Entwurf Deliverable 1.1.1

Assessment of hydrological variability and wastewater infiltration impacts (Mountain area of Cluster West)

[Titel wie DoW, um Cluster West ergänzt, Vorschläge sind willkommen]

Executive Summary

[Folgt]

1 Introduction

The study area of Deliverable 1.1.1. is located on the western side of the Lower , focussing on the Wadis Qilt, Nueima and Auja as well as on the highland areas to the west and northwest of the Wadi catchments (groundwater catchment) and is also including areas of Mediterranean surface water and groundwater drainage). Water supply in the study region is heavily dependent on spring water supplies, making the water supply systems highly susceptible to fluctuations in climate as well as to anthropogenic impacts, especially regarding the infiltration of treated and untreated sewage. Microbiological contaminants like E. coli are rapidly transported in the karst flow systems of the region and hence raw water management and appropriate treatment is of high importance for the water suppliers of the region. Water supply is often organised on municipality level from local sources, but also a few regional suppliers are present. Groundwater quality is also compromised by long-term 1 effects of wastewater infiltration and agricultural activities. For example nitrate concentrations display a rising trend for a large number of groundwater resources. Furthermore, due to the structurally highly complex geological setting and the largely unknown karstification, the catchment areas of groundwater resources are not delineated and the flow systems are poorly understood.

In order to enhance the understanding of the natural resources and to provide high resolution hydrological data for local raw water management, a comprehensive hydrological monitoring network was established and maintained. Remote data transmission was established for the most important stations. Comparative investigations were carried out in Wadi Shueib on the eastern side of the Jordan Valley. Therefore, an intensive exchange and collaboration between the research groups on both sides was conducted.

According to the original Description of Work (DoW), Deliverable 1.1.1 was organised into six tasks (1.1.1.1–1.1.1.6). In practice, the different tasks were largely carried out in a cohesive way. Therefore, the content of this deliverable is re-structured in order to enhance readability and to avoid repetitions. In the individual chapters references to the original DoW structure are provided, where appropriate.

This deliverable involves a large amount of cooperative fieldwork in the region. A major field campaign of six week’s duration was conducted in autumn 2015, involving in total five employees of Göttingen University (October–November 2015). It was intended to complete the installations before the start of the winter rainfall season 2015/2016. To achieve this task, already extensive preparations were necessary beforehand, i.e. planning for the installations (including a field survey in April/May 2015), selection and tests of the instruments, organisation of shipment to the project region etc.. Another larger campaign was conducted during February 2016 in order to calibrate the monitoring stations (e.g. by discharge measurements) and to conduct a large sampling campaign during the main winter precipitation events.

Unfortunately, these two most important fieldwork campaigns were largely affected by a very unstable safety situation in the region (see e.g.: 2015–2016 wave of violence in Israeli- Palestinian conflict, https://en.wikipedia.org/wiki/2015%E2%80%932016_wave_of_ violence_in_Israeli-Palestinian_conflict; Wikipedia). This escalation of the conflict caused in total >250 fatalities and restricted movement in the project region which also caused considerable detours during travel and field work due to roadblocks, checkpoints etc. Here it is worth noting that without the profound local knowledge and experiences gather by parts of the German research counterpart since project phases SMART I & II (Mr. Fischer, Dr. Ries and Dr. Schmidt), barely any fieldwork would have been possible during such circumstances. Nevertheless, Task 1.1.1.3 – Characterisation of subsurface spring catchments and assessment of wastewater infiltration impacts by artificial tracer experiments, was particularly affected by the restrictions. In order to conduct this task, extensive surveys and preparations were carried out particularly in the office before the start of the main autumn 2015 field campaign, e.g. survey of possible injection sites via geological maps and cross sections and satellite images (e.g. via Google Earth). The following steps during the field campaign would have been to further investigate those sites in the field and to conduct infiltration experiments at the most promising ones (e.g. by water tankers). Before the start of the tension, a few site visits could be carried out in this regard. However, they

2 were largely with an adverse result. Subsequently, according to the security situation, it was not possible to undertake any further site visits, let alone to conduct infiltration experiments etc.. Therefore, those planned experiments were largely delayed and had finally to be aborted due to insufficient manpower resources in the following. Nevertheless, a discussion of the generally planned tracer testing setup and potential injection sites is provided in chapter 4, in order to facilitate further investigations in the region.

Despite the restrictions, a considerable field presence of the German research team could be maintained during the whole SMART-MOVE project (10 field campaigns in total). In this context, a large amount of technical training on-the-job was conducted; especially involving local water supplier personnel and local water resources authorities field crews.

2 Assessment of hydrological variability and relevant water quality parameters by high-resolution monitoring & concomitant technical training

According to the original DoW this was organised into three interconnected tasks:

 Task 1.1.1.1 – Assessment of hydrological variability / Technical training of PWA personnel on high-resolution monitoring techniques (installation, maintenance, and data processing)  Task 1.1.1.2 – Assessment of relevant water quality parameters (e.g. turbidity, nitrate, microbial contamination) for selected springs  Task 1.1.1.4 – Assessment of meteorological parameters and surface runoff volumes

Those works were usually carried out in combination, e.g. the maintenance and data retrieval visits were carried out for all parts of the networks in a spatially pooled way.

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Figure 1: Overview map of the study area displaying the main springs and surface water (Wadi) catchments. [Karte wird noch ergänzt]

2.1 Spring monitoring installations and early warning system

During the phases SMART I & II, spring monitoring was carried out at the large springs in the lower Jordan Valley (Auja, Duyuk, Sultan) and important springs for spring water supply on the flanks of the Valley (Samia). Those springs are characterised by rather extensive catchment areas. Therefore, especially anthropogenic impacts and corresponding spring signals are merged with natural recharge signals. Accordingly, for SMART-MOVE, focus was on small springs and therefore spring catchments in the highland recharge area displaying different degrees of urbanisation in order to discriminate natural recharge behaviour and anthropogenic impacts more clearly. For springs used for water supply, those kinds of investigations also provide the basis for an enhanced raw water management and treatment.

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Figure 2: Examples of spring monitoring stations. Left: Assikka spring in Salfeet; Middle: Spring Areek Fouqa in Ein Areek; Right: Spring Ein Majur in Abu Qash.

[Ausgewählte Ergebnisse]

Early warning system [Kurzüberblick]

2.2 Meteorological and precipitation monitoring network

Already at the start of the SMART-project, a network of automatic precipitation monitoring stations was installed in December 2007 in order to monitor the spatial distribution of precipitation. According to the main research area at that time, those installations focused on the headwater region of Wadi Auja and surroundings. During SMART II, were a special focus was on rainfall-runoff research, the monitoring network was considerably expanded with further meteorological and precipitation stations, now covering the whole catchment area of the western wadi cluster (see further Ries, 2017). With this network it was possible to study

5 rainfall-runoff processes and to quantify the hitherto unknown surface runoff amounts (Ries et al., 2017).

During SMART-MOVE, most of those stations were continued, while a couple had to be abandoned due to high maintenance requirements, partly caused by vandalism at the respective sites. Since in SMART-MOVE, the focus was also on small spring groundwater catchments in the highland area and areas of Western (Mediterranean) drainage, the precipitation monitoring network had to be restructured in order to also cover those areas (see Figure 1).

[Ausgewählte Ergebnisse]

2.3 Surface runoff monitoring

The gauging stations established in SMART II were largely continued. A few stations with a poor accessibility, unstable stage-discharge relation and therefore a high maintenance demand were discontinued. The location of the stations is shown in Figure 1. Especially in Wadi Auja a clustered layout from small headwater catchments (4 km²) via medium scale catchments (14.5 km²) to Wadi catchments (55 km²) is realised.

Overall, the three winter seasons of SMART-MOVE were characterized by relatively little surface runoff amounts. In the large wadis, below-average amounts were observed. For example, in the headwater catchments, no or only negligible runoff generation was observed.

[Ausgewählte Ergebnisse]

2.4 Concomitant technical training of local water authority and water supplier personnel

The field investigations were carried out together with the local water authorities (main partner: Palestinian Water Authority) and the local water suppliers

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Figure XX: Training of municipality water supplier personnel at the monitoring installations and the respective computer programs for data readout and maintenance. Discussion about spring monitoring parameters and data.

Figure XX: Training of PWA and Salfeet municipality water supplier personnel at the monitoring installations and the respective computer programs for data readout and maintenance. Discussion about spring monitoring parameters and data.

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3 Local numerical models for karst system characterisation and water resources management

Besides the very large scale Transboundary model (Deliverable 1.0.2), specialised models were constructed for the western catchment cluster. Those are reservoir models for individual karst springs (Chapter 3.1 = Task 1.1.1.5, see below), a discrete karst flow model for the southern part of the catchment cluster (Chapter 3.2 = Task 1.1.1.6, see below), as well as the alluvial model Jericho-Auja (Deliverable 1.1.2).

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3.1 Characterization and scenario modelling of the karst aquifer by lumped parameter models for selected springs

Main aim of this task (1.1.1.5) is to provide the input for climate scenario calculations (e.g. Deliverable 1.0.1) and to gain further insight into the karst flow systems (e.g. the hydraulic system parameters). To achieve these aims, initially, also the applicability of statistical approaches was tested. However, the carbonate aquifers in the region exhibit abundant multi-year water storage and many springs also a restriction in discharge (Schmidt et al., 2014; Schmidt, 2014) leading to complex discharge behaviour. Accordingly, it was not feasible to model the discharge behaviour by conventional statistical or precipitation-runoff- models and specific models had to be developed.

Recharge and spring discharge were modelled by means of lumped parameter models. Those models usually consist of a soil water balance model combined with groundwater reservoirs (Figure XX). Standard models for karst aquifers (“Type 1” in Figure XX) often apply two groundwater reservoirs, one resembling the fast flow system (e.g. karst conduits) and one resembling the slow flow system (e.g. fractured rock matrix). Examples can be found e.g. in Fleury et al. (2007) and Geyer et al. (2008). These kinds of models are quite straightforward; however, in case of complex flow and discharge behaviour, more specific models need to be applied. For the Auja spring, displaying such complex discharge behaviour, a specialised reservoir model (“Type 2” in Figure XX) was established during SMART phase II (Schmidt et al. 2014). There are two main differences between the two model types: (i) the realisation of the (spring) outflow: for type 1 models this is realised by means of a recession coefficient

(calibration parameter) αfast, whereas for type 2 models there is a physical representation of a flow-restricting conduit segment; (ii) the representation of exchange between the aquifer modules: type 1 models are characterised by a slow draining aquifer storage. Outflow of this storage into the fast flow system (i.e. the karst conduit system) is realised by another recession coefficient (αslow). In the type 2 model, there can be an exchange flow between the

8 the aquifer modules (high in both directions. This exchange flow is governed by the head difference between the reservoirs and by a lumped exchange parameter, which is calibrated.

Due to its considerable variation in discharge, the model for Auja spring is expected to provide a quite robust model calibration and hence parameter estimation. This model furthermore contains two soil water balance models with different field capacities, taking spatial variations in recharge into account. The model for the Auja spring could be used for the scenario modelling. Further details about the Auja spring-model and the soil water balance model, which was also adopted for the subsequent models, can be found in Schmidt et al. (2014).

Figure XX: Different types of reservoir models, applied for the springs of the western Wadi-cluster. Right hand side of figure modified after Schmidt et al. (2014).

Individual reservoir models were developed for the other three main springs in the catchment cluster (Fawwar, Qilt and the Sultan-Duyuk spring system). In order to reduce complexity and to increase the comparability between the models, relatively straightforward models of the first kind (type 1) as described above were developed. Those consisted of two groundwater reservoirs as well as two soil/epikarst water reservoirs. Since the data basis for most springs would not allow an individual calibration of some model parameters (e.g. surface area ratio of the two soil water balance models), those parameters were adopted from the Auja model. The springs Fawwar (34%) and Qilt (35%) displayed a mean recharge (fraction of precipitation) similar to the Auja spring (34%) (Schmidt et al, 2013). Therefore, the soil water balance model could be adopted with no or minor modifications. The Sultan- Duyuk spring system exhibits with 42 % a higher mean recharge rate compared to the other springs. Therefore, the field capacities needed to be adjusted (Table XX). In the models, the recharge of the soil water balance model with the high field capacity is passed into the slow reservoir, the recharge of the soil model with the low field capacity into the fast reservoir. Those assumptions are in line with general concepts of karst hydrogeology (e.g. Ford & Williams, 2007) 9

Table XX: Main model and calibration parameters for the four reservoir models applied. Please note that model Auja is of type 2, whereas the remaining models are of type 1 (see Fig. XX).

Model parameters Auja Sultan-Duyuk Qilt Fawwar Mean recharge rate (% of precip.) 34 42 35 34 (from Schmidt et al. 2013) Percentage of area of SEM 1 (%) 28 28 28 28

Field capacity SEM 1 (mm) 70 70 70 70

Field capacity SEM 2 (mm) 190 126 182 190

α fast reservoir (–) n.a. 0.0018 0.012 0.2

α slow reservoir (–) n.a. 0.0005 0.0008 0.01

Meteorological input data (daily precipitation sums, daily minimum and maximum air temperature) for the model were obtained from the European Climate Assessment & Dataset project (https://eca.knmi.nl/) for station Jerusalem Central (IMS). The recent years were still in need for quality inspection from the data provider. Therefore, minor time misalignments and data gaps had to be corrected/filled. Since Jerusalem is located at the southern margin of the catchment cluster, a secondary time series was compiled for the north-central part (headwater of Wadi Auja) from stations installed and operated during the SMART project. For the hydrological years 2008–2011 the station Kafr Malik was used, from 2012–2018 the station at the Abu Falah Monitoring Well (AFMW) was utilised. The consistency of results was checked by data from surrounding stations (e.g. Ein Samia). The two stations exhibit very similar precipitation amounts when compared to Jerusalem station on a long-term basis, i.e. Kafr Malik 100%, AFMW 99%. Some small data gaps were filled with values from Jerusalem station. During the snow events (especially for events Jan. 2008 and Dec. 2013) due to the incapability of the unheated precipitation gauges to measure solid precipitation, larger measurement errors occurred. For those solid precipitation periods, the data from Jerusalem were used. A comparison with data from the low-lying gauging station Ein Samia (415 m a.s.l., hence only liquid precipitation) confirms the validity of this approach.

Figure XX: Upper part: Representative drought, medium and wet periods selected for the assessment and modelling of hydrological variability (Deliverable 1.0.1 – Cluster West). The selected periods are

10 indicated by arrows. Lower part: The modelled groundwater recharge (at the example of the Auja spring reservoir model) is shown together with the percentage ratio compared to the long-term average. Figure modified and extended after Schmidt (2014).

A comparison of the calibrated model parameters is shown in table XX. It should be noted that the model for Sultan/Duyuk spring systems is a quite rough conceptualisation of the very complex flow system (e.g. two outlets with a difference in elevation of >100 m). Nevertheless, the calibration worked well. It is presumed that further optimisation of the calibration would most likely require a fundamentally more complex model of type 2.

3.2 Characterization of karst network by distributive parameter model for (parts of) the spring aquifer system

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4 Recommendations for large scale tracer testing in the Jericho– region for spring catchment delineation

As described in chapter 1, a large-scale tracer testing campaign was scheduled for SMART- MOVE but had to be aborted. Here, a few hints on tracer testing design and the hitherto carried out investigations shall be given in order to facilitate further (e.g. follow-up projects).

4.1 Selected tracing setup and dye tracer substances

It was envisaged to conduct the tracer testing with fluorescent dyes in a multi-tracer setup. In these kinds of setup, several injection points are used at the same time in order to maximise the output in relation to the manpower need for sampling and potential recovery point survey. To monitor important potential springs, automatic field-fluorimeters should be used. According to the planned instruments (GGUN FL 30, Abillia, Switzerland), certain restrictions on tracer selection had to be taken into consideration. The instruments can measure three different fluorescent dye tracers simultaneously; however, the fluorescence properties (e.g. excitation and emission wavelength) of the tracers need to be sufficiently different. Here, three tracers with specific advantages and disadvantages were selected:

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● Sodium-fluorescine – This tracer is usually displaying the most conservative properties and lowest detection limits of the common fluorescent dyes. Accordingly, it is often used for the most remote injection point or to proof important assumed connections. In case of an unexpected high concentration at the sampling point it is visibly green and might therefore cause concern among water users and farmers. Therefore, tracer injection mass needs to be carefully calculated according to the (expected) hydrological properties of the flow system.

● Sodium naphthionate – This tracer is exhibiting fluorescence in the ultraviolet range. Therefore, it is largely invisible to the human eye and not causing public worry in case uf unexpected high breakthrough concentrations as might be the case with the tracers exhibiting colour in the visible range. However, also humic substances dissolved in the water exhibit fluorescence in the ultraviolet range, leading to a high natural “background” concentration of the tracer signal. With tests from regional waters with the filter fluorimeters, “background signals” as high as 5 µg L–1 were observed. Therefore, injection amounts need to be considerable higher compared to e.g. sodium-fluorescine. This disadvantage is aggravated by the comparable high cost of the tracer substance.

● Amidorhodamine G – is exhibiting florescence and colour in the red spectral range. Measurements are very little affected by natural dissolved organic matter. This might be an advantage in tracing water high in organic matter, i.e. water originating from wastewater treatment plants sinking underground. This suitability to dye surface water sinking streams is enhanced by its very low photo-degradability. Main drawback is a relatively high adsorbance and retardation in the aquifer (compare e.g. Geyer et al. 2007). Therefore, it is difficult to derive quantitative transport parameters from the tracer breakthrough curve and results should be treated more semi-quantitatively. It might be used for expected relatively short transport distances. Also the availability of the tracer substance was restricted in recent years.

A survey of possible injection points was conducted at the beginning of SMART-MOVE. However, the activities needed to be stopped due to a very unstable security situation in the region (see Introduction). In principle two multi-tracer tests were planned to be carried out: One before the winter season 2016/17 (i.e. approximately in October 2016) and one at the end of the winter season 2016/17 (i.e. approximately in February 2017). Rational behind this was: In case of an insufficient flushing amount at the injection points, the tracer substances could still be re-mobilised by subsequent precipitation events with a high intensity and amount. However, the testing in February can still be a bit risky in this respect, since the timing of the last large-scale precipitation event is not necessarily in March. Hence, if the tracer should get stuck in the vadose zone, it might remain there over the dry summer season.

The main springs GGUN-FL30

4.2 Envisaged injection and potential recovery points

A couple of possible injection points were derived from an analysis of geological maps and cross sections in combination with topographical maps and satellite images (e.g. employing 12

Google Earth). Figure XX displays points which were considered/investigated more thoroughly.

The sinking wastewater stream in upper Wadi Qilt (Wadi Makkuk): The discharge of Al-Bireh wastewater treatment plant is directed into the upper reaches of wadi XY. It flows a considerable distance and is finally infiltrating XXX

The horst of Mazraa-a-esh-Sharquiyya: The village Mazraa-a-esh-Sharquiyya is located on a high plateau composed of the well karstified Amminadav/Hebron-Formation. It is assumed that at major faults this block is in hydrological contact with the Turonian formations in the southeast and that groundwater is finally discharging at the Sultan-Duyuk springs. Here, a tracer test would be very helpful in order to prove/disprove this assumption. Construction pits in the village might provide suitable points for tracer injection. The infiltration capacity should be checked well before the test. From a logistic point of view, the flushing water supply should be relatively straightforward in the village.

Wadi Auja near Ein Samia: About 8 km upstream of Auja spring, the plain of Ein Samia is located. Here also the wellfield of the regional water supplier Jerusalem Water Untertaking is located. Can be conveniently reached by Bedouin water tanker – filling point at Ein Samia water works. Infiltration experiment should be conducted first, in order to proof the suitability of the injection site to

The plain of Ein Samia itself seems no suitable since a large depth of colluvial finegrained sediments in the valley/plain which will prevent water from entering

Sites in Wadi Auja which might still be reached by Bedouin water tanker with tractor

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Figure XX: Different sites in Wadi Samia seeming favourable to provide natural infiltration pits. Uppermost picture approximately at coordinates XXXX, XXXX (Palestinian grid).

5 SMART publications

Grimmeisen et al (2017)

Ries, F., Lange, J., Schmidt, S., Puhlmann, H., Sauter, M. (2015). Recharge estimation and soil moisture dynamics in a Mediterranean, semi-arid karst region. Hydrology and Earth System Sciences, 19(3), 1439–1456.

Ries, F., Schmidt, S., Sauter, M., Lange, J. (2017). Controls on runoff generation along a steep climatic gradient in the Eastern Mediterranean. Journal of Hydrology: Regional Studies, 9, 18–33.

Ries 2016

Schmidt, S., Geyer, T., Marei, A., Guttman, J., Sauter, M. (2013). Quantification of long-term wastewater impacts on karst groundwater resources in a semi-arid environment by chloride mass balance methods. Journal of hydrology, 502, 177–190.

Schmidt, S., Geyer, T., Guttman, J., Marei, A., Ries, F., Sauter, M. (2014). Characterisation and modelling of conduit restricted karst aquifers–example of the Auja spring, Jordan Valley. Journal of Hydrology, 511, 750–763.

Schmidt ea 2017

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Schmidt, S., Grimmeisen, F., Ries, F., Goldscheider, N., Sauter, M. (2018). Hochauflösendes Monitoring von Karst-Grundwasserressourcen beiderseits des Jordangrabens–Konzepte und Anwendungsbeispiele. Grundwasser, 23(1), 59–72.

Schmidt, S. (2014) Hydrogeological characterisation of karst aquifers in semi-arid environments at the catchment scale – Example of the Western Lower Jordan Valley. Doctoral thesis University of Göttingen, Germany, http://hdl.handle.net/11858/00-1735- 0000-0023-98E1-8.

6 Further references

Fleury, P., Plagnes, V., Bakalowicz, M. (2007). Modelling of the functioning of karst aquifers with a reservoir model: Application to Fontaine de Vaucluse (South of France). Journal of hydrology, 345(1–2), 38–49.

Geyer, T., Birk, S., Liedl, R., Sauter, M. (2008). Quantification of temporal distribution of recharge in karst systems from spring hydrographs. Journal of hydrology, 348(3–4), 452– 463.

Gunkel, A., & Lange, J. (2017). Water scarcity, data scarcity and the Budyko curve—An application in the Lower Basin. Journal of Hydrology: Regional Studies, 12, 136-149.

Rimmer, A., & Salingar, Y. (2006). Modelling precipitation-streamflow processes in karst basin: The case of the Jordan River sources, . Journal of Hydrology, 331(3–4), 524– 542.

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