Calibration of Rainfall-Runoff Models

ET&P Srl ET&P Srl Environmental Tecnologies and Products Via Don Bedetti, 20 40129 – Bologna Tel.: (+39) 051 6389099 Fax: (+39) 051 6389100 EMail: [email protected]

MUSIC – Multiple-Sensor Precipitation Measurements, Integration, Calibration and Flood Forecasting

A Project supported by the European Commission under Contract No EVK1-CT-2000-00058

Published is here the Deliverable 9.1 titled

Calibration of rainfall-runoff models

By: Michele Marsigli, Francesca Todini, Tommaso Diomede, Zhiyu Liu, Rosa Vignoli ET&P Srl

resulting from

WP 9

Integration with the real-time flood forecasting system

Contents

Contents ...... 2 1 Application of the TOPKAPI Model in the Arno River Basin ...... 3 1.1 Characteristics of the Arno River Basin...... 3 1.1.1 General description of the Arno River basin...... 3 1.1.2 Topography...... 4 1.1.3 Type and properties of soil...... 5 1.1.4 Vegetation and land ...... 6 1.2 Data preparation for the application of the TOPKAPI model ...... 7 1.2.1 DEM application...... 7 1.2.2 Hydro-meteorological data...... 8 1.2.3 Evapotranspiration ...... 11 1.3 Calibration of the TOPKAPI model...... 18 1.3.1 Calibration parameters...... 18 1.3.2 Graphical calibration results on Upper Arno basin for 1992...... 20 1.3.3 Graphical calibration results on Arno basin for 2000...... 23 1.3.4 Statistical tests...... 26 1.3.5 Considerations on calibration...... 28 1.4 Conclusions...... 28 2 Application of the TOPKAPI Model in Reno River Basin ...... 29 2.1 Characteristics of the Reno river basin ...... 29 2.1.1 General description of the Reno River basin...... 29 2.1.2 Topography...... 31 2.1.3 Type and properties of soil...... 34 2.1.4 Land use...... 35 2.2 Data preparation for the application of the Topkapi model...... 37 2.2.1 Applications to DEM...... 37 2.2.2 Hydro-meteorological data...... 37 2.2.3 Evapotranspiration ...... 40 2.3 Calibration of the TOPKAPI model...... 41 2.3.1 Calibration parameters...... 41 2.3.2 Graphical results of the calibration...... 44 2.3.3 Statistical Tests...... 53 2.4 Validation of the model...... 54 2.5 Conclusions...... 60

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 2 1 · Application of the TOPKAPI Model in Arno River Basin

1 Application of the TOPKAPI Model in the Arno River Basin

1.1 Characteristics of the Arno River Basin

1.1.1 General description of the Arno River basin The Arno River basin (Fig. 1.1) has the surface area of 8228 Km2. The river originates from the mountain Falterona (1654m a. s. l) situated in the northern border of Casentino. It initially starts in Northeast-Southwest for about 60 km in the Casentino (883 Km2), then it receives the water from the Chiana (1356 Km2) and debouches to the Valdarno Superiore (1005 Km2). The river flows in the direction of Southwest-Northeast and arrives at the confluence with the Sieve River (836 Km2), then it flows in East-West to the mouth of the river, in the course the Arno River receives some major tributaries as Greve River, Pesa Sream, Elsa River and Era River on the left side, and Bisenzio River, Ombrone River on the right side. The watercourse of the river has a total length of approximately 245 km.

The bed slope of the Arno River varies from 0.14% in the stretch of Laterina-Nave di Rosano to 0.035% in the stretch of Florence-Pisa (Fig. 1.1). In the low-lying area of the Arno River basin, the width of the channel section has the order of 100~150 m in general.

Bisenzio Fornacina Sieve Fornacina Padule Ombrone Fucecchio Pistoiese

Pesa

Casentino Pesa Greve Subbiano Valdarno Superiore

Era Laterina

S. Giovanni Ferrovia Elsa

Chiana

Firenze Nave di Rosano

Figure 1.1 Arno river basin, sub-basins and water level stations

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 3 1 · Application of the TOPKAPI Model in Arno River Basin

1.1.2 Topography The Arno River basin has the surface area of 8228 Km2, 55% of which has an elevation lower than 300 m a.s.l., 30.4% is located between 300 and 600 m a.s.l., 9.8% has an elevation between 600 and 900 m a.s.l., and 4.5% has an elevation higher than 900 m a.s.l.. The major altitudes are found in the montains of Falterona and Pratomagno in the Casentino. The mean elevation in the whole river basin is 292 m a.s. l..

A digital elevation model (DEM) data file is available in the Arno River basin on the basis of 100x100 m cells. This study, uses a DEM with a gird size of 1km×1km derived from the previous one (see Fig.1.2). The area and the average elevation for major sub-basins are listed in Table 1.1.

The local climatic conditions produce the highest flooding risk in the period from September to January in which the south-west winds dominate.

Figure 1.2 DEM map for the Arno River basin (gird size 1km×1km)

Basin Sieve Casentino Chiana Valdarno Greve Ombrone Bisenzio Pesa Elsa Era Name Superiore Pistoiese Area (km2) 836 883 1356 1005 294 486 304 327 856 587 Average Elevation 388 479 226 200 708 234 363 99 380 119 (m a.s.l.) Table 1.1 Topographic information of major subbasins in the Arno River basin

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 4 1 · Application of the TOPKAPI Model in Arno River Basin

1.1.3 Type and properties of soil A soil map of the Arno River basin, created by Milan Polytechnic on the basis of the SCS classification (U.S. Dept. Agric., Soil Conservation Service, 1972), was made available for this project in Arcview format (Fig. 1.3 and Tab. 1.2). The entire basin is divided into four classes; this schematisation is not entirely satisfactory, as will be described in the sequel, but it was the only one available. As shown in Table 1.2, the soil most present in the basin (almost 58% of the total area) is type C. This means that more than half of the soils have the following hydrologic characteristics: thin- bedded, with moderately high potential runoff, and containing a considerable quantity of argillaceous and colloidal materials; they have low infiltration and saturation capacity. In addition, the soils tend to be thin-bedded with low permeability in mountain areas, whereas they are thick and have medium-high permeability in the valleys.

Figure 1.3 Hydrological classes of soil for the Arno river basin

Hydrologic Percentage No. type of soil DESCRIPTION (%) (S.C.S/C.N.) 1 A With scarce potentiality of runoff. Contains deep sands with very scarce lime and clay, and deep gravel of high permeability. 18.5 2 B With moderately low potentiality of runoff. Contains the major part of the sandy soil that is less deep than the one in the group A, but 20.1 the group as a whole keeps high capacity of infiltration even when saturated. 3 C With moderately high potentiality of runoff. Contains the thin soils which contain considerable quantity of clay and colloidal, while 57.9 less than group D. The group has scarce capacity of infiltration when saturated. 4 D With middle high potentiality of runoff. Contains the major part of the clay soil with high capacity of expanding , but also the thin 0.965 soils almost impermeability nearby the surface. Table 1.2 Legend of pedological data (soil hydrologic types)

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 5 1 · Application of the TOPKAPI Model in Arno River Basin

1.1.4 Vegetation and land The land use map was derived from the CORINE mapping, produced by the European Union. Fig. 1.4 shows the distribution of land use in the Arno River basin, and Tab. 1.3 describes its individual characteristics and gives the percentage of area for each type present in the basin.

It is easy to see that land uses classified as B (38.6%) and S (34.7%) predominate. Mountainous zones have mainly shrubs and low or high bushes, while valleys present mainly seeded fields.

Figure 1.4. Land uses in the Arno river basin.

Percentage No. Class DESCRIPTION (%) 1 U Urban areas with continuous tissue, the surface occupied 1.38 generally over 70% 2 U1 Discontinuous urban areas with the percentage of surface 2.88 occupied lower than 79% 3 CA Forest and arboreal vegetation 4.18 4 B Vegetation of shrub, undergrowth ,overgrowth bush 38.6 5 C Herbaceous vegetation, meadow-pasture 6.85 6 CS Special cultivated areas, olive, vineyard 10.8 7 S Fit for sowing 34.7 8 NV Areas without vegetation 0.504 9 P Humid areas 0.467 Table 1.3 Legend of land use data

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 6 1 · Application of the TOPKAPI Model in Arno River Basin

1.2 Data preparation for the application of the TOPKAPI model

1.2.1 DEM application The DEM was used in the TOPKAPI model to define drainage and slope directions for each cell of the DEM grid. Three main steps were used to reach such definition:

• Identify and correct sinks and false outlets

• Identify the connections among the cells, by means of which it is possible to identify the flow pathways, to calculate the steepness for each grid cell

• Identify the channel network given a user-defined minimum drainage area

At first, DEM data were used as described above, with cell dimensions of 1x1 km for the entire basin, for correction and identification of the channel network; nevertheless, the streams identified in this way with DEM are inconsistent with those shown on the vector maps supplied by the Basin Authority for the Bisenzio and Greve basins (Fig. 1.5): this is because levels in shallower zones of the two sub- basins show little variation and were difficult to process.

inconsistent area

Figure 1.5 Comparison between the modelled river system and the given vector map.

To solve this problem, a different procedure was established. The Arno River basin was first divided into 19 sub-basins to satisfy preview requirements, and then DEM correction and identification of drainage network connections were identified for each sub-basin, after which the sub-basins were connected to one another and to the main course of the Arno. Fig. 1.6 shows the river map as generated by means of DEM, consistent with the vector map of the river. The threshold of the drained area

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 7 1 · Application of the TOPKAPI Model in Arno River Basin used to derive the drainage network was found by trial and error, and is equal to 5 Km2.

Figure 1.6 Final channel network raster map modelled by DEM.

1.2.2 Hydro-meteorological data Tab. 1.4 shows maximum annual levels for two main water-level gauging stations (Florence and S. Giovanni alla Vena) from 1992 to 2000. Note that 1992 and 1993 are years in which floods occurred. At an early stage of the work, the year 1992 was chosen for calibrating the model.

Year Station 1992 1993 1994 1995 1996 1997 1998 1999 2000 name Florence 5.32 4.26 2.53 3.95 3.7 3 2.08 2.42 3.98 S. Giovanni 6.80 6.82 3.33 3.88 5.06 3.73 3.01 4.44 6.34 Table 1.4 Yearly-maximum water levels at two stations.

By analysing the data for 1992, it was realised that rain measurement stations that were made available were located exclusively in the zone upstream of Florence (see Fig. 1.7). For the same year, rating curves and historical level records were available in six gauging stations, namely Subbiano (Casentino), Ponte della Ferrovia (Chiana), Nave di Rosano (Valdarno Superiore), Fornacina (Sieve), Florence Uff. (Arno River) and S. Giovanni (Arno River) their locations are shown in Fig 1.8.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 8 1 · Application of the TOPKAPI Model in Arno River Basin

Thus, it was decided to calibrate,for 1992, the TOPKAPI model in the catchment closed at Florence (with an area of about 4325 Km2) only. The catchment concerned is hereafter called Upper Arno catchment.

Year 2000 was used and to calibrate le Lower Arno catchment; the one bertween Florence and S. Giovanni alla Vena, and to verify calibration on the Upper Arno catchment. 1.2.2.1 Hydrometeorological data for 1992

Firenze

Figure 1.7 Distribution of rainfall measurement stations in 1992.

Hourly values for 52 rain gauges and 7 temperature stations were available while the discharges of Forncina, Subbiano, Ponte della Ferrovia, Nave di Rosano and Florence Uff. were computed from hourly levels by means of rating curves. The areal rainfall and temperature distribution were initially estimated using the Thiessen Polygon method while in a second phase, the Block-Kriging technique developed within the frame of MUSIC (deliverable D7.1), was used for the rainfall measurements.

In addition to data from the seven thermometric stations available in telemetering, the Ufficio Idrografico e Mareografico of Pisa supplied data from over 20 conventional

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 9 1 · Application of the TOPKAPI Model in Arno River Basin thermometric stations. Mean monthly temperatures were calculated for estimation of potential evapotranspiration parameters by means of Thorthwaite's equation.

Bisenzio Fornacina Sieve Fornacina Padule Ombrone Fucecchio Pistoiese

Pesa

Pesa Greve Casentino Subbiano Valdarno Superiore

Era Laterina S. Giovanni Ferrovia Elsa

Chiana

Fiirenze Nave di Rosano

Figure 1.8 Tele-hydrometric stations available for 1992 and the 44 sub-basins.

1.2.2.2 Hydrometeorological data for 2000 To obtain calibration on the lower part of the catchment, between Florence ans S.Giovanni alla Vena data of 2000 were used.

The following data were available for 2000:

59 raingauge stations, the location of which partly differs from that of 1992.

8 thermometric stations, having telemetering measure.

42 additional thermometric, non telemetring station were available for long term average temperature computation, aimed to evaluate potential evapo-transpiration.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 10 1 · Application of the TOPKAPI Model in Arno River Basin

1.2.3 Evapotranspiration Starting from the initial subdivision in 19 sub-catchments, the Arno River basin was further divided into 44 sub-basins (Fig.1.8) on the basis of the available level gauge observation network as well as of the relevance of the location in terms of floods.

Monthly reference evapo-transpiration values were first calculated with the Thornthwaite equation on the basis of ten years of monthly data and then approximated using the radiation method following the technique described in Todini, 2002, and reported in the "EFFORTS, Description of Methods" report, paragraph 2.4.

42 additional thermometric stations with data for a number of years sufficient to calculate mean monthly temperatures per station and necessary to calculate parameters for potential evapotranspiration.

These stations supplied data in different periods, as may be seen on table 1.5.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 11

1 · Application of the TOPKAPI Model in Arno River Basin

1872 1889 1950 1951 1953 1972 1973 1975 1976 1985 1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Camaldoli Montepulciano SGVald Vallombrosa Vaglia Pistoia SMiniatoCim Pescia Castelmartini CascianaTerme Stia Cortona Arezzo BorgoSanLo Consuma Prato in Tosc Empoli Prunec Poggib.Stroz SMiniato FireXimen BadiaPrata LaVerna Salutio Chianciano Capezzine Abb.Monte Bettolle FoianoChiana MSSavino MontediFò Mangona Marcoiano Le CrociBarb Antella S.Colombano PratoG S.Giusto CasePasserini Pitecchio S.Gimignano Volterra Coltano Note: data for the years in grey were supplied as daily maximums and minimums Table 1.5 Thermometric stations providing several years of data

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 13 1 · Application of the TOPKAPI Model in Arno River Basin

We then selected a time window of 10 years, using the thermometers providing the greatest number of years of station data (Table 1.6).

1989 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Camaldoli Montepulc SGVald Vallombrosa Pistoia SMiniatoCim Pescia Castelmartini Prato in Tosc FireXimen MontediFò Mangona Marcoiano Le CrociBarb S.Colombano S.Giusto CasePasserini Volterra Coltano Note: data for the years in grey were supplied as daily maximums and minimums Table 1.6 Thermometers selected to determine mean monthly temperatures

Note in particular that data for the years in grey were supplied as daily maximums and minimums. Therefore, mean daily data were preventively reconstructed, after which mean monthly temperatures were calculated.

Monthly maximum and minimum values were already available for years shown in green colour.

Table 1.7 shows the mean monthly temperatures at height of thermometers.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 14 1 · Application of the TOPKAPI Model in Arno River Basin

1.2.3.1.1.1

Temperature Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Station

m slm Camaldoli 1111 1.96 2.13 4.92 6.22 11.21 14.58 18.1 18.85 14.24 10.17 5.44 1.82 Montepulc 607 6.51 7.69 10.57 11.86 17.47 21.16 24.71 25.35 19.94 15.49 10.24 6.73 SGVald 132 4.72 6.27 9.38 11.75 16.31 20.27 23.54 23.88 18.95 14.65 9.56 5.35 Vallombrosa 955 2.91 3.13 5.9 7.4 12.11 15.25 18.89 19.13 14.42 10.96 5.84 2.71 Pistoia 88 6.85 7.35 11.01 13.25 17.98 21.08 24.75 25.38 20.26 15.98 10.67 7.14 SMiniatoCim 124 7.4 8.23 10.83 12.81 17.54 20.83 24.65 25.07 20.49 16.02 10.68 7.84 Pescia 62 7.76 8.21 11.34 13.2 17.94 21.23 24.51 25.17 20.7 16.33 11.31 7.92 Castelmartini 23 6.03 7.4 11.13 13.57 18.28 21.6 24.71 25.25 20.77 15.17 11.23 6.28 Prato in Tosc 70 6.15 7.52 10.34 13.88 18.08 21.66 24.64 24.55 21.1 16.21 10.56 6.92 FireXimen 51 7.58 8.5 11.59 13.75 18.72 22.23 25.55 25.91 21.02 16.17 11.05 7.52 MontediFò 764 3.23 3.86 6.07 8.85 14.23 17.16 19.92 20.86 15.88 11.52 6.71 3.89 Mangona 525 5.07 5.7 7.74 10.46 15.64 18.45 21.08 21.97 17.22 13.01 8.37 5.58 Marcoiano 535 5.15 5.88 8.04 10.87 16.23 19.19 22.13 22.99 17.92 13.46 8.76 5.5 Le CrociBarb 406 5.98 6.83 8.98 11.72 16.93 19.78 22.5 23.26 18.52 14.19 9.33 6.5 S.Colombano 34 6.99 7.77 10.57 13.67 19.19 22.71 25.25 25.78 20.7 16.24 10.63 7.46 S.Giusto 40 7.17 7.71 10.2 13.18 18.48 21.79 24.38 24.99 20.23 16.03 10.7 7.71 CasePasserini 36 6.5 7.26 9.81 12.81 18.19 21.64 24.2 24.99 20.04 15.63 10.1 6.96 Volterra 530 6.4 7.55 9.47 11.87 17.57 20.68 24.21 24.35 19.29 15.33 10.29 7.04 Coltano 1 7.93 8.26 10.28 12.7 17.58 20.49 23.56 24.24 20.02 16.12 11.31 7.98 Table 1.7 Mean monthly temperatures at height of thermometers.

Utilising the weights obtained from the Theissen polygons, we calculated mean temperatures weighted on the sub-basins.

This calculation considered temperature variation due to altitude by means of a thermal gradient of 0.6°C/100 m.

Tab. 1.8 shows the (average) altitudes of the sub-basins and their relative mean temperatures. Based on data from these instruments, we calculated the monthly mean in the selected period, obtaining 12 mean temperature values per station with which we defined parameters a and b for calculation of potential evapotranspiration (Tab. 1.9).

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 15 1 · Application of the TOPKAPI Model in Arno River Basin

1.2.3.1.1.

Raster n° Sub-basin Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec N°

m slm 1 BI01 585 14 4.38 5.15 7.36 10.23 15.22 18.18 20.89 21.59 17.09 12.74 7.9 4.94 2 SI01 464 12 5.47 6.22 8.34 11.09 16.31 19.16 21.86 22.69 17.92 13.62 8.83 6 3 SI02 454 22 5.52 6.32 8.56 11.31 16.57 19.54 22.37 23.14 18.26 13.85 8.98 5.94 4 OP01 467 7 4.58 5.08 8.74 10.98 15.71 18.81 22.48 23.11 17.99 13.71 8.4 4.87 5 SI03 453 24 5.71 6.41 8.66 11.24 16.44 19.38 22.35 23.07 18.19 13.94 9.11 6.04 6 PF01 205 23 6.07 6.95 10.27 12.4 17.13 20.42 23.72 24.3 19.79 14.9 10.27 6.31 7 OP02 195 27 5.81 6.7 10.04 12.79 17.36 20.66 24 24.36 19.89 15.27 9.95 6.27 8 SI04 576 25 5.16 5.4 8.16 9.68 14.46 17.64 21.23 21.58 16.87 13.25 8.21 4.99 9 BI03 182 19 5.57 6.57 9.23 12.44 17.36 20.86 23.58 24.03 19.66 15.06 9.49 6.15 10 BI02 349 5 4.76 6.05 8.77 12.18 16.54 20.01 22.95 22.99 19.34 14.54 9.01 5.49 11 CA01 966 11 2.83 3.02 5.8 7.17 12.07 15.37 18.92 19.51 14.87 10.99 6.14 2.68 12 AR03 94 42 6.44 7.39 10.18 13.4 18.56 22.09 24.75 25.12 20.49 15.91 10.3 7 13 MU01 298 4 5.97 6.87 9.9 12.16 17.17 20.67 23.91 24.32 19.42 14.62 9.45 5.97 14 SC01 363 3 5.82 6.64 9.68 11.74 16.67 20.13 23.49 23.84 18.97 14.33 9.21 5.74 15 AR01 137 41 6.7 7.54 10.44 12.98 18.14 21.63 24.62 25.1 20.18 15.53 10.23 6.88 16 FI00 267 40 6.36 7.21 10.27 12.36 17.31 20.78 24.13 24.48 19.61 14.9 9.78 6.29 17 CA03 694 31 4.15 4.46 7.28 8.71 13.63 17.04 20.55 21.21 16.56 12.52 7.72 4.08 18 VA04 432 39 5.99 6.26 9.06 10.61 15.34 18.51 22.13 22.38 17.65 14.08 8.96 5.8 19 AR02 105 43 6.88 7.78 10.63 13.06 18.05 21.42 24.7 25.17 20.44 15.8 10.59 7.3 20 CA02 909 8 3.19 3.4 6.17 7.67 12.39 15.53 19.17 19.43 14.72 11.24 6.13 2.99 21 ST01 176 1 6.24 6.91 9.55 12.6 18.01 21.43 23.99 24.56 19.64 15.3 9.83 6.75 22 GR03 107 18 6.8 7.37 9.91 12.83 18.1 21.43 24.07 24.66 19.89 15.64 10.33 7.3 23 GR02 274 13 6.05 7 10.06 12.26 17.2 20.73 24.03 24.4 19.51 14.75 9.62 6.06 24 PE01 299 20 4.98 5.88 8.62 11.43 16.54 20.09 22.88 23.39 18.52 14.25 8.94 5.54 25 VA03 409 38 4.01 5.16 8.16 10.27 14.87 18.58 21.97 22.28 17.41 13.37 8.27 4.39 26 AR04 70 44 7.72 8.55 11.15 13.13 17.86 21.15 24.97 25.39 20.81 16.34 11 8.16 27 GR01 333 9 4.67 5.65 8.43 11.1 16.08 19.67 22.59 23.07 18.23 13.95 8.73 5.2 28 EL02 117 32 7.41 8.23 10.78 12.96 17.86 21.15 24.75 25.18 20.52 16.11 10.78 7.88 29 VA02 381 37 3.3 4.82 7.92 10.27 14.83 18.77 22.05 22.39 17.47 13.18 8.09 3.91 30 CI01 143 2 7.29 8.12 10.72 12.7 17.43 20.72 24.54 24.96 20.38 15.91 10.57 7.73 31 EG01 187 6 7.3 8.19 10.66 12.72 17.64 20.9 24.66 25.03 20.35 15.98 10.7 7.78 32 ER03 112 29 7.84 8.7 11.04 13.19 18.21 21.4 25.05 25.43 20.76 16.49 11.3 8.29 33 AC01 399 34 3.9 5.11 8.14 10.24 14.91 18.73 22.06 22.5 17.65 13.4 8.4 4.34 34 EL01 263 30 7.09 8.25 10.43 12.88 18.34 21.62 25.01 25.23 20.23 16.18 11.1 7.72 35 ER02 183 28 8.21 9.3 11.35 13.67 19.19 22.33 25.91 26.11 21.14 17.08 11.99 8.81 36 VA00 313 35 3.63 5.18 8.29 10.66 15.22 19.18 22.45 22.79 17.86 13.56 8.47 4.26 37 VA01 365 36 3.32 4.87 7.98 10.35 14.91 18.87 22.14 22.48 17.55 13.25 8.16 3.95 38 ER01 300 10 7.78 8.93 10.85 13.25 18.95 22.06 25.59 25.73 20.67 16.71 11.67 8.42 39 AM00 380 16 3.23 4.78 7.89 10.26 14.82 18.78 22.05 22.39 17.46 13.16 8.07 3.86 40 CH05 278 33 6.59 7.92 10.89 12.63 17.81 21.61 25.04 25.56 20.35 15.96 10.77 6.98 41 CH01 330 15 6.05 7.4 10.38 12.17 17.3 21.11 24.53 25.03 19.84 15.46 10.29 6.45 42 CH04 361 26 7.94 9.12 12 13.3 18.9 22.6 26.14 26.78 21.38 16.93 11.68 8.16 43 CH02 351 17 7.9 9.09 11.98 13.3 18.88 22.58 26.12 26.75 21.36 16.91 11.67 8.13 44 CH03 322 21 8.22 9.4 12.28 13.57 19.18 22.87 26.42 27.06 21.65 17.2 11.95 8.44

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 16 1 · Application of the TOPKAPI Model in Arno River Basin

Table 1.8 Annual mean temperatures in sub-basins.

n° Sub-basin a b Basin name 1 SI01 -2.140495 0.557624 Sieve 2 SI02 -2.839258 0.557607 Sieve 3 SI03 -2.809705 0.557717 Sieve 4 SI04 -0.015211 0.560496 Sieve 5 CA01 3.805099 0.575259 Casentino 6 CA02 3.586326 0.574404 Casentino 7 CA03 1.225311 0.563456 Casentino 8 AC01 -0.791816 0.561367 Casentino 9 VA00 -1.214764 0.562104 Valdarno Superiore 10 AM00 -0.439797 0.562656 Valdarno Superiore 11 VA01 -0.609622 0.562496 Valdarno Superiore 12 VA02 -0.466128 0.562572 Valdarno Superiore 13 VA03 -0.615381 0.56167 Valdarno Superiore 14 CH05 -8.339919 0.569304 Chiana 15 CH04 -12.876335 0.583388 Chiana 16 CH03 -14.078629 0.589055 Chiana 17 CH02 -12.754322 0.583062 Chiana 18 CH01 -6.723244 0.564741 Chiana 19 SC01 -4.164557 0.559979 Sieci 20 MU01 -5.129472 0.561895 Mugnone 21 GR03 -6.637389 0.566219 Greve 22 GR01 -2.215513 0.56014 Greve 23 GR02 -5.38613 0.562613 Greve 24 ST01 -5.950595 0.564389 Strada 25 EL02 -8.221964 0.570444 Elsa 26 EL01 -8.713862 0.569439 Elsa 27 EG01 -7.888093 0.568566 Egola 28 CI01 -7.530275 0.568329 Cecina 29 ER03 -9.423905 0.573605 Era 30 ER02 -12.127332 0.581179 Era 31 ER01 -10.8556 0.575031 Era 32 PF01 -5.334732 0.562571 Padule Bientina 33 OP02 -5.528784 0.563036 Ombrone Pistoiese 34 OP01 -1.832206 0.558858 Ombrone Pistoiese 35 BI03 -4.758767 0.562036 Bisenzio 36 BI02 -3.256087 0.558194 Bisenzio 37 BI01 -0.037426 0.559674 Bisenzio 38 PE01 -2.937532 0.560017 Pesa 39 FI00 -5.791166 0.563249 Arno River 40 AR01 -7.319148 0.568099 Arno River 41 AR02 -7.731324 0.569518 Arno River 42 AR03 -7.661289 0.569003 Arno River 43 AR04 -8.934316 0.573256 Arno River 44 VA04 -1.807981 0.559099 Arno River

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 17 1 · Application of the TOPKAPI Model in Arno River Basin

Table 1.9 Evapo-transpiration parameters a and b for the 44 sub-basins identified in the Arno catchment

With reference to land use, crop factors were also used for adjusting potential evapo- transpiration to the different crops, as described in "Crop Water Requirements", FAO Irrigation and Drainage publications. Table 1.10 shows the monthly crop factor used for the Upper Arno Basin. Ecrop=Kcrop*Eto

No. Class J F M A M J J A S O N D 1 U 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 U1 0.18 0.18 0.24 0.24 0.31 0.31 0.31 0.31 0.31 0.18 0.18 0.18 3 CA 1.00 1.00 1.00 1.00 1.00 1.10 1.20 1.05 1.00 1.00 1.00 1.00 4 B 0.70 0.70 0.80 0.80 0.90 1.00 1.00 1.00 1.00 0.90 0.80 0.80 5 C 0.70 0.70 0.80 0.80 0.90 1.00 1.00 1.00 1.00 0.90 0.80 0.70 6 CS 0.55 0.55 0.60 0.60 0.65 0.70 0.75 0.65 0.60 0.60 0.55 0.55 7 S 0.55 0.60 0.70 0.80 0.90 1.20 1.10 0.50 0.50 0.50 0.50 0.50 8 NV 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 9 P 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 Table 1.10 Monthly crop factors.

1.3 Calibration of the TOPKAPI model

1.3.1 Calibration parameters As mentioned in the previous section, the TOPKAPI model was first calibrated upstream of Florence using the 1992 data, subsequently calibration was extended to lower part of basin, using the 2000 data. The parameter values for the three basic components in the TOPKAPI model, namely the soil water components, surface water component and channel water component, were estimated from the literature, and applied in relation to soil type and soil uses. A common set of parameters was tested and tuned for both calibration periods (1992 and 2000).

The following tables show final parameters

Tables 1.11a, 1.11b and1.11c, show the obtained parameter values: ϑ s = Soil water content at saturation ϑ r = Residual soil water content α s = Exponent in Topkapi soil saturation equation

k s = Horizontal soil permeability in saturation condition

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 18 1 · Application of the TOPKAPI Model in Arno River Basin

L = Soil thickness

Soil Class α ϑ −ϑ s k (m/s) L (m) number s r s 1 0.500 2.5 6.54E-04 1.20 2 0.535 2.5 6.06E-05 0.90 3 0.425 2.5 1.89E-05 0.60 4 0.417 2.5 1.67E-06 0.30 Table 1.11a Soil and surface components calibration parameter values for Arno catchment

ns = slope roughness

Land use n (m-1/3 s-1) Class no. s 1 0.06 2 0.10 3 0.20 4 0.15 5 0.10 6 0.09 7 0.15 8 0.10 9 0.01 Table 1.11b Overland flow component calibration parameter values based on land use for Arno catchment

nc = Channel bed roughness

Ordine (Strahler) -1/3 -1 nc (m s ) I 0.045 II 0.040 III 0.035 IV 0.030 V 0.025 Table 1.11c Channel flow component calibration parameters, based on Strahler ordering for Arno catchment.

In reality, TOPKAPI was not calibrated in a conventional manner. The only parameters utilised (modified) were soil thickness, slope roughness, and width and roughness of the drainage network reaches. These parameters were modified in relation to precise physical considerations, and intentionally a least squares technique, which could have improved the calibration to the detriment of the physical significance of the parameters, was not used.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 19 1 · Application of the TOPKAPI Model in Arno River Basin

1.3.2 Graphical calibration results on Upper Arno basin for 1992 Figures 1.9 to 1.13 present a comparison between flows observed and those calculated with the TOPKAPI model, distributed in the sections of Florence, Nave di Rosano, Fornacina, Subbiano and Ponte della Ferrovia.

Comparison between Qobs and Qcal Firenze 1992

2500

2000 Firenze_cal Firenze_obs

1500

1000 Discharge (m3/s) Discharge

500

0 92/12/2/3/0 92/12/6/7/0 92/9/22/7/0 92/11/7/3/0 93/1/4/11/0 93/1/8/15/0 93/1/21/3/0 93/1/25/7/0 92/9/26/11/0 92/9/30/15/0 92/10/4/19/0 92/10/8/23/0 92/10/13/3/0 92/10/17/7/0 92/11/2/23/0 92/11/11/7/0 92/12/27/3/0 92/12/31/7/0 93/1/12/19/0 93/1/16/23/0 92/10/21/11/0 92/10/25/15/0 92/10/29/19/0 92/11/15/11/0 92/11/19/15/0 92/11/23/19/0 92/11/27/23/0 92/12/10/11/0 92/12/14/15/0 92/12/18/19/0 92/12/22/23/0 Date ( hour)

Figure 1.9 Comparison of observed discharges at Florence. Solid line observed, thin line TOPKAPI.

Comparison between Qobs and Qcal Nave di Rosano 1992

2500

NaveRCal 2000 NaveRObs

1500

1000 Discharge (m3/sec)

500

0 92/9/22/7/0 92/11/7/3/0 92/12/2/3/0 92/12/6/7/0 93/1/4/11/0 93/1/8/15/0 93/1/21/3/0 93/1/25/7/0 92/9/26/11/0 92/9/30/15/0 92/10/4/19/0 92/10/8/23/0 92/10/13/3/0 92/10/17/7/0 92/11/2/23/0 92/11/11/7/0 92/12/27/3/0 92/12/31/7/0 93/1/12/19/0 93/1/16/23/0 92/10/21/11/0 92/10/25/15/0 92/10/29/19/0 92/11/15/11/0 92/11/19/15/0 92/11/23/19/0 92/11/27/23/0 92/12/10/11/0 92/12/14/15/0 92/12/18/19/0 92/12/22/23/0 Date (hour)

Figure 1.10 Comparison of observed discharges at Nave di Rosano. Solid line observed, thin line TOPKAPI.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 20 1 · Application of the TOPKAPI Model in Arno River Basin

Comparison between Qobs and Qcal Fornacina 1992

900

800 FornacinaCal

700 FornacinaObs

600

500

400 Discharge (m3/sec) Discharge 300

200

100

Figure 1.11 Comparison of observed discharges at Fornacina. Solid line observed, thin line TOPKAPI.

Comparison between Qobs and Qcal Subbiano 1992

1600

1400

1200 SubbianoCal

1000 SubbianoObs

800

Discharge( m3/sec) 600

400

200

Figure 1.12 Comparison of observed discharges at Subbiano. Solid line observed, thin line TOPKAPI.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 21 1 · Application of the TOPKAPI Model in Arno River Basin

Comparison between Qobs and Qcal Ponte della Ferrovia per 1992

250

Ponte della 200 FerroviaCal

Ponte della FerroviaObs

150

100 Discharge( m3/sec)

0 92/9/22/7/0 92/11/7/3/0 93/1/4/11/0 93/1/8/15/0 93/1/21/3/0 93/1/25/7/0 92/12/2/3/0 92/12/6/7/0 92/9/26/11/0 92/9/30/15/0 92/10/4/19/0 92/10/8/23/0 92/10/13/3/0 92/10/17/7/0 92/11/2/23/0 92/11/11/7/0 92/12/27/3/0 92/12/31/7/0 93/1/12/19/0 93/1/16/23/0 92/10/21/11/0 92/10/25/15/0 92/10/29/19/0 92/11/15/11/0 92/11/19/15/0 92/11/23/19/0 92/11/27/23/0 92/12/10/11/0 92/12/14/15/0 92/12/18/19/0 92/12/22/23/0 Date (hour)

Figure 1.13 Comparison of observed discharges at Ponte della Ferrovia. Solid line observed, thin line TOPKAPI.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 22 1 · Application of the TOPKAPI Model in Arno River Basin

1.3.3 Graphical calibration results on Arno basin for 2000 The following graphs (from Fig 1.14 to 1.19) compare flows calculated by the model to those observed for 2000.

In general, peaks are well represented by the model, while there are more obvious differences in the curve depletion zone because the groundwater depletion module was not applied.

Comparison between Qobs and Qcal San Giovanni alla Vena V. 2000

2500

San Giovanni alla Vena V. obs 2000

San Giovanni alla Vena V. cal

1500

1000 Discharge (m3/sec) Discharge

500

0 00/10/28 09:00 09:00 00/10/28 09:00 00/10/30 09:00 00/11/01 09:00 00/11/03 09:00 00/11/05 09:00 00/11/07 09:00 00/11/09 09:00 00/11/11 09:00 00/11/13 09:00 00/11/15 09:00 00/11/17 09:00 00/11/19 09:00 00/11/21 09:00 00/11/23 09:00 00/11/25 09:00 00/11/27 09:00 00/11/29 09:00 00/12/01 09:00 00/12/03 09:00 00/12/05 09:00 00/12/07 09:00 00/12/09 09:00 00/12/11 09:00 00/12/13 09:00 00/12/15 09:00 00/12/17 09:00 00/12/19 09:00 00/12/21 09:00 00/12/23 09:00 00/12/25 09:00 00/12/27 09:00 00/12/29 09:00 00/12/31 Date (hour)

Figure 1.14 Comparison between flows observed and flows calculated at closure of San Giovanni alla Vena basin in 2000.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 23 1 · Application of the TOPKAPI Model in Arno River Basin

Comparison between Qobs and Qcal Fornacina 2000

700

600

500 Fornacina obs

400 Fornacina cal

300 Discharge (m3/sec)

200

100

0 00/10/28 09:00 00/10/30 09:00 00/11/01 09:00 00/11/03 09:00 00/11/05 09:00 00/11/07 09:00 00/11/09 09:00 00/11/11 09:00 00/11/13 09:00 00/11/15 09:00 00/11/17 09:00 00/11/19 09:00 00/11/21 09:00 00/11/23 09:00 00/11/25 09:00 00/11/27 09:00 00/11/29 09:00 00/12/01 09:00 00/12/03 09:00 00/12/05 09:00 00/12/07 09:00 00/12/09 09:00 00/12/11 09:00 00/12/13 09:00 00/12/15 09:00 00/12/17 09:00 00/12/19 09:00 00/12/21 09:00 00/12/23 09:00 00/12/25 09:00 00/12/27 09:00 00/12/29 09:00 00/12/31 09:00 Date (hour)

Figure 1.15 Comparison between flows observed and flows calculated at Fornacina in 2000.

Comparison between Qobs and Qcal Nave di Rosano 2000

1400

1200

Nave di Rosano obs 1000

Nave di Rosano cal

800

600 Discharge (m3/sec) Discharge

400

200

Figure 1.16 Comparison between flows observed and flows calculated at Nave di Rosano in 2000.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 24 1 · Application of the TOPKAPI Model in Arno River Basin

Comparison between Qobs and Qcal Firenze 2000

1600

1400

1200 Firenze obs

1000 Firenze cal

800

600 Discharge (m3/sec) Discharge

400

200

Figure 1.17 Comparison between flows observed and flows calculated at Florence in 2000.

Comparison between Qobs and Qcal Ponte della Ferrovia 2000

300

250 Ponte della Ferrovia obs

200 Ponte della Ferrovia cal

150

Discharge (m3/sec) Discharge 100

Figure 1.18 Comparison between flows observed and flows calculated at Ponte della Ferrovia in 2000.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 25 1 · Application of the TOPKAPI Model in Arno River Basin

Comparison between Qobs and Qcal Subbiano 2000

900

800

700 Subbiano cal 600 Subbiano obs 500

400

Discharge (m3/sec) 300

200

100

0 00/10/28 09:00 00/10/30 21:00 00/11/02 09:00 00/11/04 21:00 00/11/07 09:00 00/11/09 21:00 00/11/12 09:00 00/11/14 21:00 00/11/17 09:00 00/11/19 21:00 00/11/22 09:00 00/11/24 21:00 00/11/27 09:00 00/11/29 21:00 00/12/02 09:00 00/12/04 21:00 00/12/07 09:00 00/12/09 21:00 00/12/12 09:00 00/12/14 21:00 00/12/17 09:00 00/12/19 21:00 00/12/22 09:00 00/12/24 21:00 00/12/27 09:00 00/12/29 21:00 Date (hour)

Figure 1.19 Comparison between flows observed and flows calculated at Subbiano in 2000.

1.3.4 Statistical tests A check of results for 2000 revealed a few inaccuracies in level data, especially with respect to the Sieve sub-basin at Fornacina and for the Florence Uffizi station, where instruments gave highly oscillating hydrographs with values (appropriately filtered and deleted) that were unrealistic with regard to level and, consequently, to flow.

After calibration, we calculated three coefficients used for synthetic analysis of fitting of the model for some of the main flood forecast stations:

1.3.4.1 EFFICIENCE COEFFICIENT

N ()ε −ε 2 ∑ i = − i=1 ε = − ε ε = − EV 1 N dove i Qc i Qoi e = mean of i Qci Qoi ()− 2 ∑ Qoi Qo i=1

1.3.4.2 DETERMINATION COEFFICIENT

N N ()− 2 ()ε 2 ∑ Qci Qoi ∑ i = − i=1 = − i=1 DC 1 N 1 N i = time step ()− 2 ()− 2 ∑ Qoi Qo ∑ Qoi Qo i=1 i=1

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 26 1 · Application of the TOPKAPI Model in Arno River Basin

1.3.4.3 CORRELATION COEFFICIENT

CC = DC

In the following Table 1.14 are showed the coefficients estimated for 1992 and 2000.

STATIONS PTE NAVE DI SAN FORNACINA FLORENCE COEFFICIENT FERROVIA ROSANO GIOVANNI EV 0.875 0.855 0.915 0.916 1992

DC 0.873 0.847 0.912 0.910 1992

CC 0.934 0.920 0.955 0.954 1992

EV 0.514 0.923 0.867 0.824 0.907 2000

DC 0.503 0.922 0.865 0.802 0.907 2000

CC 0.709 0.960 0.929 0.895 0.952 2000

Table 1.14 Coefficient for 1992 and 2000.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 27 1 · Application of the TOPKAPI Model in Arno River Basin

1.3.5 Considerations on calibration As mentioned above, analysis of the graphs and calculation of the indices show errors in the measurement of levels. At the Fornacina station, for example, some of the flows calculated on the basis of level data conflict with flows observed for stations such as Nave di Rosano and Florence located downstream of it. In fact, these two stations record flows lower than that of Fornacina, which is impossible because there is no runoff along the path from one station to the next.

Lack of correspondence between flows observed and flows calculated by the model were noted for a few sub-basins, such as Bisenzio, Greve, and Era. From their comparison, and from calculation of the ratio of runoff to rainfall, we concluded that this fact was due mainly to a lack of precipitation data (especially in the rainiest periods of the year), caused by failure of the pluviometers to take a reading or because there were no pluviometers at all.

As for the Greve sub-basin, which lies in a predominantly mountainous zone, a contrary phenomenon may be observed, as if there were too much rain compared to observed flows deriving from it. In this situation, the model tends to over-estimate flows. The ground is not thick, and therefore runoff should be abundant; but the runoff/rain ratio is very low, so it is probable that the error here is due to imprecise level data.

1.4 Conclusions Application of the TOPKAPI model to the zone above Florence demonstrates that the model successfully simulates floods but does not correctly simulate runoffs due to lack of a groundwater discharge module. Such a module is now being created, although it is not essential for purposes of representing floods.

The model is efficient in terms of calibration and execution times. The Arno basin was divided into 8315 cells, and the program took only 9 minutes to simulate a one- year period in one-hour intervals. The study demonstrates that the model has a simple and logical structure for simulation of hydrologic and hydraulic processes in a basin using a non-linear reservoir approximation.

The calibration phase of model parameters may therefore be considered concluded. Following the model, the error model is being calculated on a physical basis, based on a Kalman filter, and these will shortly be integrated in the EFFORTS system to be installed at the Arno Basin Authority.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 28 Errore. L'origine riferimento non è stata trovata.

2 Application of the TOPKAPI Model in Reno River Basin

2.1 Characteristics of the Reno river basin

2.1.1 General description of the Reno River basin The Reno River drainage basin (Fig. 2.1), with a total length of 210 km, is the largest in the Emilia-Romagna Region, measuring 4930 km2 and also occupying a part of the Tuscany Region.

The mountainous part covers 1051 km2 up to Chiusa di Casalecchio di Reno, where the river reaches a length of 84 km starting from its source in S.Marcello Pistoiese. Then comes a foothill reach about 6 km long, which ends immediately after the Bologna-Milan railway bridge. The valley reach conducts the waters (enclosed by high levees) to its natural outlet in the Adriatic Sea, flowing along the plain for 120 km.

Figure 2.1 Reno River basin, divided into sub-basins, and hydrographic grid

In its mountainous reach, the Reno River receives a series of tributaries, for most of which an area of pertinence (sub-basin) can be identified, in turn fluted by small streams and brooks. The main left tributaries, flowing from mountain to valley, are: the Maresca and the Orsigna (in Tuscany), the Randaragna, Rio Maggiore, the Silla, the Marano, the Vergatello, the Croara, and the Venola. Right tributaries (mountain to

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 29 Errore. L'origine riferimento non è stata trovata. valley) are: the Limentra di Sambuca, the Limentra di Treppio, the Camperolo, the Setta with sub-tributaries Brasimone on the left and Gambellato, Voglio and Sambro on the hydraulic right.

This part has four artificial reservoirs used to produce hydroelectric power and to supply drinking water:

- the Molino del Pallone reservoir on the Reno (474 m a.s.l.)

- the Pavana reservoir on the Limentra di Sambuca (470 m a.s.l.)

- the Suviana reservoir on the Limentra di Treppio (470 m a.s.l.)

- the Brasimone reservoir on the Brasimone (842 m a.s.l.).

The first three are connected in cascade. The Suviana reservoir has the largest capacity (34·106 m3), collecting the waters of the Molino del Pallone and Pavana (connected in cascade), and is in turn connected by forced channels to the Brasimone basin.

The Reno is created by the confluence of two branches (Reno di Prunetta and Reno di Campolungo), and for its first 10 km resembles a small mountain stream in perfect balance due to the absolute stability of the terrains crossed and its fairly modest slope (an average of 3.7%); in this reach, the valley is quite wide and resembles a high plain.

This is followed by a reach of 15 km with average slope of 1.8%, along which the valley progressively narrows, becoming deeply entrenched in the last 3 km before the Venturina bridge. In this reach, the Reno valley extends essentially to the left, and presents satisfactory conditions of overall hydraulic stability.

In the next trunk above Vergato, the slope drops an average of 0.8% for approximately 27 km; the bed first crosses formations composed of highly “Galestrina” clay, then an almost plastic clay, after which it flows through arenaceous marl and then returns to a plastic-type scaly clay characterised by very poor stability.

In the next 17 km, until the confluence of the Setta stream, with average slope of 0.4%, the valley leaves the scaly clay and enters a Miocene formation (predominantly sandy marl and, only locally, clayey and hard marl); the bed is entrenched quite deeply in this formation, and then passes beyond Pian di Venola to a deposit zone in which it is incised almost everywhere by ancient and recent floods.

In the last 10 km, with average slope of 0.3% until Chiusa di Casalecchio (border of mountainous zone), the river crosses recent flood planes and ancient terraced deposits (larger on the left than on the right). The geological formations present on the hill slopes are Pliocene sandstone toward the mountains and hard marl Miocene toward the valley.

This reach presents marked signs of anthropic intervention which, following heavy extraction of lithic material from the bed, have caused, among other things, formation of numerous reservoirs fed by bank storage of the river bed.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 30 Errore. L'origine riferimento non è stata trovata.

The valley reach of the Reno River, i.e., downstream of Chiusa di Casalecchio, divides into two branches: one 5.5 km long from Casalecchio to the Via Emilia bridge, the other 124 km long, from the Milan-Bologna railway bridge to its outlet in the sea.

The first represents the foothill reach of the course and, as such, has particular hydraulic importance, since it has to connect the regime of mountain basin streams with the river regime of the leveed watercourse in the valley.

Contributing to the importance of this reach is the fact that it extends practically within the city limits of Bologna.

The second branch winds through the plain zones of the provinces of Bologna, Ferrara and Ravenna; the Samoggia stream, Navile Canal, and the Savena Abbandonato, Idice, Sillaro, Santerno and Senio streams empty into it.

The morphological features of this branch are extremely variable, in that they reflect the various hydraulic episodes that through the ages have created the current structure of the Reno River.

It is known that the Reno's original natural basin closed at the confluence of the Samoggia stream, later becoming, downstream, the right tributary of the Po River.

Following large-scale hydraulic works for recovery and remediation of valleys in the lower Bologna, Ferrara and Ravenna areas, the Reno was diverted through Cavo Benedettino and the end reach of Po di Primaro, attaining (after additional work) its current configuration:

- first reach (about 19 km) until Ponte Bagno, with twisting path and wide flood plains modulating flood discharges, alternating with localised narrow levees;

- second reach (about 18 km) until the bypass canal of Cavo Napoleonico, with fairly regular path and highly restricted geometry of the stream bed;

- third reach (about 47 km) until Bastia, with channelled bed having close and very high levees compared to the plane of site; this reach has a lateral flow spillway at Gallo di Poggio Renatico - where the left bank collapsed in the 1950s - which guarantees natural flood crest abatement with discharge of excess waters in the nearby "Cembalina" channel;

- fourth reach (about 40 km) until the sea, with relatively wide leveed bed.

In the valley reach, the transverse section of the Reno is about 150-180 m wide.

2.1.2 Topography The Reno River drainage basin has a total surface area of 4930 km2, slightly more than half of which pertains to the mountain basin.

In Emilia-Romagna, the majority of this area - 3377 km2 (68.5%) - is in the Province of Bologna, with 871 km2 (11.7%) in that of Ravenna, 62 km2 (1.3%) in that of

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 31 Errore. L'origine riferimento non è stata trovata.

Modena, and a very small area - 47 km2 (0.9%) - in that of Ferrara, for an overall area of 4357 km2, corresponding to 88.4% of the entire basin.

The part in the Tuscany Region involves the Province of Florence, with 378 km2 (7.7%), that of Pistoia, with 155 km2 (3.1%), and that of Prato, with 40 km2, for a total of 573 km2, corresponding to 11.6% of the entire inter-regional basin.

The digital earth model (DEM) available (Fig. 2.2) is composed of square cells with 1 km side, about 44% of which have a level below 50 m a.s.l., 19% a level between 50 and 300 m a.s.l., 18 % between 300 and 600 m a.s.l., 14 % between 600 and 900 m a.s.l., 4 % between 900 and 1200 m a.s.l., 0.45 % between 1200 and 1500 m a.s.l., and 0.07% between 1500 and 1825 m a.s.l. The average level is about 274 m a.s.l.

Figure 2.2 Digital elevation model (DEM) of the Reno River basin

The basinwas divided into 43 sub-basins (Fig. 2.1) based on coverage in Arc-View vector format supplied by the Environmental Engineering Department of ARPA Emilia-Romagna. Tab. 2.1 shows the area and average level for each.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 32 Errore. L'origine riferimento non è stata trovata.

Sub-basin code Area (km2) Average level (m a.s.l.)

25 228 688 26 227 221.8 27 103 653.7 28 81 369.7 29 55 119.8 31 69 428.9 34 141 344.9 35 31 57.1 36 51 168.2 40 83 314.5 41 178 470.2 42 93 328.5 43 115 461.9 44 44 853.2 45 84 869.7 46 16 971.3 47 107 953.9 48 336 655.5 49 184 337.4 50 311 621.7 37 127 98.7 39 180 375.1 51 145 8 52 160 12.5 53 70 11.8 54 132 27.3 55 41 19.4 56 203 17.1 57 164 43.3 58 69 74.4 59 11 64.2 60 37 86.3 61 33 33.2 62 93 49.2 63 128 3.5 64 67 5.1 65 102 6.7 66 136 7.7 67 58 24.6 68 223 15.3 69 149 15.9 70 327 16 71 37 42.4

Table 2.1 Area and average level of 43 sub-basins of the Reno River basin

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 33 Errore. L'origine riferimento non è stata trovata.

2.1.3 Type and properties of soil Information on soil types was obtained from a digital map of the entire territory of the Emilia-Romagna region in Arc-View format, provided by the Cartography Department of the Emilia-Romagna Region.

We identified 10 categories by associating descriptions of various soil types taken from the publication “The Soils of Emilia-Romagna”, with classification in fundamental classes of texture based on relative proportions between the main granulometry fractions of the soil (Tab. 2.2).

From this we generated a raster map (Fig. 2.3) of elements, with dimensions of 1 km by 1 km. For each of these we calculated horizontal permeability values, water content at saturation θs, reduced water content θr and exponent αs of the law of transmissivity, with reference to instructions contained in the literature.

Figure 2.3 Raster map of soil types

Soil code Soil type Percentag e 1 Sand 0.59 2 Clay loam 28.23 3 Silt loam 25.86 4 Silty clay 12.12 5 Sandy loam 10.12 6 Loam 14.42 7 Clay loam 0.38 8 Loam sand 0.75 9 Sand 0.36 10 Gravel 7.17 Table 2.2 Classification of soil types

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 34 Errore. L'origine riferimento non è stata trovata.

We also generated a raster map with 1 km square cells for soil thickness, using information contained in “The Soils of Emilia-Romagna.” Fifteen classes were identified (Fig. 2.4).

As seen on Tab. 2.2, the most common soils are those with medium granulometry, found in 79% of the territory. The piedmont zone presents a belt of soils formed by floods, with rough, locally gravelly texture and high permeability. The plain is covered by predominantly by loam, i.e., by silty clay and silty clay loam, while the reach near the sea presents mostly sand.

Information regarding the part of the basin in the Tuscany Region was extrapolated by observing (on geological and pedogenic maps of the Emilia-Romagna Region) the distribution of geological formations and by exploiting the knowledge and experience of personnel at the Cartography Department of the Emilia-Romagna Region.

Figure 2.4 Raster map of basin divided into classes of thickness

2.1.4 Land use Land use data derives from the digital map produced by the European Community “CORINE Program –LAND COVER Project” (1985), available on the Emilia- Romagna region's Internet site with regard to the territory in the Region, while the map pertaining to the part in Tuscany was supplied by the Reno River Basin Authority.

Based on such information, we derived 29 different types of ground use, reducing these to 9 by aggregating types considered similar with respect to resistance to water flow. The aim was to assign each element on the raster map a roughness coefficient value for surfaces according to Manning based on ground use.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 35 Errore. L'origine riferimento non è stata trovata.

Tab. 2.3 describes the 9 classes, while Fig. 2.5 shows the raster map. Immediately evident are the widespread diffusion of agriculture (63 % of the territory) and the presence of woods in the mountainous part of the basin.

Figure 2.5 Raster map of ground use

Ground Ground use Percentage code 1 Continuous urban, industrial and business areas, roads and railways, 1.38 airports, rock, basins 2 Discontinuous urban, dumps, green urban areas, sports/recreation areas 3.31 3 Extraction area, aapamoors and shrubs, evolving vegetation, sparse 5.53 vegetation 4 Crops: farm, heterogeneous, annual/permanent, non-irrigated seed 63.17 5 Vineyards 0.08 6 Fruit orchards 0.46 7 Meadows, natural pasture 1.84 8 Woods: broadleaf, conifer, mixed, beaches 23.64 9 Watercourses, inland swamps, lagoons 0.61

Table 2.3 Ground use classification

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 36 Errore. L'origine riferimento non è stata trovata.

2.2 Data preparation for the application of the Topkapi model

2.2.1 Applications to DEM Application of the TOPKAPI model requires use of the Reno basin DEM to highlight the drainage direction and slope of each cell of the DEM grid. We achieved such definition as follows:

- identification and correction of trenches and false outlets;

- identification of connections between cells, i.e., identification of path taken by water flow to reach the closing section of the basin;

- identification of the drainage system given a minimum value of drained area.

So that automatic extraction by the drainage system may be compatible with the drainage pattern in vector format (supplied by the Environmental Engineering Department of ARPA Emilia-Romagna), we extended the Reno water basin to the north for a virtual belt of territory of 2 km. This correction was required because the watercourse formed the northern boundary of the basin in the DEM used.

Fig. 2.6 compares the real drainage pattern to the drainage system calculated on the basis of a drained area threshold of 8.37 km2, corresponding to 0.16% of total basin area.

Figure 2.6 Comparison of real drainage pattern (thin black line) to drainage system extracted automatically by model (thick light blue line)

2.2.2 Hydro-meteorological data The hydro-meteorological measurement system available for calibration and validation of the TOPKAPI model consists of 47 pluviometers and 15 thermometers in the period 1990-1995, distributed throughout the territory of the Reno River basin (Figs. 2.7, 2.8).

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 37 Errore. L'origine riferimento non è stata trovata.

Figure 2.7 Distribution of available precipitation stations

Figure 2.8 Distribution of available temperature stations

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 38 Errore. L'origine riferimento non è stata trovata.

As for level stations, we considered only those for which a runoff scale was available (Fig. 2.9).

Fig.ure 2.9 Distribution of available hydrometric stations

Rain, temperature, and level values are given on an hourly scale, and rain and temperature time series are continuous, i.e., they have no missing values due to instrument malfunction, because we reconstructed (by means of an algorithm based on the Kalman filter technique) data missing from a certain station for a defined interval by using the values given by other instruments functioning during such interval. It was therefore possible to estimate rainfall on the territory, utilising the Thiessen polygon method to identify areal distribution of precipitation (Fig. 2.10), and to determine average monthly temperatures per station needed to estimate parameters for calculation of potential evapo-transpiration.

Figure 10 Thiessen polygons for 47 pluviometers

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 39 Errore. L'origine riferimento non è stata trovata.

2.2.3 Evapotranspiration The TOPKAPI model calculates potential and actual evapo-transpiration for each cell and time interval, and, on this basis, updates the water level stored in the ground. The method utilised (linear formula) requires estimation of two parameters (a, b) by means of linear regression on average monthly evapo-transpiration values determined with Thortwaite's formula and with the radiation method in order to derive the value at the hourly level.

Calculation of evapo-transpiration according to the above criteria requires the average monthly temperature values for each instrument: for the current calibration, these were estimated on the basis of hourly data for the period 1990-2000 (Tab. 2.4).

Stat Alt Average monthly temperatures (C°) Code a.s.l. (m) Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic 1 1043 3.68 4.14 6.38 7.58 12.78 15.43 19.5 20.14 15.13 11.13 6.88 3.34 2 627 2.57 2.79 6.03 7.84 12.66 15.55 18.73 18.46 14 10.41 5.81 2.25 3 349 2.54 4.22 8.4 10.57 15.27 18.87 22.16 22.06 17.3 12.33 6.89 2.88 4 890 1.64 2.23 5.15 6.57 11.53 14.54 18.48 18.85 14.17 10.16 5.63 2.02 5 500 4.81 5.5 8.9 10.7 15.5 18.75 22.5 22.76 18.39 13.52 8.8 4.76 6 850 1.59 2.66 5.52 6.83 12.09 15.17 18.91 19.86 14.68 10.25 5.51 1.46 7 620 3.76 4.57 8.28 9.89 15.16 18.72 22.11 22.48 17.15 11.92 7.3 3.65 8 727 2.87 3.78 7.58 9.17 14.17 17.79 21.32 21.81 17 11.48 7.03 3.05 9 51 4.13 5.74 10.79 13.15 18.26 21.77 25.54 25.81 20.58 14.51 8.63 4.37 10 422 3.96 4.39 8.25 10.9 15.62 19.16 22.66 23.16 18.22 13.53 8.54 4.03 11 15 2.75 4.62 9.61 12.51 17.91 21.46 24.7 24.83 19.57 13.99 7.75 3.01 12 1 1.47 3.74 9.13 12.14 17.8 21.29 24.88 25.08 19.89 13.29 6.92 2.22 13 40 3.16 5.08 10.69 13.74 19.32 23.09 26.4 26.43 20.97 14.58 8.44 3.72 14 47 2.59 4.03 9.12 11.8 17.08 20.42 23.79 23.87 19.12 13.58 7.8 3.4 15 7 2.06 3.3 8.21 11 16.2 19.91 23.24 23.28 18.85 13.51 7.6 3.07

Table 2.4 Average monthly temperatures for 15 thermometers calculated in period 1990-2000

We therefore determined the values of parameters a and b (Tab. 2.5) valid for the area covered by each thermometer identified by means of the Thiessen polygon method (Fig. 2.11).

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 40 Errore. L'origine riferimento non è stata trovata.

Figure 2.11 Thiessen polygons for 15 thermometers

Cod Parametri staz a b 1 2.372632 0.600527 2 3.213291 0.615583 3 -1.153190 0.582511 4 3.833022 0.622146 5 -2.988199 0.580224 6 3.184438 0.613469 7 -1.239314 0.582446 8 0.074141 0.586735 9 -7.660950 0.584220 10 -2.693966 0.579880 11 -5.10180 0.578464 12 -4.242275 0.577230 13 -8.260139 0.586050 14 -3.735282 0.577741 15 -2.553840 0.578230

Table 2.5 Values of parameters a and b for calculation of estimated hourly evapo-transpiration for period 1990-2000

2.3 Calibration of the TOPKAPI model

2.3.1 Calibration parameters The TOPKAPI model requires assignment of parameters for interpretation of the dynamic of physical phenomena of soil, surface, and drainage pattern components.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 41 Errore. L'origine riferimento non è stata trovata.

Using indications contained in the literature, tables 2.6 and 2.7 shows estimated values of the following quantities for each soil type and ground use:

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 42 Errore. L'origine riferimento non è stata trovata.

- θs water content at saturation;

- θr reduced water content;

- αs exponent of law of transmissivity;

- ks horizontal permeability;

- no coefficient of surface roughness according to Manning;

- L soil thickness;

- nc coefficient of channel roughness according to Manning;

Order (Strahler) -1/3 -1 nc (m s ) I 0.040 II 0.035 III 0.030 IV 0.030 V 0.025

Table 2.6 values of coefficient of roughness nc in channels according to Manning

-1/3 -1 Codice ϑ ϑ α k s (m/s) no (m s ) L (m) mappe s r s 1 0.500 0.020 2.5 1.544E-02 0.025 0.50 2 0.524 0.040 2.5 4.555E-04 0.060 3 0.582 0.015 2.5 0.371E-03 0.050 4 0.533 0.056 2.5 2.222E-04 0.040 0.90 5 0.555 0.041 2.5 1.055E-03 0.038 1.35 6 0.551 0.027 2.5 0.666E-03 0.039 1.25 7 0.519 0.075 2.5 4.555E-04 0.035 1.60 8 0.506 0.035 2.5 6.661E-03 0.100 9 0.523 0.090 2.5 1.444E-04 0.050 1.90 10 0.450 0.010 2.5 5.000E-02 11 0.85 14 1.35 15 0.65 16 0.95 17 1.10 19 1.05 20 1.0 21 1.4 22 0.7

Table 2.7 valori dei parametri θs , θr , αs , ks , no , L

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 43 Errore. L'origine riferimento non è stata trovata.

In reality, the TOPKAPI was not calibrated in a conventional manner. The only parameters utilised were soil thickness, slope roughness, and width and roughness of drainage pattern trunks. These parameters were modified in relation to precise physical considerations, and we intentionally did not use a least squares technique, which could have improved the calibration (or, more precisely, fitting of the flows observed) to the detriment of the physical significance of the parameters.

Because continuous series of hydro-meteorological data were available only for 1990-1995, we considered it appropriate to calculate the TOPKAPI model on the Reno basin using data for 1994. This year was chosen due to the presence of major flood events occurring in different climatic periods (one in June (summer), one in September (autumn), and another in November, concomitant with the Piedmont flood event), by means of which we were able to evaluate the model's response under different physical and meteo-climatic conditions.

Analysing the maximum annual hydrometric levels recorded at the Casalecchio Chiusa section (Tab. 2.8), it appears that a major flood event occurred in 1990 (the maximum level measured in the decade 1990-2000), but the possibility of checking the model's results in the plain zone as well (in particular at the Bastia sul Reno section), supported our decision to do the calibration with 1994 data, in that 1990 hydrometric records from Bastia are not available.

YEAR 1990 1991 1992 1993 1994 1995

Casalecchio Chiusa 2.55 1.57 1.94 1.29 2.48 1.37

Table 2.8 maximum annual water levels recorded by instrument at Casalecchio Chiusa in period 1990-1995

2.3.2 Graphical results of the calibration To evaluate the results produced by the model during the calibration phase, we chose 7 transverse sections of some of the most important watercourses for which the runoff scale is available. In particular, we referred to the tele-hydrometers of:

- Vergato, Casalecchio Chiusa and Bastia for the Reno river;

- Castenaso for the Idice stream;

- Mordano for the Santerno stream;

- Sesto Imolese for the Sillaro stream;

- Calcara for the Samoggia stream.

With regard to the Reno, it may be seen (Figs. 2.12, 2.13 and 2.14) that the discharge trend is reproduced well in the Vergato and Casalecchio Chiusa sections, whereas at Bastia the check is more complex due to filling work on Cavo

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 44 Errore. L'origine riferimento non è stata trovata.

Napoleonico and because rainfall on the plain does not directly feed the river, being retained by the heavy network of canals. Therefore, definition of the amount of precipitation contributing to flow in the plain reach of the Reno is imprecise.

m3/s VERGATO 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.12 Comparison of observed and calculated discharge at Vergato section on Reno for 1994

m3/s CASALECCHIO CHIUSA 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.13 Comparison of observed and calculated discharge at Casalecchio Chiusa section on Reno for 1994

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 45 Errore. L'origine riferimento non è stata trovata.

m3/s BASTIA 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.14 Comparison of observed and calculated discharge at Bastia section on Reno for 1994

Analysing the two most significant events of 1994, one notes that the model underestimates the peaks at Vergato (Figs. 2.15 and 2.16), while at Chiusa di Casalecchio the maximum level of 22 September is correctly represented (Fig. 2.18) and there is a slight underestimate in June (Fig. 2.17).

In Fig. 2.18 , the final part of the hydrograph shows the effects of releases of upstream surface reservoirs, at the time not contemplated in simulation of the model.

m3/s VERGATO 500

450 observed 400 calculated

350 300

250

discharge 200

150 100

0 1 101 201 301 401 501 601 701 hour

Figure 2.15 Comparison of observed and calculated discharge at Vergato section on Reno from 8/6/94 - 7.00 to 7/7/94 - 11.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 46 Errore. L'origine riferimento non è stata trovata.

m3/s VERGATO 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 hour

Figure 2.16 Comparison of observed and calculated discharge at Vergato section on Reno from 17/9/94 – 13.00 to 13/11/94 - 15.00

m3/s CASALECCHIO CHIUSA 800

700 observed calculated 600

500

400

discharge 300

200

100

0 1 101 201 301 401 501 hour

Figure 2.17 Comparison of observed and calculated discharge at Casalecchio Chiusa on Reno from 9/6/94 –23.00 to 2/7/94 – 1.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 47 Errore. L'origine riferimento non è stata trovata.

m3/s CASALECCHIO CHIUSA 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 201 401 601 801 1001 1201 1401 1601 1801 hour

Figure 2.18 Comparison of observed and calculated discharge at Casalecchio Chiusa on Reno from 30/8/94 – 15.00 to 19/11/94 – 21.00

The model's best result was seen for the Idice stream at Castenaso (Fig. 2.19) (see coefficients EV, DC, CC described below). Both peaks for the June and September events are adequately reproduced (Figs. 2.20 and 2.21).

m3/s CASTENASO 400

350 observed calculated 300

250

200

discharge 150

100

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.19 Comparison of observed and calculated discharge at Castenaso section on Idice stream for 1994

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 48 Errore. L'origine riferimento non è stata trovata.

m3/s CASTENASO 400

350 observed calculated 300

250

200

discharge 150

100

0 1 101 201 301 401 501 601 701 801 hour

Figure 2.20 Comparison of observed and calculated discharge at Castenaso section on Idice stream from 7/6/94 – 11.00 to 11/7/94 – 15.00

m3/s CASTENASO 250

observed 200 calculated

150

discharge 100

0 1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 hour

Figure 2.21 Comparison of observed and calculated discharge at Castenaso section on Idice stream from 13/9/94 – 9.00 to 13/11/94 – 15.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 49 Errore. L'origine riferimento non è stata trovata.

The hydrograph calculated for the Mordano section on the Santerno stream (Fig. 2.22) produced satisfactory results as well, especially in the simulation of peaks (Figs. 2.23 and 2.24). On the other hand, there was an overestimate of base discharge, potentially attributable to groundwater percolation not considered by the model or to invalidity (due to low discharge values) of the discharge scale adopted.

m3/s MORDANO 250

observed 200 calculated

150

discharge 100

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.22 Comparison of observed and calculated discharge at Mordano section on Santerno streem for 1994

m3/s MORDANO 250

observed 200 calculated

150

discharge 100

0 1 101 201 301 401 501 601 hour

Figure 2.23 Comparison of observed and calculated discharge at Mordano section on Santerno streem from 8/6/94 – 7.00 to 3/7/94 – 7.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 50 Errore. L'origine riferimento non è stata trovata.

m3/s MORDANO 200

180 observed calculated 160

140

120 100

discharge 80

40 20

0 1 103 205 307 409 511 613 715 817 919 1021 1123 1225 1327 1429 1531 1633 hour

Figure 2.24 Comparison of observed and calculated discharge at Mordano section on Santerno streem from 5/9/94 – 21.00 to 15/11/94 – 16.00

The model did not satisfactorily reproduce the observed discharge trend in the case of Sesto Imolese (Fig. 2.25) or Calcara (Fig. 2.26). The causes may be traced to: incorrect identification of drainage directions for the DEM cells, attributed to sub- basins of the Sillaro and Samoggia streams, respectively; validity of the runoff scales used (in particular, for Calcara, there are several versions generating conflicting hydrographs, valid for some events and not for others), and imprecise definition of rain in input.

In this regard, it must be emphasised that Sesto Imolese is on the plain, thus with a part (even if minor) of its territory that does not convey waters directly to the bed as occurs at Bastia; whereas for Calcara, pluviometric values, whose areas of influence according to Thiessen fall in the Samoggia sub-basin, were lacking for some events (one instrument has no data at all for 1994), and were reconstructed with the closest functioning instruments.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 51 Errore. L'origine riferimento non è stata trovata.

m3/s SESTO IMOLESE 120

observed 100 calculated

60 discharge 40

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.25 Comparison of observed and calculated discharge at Sesto Imolese section on Sillaro streem for 1994

m3/s CALCARA 350

observed 300 calculated

250

200

150 discharge

100

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.26 Comparison of observed and calculated discharge at Calcara section on Samoggia streem for 1994

The model's suitability may be concisely analysed by defining the following three parameters:

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 52 Errore. L'origine riferimento non è stata trovata.

2.3.3 Statistical Tests

After calibration, we calculated three coefficients used for synthetic analysis of fitting of the model for some of the main flood forecast stations:

Being: i = time interval

N = total of time intervals

th Qci = calculated discharge at i time interval

th Qoi = observed discharge at i time interval

Qo = average observed discharge

2.3.3.1 COEFFICIENT OF EFFICIENCY

N ()ε −ε 2 ∑ i = − i=1 ε = − ε ε = − EV 1 N where i Qc i Qoi e = mean of i Qci Qoi ()− 2 ∑ Qoi Qo i=1

2.3.3.2 COEFFICIENT OF DETERMINATION

N N ()− 2 ()ε 2 ∑ Qci Qoi ∑ i = − i=1 = − i=1 DC 1 N 1 N ()− 2 ()− 2 ∑ Qoi Qo ∑ Qoi Qo i=1 i=1

2.3.3.3 CORRELATION COEFFICIENT

CC = DC

Tab.2.9 shows values for some of the sections considered.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 53 Errore. L'origine riferimento non è stata trovata.

Section EV DC CC Castenaso 0.836 0.679 0.824 Casalecchio Chiusa 0.761 0.672 0.820 Vergato 0.715 0.612 0.783 Mordano 0.578 0.218 0.467 Bastia 0.627 0.576 0.759 Sesto Imolese 0.490 0.057 0.238

Table 2.9 Values of coefficients of efficiency, determination and correlation for some river sections

2.4 Validation of the model Once the model was calibrated with the most appropriate parameter values, the estimates had to be validated in a different time period.

Based on the considerations described in section 7, we chose 1990 as the period for validation of the model. The most significant event in the 5-year period 1990-1995 in terms of maximum peak for the Casalecchio Chiusa section occurred on 25 November, followed by a less significant event the following month. Unfortunately, comparison with the Bastia section is impossible because water level readings are not available.

Fig. 2.27 shows reconstruction of discharges as run by the model at the Casalecchio Chiusa section on the Reno River: the results are quite satisfactory.

m3/s CASALECCHIO CHIUSA 1400 observed 1200 calculated

1000

800

600 discharge

400

200

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure2.27 Comparison of observed and calculated discharge at Casalecchio Chiusa section on Reno for 1990

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 54 Errore. L'origine riferimento non è stata trovata.

In particular, the shape of the hydrograph for the November event (Fig. 2.29) faithfully reproduces the maximum discharge value, and the descending phase, slightly fuller than that obtained with the runoff scale, can be interpreted as physically closer to the truth because water speeds during the depletion phase are lower than those preceding the crest, and are used to determine the runoff scale.

m3/s CASALECCHIO CHIUSA 300 observed calculated 250

200

150 discharge 100

0 1 101 201 301 401 501 601 701 801 901 hour

Figure 2.28 Comparison of observed and calculated discharge at Casalecchio Chiusa section on Reno from 25/3/90 – 16.00 to 2/5/90 – 4.00

m3/s CASALECCHIO CHIUSA 1400 observed 1200 calculated

1000

800

600 discharge

400

200

0 1 101 201 301 401 501 hour

Figure 2.29 Comparison of observed and calculated discharge at Casalecchio Chiusa section on Reno from 24/11/90 – 10.00 to 15/12/90 – 6.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 55 Errore. L'origine riferimento non è stata trovata.

For the Vergato section, discharges calculated by the model for 1990 (Fig. 2.30) have a trend similar to those observed. Here, as in 1994, there is underestimation of the peak at the most significant event on 25 November (Fig. 2.32). It should be considered that rain was not always reconstructed with values recorded by pluviometers in the basin (due to their absence). Therefore, and especially for the initial period shown in Fig. 2.31, reconstruction cannot be exhaustive.

m3/s VERGATO 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 701 1401 2101 2801 3501 4201 4901 5601 6301 7001 7701 8401 hour

Figure 2.30 Comparison of observed and calculated discharge at Vergato section on Reno for 1990

m3/s VERGATO 200 observed 180 calculated 160

140 120

100

discharge 80

60 40

0 1 101 201 301 401 501 601 701 801 901 hour

Figure 2.31 Comparison of observed and calculated discharge at Vergato section on Reno from 25/3/90 – 16.00 to 2/5/90 – 4.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 56 Errore. L'origine riferimento non è stata trovata.

m3/s VERGATO 1400

observed 1200 calculated

1000

800

600 discharge

400

200

0 1 101 201 301 401 hour

Figure 2.32 Comparison of observed and calculated discharge at Vergato section on Reno from 23/11/90 – 4.00 to 13/12/90 – 4.00

The question of representability of reconstructed rain applies equally to Castenaso. Nevertheless, comparison of the model's result to observed result is fairly good, and it must be considered that the high discharge levels used to build the runoff scale (Figs. 2.32 and 2.33) are absent here.

m3/s CASTENASO 90

80 observed calculated 70

40 discharge 30

0 1 101 201 301 401 501 601 701 801 hour

Figure 2.33 Comparison of observed and calculated discharge at Castenaso section on Idice streem from 25/3/90 – 16.00 to 27/4/90 – 0.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 57 Errore. L'origine riferimento non è stata trovata.

m3/s CASTENASO 180

160 observed calculated 140

120

100

80 discharge 60

0 1 101 201 301 401 501 hour

Figure 2.34 Comparison of observed and calculated discharge at Castenaso section on Idice streem from 23/11/90 – 4.00 to 15/12/90 – 6.00

For the Calcara, Sesto Imolese and Mordano sections, the simulations run are not reliable because the pluviometers in the sub-basins were out of order for extended period. Nevertheless, we show a few cases (Figs. 2.35, 2.36, 2.37).

m3/s SESTO IMOLESE 50 observed 45 calculated 40

35 30

discharge 20

15 10

5 0 1 101 201 301 401 hour

Figure 2.35 Comparison of observed and calculated discharge at Sesto Imolese section on Sillaro streem from 3/11/90 – 4.00 to 13/12/90 – 4.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 58 Errore. L'origine riferimento non è stata trovata.

m3/s MORDANO 300

observed 250 calculated

200

150 discharge 100

0 1 101 201 301 401 hour

Figure 2.36 Comparison of observed and calculated discharge at Mordano section on Santerno from 23/11/90 – 4.00 to 13/12/90 – 4.00

m3/s CALCARA 80

70 observed calculated 60

discharge 30

0 1 101 201 301 401 hour

Figure 2.37 Comparison of observed and calculated discharge at Calcara section on Samoggia streem from 23/11/90 – 4.00 to 13/12/90 – 4.00

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 59 Errore. L'origine riferimento non è stata trovata.

2.5 Conclusions Application of the TOPKAPI model to the entire Reno River basin until its natural outlet in the Adriatic Sea reveals that the model successfully simulates flood levels, with respect to both their extent and to peak time, whereas it is less successful at reproducing surface runoff (evident in some sections rather than in others).

This last aspect is no doubt linked to lack of a module (now under preparation) to simulate groundwater discharge, not essential for purposes of depicting flood levels. It may also be due to the discharge scales adopted, built on higher measured discharge values and extrapolated for minimum flow.

The technique utilised was efficient in terms of calibration and run times. The Reno basin was divided into 5229 cells, identifying 43 sub-basins, and the calculation algorithm took about twenty minutes to simulate a one-year period in one-hour intervals. The study demonstrates that the model has a simple and logical structure for reproduction of hydrologic and hydraulic processes in the basin using a non-linear reservoir approximation.

The parameter calibration phase may be deemed concluded.

A few modifications, refinements of a few components of the model, may be made at a later time, but these will not substantially change the calibration.

For example, it is expected that the digital map of soil types will soon be integrated with data on the part of the basin in Tuscany. This will provide detailed completion of the raster maps used to assign physical parameters.

We intend to introduce a rule of behaviour that simulates the filling work executed at Opera Reno to divert river flood waters into Cavo Napoleonico.

In addition, it seems useful to adopt a method to calculate the width of river sections that can exploit available data on pertinent reaches of the watercourse.

Lastly, parameters for estimation of evapo-transpiration will be calculated again for each sub-basin, using pre-1990 series of data as well.

MUSIC · Deliverable 9.1 · 6th Mar. 2002 Page 60