Mar 6, 2013 - HRU units composed by pixels with same the land use ... Discharge: simulated vs. observed ... different objective functions (NSE sediment vs.
Sediment dynamics in the Mekong basin - model development and multi-objective calibration First results from a case study in the Nam Ou watershed Stefan L¨ udtke 1 , Heiko Apel 1 , Dung-Viet Nguyen Merz 1 1 GFZ
1
and Bruno
German Research Centre for Geosciences, Section 5.4 Hydrology, Potsdam, Germany
March 6, 2013
Introduction Methods Results Discussion References
Objectives
Hydrological model: SWIM (Soil-WaterIntregrated Model) Hydraulic model: MIKE 11 (GFZ & SIWRR)
How does the hydrological model SWIM performs in an automated multi-objective calibration in the Nam Ou catchment?
Introduction Methods Results Discussion References
Table of contents
1
Methods Model description Model setup Model calibration
2
Results
3
Discussion
Model description Model setup Model calibration
Introduction Methods Results Discussion References
Model description Model setup Model calibration
SWIM Soil-Water-Intregrated Model (Krysanova, 1998) semi-distributed lumped model based on SWAT and Matsalu 3-level disaggregation basin → subbasin → hydrotop (HRU) basin area of interrest subbasin defined by the user as a preprocessing step HRU units composed by pixels with same the land use and soil type distributed within a subbasin all hydrological processes are computed on the HRU level
Introduction Methods Results Discussion References
Model description Model setup Model calibration
Overview
area 26.000km2 91 subbasins only one station for calibration (Muong Ngoy) of discharge and sediment modelled time span from 1990 to 2002 with 2 years warm up Figure: General overview over the study area
Introduction Methods Results Discussion References
Model description Model setup Model calibration
Input data and general settings dem 250m digital elevation model (Jarvis et al., 2008) soil Harmonized-World-Soil-Database(HWSD) (Nachtergaele et al., 2010) landuse GLOBECOVER (Bontemps et al., 2011) precipitation APHRODITE (Yatagai et al., 2009) climate ERA-INTERIM (Dee et al., 2011) discharge & sediment MRC Other settings: 11 parameters for calibration that control infiltration water routing sediment routing ...
Introduction Methods Results Discussion References
Model description Model setup Model calibration
NSGA II algorithm Best nPop parameter set from the montecarlo runs
generation of the new parameter sets
nPop number of model runs
nGen
Evaluation of the model results
NSGA II based sorting
nPop population size (number of model runs in each generation=140) nGen number of generations=100)
Figure: Scheme of the multi-objective calibration
Algorithm by Deb et al. (2000) and code by Dung et al. (2011).
Introduction Methods Results Discussion References
Table of contents
1
Methods Model description Model setup Model calibration
2
Results
3
Discussion
Introduction Methods Results Discussion References
Multi-Objective calibration generation
0.2
0.2
−0.4
−0.2
0.0
0.00
0.05
0.10
0.15
0.20
generation
Figure: Evolution of the goodness of fit measures for discharge and sediment during the calibration process for generation 1, 20, 60 and 100.
0.7
0.7
generation
0.6
0.6
NSE: NashSutcliffe-Efficiency (Nash and Sutcliffe, 1970)
0.5
0.5 0.4
0.4
0.3
NSE discharge
0.3
0.3
0.4
0.4
0.5
0.5
0.6
0.6
0.7
0.7
generation
0.12 0.14 0.16 0.18 0.20
0.12 0.14 0.16 0.18 0.20 0.22
NSE sediment
Introduction Methods Results Discussion References
Discharge: simulated vs. observed
4000
6000
NSE Discharge
Sediment
0.42 0.72
0.23 0.12
2000
Table: Goodness of fit measure (NSE) for the discharge and sediment time series
0
Discharge in [m³/sec]
8000
simulated observed
1992
1994
1996
1998
Time
2000
2002
Introduction Methods Results Discussion References
Sediment: simulated vs. observed
1.0
1.5
NSE Discharge
Sediment
0.42 0.72
0.23 0.12
0.5
Table: Goodness of fit measure (NSE) for the discharge and sediment time series
0.0
Sediment load in [tons/sec]
2.0
simulated observed
1992
1994
1996
1998
Time
2000
2002
Introduction Methods Results Discussion References
Summary
multi-objective calibration shows a trade off between the different objective functions (NSE sediment vs. NSE discharge) discharge is depicted well by the model seasonality of the sediment dynamics are captured but in general, the model performs rather poor on sediments
Introduction Methods Results Discussion References
Table of contents
1
Methods Model description Model setup Model calibration
2
Results
3
Discussion
Introduction Methods Results Discussion References
Open questions
What parameters cause the trade off between the simulated discharge and sediment time series? How to deal with the sparse data for sediment load? How reliable is the data on sediment load? How do extremes events like landslides effect the validation of the model results?
Introduction Methods Results Discussion References
References Bontemps, S., Defourny, P., Van Bogaert, E., Kalogirou, V., and Arino, O. (2011). GLOBCOVER 2009: Products Description and Validation Report. pages 1–17. Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000). A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J., and Schwefel, H.-P., editors, Parallel Problem Solving from Nature PPSN VI, volume 1917 of Lecture Notes in Computer Science, pages 849–858. Springer Berlin Heidelberg. Dee, D. P., Uppala, S. M., Simmons, a. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. a., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, a. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, a. J., Haimberger, L., Healy, S. B., Hersbach, H., H´ olm, E. V., Isaksen, L., K˚ a llberg, P., K¨ ohler, M., Matricardi, M., McNally, a. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Th´ epaut, J.-N., and Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656):553–597. Dung, N. V., Merz, B., B´ ardossy, a., Thang, T. D., and Apel, H. (2011). Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data. Hydrology and Earth System Sciences, 15(4):1339–1354. Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E. (2008). Hole-filled SRTM for the globe Version 4. available from the CGIAR-CSI SRTM 90m Database (http://srtm. csi. cgiar. org). Krysanova, V. (1998). Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds. Ecological Modelling, 106(2-3):261–289. Nachtergaele, F., Velthuizen, H. V., Verelst, L., Batjes, N., Dijkshoorn, K., Engelen, V. V., Fischer, G., Jones, A., Montanarella, L., Petri, M., Prieler, S., Teixeira, E., Wiberg, D., and Shi, X. (2010). Harmonized World Soil Database. Soil Science, (Report):37. Nash, J. and Sutcliffe, J. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3):282–290. Yatagai, A., Arakawa, O., Kamiguchi, K., Kawamoto, H., Nodzu, M., and Hamada, A. (2009). A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Sola, 5:137–140.
