Planning with water - an overview

1. Planning with water - an overview Paul van Walsum

2. Overview • introduction • regional influencing through GW & SW • methods for decision support • influence matrix method • embedding method

3. Regional influencing through GW & SW • pressure wave • droplet movement

4. Regional influencing, matrix

5. Regional influencing, cross section

6. SIMGRO for the regional hydrology

7. Methods for decision support • simulation models • optimization models • linked optimization-simulation models

8. Planning with water, ‘conventional style’ Stakeholders suggest measures communication simulation effects on objectives

9. Planning with water, ‘inverse approach’ measures communication optimization Stakeholders: • targets on objectives • options for measures

10. Integrated model simple reduction verification complex economy ecology hydrology Multi-level modelling

11. Optimization model using LP • x1, x2,... vector of decision variables x xi = 0 : no, you do not do it xi = 1 : yes, you do it • g1x1 + g2x2 + .. objective function gx --> max • a11x1 + a12x2 + .. <b1 constraints Ax < b • a21x1 + a22x2 + .. <b2

12. Non-linear programming • non-linear constraints and/or non-linear objective • optimality not guaranteed (lowest point potato field?) • if optimality is guaranteed, then you can probably do it with LP (piece-wise linear)

13. Piece-wise linear yield function (convex)

14. Non-linear programming (ctd) • non-linear constraints and/or non-linear objective • optimality not guaranteed (lowest point potato field?) • if optimality is guaranteed, then you can probably do it with LP (piece-wise linear) • if not guaranteed, then with integer programming you can construct non-linear functions using special sets

15. Use of special sets for constructing non-convex piece-wise linear functions

16. Approximation of quantity*quality • (a+ x1)*(b+x2)  ab + ax2 + bx1

17. Influence matrix approach

18. Building of simplified groundmodel • Boundary condition of nature area in terms of • Mean Spring Watertable MSW • Mean Lowest Watertable MLW • seepage that reaches the rootzone

19. Analytical solution for spatial interaction • steady-state • homogeneous geohydrology • radial flow • analytical solution (Groenendijk) i j Unit rise of head 0 Calculated effect 1

20. ‘Walking’ measure • Influence matrix IM for spatial interaction through groundwater Bovenaanzicht Modelcel (i) j j i IM = a(i)/p(j) a(i)/p(j)

21. K 1 eenheidsverhoging k 2 fre a berekend effect Combination with simulation model • Sensitivity analyses with SIMGRO (uniform measure) • 2) MHW, MSW, MLW (phreatic level agricultural land) • 4) MSWa en MLWa (aquifer under nature area) 1) maatregelen 6) effecten op k k landbouwgebied natuurgebied 1 2 2) grondwaterstand 5) grondwaterstand- veranderingen veranderingen 4) stijghoogte- 3) superpositie effecten veranderingen op stijghoogten

22. Regression model MSWa (1) • MSWa = fMSW · [IM]• MSW

23. Regressiemodel MSWa (2) • MSWa = fMSW · [IM]• MSW •  MSWa = fMSW · [IM]• MSW + fMHW · [IM]• MHW

24. SNCc(r) fltir,l flhir, rp, l r rp GNCr,l flbir,l Embedding approach using mixing cells

25. Software • Xpress package of DASH • interior point algorithm (not ‘Simplex”) • integer extensions (also binary variables) • use of special sets for nonlinear functions implemented with integer variables

26. Pilot study/methodology

27. What are we talking about ? 1. Problem definition

28. Pilot area Beerze & Reusel

29. What are the stakeholder objectives ? 1. Problem definition 2. Objectives - stakeholders - authorities

30. Objectives • reduce flood risk / climate change • reduce desiccation of nature areas • reduce nitrogen and phosphorous loading on groundwater & surface water • minimize loss of income from agriculture

31. Where are we now ? 1. Problem definition 2. Objectives 3. Actual situation - authorities - stakeholders - now

32. grassland arable land tree nurseries water built-up area nature area Situation Now land use

33. AlterrAqua: GIS-shell for regional hydrology waterways culverts weirs subcatchments Land use DTM top10 vector sewerage systems

34. Metamodel for leaching of nutrients Pload =f(Soiltype,Landuse,P-surplus, MHW)

35. NO3-N aquifer 2 (mg/l) Situation NowNitrate concentration(in the long-term,after endlessly repeating manuring)

36. Catchment accumulation of NO3-N loadingon surface water

37. 470 kg/ha/year Situation Now : N-loading on surface waternitrogen surplus

38. Where are we heading ? 1. Problem definition 2. Objectives 3. Actual situation - authorities - stakeholders - now - autonomous developments

39. Autonomous developments + climate scenario Discharge (m3/s) Situation Now Pwinter +17% Autonomous dev.

40. Autonomous developments: drainage & nature Current Situation Autonomous development

41. What should we focus on ? 1. Problem definition 2. Objectives 3. Actual situation compare - authorities - stakeholders - now - autonomous developments 4. Focal points

42. What are the options ? 1. Problem definition 2. Objectives 3. Actual situation compare - authorities - stakeholders - now - autonomous developments 4. Focal points 5. Measures (options)

43. Measures(options) • land use • water management

44. What is the best strategy ? 1. Problem definition 2. Objectives 3. Actual situation compare - authorities - stakeholders - now - autonomous developments 4. Focal points 5. Measures (options) 6. Strategies

45. Planning with water, ‘inverse approach’ measures communication optimization Stakeholders: • targets on objectives • options for measures

46. DRAM Waterwijs market prices (elasticity) 15 Integration with agricultural model DRAM

47. Contribution to peak flow, per subcatchment

48. Contribution topeak flow in reference run

49. Optimisation-model (Beerze-Reusel) • 60 000 constraints • 200 000 continuous decision variables • 2 million non-zero coefficients in de matrix • CPU-time ~0.5 hour on a P4-2.4

50. Strategy 1 : flood risk  Discharge (m3/s) Situation Now Pwinter +17% Autonomous dev. Strategy 1