multi-criteria decision making in polder planning A.H.Schumann , - PowerPoint PPT Presentation

Flood scenarios, imprecise probabilities and multi-criteria decision making in polder planning A.H.Schumann , D.Nijssen, B. Klein, M.Pahlow Ruhr- University Bochum Germany Institute for Hydrology and Water Management

Flood scenarios, imprecise probabilities and multi-criteria decision making in polder planning Introduction 1. Case Study 2. Characterisation of uncertainties of hydrological loads by scenarios 3. Decision support 4. Summary 5.

CONFERENCE THEMES and TOPICS Theme A1 : How can we identify and quantify water-related changes due to direct human interventions (analysis of long-time past  records, future developments); Theme A2 : How can we identify and quantify water-related changes due to climate change (analysis of long-time past records,  future developments); Theme B : How can we discriminate among impacts of direct human interventions and impacts caused by climate change, and  how can we quantify the impacts; Theme C : How can we quantify/ predict changes in water-related hazards;  Theme D : How can we adapt to / mitigate water-related hazards? -  resilient and robust ways to adapt to water-related disasters. „robust“: „ capable of performing without failure under a wide range of conditions ”

Hydrological Risk of flood protection by technical retention No flood protection Flood protection by technical retention facilities depends on characteristics of events! Full flood protection

From Safety to Risk-Oriented Approaches Motivatio n Safety-oriented Approach Risk-oriented Approach Choice of a design flood 100 % safety can not be Q design (e. g. 100 year flood) achieved by technical measures Design Risk of failure Technical flood control fully Hydrological Risk functional for Q  Q design Operational &Technical Risks Assumption: No risk of failure Risk Management is for Q  Q design and negligible required risks beyond Q design

Design Floods based on standardized and scaled Kozeny- hydrographs for different return periods 11 Maximum of Water Level within HQ(1000) 10 HQ(500) 9 HQ(200) 8 HQ(100) 7 HQ(50) Abfluß in m3/s Spill-off reservoir 6 HQ(20) Flood Peak Inflow 5 HQ(10) HQ(5) 4 (non-linear) relationship 3 between flood peak and HQ(2) reservoir performance 2 1 0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 Zeit in Stunden

More complex design floods: Stochastic generation of hydrographs with two peaks, derived from overlaying of two Kozeny-Curves         flood storage capacity    I II 80 Q t Q t Q t Q t d I-II P 60 Z B d d Q [m³/s] Hy 40 Q Z (t) Q A (t) 20 Shape Parameters: 0 T 0 20 40 60 t 1 Q I sd , Q II sd : Peaks of single flood waves t 2 t 3 m I , m II : shape parameter of flood waves Duration of the flood event t 4 t I A ,t II A : time to peak 80 60 d I-II : temporal distance between the m I Q [m³/s] 40 I overlaying floods I Q Q d (t) 20 Sd t 2 : lag time until begin of the 0 0 20 40 60 second flood wave I t 1 =t A 80 t 3 : total time span until second 60 I t 3 =t A +d I-II Q [m³/s] peak occurs 40 m II II 20 II Q d (t) Q Sd 0 0 20 40 60 time t II t 2 t A

Monte- Carlo- Simulations of Hydrographs resulting from a design rainfall with duration D=24 h and a return periode of 1000 years(Reservoir Gottleuba, Germany) (Klein, 2009) Maximum of Water Level Simulation of hydrographs with one peak within reservoir Spill-off Simulation of hydrographs with two peaks Flood Peak Inflow Maximum of Water Level Inflow Spill-off within reservoir time Flood Peak Inflow  more complex relationships between flood peaks and resulting storage content of the reservoir

Flood scenarios, imprecise probabilities and multi-criteria decision making in polder planning Introduction 1. Case Study 2. Characterisation of uncertainties of hydrological loads by scenarios 3. Decision support 4. Summary 5.

Considering complex flood risks in planning of flood retention systems Unstrut River Basin in Germany, 6343 km 2 Germany

6 Different System States of the Technical Flood Control System 1 2 3 System State 2: System State 1: System State 3: Current system is Status Quo: Addition of small polders in the upstream region fully functional 2 dams (~57 million m 3 ) 2 dams (~57 million m3) 2 dams (~57 million m3) 5 polders (~45 million m 3 ) 9 polders (~77 million m3) 5 polders (~45 million m3) 4 5 6 System State 4: System State 6: System State 5: Addition of larger polders in the Implementation of larger polder Controlled operation of the polders upstream region inlet structures 2 dams (~57 million m3) 2 dams (~57 million m3) 2 dams (~57 million m3) 9 polders (~85 million m3) 9 polders (~85 million m3) 9 polders (~85 million m3)

12 Spatial characteristics of hydrological loads and flood retention facilities Flood protection depends on: • spatial distribution of precipitation • coincidences of floods in tributaries • performances and interactions of flood retention facilities

Flood scenarios: 13 Coupling a stochastic generator of precipitation fields ( Bárdossy & Plate (1992)) with a hydrological model Mixed Distribution: • Gamma-DF for small values, • Generalized Pareto- DF for high values             1 p z,u h z,u p z,u g z,u      f z,u   K u 122 gauges precipitation Simulation of 10 times 1.000 years runoff (daily values)

14 Transfer of simulated daily values into flood events, consideration of reservoirs peak volume Flood statistics, derived from observed and simulated data

15 Impact analysis: Hydro-dynamic simulations of runoff, polder flooding and inundations (RWTH Aachen, Prof. Schüttrumpf) Coupled 1D- 2D- Model implicid 4-point discretisation scheme „Storage - Cell“ -Approach

16 Selection of flood scenarios Criteria Characteristics Performance of existing reservoirs Flood Peak, Volume, Hydrograph Interaction of tributaries Distribution of runoff, Flood retention by polders and reservoirs Spatially uneven distributed damages Damages related to political units Event-specific damages Number of affected people, innundated areas, economic losses

Flood scenarios, imprecise probabilities and multi-criteria decision making in polder planning Introduction 1. Case Study 2. Characterisation of uncertainties of hydrological loads by scenarios 3. Decision support 4. Summary 5.

18 Scenarios to assess the performance of existing retention facilities Example: Two 100- years floods with different volumes and shapes Reservoir Straussfurt Inflow Outflow Water level Inflow Outflow Water level

19 Differences between flooded areas resulting from two different floods with T=100 years

20 Bivariate Statistics with Copulas Sklar- Theorem (1959):           F x y , C F x , F y C u v ( , )   X Y , X Y F X,Y (x,y) bivariate Distribution Function F X (x), F Y (y) Boundary distributions of random variables X and Y C Copula- function describing interdependencies between X and Y independent from boundary distributions              1 , , C u v u v  Generator

21 Bivariate Statistics with Copulas Non-exceedance Probability of x and y               P X x,Y y F x,y C F x ,F y   X,Y X Y Exceedance Probability of x and y                P X x Y y 1 F x F y F x,y X Y X,Y               1 F x F y C F x ,F y X Y X Y 1 1       Return Periode T Max T ,T                 X,Y X Y P(X x Y y) 1 F x F y C F  x ,F y  X Y X Y Exceedance Probability of x or y :                  P X x Y y 1 F x,y 1 C F x ,F y X,Y X Y 1 1       Return Periode T Min T ,T           X,Y X Y P(X x Y y) 1 C F  x ,F y  X Y

Bivariate Analysis: Flood Peak-Volume at dam sites Joint return periods:  A large variety of different hydrological scenarios has to be considered in design E.g. return period of flood peak of about 250 years at reservoir Straußfurt, the corresponding return periods of the flood volumes ranges between 50 and 500 years

Bivariate Analysis: Flood Peak-Volume at dam sites Joint return periods:

24 Hydrological Scenarios of different return periodes 250 T=25 years 200 500 T=50 years 450 s ] 150 400 Q [m³/ 350 350 T=100 years 100 300 Q [m 3 /s ] 300 250 250 50 200 Q [m 3 /s ] 200 150 0 100 150 0 200 400 600 800 1000 50 Dauer [h] 100 0 0 200 400 600 800 1000 50 Dauer [h] 0 400 0 200 400 600 800 1000 1200 1400 T=200 years 350 Dauer [h] 300 300 T=500 years 250 Q [m 3 /s ] 250 200 200 150 Q [m 3 /s ] 100 150 50 100 800 0 700 0 200 400 600 800 1000 50 600 Dauer [h] T=1000 years 0 500 Q [m 3 /s ] 0 200 400 600 800 1000 1200 400 Dauer [h] 300 200 100 0 0 100 200 300 400 500 600 Dauer [h]

25 Utilization of Copulas Performance of single reservoirs Interactions of tributaries