HYDROLOGIC MODEL DEVELOPMENT FOR THE DALIA- TANINIM WATERSHEDS IN ISRAEL

Transcription

1 HYDROLOGIC MODEL DEVELOPMENT FOR THE DALIA- TANINIM WATERSHEDS IN ISRAEL by Eylon Shamir and Konstantine P. Georgakakos Hydrologic Research Center, San Diego, CA in collaboration with Nadav Peleg Hydrology and Water Resources Program, Hebrew University of Jerusalem, Israel Efrat Morin Geography Department, Hebrew University of Jerusalem, Israel HRC TECHNICAL NOTE NO. 66 Hydrologic Research Center High Bluff Drive, Suite 255 San Diego, CA 92130, USA 10 February 2014

2 TABLE OF CONTENTS 1. Introduction Data Hydrometeorological Data Basin Delineation Rainfall-Streamflow Analysis Sacramento Soil Moisture Accounting Model SAC-SMA Model A priori Parameter Estimation Channel Routing Evapotranspiration Demand (ETD) Model Calibration References Appendix A: Annual Streamflow Hydrographs... 25

3 ACKNOWLEDGMENTS We wish to thank Danny Sherban from Yodfat Engineering for the logistical support and Yair Rinat, Matan Ben-Asher, and Shlomi Viner for providing the soil survey data. The research work was partially supported by the HRC Technology Transfer Program. This report should be cited as follows: Shamir, E., K.P. Georgakakos, N. Peleg, and E. Morin. 2014: Hydrologic Model Development for the Dalia-Taninim watersheds in Israel. HRC Technical Note No. 66. Hydrologic Research Center, San Diego, CA, 10 February 2014, 46 pp.

4 1. INTRODUCTION This report describes the development of the Sacramento Soil Moisture Accounting (SACSMA) model for the Dalia and Taninim watersheds in Israel. The motivation for the study is to develop a hydrologic model that runs in conjunction with ensembles of synthetic rainfall realizations from a weather generator. The weather generator developed by Peleg et al. (under review), produces rain fields that are based on statistical characterizations of convective and nonconvective rainfall events derived from gauge-corrected weather radar data. The weather generator rain fields have a 5-minute temporal and a 0.25 km 2 spatial resolution, similar to the radar data. The SACSMA model was developed using 12 years of rainfall data and their corresponding streamflow data for 3 hydrologic stations: Dalia-Bat Shlomo, Dalia Coastal Highway, and Taninim-Amikam. The SACSMA model was constructed for 14 subbasins within the two watersheds and the model precipitation forcing time series was aggregated to reflect estimated mean areal values for these subbasins. Initial SACSMA model parameters were estimated using soil field survey data collected by The Hebrew University of Jerusalem and GIS layers of terrain, soil, and lithology. The model was implemented to run in 5-minute intervals which is the native resolution of the radar data and is required because of the small sub-basins size and their rapid response time. Following the calibration rational described at the U.S. National Weather Service SACSMA calibration manual (NWSRFS 1999), the model parameters were tuned to accomplish a sound match between the observed and simulated streamflow. In the following we state the objectives that guided the model calibration, the caveats and uncertainty sources, and provide recommendations for the future use and interpretation of the model simulations. The objectives of the study dictated the focus of the model development. The SACSMA model developed herein is intended to run with input from the weather generator synthetic rainfall output, in order to examine the overall regional hydrologic regime and the potential impact of 1

5 projected climatic changes. Therefore, the model was calibrated to capture a general hydrologic regime as observed in the 12 years of record. Notice that the model was not calibrated to simulate the peak and the timing of a specific large event. The calibration performance criteria were based on visual inspection of the hydrographs, exceedance distribution plots, and quantitative performance indices that measure the bias and variance of the residuals. In addition, we inspected the model capacity to describe the relatively large (upper 1 percentile) events with respect to quantity, magnitude and frequency of occurrence. The SACSMA model is regarded as a robust hydrologic model that is used in various operational setups for various water resources management and flood warning practices (Smith et al., 2004; Reed et al., 2004; Shamir et al., 2006). However, the implementation of the SACSMA model for the Dalia and Taninim watersheds presented a unique set of challenges. The implementation of the SACSMA model over 5-min intervals and in basins of area on the order of 10 km 2 is challenging. Most studies and implementation recommendations for the SACSMA model is for coarser resolutions. For instance, the River Forecast Centers of the U.S National Weather Service commonly implement the SACSMA model in basins larger than 50 km 2 and with hourly or longer temporal resolution (e.g. Shamir et al. 2006). The coarse resolution allows for averaging in time and space, and the hydrologic response is less abrupt contributing to robust model performance. In addition, although the model implemented herein is the continuous time form formulation (Georgakakos, 1986), which is being integrated over different time intervals, the SACSMA parameters are known to be scale sensitive (e.g. Finnerty et al., 1997). Another challenge is the implementation of the model in ephemeral streams with predominantly dry soil moisture conditions. The SACSMA model was originally developed for perennial streams and the model structure mainly relies in saturation excess physics, which is the dominant runoff generation process in humid environments. Infiltration excess which is thought to be the dominant runoff generation process in semi-arid environments is only implicitly being simulated by the model. Despite these challenges, we believe that the SACSMA model implemented herein presented reasonable results and the model can be used to study hydrological processes in the ephemeral 2

6 small watersheds of this Mediterranean climate region. We anticipate that the hydrologic model simulations forced by the synthetic rainfall from the weather generator will be a valuable dataset that can be used for analysis of the variance and range of likely hydrologic regimes. The model can also be used to determine the impact of future climate projections on the local hydrologic regime. Notice that the results from the model simulation should be carefully interpreted. This report provides performance indices for the model simulation, quantifying model simulation errors with respect to the observed record. These performance indices do not account for uncertainty in the hydrometeorological rainfall and streamflow records. Conclusions regarding the interpretation of the actual water regime and projected changes in the water regime require the consideration of this uncertainty found in the streamflow simulation. In addition, the model was calibrated to handle specific characteristics of rainfall. Thus, model input with very different rainfall characteristics will yield higher simulation uncertainty since the model was not tested to accommodate those conditions. For instance, because of the model non linearity, rainfall with intensity higher than in the observed record may generate model response that is unexpected. Although the model produces reasonable seasonal soil moisture patterns these cycles were not validated with in situ data. Therefore interpretation of soil moisture regimes should be considered with respect to the nominal simulations forced by the observed records. 2. DATA 2.1 HYDROMETEOROLOGICAL DATA The rainfall data used in this study was estimated from data recorded by the Shacham (EMS) Mekorot company weather radar (34.7E, 32.5N), located about km south of the study area. The radar is a non-doppler C-band system with a temporal resolution of about 5 minutes per volume scan and a spatial polar resolution of 1.4 o X 1 km in space. The rainfall estimates were adjusted using data from 39 rain gauges, all within a 100-km distance from the radar. The data was available for twelve hydrological years (October 1-September 30; 1991/92, 1992/93, 3

7 1993/1994, 1994/95, 1995/96, 1996/97, 1997/98, 1999/00, 2000/01, 2001/02, 2002/03 and 2004/05). Detailed description of the derivation of the rainfall time series and evaluation for the study area is in Peleg et al. (2012). Although within Dalia Taninim there are five hydrometric stations operated by the Israeli Water Authority, only three stations were found to have observed record that matched the rainfall record and with adequate quality. The three stations: Dalia Bat Shlomo (ID-12130), Dalia Coastal Highway (ID-12140), and Taninim Amikam (ID-13105) have drainage areas of 42, 69 and 51 km 2, respectively. The instantaneous streamflow dataset was further interpolated to 5 minute intervals using linear interpolation between the observed instantaneous records. The streamflow record was originally digitized from station s charts, and this work assumes that the digitization captures the hydrograph, with instantaneous events that describe changes in flow regime. Thus, the hydrograph between two instantaneous points can reasonably be expressed by linear interpolation. 2.2 BASIN DELINEATION Using 5 m Digital Elevation Model (DEM) from the Survey of Israel, 14 subbasins were delineated within the Dalia and Taninim watersheds. Figure 2.1 shows the delineated subbasins and schematics of channel connections relatives to the hydrometric stations in the two watersheds. The properties of the 14 subbasins are summarized in Table 2.1. The hydrologic model, described below, was implemented for 14 subbasins and the gridded rainfall was averaged over the subbasins to generate 5-minute mean areal rainfall as the model forcing. Table 2.1: Summary of 14 Sub-basins properties Subbasin ID Area (km 2 ) Soil Depth (mm) Top Soil Texture Sub soil Texture Clay Clay Clay Clay Clay Loam Silty Clay Loam Clay Clay Clay Clay Clay Clay Loam Loam Clay Loam 4

8 Clay Clay Clay Clay Clay Clay Clay Clay Loam Clay Loam Clay Clay Clay Clay Figure 2.1: Basin delineation and subbasin channel connections for the Dalia and Taninim watersheds (left and right, respectively). Also indicated are the hydrometric stations used in the study (black dots). 2.2 RAINFALL-STREAMFLOW ANALYSIS The maximum gridded rainfall intensity in the study area is 20.8 mm/5-min and the mean areal rainfall over the drainage area of the hydrometric stations yields maximum intensity of 10, 6.7, 8.8, and 7.3 mm/5-min for Bat Shlomo, Dalia Coastal Highway, Amikam, and Taninim outlet at Gesher Eats, respectively. 5

9 Comparing the average mean areal rainfall over the hydrometric stations drainage area with the streamflow clearly showed that for each year an initial cumulative rainfall is required in the beginning of the rainy season before streamflow is recorded in the stations. Figure 2.2 exemplifies this initial required rainfall for the Bat Shlomo and Amikam stations. The upper [lower] row of the Figure shows the required rainfall during relatively wet [dry] years. Note that even very intense and long rainfall events in the beginning of the season as seen in the upper row for 1991/92 and 1994/95 did not generate measurable streamflow at the stations. A summary of the 12 available years and their required cumulative rain before the first appearance of streamflow is in Table 2.2. In the Dalia Coastal Highway station there are very small and short-lasting streamflow events that occur during rainfall events and therefore these thresholds for initial streamflow are seen after about 2 mm (~300 m 3 ) of cumulative streamflow. These small flow events might be attributed to the urban or road sewer network. It is also possible that this station has a lower observation flow threshold in its hydrometric station and it is capable of measuring lower flows than the other stations. In general, it is seen that in the three stations, on average, more than 230 mm of cumulative rainfall is required for initial streamflow to occur. This initial rainfall is a significant portion of the annual rainfall (e.g. average annual rainfall at Even Yithaq climate station ~650 mm/year). Table 2.2: Date of initial runoff in the hydrometric stations and the total annual mean areal rainfall that preceded the initial flow WY Dalia Bat Shlomo Dalia Coastal Hy Streamflow > 300(m 3 ) Taninim Amikam Date (mm) Date (mm) Date (mm) 91/ /12/ /12/ /12/ / /12/ /12/ /12/ / /2/ /3/ /2/ / /11/ /11/ /11/ / /1/ NA- -NA- 1996/1/ / /12/ /1/ /2/ / /12/ /1/ /1/ / /1/ /1/ /1/ / /12/ /12/ /12/

10 01/ /12/ /11/ /12/ / /12/ /12/ /12/ / /1/ /12/ /1/ Avg We think that this initial required rainfall is too large to be solely attributed to the upper soil tension capacity. Since there are no known diversions or upstream dams in these basins, this initial needed volume of rainfall is probably due to initial aquifer water deficit. Further analysis is required to identify if the effect on the initial streamflow is due to the regional aquifer or a local perched aquifer. Figure 2.2: Cumulative water year (October 1-September 30) streamflow (red) and mean areal rainfall (black). The blue lines estimate the date and quantity of rain that is required to initiate streamflow. The upper [lower] panels are examples for wet [dry] years. The left [right] panels are for Bat Shlomo [Amikam] stations. 7

11 As will be further explained below the SACSMA model structure which accounts for the soil capacities to hold water was not originally developed to handle such initial loses. Thus, we modified the model and added an initial loss reservoir that starts empty every year (October 1) and has to be filled first before rainfall is used as input to the rest of the SACSMA model components. In future development, we should consider implementing this initial loss reservoir to be filled in concert with the upper tension reservoir, such that the model simulation better reflects soil moisture changes at the beginning of the rainy season. 3. SACRAMENTO SOIL MOISTURE ACCOUNTING MODEL 3.1 SAC-SMA MODEL The Sacramento Soil Moisture Accounting Model (SACSMA) is a conceptual, continuous, aerial-lumped model that describes the wetting and drying processes in the soil. It simulates soil column response to tension and gravity forces to determine the water content of various soil layers, the evapotranspiration flux and the surface and subsurface flow components (percolation to deeper soil layers, interflow and baseflow to streams). The model is forced by mean areal precipitation and evapotranspiration demand and simulates the state of the soil moisture and runoff production. The wide applicability and robustness of the SACSMA as a hydrological model was demonstrated by the results of the Distributed Modeling Intercomparison Project (Smith et al., 2004; Reed et al., 2004). In the following we provide a concise description of the model while a detailed SACSMA description and formulation is in Burnash et al. (1973) and Georgakakos (1986) for the discretetime and continuous-time forms, respectively. The model formulation that is implemented in this study is based on the continuous-time form (Georgakakos 1986). 8

12 The SACSMA model (Figure 3.1) is comprised of a two-layer structure with a relatively thin upper layer that represents the surface soil regime and interception storage and a thicker lower layer that supplies moisture to meet the evapotranspiration demand and channel baseflow (flow that is not in direct response to a rainfall rate). Each layer tracks water content changes to estimate soil-moisture states through conceptual tension water storages and free gravitational water storages. The former can only be depleted by evapotranspiration whereas the rate of depletion of the latter is a function of their moisture content and assigned depletion coefficients. Partitioning the rainfall into surface runoff and infiltration is constrained by the moisture state of the upper layer and the percolation potential to the lower layer. The percolation rate into the lower layer is a nonlinear function of the saturation at the lower storages and the upper layer free-water reservoir. The percolated water into the lower layer is divided into two storage categories, tension and free gravitational water, with the latter being modeled by two linear reservoirs, a free-supplemental, and a free-primary, to account for the variable baseflow response to past rainfall events. Surface runoff that contributes significantly to flash flooding is the result of filled upper soil water tension and free water reservoirs and the relationship between rainfall rates and percolation rates to the deeper soil layers. Direct runoff contributes to flooding from permanently impervious areas and variable impervious-area surface runoff allows for flooding contribution by the increase and decrease of the saturated soil area near the streams. Despite the physical conceptualization embedded in the model structure, the model s 14 parameters cannot be directly measured from in-situ field observations (Table 3.1). When streamflow, rainfall and soil moisture data are available for a specific basin the model parameters are often estimated with an interactive calibration methodology that recognizes explicitly the function of the physical/conceptual components of the models (e.g., NWS 1999). Table 3.1: parameters UZTWM UZFWM LZTWM LZFPM LSFSM UZK SAC-SMA model parameters Description Upper zone tension water capacity (mm) Upper zone free water capacity (mm) Lower zone tension water capacity (mm) Lower zone primary free water capacity Lower zone supplemental free water capacity Upper zone drainage depletion coefficient (1/day) 9

13 LZPK Lower zone primary drainage depletion coefficient (1/day) LZSK Lower zone supplementary drainage depletion coefficient (1/day) ZPERC Percolation equation coefficient REXP Percolation equation exponent PFREE Fraction of percolation assigned to the lower zone free storages SIDE Fraction of flow lost to baseflow ADIMP Percent of additional impervious area PCTIM Percent of permanently impervious area Figure 3.1: A schematic of the SACSMA model (adopted from Burnash et al. 1973) 3.2 A PRIORI PARAMETER ESTIMATION The link of the SACSMA model parameters to soil and land cover characteristics (Duan et al. 2001; Koren et al., 2000) is based on empirical association between soil-texture classes and soil hydraulic indices such as saturated hydraulic conductivity, wilting point, field capacity, and saturated retention conditions (e.g. Cosby et al. 1984). Initial model parameters estimation relies on spatially available land surface datasets such as soil texture, soil depth, terrain, and land use and cover. 10

14 A GIS layer of the soil survey for Israel, available from the Israeli Ministry of Agriculture, was used in addition to data from a field survey of soil pedons that was conducted by students from the Lab of Hydrometeorology at the Hebrew University. The soil group associations are in Figure 3.2 and the soil association classes are in Table 3.2. The soil association definitions were interpreted from Dan and Raz (1970). Table 3.2: Legend taken from the Soil Association map of Israel, (Dan and Raz 1970) A-1 Terra rosa on steep slopes (gt.20%) A-2 Terra rosa on slopes sm. 20% A-3 Terra rosa & rendzina on steep slopes (gt. 20%) A-4 Terra rosa & rendzina on slopes sm. 20% B-1 Brown rendzina on steep slopes (gt. 20%) B-2 Brown rendzina on slopes sm. 20% B-3 Brown rendzina & light rendzina on steep slopes (gt. 20%) B-4 Brown rendzina & light rendzina on slopes sm. 20% H-3 Calcareous accumulative brown grumusols & residual dark brown soils H-4 Hydromorphic grumusols & grumusolic gley H-5 Alluvial brown grumusols & hydromorphic grumusols H-7 Colluvial-alluvial soils & grumusols Figure 3.2: Soil group associations 11

15 In Figures 3.3 and 3.4 the soil texture and soil depth (cm) of the survey are shown overlaid on the soil association map, respectively. The points and the soil groups were used for initial estimate of depth and texture for the soil association groups first and then for the subbasins, which later were used for the a priori estimate of the SACSMA model parameters (Table 3.1). The estimated soil depth and soil texture of the sub and top soil for the 14 subbasins are in Table 2.1. Figure 3.3: Soil texture from pedon analysis Figure 3.4: Soil depth (cm) from pedon samples. 12

16 3.3 CHANNEL ROUTING The SACSMA model simulates total channel flow for the subbasins. We implemented the twoparameter linear reservoir routing scheme (Chow et al., 1988) to convey the flow to the three hydrometric stations. The upstream subbasin runoff is converted to mean areal channel inflow that is routed to the locations of interest. The linear reservoir parameters were adjusted to match the observed flow and were set to 0.1 (day -1 ) and 10 for the depletion coefficient and the number of reservoirs, respectively. 3.4 EVAPOTRANSPIRATION DEMAND (ETD) Daily evapotranspiration demand (ETD) is an input variable that is expected by the SACSMA model. We used monthly climatological values obtained from the Even Yithaq meteorological station which has similar climate to the study area. The monthly ETD values starting in January are set to 2, 2, 3, 4, 4.5, 5, 5, 5, 4, 3, 3, and 2 (mm/day). 4. MODEL CALIBRATION The model calibration was carried following guidance and rational of the NWSRFS calibration manual. An initial parameter set using the a priori scheme described above was used for an initial reference run. The model was set to run continuously in 5-minute intervals for each year from 1 October to 31 May. Each year the initial conditions of the 5 SACSMA soil components were set to dry condition (~ 1 percent of capacity). The Initial values required for cumulative rainfall before the mean area rainfall is used as forcing for the SACSMA model are shown for ascending basins IDs: , 210, 170, 170, 170, 150, 170, 150, 150, 170, 150, 210, and 150 mm per year. Note that these values are smaller than the values indicated in Table 2.2 because water is also needed in order to saturate the SACSMA upper tension and free reservoirs before runoff is produced. The model calibration was based on visual inspection of the hydrographs to achieve 13

17 simulation behavior that represents the hydrologic response at the stations for all the study years. The calibrated parameters are provided in Table 4.1. Figure 4.1 shows a sample of the 5 minute observed (black) and simulated (red) hydrographs for 1991/92 at Bat Shlomo. The mean areal rainfall hyetograph (mm/5-min) is shown on the left y- axis and the annual cumulative rainfall (mm) is in the lower panel. Similar plots for each of the 12 years and for the three hydrometric stations are in Appendix A. It is seen that although the specific large event in the beginning of the year was underestimated, the overall simulated hydrograph matches well the observation, particularly as regards the shape of the recession limb and the duration of baseflow. Table 4.1: Calibrated SACSMA parameters ID UZTM UZFM LZTM LZFPM LZFSM UZK LZPK LZSK ZPERC REXP PFREE SIDE ADIMP PCTIM

18 Figure 4.1: The left y-axis of the upper panel is for the simulated (red) and observed (black) streamflow (cms) and the right y-axis is for the mean areal rainfall (mm/5-min). The lower panel shows cumulative rainfall (mm). The 2002/03 water year was found to behave as an outlier year. The radar data indicates a relatively wet year with observed low flows at the stations and streamflow simulations that substantially overestimate the observation (Appendix A). Adjusting the model parameters to account for this year biases would compromise substantially the model performance in the other years. Since the observed flow is consistently low in all three hydrometric stations this disagreement with the other years is thought to be attributed to high biases in the radar rainfall estimate for this year. Thus, we decided to omit this year from the calibration record. The calibration record cumulative probabilities of the 30-minute streamflow (upper [lower] panel cms [natural log of cms]) are shown for the three stations (Figure 4.2). The probability distributions show good agreement between the simulation and observed distributions. The lower panel shows that simulation in Dalia Coast overestimated the low flows. 15

19 The subbasins that are upstream of the Bat Shlomo were calibrated to match the flow at Bat Shlomo and the calibration at the coast was conducted using the contribution from Bat Shlomo and two additional subbasins. In order to reduce the flow at the coast and achieve better agreement between the cumulative distributions, the hydrograph performance at Bat Shlomo will have to be compromised. It is possible that channel transmission loses between the two gauges reduce the flow downstream at the coast. The mismatch between the gauges can be seen in the lower panel. The flow between ~92-96 percentiles as Coastal station (solid red line) is lower than the corresponding flow at Bat Shlomo (solid black line). In addition, it is understood that data from the Dalia Coastal Highway station are of lower quality than the upstream Bat Shlomo station. As described above, the SACSMA model simulates the vertical soil moisture in five soil reservoirs (i.e. tension and free at the top soil and tension and two free reservoirs at the lower soil). Figure 4.3 is an example of simulation from subbasin 3 for 1991/92 and it well demonstrates the dynamic of the soil moisture in each soil component. It is seen that the upper zone is highly reactive to rainfall events. The upper free component has episodic pulses and the upper tension that is being depleted by evapotranspiration has a gradual seasonal depletion. The lower zone reservoirs, in general, show smoother changes that are less reactive to individual storm events and the differences in depletion rates between the free reservoirs is clearly seen. However, we note that the depletion coefficients of the lower free components (primary and supplemental) were assigned relatively high values which imply short durations of base flow. 16

20 Figure 4.2: The cumulative distributions of the average 30 minutes observed (solid lines) and simulated (dashed lines) streamflow in units of cms and natural log of cms for the upper and lower panels, respectively. 17

21 1 Upper Zone Tension Content Oct May 1 Upper Zone Free Content 0.5 Soil Moisture (Fraction) 0 Oct Oct 1 Lower Zone Tension Content Lower Zone Free Primary Content May May Oct May 1 Lower Zone Free Suplamentry Content Oct May Figure 4.3: An example of soil water saturation fraction of the 5 model components for subbasin /92. We reiterate that these soil water estimates reflect calibration of the model with the observed streamflow records and, although their annual behavior seems rational, they were not validated with in-situ data. The interpretation of these simulations should therefore be made using qualitative and comparable measures. For example, the spatially aggregated soil moisture of the upper and lower zones for Bat Shlomo drainage area is presented for 2000/01 and 2001/02 water years which are relatively wet (rainy) and dry years (Figure 4.4). The resulted simulated soil moisture clearly reflects the differences between the years. It is seen that during the wet year the upper soil frequency of saturation is higher and the lower soil moisture generally maintains higher saturation levels. 18

22 The soil moisture results are simulated for each subbasin and the results presentation can be done at the subbasin level or aggregated in space. In addition, the five components can be aggregated as average moisture at the upper and lower soil or aggregated to represent the soil water condition of the entire vertical soil layer. Upper ASM (fraction) Lower ASM (fraction) 1 Bat Shlomo Drainage Area /01 Dry Year x /02 Wet Year x 10 4 Figure 4.4: Bat Shlomo drainage area average upper and lower (upper and lower panels, respectively) soil water saturation fraction for 2000/01 (dry year) and 2001/02 (wet year). Last, we evaluate the performance and uncertainty associated with the simulations using various performance indices. The selected performance indices are the relative bias, relative variance, correlation coefficient and root mean square error. The relative bias and relative variance formulation is in the following: (1) (2) 19

23 where O and S are the observed and simulated values, respectively, and var() signifies the variance. The performance measures were calculated for aggregated time intervals ranging from 30 minutes to 24 hours. A summary of these four indices for the 3 hydrometric stations is in Figure 4.5. The solid line is for the entire time series while the markers represents the 10 and 90 th percentiles (pluses and triangles, respectively) estimated from the distribution calculated for the water years. The optimal relative bias is equal to zero. The different time aggregations do not change the relative bias. It is seen that the 11 years of simulation reached a bias that is fairly close to zero with slight overestimation (negative) of the simulation. The annual values have a wide range of relative bias and are skewed towards over estimation. The optimal relative variance is zero. In Figure 4.5 it is seen that when calculated for the entire duration of the record the relative variance is close to one. However as in the case of the bias the annual values have a relatively wide and skewed distribution. It is also seen that the Bat Shlomo [Dalia Coast] gauges has the lowest [highest] relative variance. The RMSE which is an aggregate residuals measure of the bias and variance also indicated that the Bat Shlomo gauge has the best performance. In addition, it is seen that the aggregation of intervals greater than 12 hours yields better performance values of relative variance, correlation coefficient and RMSE. However, up to 12 hours, the time aggregation did not improve the simulation performance. 20

24 1 30 Relative Bias Bat Shlomo Dalia Coast Amikam Relative Variance Correlation Coefficient Root Mean Square Error Time (Hours) Figure 4.5: Performance indices calculated for different aggregated time scales for the three stations. The performance indices are: relative bias (upper left), relative variance (upper right), correlation coefficient (lower left) and root mean square error (lower right). The solid lines are the indices calculated for 11 years. The 10 and 90 th percentiles of the annually calculated indices are indicated as plusses and triangles, respectively. It is also important to evaluate the performance of the model simulations as regards the overall representation of the large flow events. We compared the 1-percentile values between the observed and simulated flow for the different aggregation intervals. The 1-percentile was first determined from the observed record and the simulation samples were constructed as values that exceed the observed value at the 1-percentile threshold. 21

25 6.5 x percentile - Total Flow percentile - Average Flow Rate Bat Shlomo Sim. Dalia Coast Sim. Amikam Sim. Cubic Meter Bat Shlomo Sim. Dalia Coast Sim. Amikam Sim. Bat Shlomo Obs. Dalia Coast Obs. Amikam Obs Ratio (sim/obs) Time (Hours) Time (Hours) 1 1-percentile - Frequency of Occurence Ratio (sim/obs) Bat Shlomo Sim. Dalia Coast Sim. Amikam Sim Time (Hours) Figure 4.6: Performance indices calculated for 1 percentile flow events of different aggregated time scales and for the three stations. The performance indices are: total flow (upper left), ratio of mean flow (upper right), and ratio of frequency of occurrence. The measures seen in Figure 4.6 are the total flow for calibration record (m 3 ), the ratio (simulation/observed) between the average flows, and the frequency of occurrence of large events. The evaluation of the large events indicates reasonable agreement. For the average flow the ratio is less than 20% in Amikam and less that 7% in Bat Shlomo. The frequency of simulated large events (1 percentile) is less than the observed for all stations. 22

26 REFERENCES Burnash, R.J.C., Ferral, R.L., McGuire, R.A., 1973: A generalized streamflow simulation system: conceptual modeling for digital computers. US National Weather Service and California Department of Water Resources Rep., Joint Federal State River Forecast Center, Sacramento, California. Chow V.T., D. R. Maidment, L.W. Mays, Applied Hydrology, McGraw-Hill 572pp. Carpenter, T.M., and Georgakakos, K.P., 2004: Continuous streamflow simulation with the HRCDHM distributed hydrologic model. Journal of Hydrology 298: Cosby, B.J., Hornberger, G.M., Clapp, R.B., and Ginn, T.R., 1984: A Statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resources Research 20(6): Dan J., Z. Raz. 1970: Soil Association map of Israel, Ministry of Agriculture, The Volcani Institute of Agriculture Research, Soil Conservation and Drainage Department. Finnerty, B. D., M. B. Smith, D-J Seo, V. Koren, and G. E. Moglen 1997: Space-time scale sensitivity of the Sacramento model to radar-gage precipitation inputs. Journal of Hydrology, 203: Georgakakos, K.P., 2006: Analytical Results for Operational Flash Flood Guidance. Journal of Hydrology 317: Georgakakos, K.P., 1986(b): A generalized stochastic hydrometeorological model for flood and flash flood forecasting, 1: Formulation. Water Resources Research, 2213: Koren, V., Shaake, J., Duan, Q., Smith, M., and Cong S., 1998: PET upgrades to NWSRFS Project Plan, HRL internal report, Hydrology Laboratory, Office of Hydrologic Development, NOAA, National Weather Service, Silver Spring, MD. Koren, V., Smith, M., Wang, D., Zhang, Z., 2000: Use of soil properties data in the derivation of conceptual rainfall-runoff model parameters, American Meteorological Society15th Conference on Hydrology, Long Beach, CA pp January National Weather Service River Forecast System (NWSRFS).,1999: User Manual. National Weather Service Office of Hydrologic Development, Hydrology Laboratory, Silver Springs, MD Peleg, N., and E. Morin (2012), Convective rain cells: Radar-derived spatiotemporal characteristics and synoptic patterns over the eastern Mediterranean, Journal of Geophysical Research, 117, D15116, doi: /2011jd

27 Peleg, N., and E. Morin, Stochastic convective rain-field simulation using a high-resolution synoptically conditioned weather generator (HiReS-WG), Water Resources Research, 50, doi: /2013wr Reed, S., Koren, V., Smith, M., Zhang,.Z, Moreda, F., Seo D.-J., and DMIP participants., 2004: Overall distributed model intercomparison project results. Journal of Hydrology, 298: Shamir, E., Imam, B., Gupta, H.V., Sorooshian, S., 2005: Application of temporal streamflow descriptors in hydrologic model parameter estimation. Water Resources Research, 41(6):W Doi: /2004WR Shamir, E., TM. Carpenter, P. Fickenscher, and KP. Georgakakos Evaluation of the NWS operational hydrologic model for the American River Basin. Journal of Hydrologic Engineering, ASCE. 11(5): Smith, M.B., et al., 2004: The distributed model intercomparison project DMIP: Motivation and experiment design. Journal of Hydrology 298:

28 APPENDIX A: ANNUAL STREAMFLOW HYDROGRAPHS As in Figure 4.1 for all water years and for the three stations: 25

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