Recharge Estimation and Soil Moisture Dynamics in a Mediterranean Karst Aquifer F

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect of the ff 2 , and M. Sauter 3 8804 8803 , H. Puhlmann ed Complex Evolution Metropolis algorithm. To gen- 2 ffl , S. Schmidt 1 , J. Lange 1,2 Soil moisture measurements showed a pronounced seasonality and suggested rapid This discussion paper is/has beenSciences under (HESS). review Please for refer the to journal the Hydrology corresponding and final Earth paper System in HESS if available. Chair of Hydrology, University of Freiburg,Geoscience Freiburg, Center, Germany Applied Geology, University ofForest Göttingen, Research Göttingen, Institute Germany Baden-Württemberg, Freiburg, Germany well were observed synchronouslygroundwater with recharge simulated percolation mechanisms. pulses, Thetion indicating 62 fluxes rapid year of model updry run to years. yielded Furthermore, 66 % a annual dependencecould of percola- of be precipitation recharge shown. on depths the Strong duringalso temporal correlations observed. rainfall wet between distribution years depth and of of recharge 0 and % soil during depth were the calibrated model to a time series with 62 yearsinfiltration of during meteorological heavy data. rainstorms. Hydrus-1Dterm successfully soil simulated moisture short patterns, andduring with long- the a majority few of intensive simulated deep rainfall percolation events. occurring Temperature drops in a nearby groundwater of above-average wet winters.cesses To elucidate during process these understandingmeteorological extreme of stations, events, and infiltration a soil pro- climatic monitoring moisture gradient plots network in was of the installedwas precipitation Jordan in used gauges, Valley an to region. simulate area waterparameters In with estimated movement three a by in the steep soil the Shu moisture unsaturatederalize our soil plots, results, zone Hydrus-1D we with modified soilpronounced soil climatic hydraulic depth gradient and and rainfall soil input depth variability to on simulate percolation the fluxes e and applied Knowledge of soil moistureinformation dynamics on the in temporal the andpecially unsaturated spatial true variability soil of for zone groundwater provides the recharge.groundwater This recharge valuable Mediterranean is is region, es- expected to where occur a during high substantial magnitude fraction precipitation of events long-term Abstract Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/11/8803/2014/ 11, 8803–8844, 2014 doi:10.5194/hessd-11-8803-2014 © Author(s) 2014. CC Attribution 3.0 License. Recharge estimation and soil moisture dynamics in a Mediterranean karst aquifer F. Ries 1 2 3 Received: 3 July 2014 – Accepted: 14Correspondence July to: 2014 F. – Ries Published: ([email protected]) 28 July 2014 Published by Copernicus Publications on behalf of the European Geosciences Union. 5 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | gener- ff /sediment ff cult to distinguish ective water use. ff ffi ects on overall recharge rates. ff 8806 8805 er unique insights into near surface hydrolog- ff er et al., 2010), and chloride mass balances (Marei ff erences in climate, geology, land use, topography and soil properties erence between artificial/natural rainfall and measured overland flow ff erentiate between recharge and evapotranspiration. On the other hand, ff er et al. (2010) coupled a water budget model with a groundwater flow ff ff Observations of soil moisture may o Only limited knowledge on recharge dynamics is available for the carbonate aquifer A large variety of methods suitable for estimating recharge rates were developed in ical processes, because water fluxes are susceptible to conditions and properties of cal rainfall–recharge relationships (Baida and1995; Burstein, Zukerman, 1970; 1999). Guttman Average and recharge Zukerman, ratesance were approach assessed by (Hughes a et simple2010). water al., Sche bal- 2008) andmodel by for a the entire chlorideand western mass groundwater part balance level of (Marei data thetween et for carbonate 9 calibration. al., aquifer % They and and reported usedrainfall 40 spring within recharge % the discharge rates of winter ranging season annual be- had rainfall considerable e and showed that the temporal distribution of (Lange et al., 2010). system shared between the West Bankon and Israel. long-term First recharge estimates spring wereand based discharge Jacobs, and 1958). groundwater Later, well groundwater abstraction flow data models (Goldschmidt were used to establish empiri- vided insights into the deeperability unsaturated of zone percolation in and terms flowin of paths. water recharge Their storage, data modelling spatial was (Hartmann vari- alsobetween used et processes to al., incorporate in 2012). variability the However,uncertainty it unsaturated remains was regarding soil the di zone representativenessinfiltration of and processes. cave This in drip is data the mainly withdrips due underlying respect are to epikarst, to unknown the and and facts caves thatTherefore might the cave have contributing drips developed areas are their of own not cave hydraulic necessarily environments. representative for the bulk karst vadose zone loca et al., 2014),balance groundwater conceptual models (She models (e.g.et al., Hartmann 2010; et Schmidt et al.,including al., both 2013a), 2013). the However, unsaturated coupled these and studies water- thespatial treat saturated karst resolution. zones, and systems Studies are as on limited units, in cave temporal drips and (Gregory et al., 2009; Arbel et al., 2010) pro- knowledge (GIS)-based mapping (Andreo et al., 2008), multiple linear regression (Al- ments from spring discharge measurements or hydraulic head data. Methods include colation and recharge quantities infrom Mediterranean two karst sides: have on mainly the beensurface one approached hand, water hydrologists balance and on geomorphologistsmeasurements small characterized during the plots natural by rainstorms (e.g. sprinklingscale Cerdà, experiments 1998; experiments or Lavee et by also al., runo 1998).ation included Large- processes tracers (e.g. and Langetion facilitated et by statements al., the on 2003).but runo di did However, these not di studieshydrogeologists quantified frequently infiltra- assessed average recharge rates of entire karst catch- future climate change (Giorgi, 2006;limited water IPCC, resources. 2013), Hence, imposing more additionalin insights pressure Mediterranean into karst on processes regions its of are aquifer of replenishment vital importance. the last decades (De Vries and Simmers, 2002; Scanlon et al., 2002). Infiltration, per- cause a high spatialsessment and temporal and variability predictions of ofareas groundwater are recharge recharge important making a the in challengecipitation as- this (e.g. may respect, rapidly Zagana because infiltrate etrates during into al., (De high exposed 2007). Vries intensity karst Karst winter and surfaces(Ford and storms and Simmers, Williams, pre- induce 2002), 2007). high which Aled recharge rapidly are to increasing a common water widespread in demand overexploitationmore, in of the groundwater the the Mediterranean resources last (EUWI, Mediterranean area decades 2007). has region Further- has been identified as a “hot spot” of current and In the Mediterranean region, groundwatertural is water the supplies main (EUWI, 2007). source Knowledge foris on domestic the a and quantity agricul- of prerequisite groundwaterSmall-scale recharge for di sustainable water resources planning and e 1 Introduction 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sarcopo- ety, 1963). Soil ff ed Complex Evolution ffl erent sizes alternate with soil ff 8808 8807 ) are the dominant vegetation types. They are used for extensive On hillslopes, annual plants and Mediterranean shrubs (predominantly Outcropping geological formations consist of carbonate rocks of Cretaceous age The objective of this study is to investigate the spatial and temporal variability of ence of biogenic crusts wasof reported scattered (Kutiel et built-up al., areas, 1998).rigated olive Minor agricultural land plantations land use on types (annual terraced consist bottoms. and land perennial and crops, rainfed herbs or and partly vegetables) ir- in valley deforestation during the last 5000 years (Yaalon, 1997). terium spinosum grazing by goats and sheep.a South-facing higher slopes proportion show of a bare lower soil vegetation and density outcrops and than north-facing slopes, where the pres- and heterogeneous topography, soilping depth is massive highly bedrock variable. andpockets On loose of the rock variable slopes, fragments dimensions, outcrop- karst of shapes, fissures di and are often depths filledalluvial (Fig. with soils 2). eroded (Vertisols) Dissolution soil with cracks material.low soil In and Brown valley Lithosols depths bottoms, and up fineareas loessial to textured receiving Arid several less Brown meters rainfall Soilsnificantly have (Shapiro, dominate been developed. 2006). transformed in Shal- by In the human general, eastern, activities such soils low-lying as in land the cultivation, terracing, region and have sig- from the aeolian input of(Yaalon, 1997). dust Predominant (silt soil and types clay are fraction)ized Terra originating by Rossa high from and clay the Rendzina, contents. Sahara both Rendzinaner desert character- soils and contain still carbonate in show the recentpast soil development, climatic matrix, whereas conditions are Terra thin- (Shapiro, Rossa 2006). soils As were a formed result under of irregular carbonate weathering parent material consists of residual clay minerals from carbonate rock weathering and considered partial aquicludes (Weiss andian Gvirtzman, Chalks 2007). form In outcrops the of southeast, low Senon- hydraulic conductivity (Rofe and Ra ter season from October tomountain April range (highest (Goldreich, elevation: 2003). 1016 The meast a.s.l.) topographic in results gradient the in from west a the todecreases strong the from Jordan precipitation 532 Valley gradient. mm in Long-term in the (Morin Jerusalem average et (810 annual m al., precipitation a.s.l.) 2009). to 156 mm in Jericho (290 m(Begin, b.s.l.) 1975). They arestone composed and of dolomite fractured alternating and highly with permeable marl layers and of chalk lime- layers of low permeability, often 2 Study area Our study area is locatedeast on of the western Jerusalem marginfronts of (Fig. (mainly the 1). Cyprus Jordan lows) Rift Precipitation carrying Valley shows 25 moisture km from a north- the pronounced Mediterranean Sea seasonality during with win- cold percolation, and hence groundwater rechargebonate rates, for an aquifer. Eastern We Mediterranean car- dimensional use continuously water recorded flowMetropolis soil (SCEM) models algorithm. moisture (Hydrus-1D) The dataand calibrated with temporal to models patterns the are of calibrate then soilcharacterized one- Shu water used by percolation strong to in climatic assess a gradients spatial Mediterranean and karst variable area, soil which depths. is ture is rarely measured inpurposes. semi-arid Scott areas et and is al.calibrate seldom (2000) Hydrus-1D used exemplified for soil the recharge hydraulic estimation demonstrated potential parameters of the in soil high southeastern moisture inter-annualwhere Arizona. time considerable variability Their percolation series of only results to occurs water during fluxes above average in wet these years. environments the vadose soil zone across several scales (Vereecken et al., 2008). Yet, soil mois- 5 5 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | t E erence of di- is composed C within 24 h ◦ ff a ) for long-term E is the percolation L 2 cm as suggested − (soil evaporation) and can be described with: s ff E ected by this phenomenon. In ff nek et al., 2013) for a period of ů im Š is the precipitation, http://www.data.gov.il/ims P ect of non-linearity of the hydraulic conduc- , (1) ff t 8810 8809 E + s E + i E = is the evapotranspiration pet time interval. a a E E with (evaporation of intercepted precipitation), i L is the storage change over time, E − t a d E s/ − P nek et al., 2013 for more details). Potential evapotranspiration was calculated ů = For our three soil moisture plots, soil water content and water fluxes were simulated The dielectric permittivity of water changes with temperature (e.g. Wraith and Or, t im s d d due to a strongture radiation data input applying and multiple partly linearCobos regressions uncovered and against soil. Campell soil We (2007). temperature corrected as our described soil by mois- 3.3 Modelling of the soil zone Water balance at the plot scale in absence of surface runo SM3). Characteristics of the plots are summarized in Table 1. 1999). Hence, measurement techniques ofelectric soil permittivity moisture between based water on and theour soil di case, matrix soil are temperature a was highly variable and changed by up to 20 measurement period (October 2011 to May 2013) from only three locations (SM1– to instrument malfunction and vandalism, we obtained continuous data of our entire inserted the sensors verticallybetween 35 into and the 80 upslope cm).terial wall After and installation, of compacted we approximately manually to refilledconnected dug to pre-disturbance the soil data bulk pits loggers pits density. with The (EM50,tic (depth the Decagon probes bags parent Devices were Inc.), soil and which ma- function buried were for sealed in mineral by the soils plas- withcontent soil a (VWC). to measurement The measurement accuracy avoid interval of vandalism.on was 4 the % We set performance of at used of ten the the the minutes. volumetric Further employed water internal sensors information calibration can be found in Kizito et al. (2008). Due Seven soil moisture plots were installed, eachture equipped sensors with four (5TM/5TE, capacitance soil Decagon mois- not Devices receive Inc.). lateral We surplusfrom paid water rock attention outcrops from that and upslope at the overland locations plots flow with did by minimum slope. placing To them minimize disturbance, distant we pendant event data logger (Onset Computeribrated Corporation) before (Fig. employment, 1). maintained, All and gaugesrainfall cleaned were season. cal- twice Temperature was a measured year at beforeOnset and four Computer climatic after Corporation). stations the Additional (Thiesthe rainfall GmbH and and Israel climatic Meteorological data Serviceanalysis. was Additionally, obtained database groundwater from levels ( and temperatureswell were (Mini-Diver, recorded Eijkelkamp). in a nearby 3.2 Soil moisture measurements 3.1 Hydrometeorological measurements To capture the spatial variation ofa rainfall along rain the gauge strong network climatic gradient, consisting we of installed 14 tipping buckets (RG3-M) connected to a HOBO 3 Material and methods tivity function close toby saturated Vogel conditions, et an al. airan entry (2001) empirical value was equation of used. including InterceptionŠ the by leaf area the index plant and canopy daily was precipitation calculated values by (see on a daily basis32 with months. Hydrus-1D Hydrus-1D (version solvesin 4.16; the variable saturated Richards media. equationconductivity Matric numerically potential were for dependent calculated water water(van using transport retention Genuchten, the and 1980). hydraulic Mualem/van-Genuchten To reduce soil the hydraulic e model where d at the profile bottom and of the terms (plant transpiration). 5 5 20 25 15 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | is (2) so is a dimen- β is a dimensionless measure α ered noticeably. As a result, a par- ff ciently solve global optimization problems ffi are the standard deviations of simulated and 8812 8811 s , 2 σ ) o s 1 /µ /µ − and s o σ β s σ ( σ cient between simulated and observed VWC (Cov ciency (KGE; Gupta et al., 2009) in a modified version = + ffi ffi 2 β ) 1 − α are the mean simulated and observed VWC) and and ( o + µ 2 s o ) µ µ 1 and = − s r α ( µ q , ed Complex Evolution Metropolis optimization algorithm (SCEM; Vrugt et al., − o is the correlation coe so ffl σ 1 r · = s σ Cov = KGE determined soil hydraulic parameters forthe every soil Shu material by inverse2003) modelling using and the Kling–Guptafrom e Kling et al. (2012) as the objective function: (October 2011 to April 2013) were used for calibration of Hydrus-1D. We individually ticular soil material withmoisture singular probe. Observed soil soil hydraulic moisture properties data from was two assigned winter for and one each summer soil season fluxes at user defined observation pointsat (here: the depths of lower the profile soilranges, boundary. moisture and probes) Model the and corresponding input data data,in sources Table and selected 2. calculation parameter methods values are summarized and their 3.4 Calibration procedure, uncertainty analysis andAn parameter increase sensitivity of clay content andthe bulk individual density probes with in depth various depths was at observed our at plots all di profiles and took up water wasof controlled root by a density root withreported distribution soil for function. the depth An Mediterranean was exponentialet region decrease al., assumed, (e.g. 2008). observed De Temporal at variations RosnayWith of and the these rooting Polcher, study components, depth 1998; Hydrus-1D sites and De continuously and root Baets computed density often water were content disregarded. and water water uptake model (Feddes etenergy al., balance 1978) surface applying evaporationarea, plant model vegetation parameters (Camillo cover for shows and grass a Gurney, and strongity 1986). seasonality an during due In the to our the dryimplemented study restricted season. water into To availabil- the account for modelcording this, to with time field intra-annual observations, dependent variation the plantand start of growth of the surface the data maximum cover growing was season vegetation fraction.the was density largest Ac- set monthly was to precipitation mid assumed amounts November were for observed. February/March The depth shortly from after which plants a lysimeter station intranspiration California, under this semi-arid method climates adequatelyPotential (Jensen evapotranspiration reproduced et was potential al., split 1997; evapo- and into Weiß potential and potential transpiration Menzel, evaporation from 2008). from plantsable according surface the to cover soil fraction. Beer’s surface Both law fluxes based were on reduced the to time actual vari- values based on a root by the Hargreaves-equation (Hargreaves and Samani, 1985). Originally developed for all parameters. Predicted soilassessment. They moisture were ranges determined wereobtained by through used running the for Hydrus-1D SCEM algorithm parameter with after 1000 uncertainty reaching parameter convergence. sets optimized), the population sizeinfer was posterior set distribution to was 144 setreduction and to score 1000. the (SR), The number a SCEMwas of convergence routine fulfilled. accepted criterion was As draws defined run to proposeddicating until by by that the Gelman Vrugt scale the and Markov et Rubin chain al. (1992), had (2003), converged a to SR a stationary value posterior of distribution 1.2 for was chosen, in- where the covariance between simulated and observedfor VWC), the bias ( sionless measure for variability ( observed VWC). SCEM is widely(e.g. used to Vrugt e et al.,and 2005; to Schoups find et optimalcomplexes/parallel sequences model al., were selected parameter 2005; (equal sets. Feyen, to the 2007; As number Hartmann algorithmic of parameters parameters et to for be al., SCEM, 2012) 24 with: r 5 5 20 25 15 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erence (Fig. 3). ff the profile according ff ), we assessed the an- www.data.gov.il/ims 8814 8813 C higher at Auja Village whereas relative humid- ◦ ed Complex Evolution Metropolis algorithm. Between 20 000 and ffl erences in cumulative rainfall amount and distribution. ff erences between the Jerusalem station (810 m a.s.l.) and the three plots based ff Using a 62 year record of rainfall and temperature (1951–2013) available for ally, using the Shu 36 000 model runs were conducted until the convergence criterion was fulfilled. The a few weeks andwhole dry the summer soil period. moisture content further4.3 declined at a Modelling low of the rate soil during zone 4.3.1 the Parameter optimization, uncertainty analysis andSoil model validation hydraulic parameters were optimized for the three soil moisture plots individu- sensors. During rainfall events with highshowed amounts rapid and intensities, infiltration the of soil waterplot moisture SM-1, into data saturated the conditions deeper started from portionsinterface, the where of bottom these the probe conditions close profile. persisted to for Particularlystrongest the several soil–bedrock at hours rainfall up events to also twoshorter days. upper During periods soil the (Fig. layers 4b). reached At the saturation, end however of for the much rainy season, the soil was drying out within tion. Stations from the Israel Meteorologicalnearby Service showed with similar long-term characteristics. records at locations 4.2 Soil moisture dynamics Observed soil moisture atthe all annual soil course can profiles be (Fig. dividedson, into 4) the distinct previously showed phases. dry At a soil the strong profile beginning seasonality of was the stepwise where rainy wetting sea- up starting from upper to lower was 526 mm (380–650(270 mm), m b.s.l.) while in mean thesonal Jordan annual Valley rainfall accounted rainfall gradients for between at 106Mean mm 6.4 the (97–120 annual mm) to Auja leading temperature 7.2 % to Villageity, was sea- wind per station 7 speed, 100 m and elevation net di solar radiation were slightly higher at the more elevated sta- station (810 m a.s.l.) situated close to the Mediterranean Sea–Dead Sea water divide seasonal di Jerusalem (Israel Meteorological Service – assumed that the rootingvertical depth root distribution was or limited plantto to surface the cover the simulated fraction. soil soil We depth, cut depththe which o reduced with depths the no fell number changes of belowextended independent in the 60, soil bottom the layers 32.5 when layer. To and simulatebetween the 17.5 400 cm. range and of For 1000 climatic soil mcalculated a.s.l., conditions thicknesses mean with we elevations exceeding modified annual 1 rainfall gradientshad m, and based three we air on seasons temperature observed of according rainfall to measured and climate climatic data, data. which We we analysed separately due to To extrapolate our pointand climatic measurements input of parameters (precipitation soilour and study temperature) water area. over balance, We ranges used observedsoil we in the moisture varied calibrated plot soil soil hydraulic (SM-1), depth parameters which of had our deepest sensor (1 at m) 10, 25, 40 and 80 cm. Moreover, we cal parameters revealedgradient, considerable especially interannual in variability terms and of a rainfall. strong Mean annual elevation precipitation at the Kafr Malek 3.5 Spatial and temporal extrapolation of percolation tion di on calculated elevation gradients. 4 Results 4.1 Hydrometeorological conditions The three years of high resolution measurements of precipitation and meteorologi- nual variability of water balanceplots. Rainfall components and at temperature the data location from of Jerusalem our three station soil were moisture corrected for eleva- 5 5 25 20 10 15 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erences in predictive capac- ff erence between simulated volumetric ff 8816 8815 erences in annual values between single years (Ta- ff C. ◦ C (Fig. 7). These events coincided with the main peaks of modelled ◦ C by 0.7–4 ◦ Parameter uncertainty was assessed by simulation of water contents using param- Water temperature in a groundwater well near soil moisture plot SM-3 (cf. Fig. 1) in- reached up to 69 % ofof rainfall lowest and rainfall declined amount to and valuescan soil close depths to be greater 0 regarded % than only as 160Percentages under cm. a of conditions The year percolation third with were simulated comparable year averagerainfall to rainfall was the conditions considerably previous less. (sums year This of althoughing could cumulative 400 be 2012/13 to attributed when to 600 daily mm). higherrainfall rainfall rainfall accounted amounts intensities for dur- exceeded almost two 50 % times of 80 the mm seasonal and amount. four days of tals ranging between 27550 mm. and In 425 this mm season (Fig.ulated with 9) for below and soils average rainfall a withincreased amounts, maximum depths to percolation a daily up was maximum only amount toceiving proportion sim- below 60 of the and 40 highest % 110 rainfall for cm,seasonal shallow input. rainfall respectively. soils For Modelled ranged with the between percolation depths 450 following of and above 10 cm 725 average mm. re- wet Then year simulated 2011/12, percolation rates 45 % of cumulative rainfallportion during of 2011/12 percolation and was 2012/13, calculatedmore during respectively. The a than few largest 50 strong % pro- rainstorms. ofwithin On a all the three time total plots, period percolation of of five the to ten three days. 4.3.3 years simulation period Spatial occurred extrapolation of deep percolation During the hydrological year 2010/11, cumulative rainfall was below average with to- ceased within few weeks afterall the losses last rainfall through events evapotranspiration ofValues the and slightly winter interception above season. 100 accounted % Meanconditions over- for for at the 73 % the dry of beginning yearnegligible rainfall. 2010/11 of amounts resulted the during from simulation the elevated period. dry moisture Percolation year strongly 2010/11 varied to from values ranging between 28 % and the winter season, depending on the length of dry spells between rainfall events and 240 mm at plot SM-1, 200portion mm at of plot interception, SM-2, and soil 150 mm evaporation, at and plot SM-3. transpiration The was relative pro- highly variable during 4.3.2 Plot scale water balance Modelled fluxes of the variousability water (Fig. 8) balance and components considerable showedble di high 4). temporal vari- Evaporationthe and winter transpiration season started whenPercolation shortly the from water after the bottom content theing of in first the winter the rainfall soil season upper zone events exceeded soil only of a layer started certain began after to threshold. cumulative increase. This rainfall dur- threshold was found to be ca. dicated five distinct recharge events19 lowering the mean groundwater temperaturepercolation from from the soil moisturetemperature monitoring was sites. less During than these 6 events, mean daily air to saturated conditions except for deepertion sections of at simulated plot water SM-1 contents where was an observed. underestima- eter sets obtained withmoisture SCEM confidence after interval showed fulfilling a theexemplary narrow band for convergence around criterion. plot the SM-1). The optimumwater At 95 model % content (Fig. all soil 8 for sensors the thebelow best di 4 %, parameter i.e. set less than and the the measurement 95 error % of confidence the sensors. interval remained ance, are given inwere Table generally 3, and able their toKGE distributions values reproduce between are the 0.82 illustrated observed andities in 0.94 temporal at Fig. (Fig. distinct soil 6). 5. water However, moisture All content di plots patterns (Figs. levels models 6 could with and be 7). observed, In which general, the vary model between tended the to overestimate single water contents close calibrated parameter sets used for further assessment of the plot scale soil water bal- 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erences ff ects on infiltration, water ff 8818 8817 erences between our plots could be attributed to di ff erence between the plots was observed during rainfall events of high ff ected. Hence, a high stone content in the soil and bedrock outcrops in the ff A noticeable di less, measured soil moisturehigh fell and outside low moisture the conditions 95 (Fig. % 8). uncertainty This suggests band that especially a Mualem/van-Genuchten during high permeability (Keshet and Mimran,the 1993), vicinity we of observed SM-1, Nari whichiments may Crust on have (Dan, the reduced 1977) same hydraulic in and conductivity. geological subsequent Sprinkling material overland exper- flow type generation had (Lange already et documented al., 2003). soil5.2 saturation Simulation of the plot scaleThe water balance cumulative distributionranges and functions hence of good identifiability the for parameters most model suggested parameters rather (Fig. 5). narrow Neverthe- a similar behaviour at SM-3uration but were not caused at by SM-2. impounded Wesoil–bedrock percolation hypothesize interface. that water Di these due phases to of limitedin sat- conductivity the of permeability the ofand SM-3) the and underlying Turonian strata. limestone These (SM-2). While are both Cenomanian formations dolomite are (SM-1 known to have vicinity, as observed particularly at SM-1,retention may and have multiple water e movement in the soil (Cousin et al.,magnitude. 2003). At SM-1 (Fig.between 4b), 2 the and bottom 90 h. probe Durationsitation suggested were (24 apparently soil to linked saturation 191 to mm) forshowed the and periods depth saturation to of only the the during duration eventration. precip- of the Volumetric the largest soil event moisture rainfall (16 events at to 72 and 10 h). cm for The always a upper remained probes much below shorter 30 %. du- We observed and Bouma (1991) forperiments structured conducted clay in soils. the Brilliant2014). vicinity Blue These of patterns experiments our highlighted from plots the infiltrationing supported influence ex- preferential of these outcrops flow findings on at (Sohrt infiltrationlargely the et by una soil–bedrock initiat- al., interface, while the remaining soil matrix was plots that gave no indication of significant bypass flow as reported by e.g. Booltink appeared during dry summerseason. Moreover, months a closed successive soon propagation of after the the wetting beginning front of was observed the at rainfall the et al., 2011). At allinto soil deeper moisture sections plots, our ofamounts soil the (e.g. moisture soil plot data profile suggested SM-1most fast during in and infiltration rainfall the Fig. lowermost events 4b). probe with was Theof often high time the less intensities than soils. lag two These and between hours fastpreferential despite the reactions flow high reaction within might clay the contents be of vadose an soil theet zone indicator upper- as al. for reported (1998), concentrated for Öhrström infiltration the et Mediterranean and macropores by al. Cerdá were (2002) and rarely Van observed Schaik et in al. the (2008). Nevertheless, field larger and superficial desiccation cracks that 5.1 Soil moisture dynamics The observed seasonal dynamics ofduring soil and moisture, a dominated rapid by decreaseported short after wetting in phases the other rainfall studies season, in were the comparable Mediterranean with region those (Cantón re- et al., 2010; Ruiz-Sinoga 82 and 150 mm (standardbetween deviation: 93–141 0 mm). and Percolation 66 at %24 the %. of three Other cumulative plots seasonal seasonal varied fluxes rainfallficient varied with of much an determination less average during between betweenranged the seasonal 16 between simulation 0.82 and sums period. and of 0.88 The simulated on coef- the percolation three and plots. rainfall 5 Discussion Modelling water balance componentsferences for of 62 simulated years seasonal (1951–2013) percolation resulted reflecting(Fig. the in 10). high Mean strong variability annual dif- of rainfall rainfalland was input 537 calculated mm for (standard the deviation: three plots 128–168 mm) to and range mean between 408 percolation fluxes between 4.3.4 Temporal extrapolation of deep percolation 5 5 25 20 10 15 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | typ- ff ect wa- ff for the 2010/11 season 3 were measured for the winter sea- erent seasonal rainfall depths. Al- ff 3 8820 8819 er et al., 2011) measured similar thresholds for ff ective evapotranspiration rates of more than 64 % ff or by preferential infiltration along the soil–rock inter- ects on percolation rates and groundwater recharge ff ff erences for the three winter seasons. During the very dry year 2010/11, ff erent plant species and vegetation cycles may alter soil moisture conditions (Lange et al., 2010). Regarding the initiation of percolation at the basis of ff 1 − A high temporal variability in percolation fluxes is also apparent from the long-term Nevertheless, we cannot exclude that frequently outcropping bedrock may a In contrast to humid environments, lateral subsurface flow on rocky semi-arid hill- Simulated mean evapotranspiration at our plots over the three-years simulation pe- modelling of water balance components (Fig. 11). For the 62 year simulation period, soil moisture exceeded field capacity onlying at the locations wet with years relatively shallow ofat soils. 2011/12 all Dur- and plots 2012/13, andlead field soils to capacity substantial even was percolation reached exceeded and severalings saturation groundwater times are recharge during in to good strong local agreement aquifers. rainfallspring with These in events. discharge find- the measurements This Jordan at may Valley, where Aujasons 7 spring, 2011/12 and a and 8 large million 2012/13 karst m respectively,(Schmidt but et only al., 0.5 million 2014). m that influences percolation rates. 5.3 Spatial and temporal extrapolation ofWater deep balance percolation modelling forsiderable variable di soil depths and rainfall gradients revealed con- on the regional scale island subject flow to generation current cannot research.ically be During accounts excluded heavy (Lange for storm et events,A only over- al., second a limitation 2003), of few but ourtion percent surface investigations of runo of of an plot annual scale identical percolationa vegetation rainfall fluxes cover constant (Gunkel is at vegetation the and the cycle assump- singlethough Lange, throughout sites di years 2012). along of the di climaticprior gradient to and rainfall events, we could show that the event rainfall amount is the main factor that one-dimensional modelling ofunderstand the percolation soil and water recharge. balance is a reasonable approachter to redistribution by surface runo face. The importance of these e concentrated infiltration from the soil zone (Williams, 1983). We can therefore conclude development of vertical structural pathways in karst areas (dolines, sinkholes) inducing the soil profiles, weand found 240 seasonal mm rainfall for thresholdset the al., of deep 2010; ca. soil Lange 150the mm moisture et initiation for plots. al., of the Cave 2010; percolation shallow through drip She the studies epikarst (100 in to theslopes 220 mm). rarely region develops, (Arbel since they consistnected of due individual to soil frequently pockets outcropping thatpacity bedrocks. are given Soil poorly that moisture evapotranspiration con- seldom exceeds precipitation exceedsyear depth field (Puigdefabregas throughout ca- et most al., of 1998). the Furthermore, highly permeable bedrocks favour the of strong rainfall. Theseperatures findings observed are in supported a bythe nearby the water well response table indicating (Fig. of the 8). groundwatercolating arrival Tracer tem- experiments water of in can groundwater a recharge pass4.3 similar m at setting h the demonstrated vadose that soil per- and the epikarst at flow velocities of up to riod accounted for 73 %by of Schmidt et rainfall, al. i.e. (2014) very foret the close al. same to (2010), area. Our the who values derived long-termof also annual fall annual average into e calculated rainfall the based range on ofSpain. Cantón eddy Our covariance simulated measurements in percolation(arithmetic southeastern mean: rates semi-arid 28 ranged %) indicating betweendency on strong 0 depth inter-annual and and variability 45 temporalperiod, and % more distribution a of than of strong 50 precipitation. precipitation % depen- During of the overall percolation entire fluxes three occurred year during less than 10 days resent the heterogeneous pore structurepersistent of our saturated clay-rich conditions soil during (Durner,section 1994). major Moreover, could rainstorms not as benot discussed simulated, implemented in in as the the a previous model and percolation a impounding free soil–rock drainage had interface to was be assumed. soil hydraulic model based on a unimodal pore-size distribution may not be able to rep- 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect ff erences in percolation fluxes ff er et al. (2010) and Abusaada ff erent approaches. Our results sug- ff ected by predicted climate change in ff erent process parameters. ff 8822 8821 We therefore corroborate the statement of De Vries and Simmers (2002) about the Our long-term point calculations suggest substantial di since recharge rates areand sensitive characteristics, with which respect are to most highly probably variable a rainfall distributions by other studies ingest the that groundwater same recharge is region most basedenough prominent when to on single rainfall exceed di events field are strong thermore, capacity our of data soil revealed pockets thaton over the event a temporal and distribution wide seasonal of range recharge rainfall of amounts. had soil a depths. strong Fur- e dependence of groundwaterevents. recharge The in use semi-(arid) of areas empirical rainfall–recharge on relationships high can intensity lead rainfall to large errors, characteristics determining the strongthe annual strong variability dependency in onitation the soil depth. percolation thickness, Although fluxes temporal and ourresults distribution closely calculations of match are rainfall long-term based andvariability, observations and on precip- reflect and the plot the thresholds scale patterns for the measurements, of initiation event the of and groundwater seasonal recharge reported information on the relative importance of di 6 Conclusions This study contributesture to measurements the along a assessment steepshowed climatic that of soil gradient percolation moisture in measurements a ratester together Mediterranean flow with based karst in numerical area. on the modelling We unsaturated of soil soil the zone mois- wa- are powerful tools to investigate mechanisms and additional information from springsmodel (time series parameter of estimation, discharge whichtion and are fluxes water rarely from chemistry) available. plot-scale for to Although soil regional, simulated moisture i.e. percola- measurements catchment cannotresponsible scale, be for they the directly temporal can transferred and provide spatial insight variability into of the groundwater recharge various as processes well as approaches including hydrochemical and isotopic data (Hartmann et al., 2013b) require cent advances in the determination ofment groundwater recharge of in the karst spatial areas, the and assess- temporal distribution of recharge is still a challenge. Modelling 569 mm). These results are(2011) in about line the with importance findings of of temporal She rainfall distribution on5.4 groundwater recharge. Implications for recharge in MediterraneanThe karst steep areas climatic gradient, theate hydraulic rocks, properties the and heterogeneous characteristics soil ofon cover the event and and carbon- a seasonal high scalesarea. temporal are Similar variability dominating settings of can hydrological precipitation be characteristics found in across our the study entire Mediterranean region. Despite re- areas with strongly developed epikarst and shallow or missing soilbetween cover. years of similarthe rainfall seasons depths. 1976/77 Simulatedrespectively, and although percolation 2004/05 both for seasons accounted plot had for SM-1 very 16 similar during and above average 35 % rainfall (578 of and seasonal rainfall, period. In our studyFurthermore, we seven compared individual seasonal percolation years offrom our provided perched sites one with aquifers recharge third feeding estimations the small of entire karst the carbonate springs total aquiferresults (Weiss recharge. (Guttman and plotted und Gvirtzman, within Zukerman, 2007) theemphasize 1995) and range that (Fig. of our 11). these calculationsbelow Although large-scale average display our rainfall, recharge point a estimates, percolation certainobserved we (EXACT, rise fluxes. 1998; want in Even Schmidt to the et in groundwater al., years table 2014). with and Then spring recharge flow presumably can occurs be on for our plots.1991/92 The (five highest times valuetime the was period, modelled mean Schmidt for annual et al.Auja the percolation (2014) spring extremely of calculated catchment wet an 150 mm). with averageof winter recharge For a only season rate a conceptual five of slightly reservoir individual 33 % years model. shorter for accounted the They for found one that third recharge of total recharge of the 45 years we calculated seasonal percolation rates between 0 and 66 % (average: 20 to 28 %) 5 5 25 15 20 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , cient ffi 10.5194/hess-2-239-1998 8824 8823 , 2014. 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Water Requirements, Irrigation and Drainage Paper 56, FAO, Rome, 1998. 2012). the Mediterranean (Giorgi and Lionello, 2008; Samuels et al., 2011; Reiser and Kutiel, EUWI – European Water Initiative: Mediterranean groundwater report, technical report on Durner, W.: Hydraulic conductivity estimation for soils with heterogeneous pore structure, Water De Vries, J. J. and Simmers, I.: Groundwater recharge: an overview of processes and chal- de Rosnay, P. and Polcher, J.: Modelling root water uptake in a complex land surface scheme De Baets, S., Poesen, J., Reubens, B., Wemans, K., De Baerdemaeker, J., and Muys, B.: Dan, J.: The distribution and origin of Nari and other lime crusts in Israel, Israel J. Earth Sci., Cousin, I., Nicoullaud, B., and Coutadeur, C.: Influence of rock fragments on the water retention Cobos, D. R. and Campbell, C.: Correcting temperature sensitivity of ECH2O soil moisture Baida, U. and Burstein. 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S., Raes, D., and Smith, M.: Crop Evapotranspiration: Guidelines for Abusaada, M. J.: Flow Dynamics and Management Options in Stressed Carbonate Aquifer References tion (DFG) and the AlbertPublishing. Ludwigs Furthermore University Freiburg we in wantClemens the funding Messerschmid to programme for thank Open hospitality Access and Amer support Fraejat, during Awad fieldwork. Rashid, Kayan Manasra and BMFB–MOST Young Scientist Exchangeof Program Haifa/Israel. during The a article two-month processing stay charge at was the funded University by the German Research Founda- project “SMART –Technologies” Sustainable funded Management by BMBF oferences (German Available No. Federal Water 02WM0802 Ministry Resources and of with Education 02WM1081. 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T., and Cislerova, M.: E 5 30 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | KGE 2.01.2 0.91 0.92 2.0 0.94 2.0 0.90 2.0 0.89 nek (2013) ů L − − − − − im Š ) (–) (–) d d c 1 − a a a a a a s erent plots and probe depths. Calculated according(1998) to Allen at al. Hydrus-1D internal database (grass) Hydrus-1D internal database (grass) Calibrated Calibrated Calibrated Calibrated ff 1 1 1 − − − 1 2 3 3 − − − − ) (–) (mm day 1 m m 3 3 ciency as optimization criterion. − C Measured time series C Measured time series ffi ◦ ◦ MJ m α n K 8832 8831 were obtained from the lowest respectively highest measured volumetric soil ) (1 mm s 3 100250300010 000 mm80 000 mm mm mm mm Hydrus-1D internal database (grass) Hydrus-1D internal database Hydrus-1D (grass) internal database Hydrus-1D (grass) internal database (grass) Hydrus-1D internal database (grass) 2–2 – Calibrated Θ − − − − − − 0–0.30.3–0.60.0001–0.1 m 1 mm m 1.01–3− – Calibrated m 3 s Θ ) (m 3 b − b m 3 r (m Θ and the lower parameter limit of r Θ 5 cm)10 cm) 0.0020 0.00 cm)35 cm) 0.05 0.11 0.60 0.56 0.46 0.60 0.008 0.004 0.003 1.23 0.001 1.23 482 9908 1.22 1.66 9976 5751 1.2 2.0 0.91 0.94 10 cm)25 cm) 0.01 0.12 0.415 cm) 0.49 0.00 0.004 0.026 1.23 0.49 1.30 427 8159 0.041 2.0 1.18 126 0.91 40 cm)80 cm) 0.11 0.1010 cm) 0.5918 cm) 0.05 0.5955 cm) 0.12 0.018 0.13 0.40 0.028 0.59 1.54 0.51 1.36 9468 0.002 8732 0.012 0.013 1.23 1.37 5094 0.1 1.43 9288 2679 0.82 0.6 2.0 0.90 1.0 0.87 0.90 − − − − − − − − − − − − Extraterrestrial solar radiation (forreaves equation Harg- only) Daily minimum temperature Fedde’s parameter Fedde’s parameter Fedde’s parameter Fedde’s parameter Fedde’s parameter 5 mm day Daily maximum temperature Fedde’s parameterDepth of soil profile 1 0.5–1 mm day m Measured at experimental plots Rooting depthFedde’s parameter Interception constant 0.5–1 m Estimated 1 based on field observations mm Estimated Meteorological parameter Daily precipitationVegetation parameter mm Measured time series ParameterSoil hydraulic parameter Residual soil water content Saturated hydraulic conductivity Value/Range Unit 5–10 000 Source/calculation method mm day Saturated soil water content Van Genuchten parameter relatedentry to suction air Van Genuchtenpore parameter size distribution related to Van Genuchten parameter related totuosity tor- 2 ( 3 ( 4 ( 2 ( 3 ( 4 ( 2 ( 3 ( 4 ( SCEM optimized hydraulic parameter sets for the di Parameters and value ranges for Hydrus-1D modelling. r s s r a s i 0pt 2H 2L 3 0 min max 2H 2L Parameter calibrated for each soil materialThe with upper SCEM parameter algorithm limit and of Kling–Gupta e Maximum and minimum daily air temperature at the soil moisture plots is estimated by calculation of an elevation-temperature gradient based on R T P P P P r r T SCF SurfaceLAI Cover FractionP Leaf Area Index 0.1–1D m/m Estimated based on field observations m/m Calculated according to D α Θ P Θ α n K L Rainfall at plot SM-2 is estimated by inverse distance weighted interpolation with elevation as additional predictor. a b moisture value ofc each layer in the respectived soil moisture plot. meteorological stations in the Jordan Valley and the mountains. SM-3 1 ( Plot Layer SM-1 1 ( SM-2 1 ( Table 3. Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | s u c s a m a D n ! a m

e f m n e o

A l t

i ! o d u e l a a i r i a a t e e e e d e i

S S G m

h a d l

! t

D e

r e v i R i l

a t l v l n a d r o J l i a o a v s e s l i

u m d r o l f a

e p d w a R J r

a Y ! ! - r r e v e c i i t e v r e e A t t e 20 years) according

g a g l u e t a a g T o a w l l ! s a u l i w w z i n o a ≥ e a o d r d G a V c ! g

o n n e

/ m a

u u n f n l t i y r i o o a a t e r r r i a u o r e G S C M R S G e a S i t s i o c d i ! N e ! ! ! M ! ! ! ! ! !P # A ! ! ! !

0 4 1 5 3 1 0 3 1 5 6 1 0 6 1 5 5 1 0 5 1 5 4 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a e S

0 m 0 m

0 0 0

1 a

0 r 0

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o e

2 r 2

d R

a D n S y e l l a V n a d r o J d a e e g a D l l o i 0 0 !P 0 0 h v 0 0

c 5 5 i a 9 9 r j 1 1 e u J A 8834 8833 ! ! ! 0 0 0 0 0 0 0 0 9 9 1 1 ! ja u A 0 0 0 0 i 0 0 5 5 d 8 8 a 1

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! ! ! T ( 547 39346 7 33 102380 19 10 84 179 30 33 24 8 224 127 111 41 37 29 101 105 29 28 125 33 ! (mm) (mm) (%) (mm) (%) (mm) (%) (mm) (%) !

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2011/122012/13 6502011/12 592012/13 418 92011/12 55 932012/13 436 13 14 89 53 209 21 32 12 120 159 294 27 48 45 130 118 30 28 135 31 u ! R r

! e

!Study area with location of meteorological stations, rain gauges, soil moisture plots

0 !

0 5 1 5 4 1 0 4 1 5 3 1 0 3 1 5 6 1 0 6 1 5 5

Cumulative sums of the simulated water balance components in mm and % for the 1 !

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 !

! ! !

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The hydrological year 2012/13 was modelled until 30 April 2013. !

SM-2 2010/11 248 53 21 81 33 117 47 0 0 ! Plot YearSM-1 2010/11 Rainfall 381 Interception Evaporation Transpiration 62 Bottom ﬂux 16SM-3 2010/11 99 237 26 209 47 55 20 13 119 50 3 84 35 2 1

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! ! ! Figure 1. (SM-1, SM-2, SM-3) andto isohyets data of from long-term ANTEA (1998). average Coordinates annual in rainfall the ( detailed map are in Palestinian Grid format. Table 4. three consecutive hydrological years 2010–2013. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 30 ) ) ) −400 ) ) ) 0.68 0.79 0.74

0.73 0.81 0.78

= = =

2 2 2 r r r 2 2 2 ( ( ( r r r

( ( (

−200 25 2010/2011 2011/2012 2012/2013 0 2010/2011 2011/2012 2012/2013 20 200 15 400 8836 8835 station elevation (m) station elevation 10 600 −East axis (km) distance along West 5 800 0

1000

0 0 800 600 400 200 800 600 400 200

Typical hillslopes in the study area. The image shows the plain of Ein Samia with Correlation between average annual rainfall and station elevation for the individual precipitation (mm) precipitation precipitation (mm) precipitation hydrological years during the observation period 2010–2013. Figure 3. semi-arid climatic conditions,and where the the hillslopes valley are used bottom as is extensive grazing used land for for goats partly and irrigated sheep. agriculture Figure 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 1 0 (−)

L −1 −2

8000 6000 (mm/day)

t 4000 a s K 2000 0 3.0 2.5 (−) 2.0

n 1.5 erent depths of the three experimental plots 1.0 ﬀ 0.10

29 - 0.08 8838 8837 0.06 and details on the winter season 2011/12 for plot (1/mm)

0.04 α (a) 0.02 0.00 0.60 0.50

(−)

s Θ 1 (b). - 0.40 Observed volumetric soil moisture at different depths of the three experimental

. 4 0.30 0.30 Figure Figure plots during the complete monitoring period (a) and details on the winter season 2011/2012 for SM plot

1 2 3 4 0.20 (−)

r Θ 0.10 0.00 Observed volumetric soil moisture at di Cumulative distribution functions of parameters following 1000 model runs after con-

0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8

. probability (−) probability probability (−) probability (−) probability

SM−3 SM−1 SM−2 (b) Figure 5. vergence. The line colours correspondx-axis to represent the the selected various parameter soil ranges. depths (see Fig. 6). The limits of the during the complete monitoring period Figure 4. SM-1 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

rain (mm/day) 3 3 3 3 gw temp. (°C) 0.5 vsm (m /m ) vsm (m /m ) )

3 1:1 line 1:1 m 0 25 50 75 100 0.5 0.4 0.3 0.2 0.1 0.0 0.5 0.4 0.3 0.2 0.1 0.0 20 18 16 14 / 3 f 0.4 m c a b d e g ( 04/13 0.3 0.2 01/13 and water temperature in 0.1 (f) − 5 cm (0.91) − 10 cm (0.92) − 18 cm (0.91) − 55 cm (0.94) 10/12 SM−3 observed soil moisture 0 0 0.3 0.2 0.1 0.5 0.4 0.5 ) 3

07/12 1:1 line 1:1 m / 3 0.4 m ( 04/12 0.3 0.2 01/12 8840 8839 0.1 − 5 cm (0.89) − 10 cm (0.9) − 20 cm (0.87) − 35 cm (0.9) SM−2 observed soil moisture 10/11 0 , simulated and observed volumetric water content for 0 0.5 0.4 0.3 0.2 0.1 (a) 0.5 )

3 1:1 line 1:1 m / 07/11 3 . The grey shaded area represents the 95 % conﬁdence interval 0.4 m , Hydrus-1D simulated percolation ( (g) 0.3 04/11 (b–e) 0.2 01/11 0.1 Observed vs. simulated volumetric water contents − A Kafr Malek (SM−1) − 95 water volumetric simulated Observed vs. −10 cm −25 cm −40 cm −80 cm − 10 cm (0.91) − 25 cm (0.94) − 40 cm (0.9) − 80 cm (0.82) KGE = 0.9 KGE = 0.82 KGE = 0.91 KGE = 0.94 SM−1 observed soil moisture 0 0 10/10 0

0.5 0.4 0.3 0.2 0.1 0.0 0.5 0.4 0.3 0.2 0.1 0.0

0.5 0.4 0.3 0.2 0.1 Time series of rainfall −20 −40 −60 −80

) m / m ( vsm ) m / m ( vsm percolation (mm/day) percolation

m / m ( moisture soil simulated

) Measured against simulated volumetric soil water content for the three soil moisture

3 3 3 3 3 3 Figure 7. soil moisture plot SM-1 of soil moisture basedconvergence criterion. on model parameter sets obtained using SCEM after fulﬁlment of the a nearby groundwater well Figure 6. plots (SM-1, SM-2, SM-3) atin individual brackets depths in for the the legend. simulation period. KGE values are shown Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

percolation (%)

rain (mm/day) rain (mm/day) rain (mm/day) 100 90 80 70 60 50 40 30 20 10 0 200 0 50 100 0 50 100 0 50 100 180 160 140 120 02/2013 100 80 2012/2013 soil depth (cm) 60 40 10/2012

20 700 650 600 (mm) 550 500 rainfall 450 400 350 cumulative 300 250 200 180 06/2012 160 140 120 erent rainfall depths and distribution patterns. ﬀ 02/2012 100 8842 8841 80 soil depth (cm) 2011/2012 60 40

10/2011

20 700 650 600 (mm) 550 500 rainfall 450 400 350 cumulative 300 250 200 180 06/2011 160 140 120 02/2011 100 Interception Evaporation Transpiration Percolation Interception Evaporation Transpiration Percolation Interception Evaporation Transpiration Percolation 80 soil depth (cm) 2010/2011 60 SM−3 SM−1 SM−2 40 0 0 0

20 20 80 60 40 20 80 60 40 80 60 40

10/2010

20 Simulated percolation vs. soil depth and rainfall amounts along the climatic gradient Simulated daily water ﬂuxes at the single soil moisture plots for the simulation period flux (mm/day) flux flux (mm/day) flux (mm/day) flux 700 650 600 (mm) 550 500 rainfall 450 400 350 cumulative 300 250 Simulations were based on calibratedareas soil display hydraulic properties rainfall of depths,of plots which 400 SM-1. have The to not grey 1000 shaded m beenscale a.s.l. simulated reached according percolation in ﬂuxes to using calculated the optimaland rainfall parameter study SM-3. gradients. sets area for The the within points single altitudes represent plots SM-1, the SM-2 plot Figure 9. for three consecutive winter seasons with di Figure 8. 2010–2013. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) and ● ● ● ● SM−3 ● ● ● ● SM−2 1200 Percolation ● ● ● ● ● SM−1 ● ● ● ● SM−3 1000 ● ● ● SM−2 http://www.data.gov.il/ims ● ● ● ● ● ● ● ● Transpiration SM−1 ● 800 ● ● ● ● ● ● ● ● ● ● SM−3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SM−2 ● 600 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Evaporation ● ● ● ● ●● ● seasonal rainfall (mm) seasonal rainfall ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SM−1 ● ● ● 8844 8843 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 400 ● ● ● ● ● ● ● ● ● SM−3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SM−2 ● ● ● ● ● ● ● Interception ● ● ● ● ● ● 200 ● SM−1 ● ● Plot SM−3 (this study) Plot SM−1 (this study) Plot SM−2 (this study) ● Guttman and Zukerman (1995) and GvirtzmanWeiss (2007) − spring 1 and GvirtzmanWeiss (2007) − spring 2 and GvirtzmanWeiss (2007) − spring 3 SM−3 ● ● ● 0 ● SM−2

Rainfall 400 200 0 800 600

● simulated percolation (mm) percolation simulated SM−1 0 Simulated seasonal percolation at the plot scale (SM-1, SM-2, SM-3) for the pe-

Seasonal sums of simulated water balance components for the period 1951 to 2013 800 600 400 200

1000 1200 seasonal sums (mm) sums seasonal riod 1951–2013 in(Guttmann comparison and with Zukerman, rainfall–recharge 1995)aquifers relationships and (Weiss for and three Gvirtzman, the small 2007). carbonate karst aquifer springs emerging from local perched Figure 11. Figure 10. using the calibrated soildata were hydraulic obtained parameters from of the the nearby Jerusalem various central plots. station Rainfall ( and temperature corrected for the single locationstor. by applying a simple elevation gradient-based correction fac-