Brigham Young University BYU ScholarsArchive 1st International Congress on Environmental International Congress on Environmental Modelling and Software - Lugano, Switzerland - Modelling and Software June 2002

Jul 1st, 12:00 AM Estimation of High Floods by Three Rainfall- Runoff oM dels with Short Rainfall-Runoff eS ries (Alzette River Basin, Luxembourg) A. El Idrissi

G. Drogue

L. Pfister

J. F Iffly

L. Hoffmann

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El Idrissi, A.; Drogue, G.; Pfister, L.; Iffly, J. F; Hoffmann, L.; Hingray, B.; and Guex, F., "Estimation of High Floods by Three Rainfall- Runoff odeM ls with Short Rainfall-Runoff eS ries (Alzette River Basin, Luxembourg)" (2002). International Congress on Environmental Modelling and Software. 90. https://scholarsarchive.byu.edu/iemssconference/2002/all/90

This Event is brought to you for free and open access by the Civil and Environmental Engineering at BYU ScholarsArchive. It has been accepted for inclusion in International Congress on Environmental Modelling and Software by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Presenter/Author Information A. El Idrissi, G. Drogue, L. Pfister, J. F Iffly, L. Hoffmann, B. Hingray, and F. Guex

This event is available at BYU ScholarsArchive: https://scholarsarchive.byu.edu/iemssconference/2002/all/90 Estimation of High Floods by Three Rainfall-Runoff Models with Short Rainfall-Runoff Series (Alzette River Basin, Luxembourg)

El Idrissi, A.1 Drogue, G.1,2, Pfister, L.1, Iffly, J. F.1, Hoffmann, L.1, Hingray, B.3, Guex, F.3

1 CREBS-Cellule de Recherche en Environnement et Biotechnologies, Centre de Recherche Public – Gabriel Lippmann, 162a, Avenue de la Faïencerie, L-1511 Luxembourg, Grand-duchy of Luxembourg. 2 Centre d'Ecologie Végétale et d'Hydrologie, UMR-MA 102 ENGEES-ULP, 1, quai Koch, F-67070 Strasbourg cedex, France. 3 Ecole Polytechnique Fédérale, IATE - HYDRAM, CH-1015 Lausanne, Switzerland. ([email protected])

Abstract: This paper presents a comparison of hourly high runoff simulations using short discharge series for three parsimonious rainfall-runoff models that differ substantially in their conceptualisation (two reservoir models and one physically-based model). The models were applied to eight monitored sub-basins characterised by different physiographical properties and hydrological behaviour, located in the experimental Alzette river basin (Luxembourg). The model calibration procedure consists in selecting only rainfall-runoff events with highest peak flows and highest runoff production that occurred during the available measurement period (1997-2001). Simulated extreme values of peak flow and stormflow volumes were analysed and compared to observed runoff series. Results show that the models are able to provide good fits to the rainfall-runoff event’s hydrographs. Nevertheless, the physically-based MHM model gives the best results in terms of predictive accuracy.

Keywords: rainfall-runoff; modelling peak flows; stormflow volume; Alzette river basin; Luxembourg.

1. INTRODUCTION 2. METHODS

Rainfall-runoff models are important tools in 2.1 Study area and data operational hydrology and can differ in terms of mathematical representation of processes, spatial The three models were applied to eight sub-basins discretisation of the basin and data requirements. (Figure 1, Table 1), located in the experimental In practice, the superiority of distributed physically Alzette basin (1176 km2, Grand-duchy of based models over simpler models for operational Luxembourg). The selected sub-basins were purposes is currently an open question. This chosen to be representative of the variability of question is important for users of models, who basin sizes, geological conditions, physiographical may need to judge whether the increased costs of properties, as well as the availability and quality of obtaining and processing spatially-distributed streamflow data (Table 1). basin data can be justified in terms of increased Three sub-basins are homogeneous from a reliability of model predictions [Donnelly- lithological point of view with essentially marls Makowecki and Moore, 1999]. Therefore, a (Mierbech, Mess, Eisch). Except for the Alzette in comparison of models is required to provide a Pfaffenthal, all other sub-basins can be considered basis for choosing a model that will yield an as rural and forested. Note that former mining adequate performance in a specific application for activities in the right bank tributaries disturb the the lowest cost. hydrological behaviour of the Alzette in The purpose of this paper is to compare the outputs Pfaffenthal. of three rainfall-runoff models using short observation data series.

470 using the Penman-Monteith formula [Monteith and and [Monteith formula Penman-Monteith the using (PET)wasestimated evapotranspiration Potential reference raingauges. hourly 5 of the of rainfall structure temporal time disaggregatedaccording tothe polygons) was Thiessen via (interpolated arealrainfall daily hourlyrainfallTo compute period. modelling the for andavailable areas study the covering time-step, an hourly at recording and4raingauges raingauges daily recording non- via 19 wascollected (Pt) Total rainfall Pervious formations: Permeable geology is substratum with dominance of sandstone dominanceof with issubstratum geology Permeable formations: Pervious siltmarls, or of schists,clay issubstratum withdominance geology Impermeable formations: Impervious at a15-minute time step. levels water are recording 1995. 16streamgauges basin since the experimental been setupin Alzette has network observation hydrological A dense Figure 1. let ffeta 39 65. 349 59. Alzette Pfaffenthal 166 47.1 51. Reichlange Attert 84.7 47.2 58.4 SchoenfelsMamer Eisch Hagen Platen Roudbach 36.1 91. Mess Pontpierre 7. Pall Niederpallen 34.6 Huncherange Mierbech 66.

N Basin Reichlange %[ Study area and measurement networks measurement networks and area Study %[ %[ Niederpallen %[ 001 0Kilometers 20 10 0 10 Hagen %[ Platen %[ # #Y # # Pontpierre # %[ #Y %[ %[ %[ %[ Table 1. %[ %[ # Schoenfels

Huncherange Outlet # %[ # # Pfaffenthal %[ #Y #Y %[ Ettelbruck # %[ for each sub-basin, the the for each sub-basin, Physiographical characteristics Physiographical characteristics %[ %[ %[ #Y # National boundaries River network Daily rain gauge Hourly rain gauge Stream gauge 2 52484. 58 2 6.2 45.9 3 95.24.8 32 15.8 Area (km )

341. 333. 49 4 83.4 16.6 23.3 37.6 34.9 Impervious 7 34.3 25.4 26.8 25.2 19.2 9 48.1 22.7 33.9 31.6 11.6 4.8 1 40.9 32.4 25.8 36.7 21.1 6 8.4 3.9 8.7 11.1 59.1 25 51.6 19.1 8 33.2

formations (%) 471

41.6 30 50.6 10.5 8.9 The lumped SOCONT model model SOCONT lumped The method. the Rosenbrock by be optimised formations. Inthiscase,six mustfree parameters of lithological thepermeability for accounts which semi-distributedversion, inits applied model was HRM the following, the In input). lateral routing+ (namely, simpletransformation a by n-1 atorder structure reservoir from obtained because thereservoirstructure atordernis iscalled recursive etal.,1994] [Leviandier HRM, Recursive Model, The Hydrological 2). (Table functions and routing production runoff their in substantially differ They mean weretested. discharge hourly simulating models, parsimonious rainfall-runoff Three modeldescription Hydrological 2.2 the CRP-GL(CREBSlaboratory). by validated and built-up database climatological hydro- the fromcome study this in used Data sets to thewholestudyarea. climatologicaldata serieswereuniformly applied Thesame airport. Luxembourg at the measured data meteorological daily with 1990] Unsworth, components representing the storm runoff (Qr) and (Qr) storm the runoff representing components two into 1994] [Kaden, software (BFI) Index Flow Base viathe isseparated (Qt) discharge total modelling, rainfall-runoff of In view m. is100 thisstudy for sizeused grid the System; Information usingaGeographical by cells grid squared regular into distributed spatially characteristics of thebasins(Table2)were model, physical this running For model. event runoff storm isa 1984] MHM [Batardy, Model, Hydrological model, theMeshed The third found. characteristics couldbe basin specific with relationships significant though even meaning nophysical have parameters three parameters tobefitted. TheSOCONT Pervious

of theselectedsub-basins formations (%)

Cultivated land (%)

Grassland (%) [ Guex, 2001] has has 2001] Guex, Forested land (%)

Urbanised land (%) MHM HRM SOCONT Type of the model Distributed Semi-distributed Lumped deterministic Production function Runoff coefficient (constant) Soil reservoir Soil reservoir Routing function Iso-chronal map Geometrical isochrones Drainage reservoir Base flow Not considered Drainage reservoir Drainage reservoir Calibration procedure Trial-error (manual) Automatic Two steps (automatic) Calibration criterion Nash-Sutcliffe coefficient 1-Nash-Sutcliffe coef. Nash-Sutcliffe coef. Hydroclimatic inputs Pt, Qt, Qr, Qb Pt, PET Pt, PET Geographical inputs Grided maps: geology, river Geology, drainage area, Drainage area network, slope, flow directions isochronal zones Outputs Qr, isochronal map Qt Qt, Qb, PET Parameters meaning Physical meaning Physical meaning No physical meaning

Table 2. Main characteristics of the three tested models for this case study the base flow (Qb). To produce stormflow (Qr), Next, events that have the lowest runoff production the MHM uses two matrices combining two volumes are rejected (Figure 3). The rainfall- geological classes and three slope classes: the first runoff events finally retained (no more than 4 to 5 matrix contains the stormflow coefficients (2x3=6 per sub-basin) present similar saturated soil parameters) and the second one contains the runoff moisture conditions. velocities (2x3=6 parameters). To obtain the total In the second step, the model parameters were discharge, the Qr values are added to the estimated optimised according to three different methods: i) Qb. MHM model calibration consists in fitting all rainfall-runoff events were aggregated into a manually the 12 parameters values [El Idrissi et al., unique sequence preceded by a one month period 1999]. for initialising storage reservoirs; ii) each event was separately calibrated with an initial warm-up period for base flow. Optimal model parameters 2.3 Modelling calibration procedure were averaged; iii) using the continuous data series (usual application of the SOCONT and the HRM The available streamflow measurement period models). The 3 methods give results with slight extends over 4.5 years from January 1997 to differences and will be thus considered as March 2001; since the number of high flood events comparable. is limited, the series are not long enough to 60 perform a statistical analysis of high floods. Selected events However, it is possible to calibrate the model 50 Unselected events parameters by extracting medium and high flood events from the available data series. 40 In the first step, only hydrographs with the highest peak flows are retained; a threshold is arbitrarily selected for a given sub-basin (Figure 2). 30

14 20 Stormflow (mm) volume 12 10 /s) 3 10 0 0 20406080100 8 Total rainfall (mm) 6 Figure 3. Selection of events according to the 4 stormflow volume criterion (Eisch at Hagen)

Recorded peak flow (m 2 Selected events Unselected events 0 2.4 Criteria for model performance 0 20406080100

Total rainfall (mm) Both numerical and graphical criteria were considered to provide a good overall indication of Figure 2. Selection of events according to the peak the model’s capabilities. The accuracy criteria flow criterion (Mamer at Schoenfels) concern the stormflow shape (Qr), peak flows and stormflow volumes.

472 For assessing hourly global model efficiency, the The most homogeneous and the less homogeneous Nash and Sutcliffe [1970] coefficient was tested sub-basins (Mierbech and Alzette in computed for each sub-basin: Pfaffenthal) have an unexpected significant influence on model performance. The best results n 2 are obtained for the less homogeneous one. This ∑ ()Qsimi − Qobsi CNS = 1 − i =1 (1) could be explained by Mosley [1981]: ‘where a n 2 number of hydrological factors are equally ∑ ()Qobsi − Qobs i =1 important in controlling hydrologic regime, and where the heterogeneity differs from one property where n is the number of discharge values of the to another, a complex mosaic of hydrologically selected events hydrographs, Qsimi and Qobsi are apparent homogeneous areas may result. the simulated and the observed stormflow values 1.0 MHM MHR SOCONT respectively, and Qobs is the average of observed 0.9

0.8 stormflow values. CNS is less than 1 (equal to 1 0.7 when Qsim = Qobs). To assess goodness of fit and accuracy of 0.6 0.5 simulated peak flows and stormflow volumes, CNS 0.4 mean absolute error (MAE), expressed in percentage was determined for each sub-basin : 0.3 0.2

0.1 n 1 0.0 MAE (%) = ∑ ()Ysimi − Yobsi /Yobsi (2) i =1 n e en ierr ange range pallen Plat enfels r ntp Hagen o che where n is the number of discharge values of the n Po Sch Reichl Pfaffenthal Hu Niede selected events hydrographs, Ysimi stands for the computed values and Yobs stands for the Figure 4. Comparison of model efficiency (Nash i and Sutcliffe coeffcient) measured values. The nearer the MAE is to zero the better the method. The MAE and MBE criteria are used to To quantify the importance of the difference characterise the stormflow volume (SFV) between observed and simulated stormflow variations for the eight chosen sub-basins. The volume, bias or magnitude of mean errors (MBE), comparison of the results provided by the three expressed in mm, was determined for each sub- models concerning SFV is shown in Figure 5. basin : 100 MHM HRM SOCONT 1 n 90 MBE = Ysim − Yobs (3) ∑ ()i i 80 n i =1 70 60 3. RESULTS 50 (%) MAE 40 30

3.1 Model efficiency 20 10

0 The CNS variation for the tested sub-basins is s e depicted in Figure 4. In terms of general aten gen ng thal pierre Pl a la en t H h ff performance, the MHM model gives slightly better eic fa iederpallenPon Schoenfel R P results than SOCONT and HRM. The average HuncherangeN CNS is of 0.76, 0.73 and 0.72 respectively. However, MHM seems to be less efficient on the Figure 5. MAE criterion for simulated stormflow largest sub-basins (Attert at Reichlange and volumes Alzette at Pfaffenthal). The three models obtain their worst CNS for the Mess sub-basin. It is Averaged MAE values obtained by MHM and possible that the influence of a highway on the SOCONT are close and thus comparable (21.4 and natural hydrological behaviour of this 22.5 respectively). Excepted for the contrasted homogeneous sub-basin does not permit an result recorded for the Roudbach at Platen sub- accurate simulation of observed hydrographs. basin, no specific tendency is observed for the remaining sub-basins.

473 The MHM and SOCONT models have slightly sized sub-basins with a relatively homogeneous positive (3.5 mm) and negative bias averages (-4.6 spatial distribution of geological formations: in the mm), respectively. However, HRM largely upstream-downstream sense for the Mamer and underestimates the volume (-14.2 mm), especially north-south sense for the Attert. for the smaller sub-basins (Figure 6). As the number of sampled rainfall-runoff events 100 does not exceed 5 for each chosen sub-basin it is MHM HRM SOCONT inadequate to use them to carry out any trend 80 concerning the very high peak flows and volumes. 60 In Figure 8 (page down) are represented for the 40 three models, scatter plots of observed and 20 simulated storm peak flows of all selected sub- 0 basins. Observed peak flow values ranging from 35 m3/s to 50 m3/s are lacking. MBE (mm) -20 The SOCONT model shows a systematic under- -40 estimation for the entire range of peak flows Platen -60 Hagen (Figure 8). HRM simulates well low observed Pontpierre Pfaffenthal -80 Schoenfels Reichlange peak flow values and slightly overestimates Niederpallen

Huncherange 3 -100 measured peak flow values superior to 40 m /s (Figure 8). Model predictions towards extreme Figure 6. Bias recorded by the three models (MBE values might be exaggerated. On the contrary, the criterion) SOCONT model will systematically under- estimated extreme values (Figure 8). MHM model For the storm peak flow (SPF) values, MHM gives results suggest a slight trend towards an under- better results than the two other models and the estimation of peak flows superior to 25 m3/s. difference is rather significant (Figure 7). Note that In terms of stormflow volume (Figure 9), both the simulated peak flow values were extracted in a HRM and SOCONT show progressive under- window of ± 3 hours around the observed peak estimation of volume from low to high values. flow values. However, MHM gives a good trend estimation of 100 observed volume discharge and provides a better MHM HRM SOCONT tendency for volumes of extreme events. 90

80

70 4. CONCLUSIONS AND PERSPECTIVES 60

50 A procedure to analyse and compare hourly high

MAE (%) MAE 40 floods simulated by three rainfall-runoff models 30 with short rainfall-runoff series is presented. 20 The results based on the CNS criterion show that 10 the three tested models are well suited to assess the 0 simulation of high event’s discharges for the eight e en s g ll en el selected sub-basins in terms of general shape of the n a g f nthal p Platen en era er ntpierre Ha o h o observed storm hydrographs. The results based on nc ed P ch u i S ReichlangePfaffe H N the MAE and MBE criteria suggest that the MHM model is best suited for identifying the peak flows Figure 7. Comparison of simulated peak flow and stormflow volumes within the study area, (MAE criterion) regardless of the sub-basin’s scale and characteristics. The SOCONT model shows a The MHM leads to a MAE of 16.3% on average. systematic under-estimation of both peak flow and This value reaches 29.9% and 28.5% for HRM and stormflow volumes and should not be used for SOCONT, respectively. The same, but less extreme values. pronounced tendency is found for the MBE The accuracy of the calibration procedure would criterion. The manually ‘oriented’ calibration be better if sufficient higher rainfall-runoff method and the simulation of only net runoff can observed events would exist. In the future, each partially explain the observed differences. But the sub-basin will be separately examined. Concerning better results recorded for the CNS and the SFV the method verification, the lack of hourly confirm the overall better accuracy of MHM. Intensity-Duration-Frequency curves in the Alzette The best averaging MAE results of SFV basin does not allow the use of a range of designed simulations are obtained on the Mamer (13.1%) storm events with adequate return periods. and Attert (13.8%) sub-basins. These are medium

474

70 70 70 MHM HRM SOCONT 2 2 60 R = 0.95 60 R = 0.97 60 R2 = 0.95

/s) 3 /s) /s) 3 50 50 3 50

40 40 40

30 30 30

20 20 20

flow peak Modelled (m Modelled peak flow (m peak Modelled Modelled peak flow(m 10 10 10 0 0 0 0 10203040506070 0 10203040506070 0 10203040506070 3 3 3 Observed peak flow (m /s) Observed peak flow (m /s) Observed peak flow (m /s) Figure 8: Comparison of observed and simulated peak flow

60 60 60 HRM MHM SOCONT 50 R2 = 0.76 50 R2 = 0.78 50 R2 = 0.77

40 40 40

30 30 30

6 20 20 20

10 10 10 Modelled stormflow (mm) volume Modelled Modelled stormflowvolumeModelled (mm) (mm) stormflow volume Modelled 0 0 0 0 102030405060 0 102030405060 0 102030405060 Observed stormflow volume (mm) Observed stormflow volume (mm) Observed stormflow volume (mm)

Figure 8: Comparison of observed and simulated stormflow

Polytechnique Fédérale de Lausanne, 67 p., Acknowledgement 2001. Kaden, U., Etude de la séparation des écoulements Thanks to Leviandier T. for providing the HRM sur différents bassins d’Alsace. Application model and for his comments. et automatisation de la technique du Base Flow Index (BFI), Master thesis, Université Louis 5. REFERENCES Pasteur, Strasbourg, 55 pp., 1994. Leviandier, T., G. Mantilla-Moralles, A. Zermani, Batardy, J., L’élaboration d’un modèle Etude comparative de la résolution spatiale hydrologique conceptuel maillé pour la des modèles hydrologiques sur le bassin de prévision des débits en temps réel. la Charente. Symposium sur les relations Application au bassin versant de la Lesse à continent-zones côtières, GIP Daverdisse. Rapport final, S.A. LGR N.V., Hydrosystèmes, La Rochelle, 17 pp., 1994. Athus, 254 pp., Belgique, 1984. Monteith, J. L. and M. Unsworth, Principles of Donnelly-Makowecki, L.M., and R.D. Moore, Environmental Physics, 2nd ed., Edward Hierarchical testing of three rainfall-runoff Arnold, London, 1990. models in small forested catchments, Mosley, M.P., Delimitation of New Zealand Journal of Hydrology, 219, 136-152, 1999. hydrologic regions, Journal of Hydrology, El Idrissi, A., I. Ruthy, P. Hang, M. VanClooster, 49, 173-192, 1981. E. Persoons, Regionalization of rainfall-runoff Nash, J.E., and J.V. Sutcliffe, River flow parameters in the catchment of the Dyle forecasting through conceptual models. Part (Belgium), Act of: International conference on I: a discussion of principles, Journal of Modelling of transport process in soils at Hydrology, 10, 282-290, 1970. various scales in time and space, Edited by J., Feyen & K. Wiyo, Leuven, Belgium, November 24-26, 1999. Guex, F., Modélisation hydrologique dans le bassin versant de l’Alzette (Luxembourg), régionalisation des paramètres d’un modèle global, Travail pratique de Diplôme, Ecole

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