploration of future changes by: Specifying changes as decision variables controlled by planner; or Creating scenarios of development for factors outside planner s control (e.g., assumed changes in hydrology, or population) Typical misconception: Hydro-economic modeling is usually for planning purposes, not for operations 7

8 Problems with the traditional apprach Planners do not have perfect foresight Models that solve for optimal allocation are perhaps unconvincing Operations may be quite different Water planners are risk averse They may not care much about average results, but want to reduce risk They tend to think in terms of satisficing, not maximizing $ Water planning challenges optimization Many-dimensional uncertainty, deep uncertainty Near optimality (flatness of objective), or non-dominance of solutions Political economy of water Valuation difficulties But various methodological advances allow improvements over the traditional approach (stochastic, robust optimization, fuzzy mathematical programming) 8

9 Basic design choices in hydro-economic models Options Description Comments Model type: Optimization Flows: Historic Stochastic Simulation Uncertainty: Scenarios Risk-based Other Integration: Modular Holistic -What strategy is best? -How does strategy perform if? -Measured series -Sampling from stochastic series -Best-worst / ranges -Well-specified prob. distributions -No or partial prob. distributions -Linked models run separately -Single integrated platform -Flexible solution space, but may be unconvincing or challenge solvers -Computational advantages, realistic rule representation, but may underperform -Intuitive comparisons for managers / decision makers, but future may not be like the past -More thorough incorporation of variability, but less intuitive, may still misrepresent future -Deterministic and simple, but limited -Amenable to standard methods in decision analysis, assume away deep uncertainty -Perhaps most relevant, but challenge for decision-making -Well adapted to limited expertise, but can be clunky to use -Allow modeling of feedbacks; but may be 9 difficult to run

10 Choosing a particular architecture Need to know objective: Inform policy, promote uptake, or work at the cutting edge of methods? Tension between promoting uptake and maintaining objectivity: Users often impose assumptions to improve realism These assumptions may reduce transparency 10

11 II. Brief overview and discussion of examples from the Nile (Why the Nile?) 11

12 Hydro-economic examples from the Nile Author(s) Year Model type Flows Uncertainty Integration Wu et al. (NEOM) Strzepek et al ; 2006; 2016; 2017 Optimization Optimization (CGE) Deterministic Block (IMPEND) 2010 Optimization Deterministic Jeuland (3 linked models) Halleux; Goor; Arjoon et al. (Nile SDDP) 2010a, b; 2014 Simulation Stochastic 2009; 2010; 2014 Flow, dams, energy value Holistic (Full basin) Deterministic Capital shocks Holistic (Egypt) Climate scenarios Flow; development Holistic (Eastern Nile) Modular (Full basin) Optimization Stochastic Infrastructure Holistic (East. Nile) Dinar 2013; 2015 Optimization Deterministic Initial rights Holistic (East. Nile) Geressu et al Optimization Deterministic Satti et al. (SHOM) 2015 Optimization Deterministic Weights for decision vars. Flow; prices; development Holistic (East. Nile) Holistic (Nile in Sudan) Irrigation; Hydropower Economy-wide Irrigation; Hydropower; Flood control Irrigation; Hydropower; 12 Siltation

13 III. Interdependence and tradeoffs: Example from the Ganges 13

14 Standard narrative in water resources development To achieve optimal outcomes, one needs to consider the complete water resource system Several reasons: dependencies across Space; e.g. spillovers/externalities not considered by agents located upstream Time; e.g. depleting water storage (in soils, surface or groundwater) could lead to permanent changes in land cover and hydrology Outcomes; e.g. markets for goods and services, nonmarket values, environmental quality The need to consider the system resonates with ecologists / engineers / economists 14

15 Where do we see these ideas reflected? Two examples (among others): Wide inclusion in national policy of Integrated Water Resource Management (IWRM) : A process which promotes the coordinated development and management of water, land & related resources in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems Hydro-economic optimization models: How should one choose where to build infrastructure, how to allocate water, etc., to maximize economic benefit? 15

16 Ganges Basin: One of very first international case studies for hydro-economic optimization Objective: Maximize economic benefits (Hydropower + irrigation flood damages) Source: Rogers (1969). A game theory approach to the problems of international river basins. Water Resources Research 5(4):

17 More recent Ganges model HIMACHAL PRADESH Mahakali (Sarada) River Ghagara River Karnali (Ghagra) river Babai (Surya) river Kaligandhaki ii DAM207_2 Rapti River North Kael River Banganga River Kaliganghaki I DAM207_1 Seti DAM207_4 Marsyandi DAM207_5 Gandhak River Burhi Gangaki DAM207_6 Bagmati River Trisuli Reservoir DAM207_7 Kamala River Sunkosi II DAM209_2 Saptakosi River Arun III Upper Arun DAM209_ DAM209_3 Lower Arun 4 DAM209_5 Tamur DAM209_6 Kosi River Mechi River Mohananda River (India) Banghirathi / Hoogly Mohananda River (Bangladesh) Jamuna River Upper Meghna River Lower Meghna River UTTARKHAND INF100_3 INF205_3 INF207_5 INF209_3 INF209_4 INF100_1 INF100_4 INF205_1 INF205_2 INF205_4 INF207_1 INF207_4 INF209_2 RAJASTHAN Chambal left bank IRR101_5 IRR101_8 INF101_2 Yamuna West Canal IRR101_1 INF101_3 Agra Canal IRR101_3 IT01_1 IT01_2 GWR101_2 Delhi Water Supply WS101_1 Agra City Supply WS101_2 Parwan Irrigation IRR101_4 GWR101_8 IT01_3 GWR101_6 Dhaolpur municipal area WS101_3 INF101_7 FL1 Yamuna River Vyasi Dam DAM101_2 Yamuna East Canal IRR101_2 IT01_4 GWR101_7 Kota Dam DAM101_5 Ranapratapsagar DAM101_4 Gandhisagar Dam DAM101_3 Lakhwar Dam DAM101_1 GWR101_1 INF101_5 IT01_5 IT01_6 IT01_7 INF101_1 Upper Ganga Canal IRR100_1 Hinden R. INF101_4 INF101_9 IRR101_10 INF101_8 INF100_2 Madhya West Ganga IRR100_3 Kanpur city Supply WS100_1 GWR101_14 Chambal right bank IRR101_6 IT01_8 Lower Ganga Canal IRR100_5 IT00_4 INF101_10 INF100_5 GWR00_2 IT01_9 IT01_14 INF101_12 GWR101_10 IT01_10 IT01_13 GWR00_3 IT01_15 INF101_13 IT00_1 IT00_2 IT00_3 GWR101_13 INF101_16 IT01_11 Kotti Behl Reservoir DAM100_6 East Ganga Canal IRR100_2 GWR00_1 Madhya East Ganga IRR100_4 FL2 IT00_5 INF101_18 IRR101_11 INF101_11 IT00_6 IT01_12 IT01_16 Rangwan Dam DAM101_11 INF101_17 Keolari Ken River IRR101_7 IT02_2 Daudhan Dam DAM101_10 INF101_15 INF101_14 INF100_8 INF103_1 INF100_9 GWR101_16 INF100_6 IT02_1 INF100_10 INF102_1 INF100_7 IT00_7 Tons River MADHYA PRADESH Gomti river IT03_1 IRR101_12 GWR102_2 IRR102_2 INF102_2 Pancheswar dam DAM205_1 Purnagiri dam DAM205_2 Banbhasa Headworks DAM205_3 Lucknow City Supply WS103_1 Sarda Sahayak IRR105_2 UTTAR PRADESH INF100_11 IT00_8 Karmanasa River INF106_1 IRR205_1 IT04_1 IRR106_3 GWR106_2 Girija B GWR105_2 INF100_12 IT00_9 IT00_10 Ganges River IT00_11 IT00_12 IT00_13 Musakhand Dam DAM104_2 Naugarh Dam DAM104_1 INF104_1 IT05_1 INF205_6 IT05_2 INF106_3 IT05_3 IT06_2 IT05_4 IT05_9 FL3 Chisapani (kamali) Dam DAM205_8 INF205_7 IT05_5 INF105_9 Surya B IRR105_4 INF105_11 IRR205_3 Adhuara Municipal area WS104_1 IT06_1 INF105_10 INF106_2 INF205_5 Rapti Dam DAM205_9 Rapti Nepal IRR205_5 Son left IRR106_1 Rapti B IRR105_6 IT05_8 INF100_13 INF207_2 Andhi Khola Dam DAM207_3 INF205_8 IT05_6 IT05_7 INF207_6 Western Gandhak GWR105_7 IRR107_2 FL4 IRR207_1 INF100_14 INF207_3 FL5 GWR106_1 Son Right Bank IRR106_2 IT06_3 Sapta Ganghaki Dam DAM207_8 INF208_1 IT07_1 Kulekhani DAM208_1 INF208_2 IT07_2 Eastern Gandhak IRR107_3 INF100_15 INF106_4 IT08_1 FL6 INF111_2 INF111_3 IT09_1 INF111_4 BIHAR JHARKHAND INF209_1 Kosi High Dam DAM209_7 Kamala Dam DAM209_1 Kosi western canal IRR109_3 INF100_16 INF111_1 IRR209_1 Maithan DAM111_2 Panchet Resevoir DAM111_3 IT09_2 INF209_6 IRR209_2 IT09_3 Kosi Eastern canal IRR109_4 FL7 IT00_14 WEST BENGAL NEPAL Mayurakshi River Massanjore Dam DAM111_1 IT11_2 Kangshabati DAM111_4 INF209_5 INF110_2 INF100_17 Damodar River Haldia River INF210_1 IT00_15 Farakka IT11_4 IT11_3 IT11_1 IT10_1 FL8 END1 IT00_19 Ganges River INF312_1 BANGLADESH INF313_1 Ganges Barrage (planned) IT00_16 END2 IRR300_6 INF314_1 Padma River IT00_17 END3 IT00_18 INF101_6 Chambal River Pardkh IRR101_9 CHHATTISGARH 17

18 Insights from these models The largest dams in the Himalaya do not provide much storage compared to annual flow of Ganges Tributary flows can be reduced somewhat with storage, but ability to control floods is limited Dams would provide low flow augmentation, but these induce tradeoffs between: Irrigation (mostly in India) and Ecosystem and water quality benefits (mostly in Bangladesh) Few other tradeoffs, at least at large scale What about at smaller scale (in Western Nepal?) 18

19 More recent Ganges model HIMACHAL PRADESH Mahakali (Sarada) River Ghagara River Karnali (Ghagra) river Babai (Surya) river Kaligandhaki ii DAM207_2 Rapti River North Kael River Banganga River Kaliganghaki I DAM207_1 Seti DAM207_4 Marsyandi DAM207_5 Gandhak River Burhi Gangaki DAM207_6 Bagmati River Trisuli Reservoir DAM207_7 Kamala River Sunkosi II DAM209_2 Saptakosi River Arun III Upper Arun DAM209_ DAM209_3 Lower Arun 4 DAM209_5 Tamur DAM209_6 Kosi River Mechi River Mohananda River (India) Banghirathi / Hoogly Mohananda River (Bangladesh) Jamuna River Upper Meghna River Lower Meghna River UTTARKHAND INF100_3 INF205_3 INF207_5 INF209_3 INF209_4 INF100_1 INF100_4 INF205_1 INF205_2 INF205_4 INF207_1 INF207_4 INF209_2 RAJASTHAN Chambal left bank IRR101_5 IRR101_8 INF101_2 Yamuna West Canal IRR101_1 INF101_3 Agra Canal IRR101_3 IT01_1 IT01_2 GWR101_2 Delhi Water Supply WS101_1 Agra City Supply WS101_2 Parwan Irrigation IRR101_4 GWR101_8 IT01_3 GWR101_6 Dhaolpur municipal area WS101_3 INF101_7 FL1 Yamuna River Vyasi Dam DAM101_2 Yamuna East Canal IRR101_2 IT01_4 GWR101_7 Kota Dam DAM101_5 Ranapratapsagar DAM101_4 Gandhisagar Dam DAM101_3 Lakhwar Dam DAM101_1 GWR101_1 INF101_5 IT01_5 IT01_6 IT01_7 INF101_1 Upper Ganga Canal IRR100_1 Hinden R. INF101_4 INF101_9 IRR101_10 INF101_8 INF100_2 Madhya West Ganga IRR100_3 Kanpur city Supply WS100_1 GWR101_14 Chambal right bank IRR101_6 IT01_8 Lower Ganga Canal IRR100_5 IT00_4 INF101_10 INF100_5 GWR00_2 IT01_9 IT01_14 INF101_12 GWR101_10 IT01_10 IT01_13 GWR00_3 IT01_15 INF101_13 IT00_1 IT00_2 IT00_3 GWR101_13 INF101_16 IT01_11 Kotti Behl Reservoir DAM100_6 East Ganga Canal IRR100_2 GWR00_1 Madhya East Ganga IRR100_4 FL2 IT00_5 INF101_18 IRR101_11 INF101_11 IT00_6 IT01_12 IT01_16 Rangwan Dam DAM101_11 INF101_17 Keolari Ken River IRR101_7 IT02_2 Daudhan Dam DAM101_10 INF101_15 INF101_14 INF100_8 INF103_1 INF100_9 GWR101_16 INF100_6 IT02_1 INF100_10 INF102_1 INF100_7 IT00_7 Tons River MADHYA PRADESH Gomti river IT03_1 IRR101_12 GWR102_2 IRR102_2 INF102_2 Pancheswar dam DAM205_1 Purnagiri dam DAM205_2 Banbhasa Headworks DAM205_3 Lucknow City Supply WS103_1 Sarda Sahayak IRR105_2 UTTAR PRADESH INF100_11 IT00_8 Karmanasa River INF106_1 IRR205_1 IT04_1 IRR106_3 GWR106_2 Girija B GWR105_2 INF100_12 IT00_9 IT00_10 Ganges River IT00_11 IT00_12 IT00_13 Musakhand Dam DAM104_2 Naugarh Dam DAM104_1 INF104_1 IT05_1 INF205_6 IT05_2 INF106_3 IT05_3 IT06_2 IT05_4 IT05_9 FL3 Chisapani (kamali) Dam DAM205_8 INF205_7 IT05_5 INF105_9 Surya B IRR105_4 INF105_11 IRR205_3 Adhuara Municipal area WS104_1 IT06_1 INF105_10 INF106_2 INF205_5 Rapti Dam DAM205_9 Rapti Nepal IRR205_5 Son left IRR106_1 Rapti B IRR105_6 IT05_8 INF100_13 INF207_2 Andhi Khola Dam DAM207_3 INF205_8 IT05_6 IT05_7 INF207_6 Western Gandhak GWR105_7 IRR107_2 FL4 IRR207_1 INF100_14 INF207_3 FL5 GWR106_1 Son Right Bank IRR106_2 IT06_3 Sapta Ganghaki Dam DAM207_8 INF208_1 IT07_1 Kulekhani DAM208_1 INF208_2 IT07_2 Eastern Gandhak IRR107_3 INF100_15 INF106_4 IT08_1 FL6 INF111_2 INF111_3 IT09_1 INF111_4 BIHAR JHARKHAND INF209_1 Kosi High Dam DAM209_7 Kamala Dam DAM209_1 Kosi western canal IRR109_3 INF100_16 INF111_1 IRR209_1 Maithan DAM111_2 Panchet Resevoir DAM111_3 IT09_2 INF209_6 IRR209_2 IT09_3 Kosi Eastern canal IRR109_4 FL7 IT00_14 WEST BENGAL NEPAL Mayurakshi River Massanjore Dam DAM111_1 IT11_2 Kangshabati DAM111_4 INF209_5 INF110_2 INF100_17 Damodar River Haldia River INF210_1 IT00_15 Farakka IT11_4 IT11_3 IT11_1 IT10_1 FL8 END1 IT00_19 Ganges River INF312_1 BANGLADESH INF313_1 Ganges Barrage (planned) IT00_16 END2 IRR300_6 INF314_1 Padma River IT00_17 END3 IT00_18 INF101_6 Chambal River Pardkh IRR101_9 CHHATTISGARH 19

20 IV. Western Nepal model: Basic structure 20

21 Modular structure From Vishnu s SWAT model Figure 8. Module interconnections for HEM model 21

22 More details on connections Figure 2. Interactions between production domains included in the WEEF framework 22

23 Nodes and links Figure 9. River Node network scheme 23

24 Model schematic 24

25 Mathematical equations Objective is to maximize economic value (from hydropower, irrigation, what else?) Nodal water balance (inflow, storage, diversion, outflow) Production using water: Energy, food crops, industry, other services (including environmental) Consumption by end users (households) Use of energy by water sector (nexus!) Institutional & other constraints (minimum flows, specific allocation rules) 25

26 Questions we hope to ask What is the value of new water infrastructure? (storage/hydropower dams, expanded irrigation canals) Are there tradeoffs between uses? (e.g., irrigation vs. hydropower, ecosystem services vs. hydropower) Are there tradeoffs across space and time? (groundwater irrigation now vs. later; micro-irrigation in hills vs. irrigation in Terai) What is the effect of institutional constraints? (guaranteed flow to Terai irrigation, or power supply to India) 26

27 Thank you! 27