Planning with water

Planning with water - an overview: 

Planning with water - an overview Paul van Walsum

Overview: 

Overview introduction regional influencing through GW andamp; SW methods for decision support influence matrix method embedding method

Regional influencing through GW & SW: 

Regional influencing through GW andamp; SW pressure wave droplet movement

Regional influencing, matrix : 

Regional influencing, matrix

Regional influencing, cross section: 

Regional influencing, cross section

SIMGRO for the regional hydrology: 

SIMGRO for the regional hydrology

Methods for decision support: 

Methods for decision support simulation models optimization models linked optimization-simulation models

Planning with water, ‘conventional style’: 

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

Planning with water, ‘inverse approach’: 

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

Multi-level modelling: 

Multi-level modelling

Optimization model using LP: 

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 --andgt; max a11x1 + a12x2 + .. andlt;b1 constraints Ax andlt; b a21x1 + a22x2 + .. andlt;b2

Non-linear programming: 

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)

Piece-wise linear yield function (convex): 

Piece-wise linear yield function (convex)

Non-linear programming (ctd): 

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

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

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

Approximation of quantity*quality: 

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

Influence matrix approach: 

Influence matrix approach

Building of simplified groundmodel : 

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

Analytical solution for spatial interaction: 

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

‘Walking’ measure: 

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

Combination with simulation model: 

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

Regression model MSWa (1): 

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

Regressiemodel MSWa (2): 

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

Embedding approach using mixing cells : 

Embedding approach using mixing cells

Software: 

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

Pilot study/methodology: 

Pilot study/ methodology

What are we talking about ?: 

What are we talking about ? 1. Problem definition

Pilot area Beerze & Reusel: 

Pilot area Beerze andamp; Reusel

What are the stakeholder objectives ?: 

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

Objectives: 

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

Where are we now ?: 

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

Situation Now land use: 

Situation Now land use

AlterrAqua: GIS-shell for regional hydrology : 

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

Metamodel for leaching of nutrients: 

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

Situation NowNitrate concentration(in the long-term,after endlessly repeating manuring): 

Situation Now Nitrate concentration (in the long-term, after endlessly repeating manuring)

Catchment accumulation of NO3-N loadingon surface water: 

Catchment accumulation of NO3-N loading on surface water

Situation Now : N-loading on surface waternitrogen surplus: 

Situation Now : N-loading on surface water nitrogen surplus

Where are we heading ?: 

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

Autonomous developments + climate scenario: 

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

Autonomous developments: drainage & nature: 

Autonomous developments: drainage andamp; nature Current Situation Autonomous development

What should we focus on ?: 

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

What are the options ?: 

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

Measures(options): 

Measures (options) land use water management

What is the best strategy ?: 

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

Planning with water, ‘inverse approach’: 

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

Integration with agricultural model DRAM: 

Integration with agricultural model DRAM

Contribution to peak flow, per subcatchment: 

Contribution to peak flow, per subcatchment

Contribution topeak flow in reference run: 

Contribution to peak flow in reference run

Optimisation-model (Beerze-Reusel): 

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

Strategy 1 : flood risk : 

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

Strategy 1 (ctd) : generated pattern of measures: 

Strategy 1 (ctd) : generated pattern of measures

Strategy 2a:- desiccation  - option for new natural grasslands discourageddY = - 1.5 M€ /ydN = 64 ha: 

Strategy 2a: - desiccation  - option for new natural grasslands discouraged dY = - 1.5 M€ /y dN = 64 ha

Strategy 2b:- desiccation  - option for new natural grasslands encourageddY = - 0.7 M€ /ydN = 250 ha: 

Strategy 2b: - desiccation  - option for new natural grasslands encouraged dY = - 0.7 M€ /y dN = 250 ha

Strategy 3: combined targets of 1&2 dY=-3.3M€/y: 

Strategy 3: combined targets of 1andamp;2 dY=-3.3M€/y 3. flood risk  desiccation  1. flood risk 

Strategy 3 (ctd.) : field drainage: 

Strategy 3 (ctd.) : field drainage Field drainage 1. flood risk  3. flood risk  desiccation 

Landuse in strategy 2b: 

Landuse in strategy 2b 3. flood risk  desiccation  2b. desiccation 

Implicit conflict: flood risk  <> desiccation : 

Implicit conflict: flood risk  andlt;andgt; desiccation 

Strategy 4:- flood risk  - desiccation  - N-loading SW  : 

Strategy 4: - flood risk  - desiccation  - N-loading SW  dY = - 17.5 M€ /y

N-loading on surface water generic measures <> optimisation : 

N-loading on surface water generic measures andlt;andgt; optimisation

Slide60: 

4a N-loading SW  via generic measure dY = -25 M€/j dY = -17 M€/ j 4 N-loading OW  via optimisation

Tradeoff curve for SW-objective and income: 

Tradeoff curve for SW-objective and income With (rood) and without (blue) transport via deep groundwater

Trade-of curves desiccation nature: 

Trade-of curves desiccation nature naturezones 1-20

Trade-off curves nature areas: 

Trade-off curves nature areas

Concluding remarks (modelling): 

Concluding remarks (modelling) integrated modelling of hydrology, ecology, and economy combined use of simulation andamp; optimization turns the regional system ‘inside-out’ ideas for solutions, gives insight

Concluding remarks (stakeholder participation): 

Concluding remarks (stakeholder participation) stakeholders must get really creative about options (including multifunctional forms of landuse) protocol for interaction with stakeholders must be further developed (in the form of a game?)

Increased adaptive capacity through risk diversification: 

Increased adaptive capacity through risk diversification

Slide67: 

GHG veldinventarisatie SIMGRO vóór calibratie na downscaling (25*25m) Afvoer (l/s)

Slide68: 

GHG veldinventarisatie SIMGRO na calibratie en downscaling (25*25m) Afvoer (l/s)

Calibration factors for MSWa: 

Calibration factors for MSWa fGHG (-) fGVG (-)

Verification with SIMGRO (1): 

Verification with SIMGRO (1)

Verification with SIMGRO (2): 

Verification with SIMGRO (2)