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Effect of rapid urbanization on Mediterranean karstic mountainous drainage basins
Sustainable Cities and Society 51 (2019) 101704
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Effect of rapid urbanization on Mediterranean karstic mountainous drainage basins
T
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Yaakov Ankera, , Vladimir Mirlasb, Alexander Gimburgb, Michael Zilberbrandb, Faina Nakonechnya, Itzhak Meirc, Moshe Inbard a
Department of Chemical Engineering and Materials & Biotechnology, Ariel University, Ariel, Israel Department of Environmental Research, Eastern Israel R&D Center, Ariel, Israel c The Municipal Environmental Associations of Judea and Samaria, Ariel, Israel d Department of Geography and Environmental Studies, University of Haifa, Haifa, Israel b
A R T I C LE I N FO
A B S T R A C T
Keywords: Urbanization Hydrology Runoff coefficient Israel Catchment basin Karstic terrains Remote sensing
Transformation of permeable natural areas into impervious surfaces (e.g. roads, parking lots, and buildings) is altering the watershed response to precipitation, generating bigger runoff volumes with increased peak discharges. The runoff travels faster to the watershed outlet, accumulates, and might cause flooding. Urban areas are affected by flooding, with consequences that might result in severe economic damage and even hazard to life. The paper will estimate the effect of rapid urbanization on runoff and on groundwater recharge. The model includes the Shiloh catchment basin located in a karstic mountainous terrain in Israel for a time period of rapid development from 1978 to 2018. Evaluation of surface permeability was based on land-use classifications from Landsat datasets using the Q-GIS 3.4 platform. Multiyear runoff coefficient estimation was calculated based on annual runoff and rainfall series. The evaluation suggests natural permeable areas were constantly declining from about 400 km2 (nearby 100% of the Shiloh basin) in 1978 to about 100 km2 in 2018 and that nonpermeable areas increased to 95 km2 and semi-permeable areas to 250 km2. Despite annual precipitation decrease during the last two decades, the annual runoff coefficient has a pronounced tendency to increase. This research suggests that the anthropogenic impact on the Shiloh basin and, hence, similar watersheds encourages runoff accumulation, resulting in extreme flood events recurrence while significantly reducing the groundwater recharge.
1. Introduction One consequence of urbanization is the proliferation of impervious surfaces such as roads, sidewalks, parking lots and buildings, which cover previously natural permeable areas. Another outcome is the modification of natural flow paths into paved gutters, storm sewers, or other elements of artificial drainage. Urbanization changes the overall watershed’s response to precipitation such that developed urban watersheds generate increased runoff volumes, with increased peak discharges. Runoff that travels faster to the watershed’s outlet might cause flooding once the watercourse slope moderates. The increase in flooding magnitude and reoccurrence affects expanding urban areas with consequences that might result in severe economic damage and even hazard to life (Shaad, Ninsalam, Padawangi, & Burlando, 2016). The surplus runoff, due to relatively impervious urban areas, is conducted either by the natural conveying system or by artificial systems to concentration points where runoff mitigation procedures may
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be applied (Hollis, 1988; Marsalek, 1993). Urban hydrology characterization (e.g. Dunne & Leopold, 1978; Lazaro, 1979; Leopold, 1968) has yielded empirical and computer modeling of urban runoff, primarily to assist engineers in designing stormwater drain facilities. Some of these models have achieved widespread usage, especially in the United States (Abbott, 1978). Miller and Hutchins (2017) reviewed the combined impact of urbanization and climate change on inland urban catchments in the United Kingdom (UK). They found high spatiotemporal variability and uncertainty in climate change and population growth, suggesting that the UK needs to design future climate-proof cities to prevent flooding and water quality deterioration. Urban runoff quality can degrade as the urbanized surrounding might induce microbial contaminants, elevated concentration of nutrients, and suspended solids. It can also carry inorganic and organic chemicals, hydrocarbons, pesticides, heavymetals, and potential endocrine disruptors. Nevertheless, these potentially hazardous contaminants are usually at low levels due to a high dilution factor (Bichai
Corresponding author. E-mail address: [email protected] (Y. Anker).
https://doi.org/10.1016/j.scs.2019.101704 Received 11 February 2019; Received in revised form 24 June 2019; Accepted 5 July 2019 Available online 20 July 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.
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created by urban development affect hydrological processes in the following ways:
& Ashbolt, 2017). Modeling the effect of urban development on runoff quality is currently indecisive, owing to urban densification and expansion quantification, population distribution, and high-resolution rainfall intensity and frequency changes (Anker, Tal, Nisnevitch, Gimburg, & Inbar, 2013; Asaf, Nativ, Shain, Hassan, & Geyer, 2004). Our study region is located at a triple junction of the Arabian Plate (western Asia), African Plate, and Anatolian Block, and is lengthwise the Arabian and African plates’ bounding fault (Rojay, Heimann, & Toprak, 2001). While this region is still considered rural, in recent years, surface water quantity and quality issues may jeopardize political trends, motivating region-wide urban development (AlHirsh, Battisti, & Schirone, 2016). An initial study of the urbanization effect along the coastline (the Tel Aviv area) already indicated that constructed water harvesting is essential to prevent runoff intensification, which otherwise may over-flood drainage, leading to ecologic and economic hazards (Nachshon, Netzer, & Livshitz, 2016). Urban runoff research in Israel began in the early 1970s (Ben Zvi, 1970), aiming mostly at runoff coefficient estimation for drainage system planning. Rainfall-runoff-infiltration models were later used for discharge and total urban runoff volume estimation, and for urban drainage design and for watershed management. Water volume modeling is used for downstream flooding prevention by water harvesting systems and detention reservoirs that should infiltrate surplus runoff to groundwater or repurpose it for municipal use reclamation (ANUPU, 2010; Beven, 2002). In recent years, high resolution mapping has enabled hydrodynamic modeling of highly and rapidly populated areas (Shaad et al., 2016) as well as of heterogeneous areas such as mountainous karstic terrains (Ne’eman, 2016). Urban watershed characteristics such as land-use heterogeneity and small-scale topographic change affect flow directions and velocities and should be considered in any hydrological model (McCuen, 1998). Preliminary research evaluating these parameters in the runoff coefficient demonstrated that for a city on the Israeli coastal plain (e.g. Ashdod), the averaged runoff yield is about 160,000 cubic meters/sq km, with a runoff coefficient of 0.22 (Asaf et al., 2004). A study of the mountainous Haifa region (i.e. the Mt. Carmel basins) indicated that the average runoff coefficient values range from 0.18 to 0.32, increasing with rain amount and impervious area percentage (Ziegel, 2007). A pilot study in the Jerusalem area, like Haifa a large city located on mountainous karstic terrain, demonstrated that for large storms the runoff coefficient reaches a value of 0.25 (Ziegel & Inbar, 2008). Since Ben-Zvi (1984), most projects carried out in Israel studied the urbanization impact of coastal cities and the impact on the coastal aquifer. For example, Burmil, Shamir, and Carmon (2003) developed a neighborhood scale (for the city of Rishon LeZion) hydrological model based on an empirical study. Preliminary studies of mountain ranges applied methods that were initially developed for different regions. For example, rational method exponent values (CIA) were calculated for several catchments in Haifa and Yokneam (Ziegel, 2007), and the ZIN model, originally developed for the Negev Desert, was also aligned for regional urban settings (e.g. Fritz et al., 2005; Lange & Guwang, 2004). The KINEROS2 model of the Yarkon-Ayalon watershed is the first to emphasize the harsh effect of the study area (Shiloh basin, Fig. 1) urbanization processes on runoff accumulation (Ohana et al., 2013). Another work carried out in the city of Ramallah, which is in the study area (Fig. 1), indicated that the rainwater harvesting effect and different land use scenarios could mitigate urban runoff (Shahin, 2006). As an initial research initiative, a modeling strategy and urban runoff chemical evaluation were performed for the city of Ariel (Anker et al., 2013; Inbar & Gimburg, 2010), and long-term studies including different urban patterns, lithology, and catchment size were used to improve urban mountainous karstic terrain hydrological modeling. This detailed study revealed that while the rural regions in Ariel had a runoff coefficient that did not exceed 0.1, the urban regions were in the range of 0.3 to 0.4 (Ne’eman, 2016). Integration of these early works suggests that impervious surfaces
1 Increase of urban basin runoff total volume, especially when compared to similar non-urban physiographic areas (Stephenson, 1994; White & Greer, 2006; Yamashita, Watanabe, & Shimatani, 2016). 2 Increase of peak discharge of runoff draining to urban channels (Kendall et al., 2005; Leopold, 1968; Mahaut & Andrieu, 2019). 3 Reduction of the lag time between rainfall and runoff events (Sala & Inbar, 1992; Wang & Cheng, 2002; Yamashita et al., 2016). 4 Reduction of the recurrence interval of extreme flood events (Lazaro, 1979). 5 Increase in the number of runoff episodes in a storm integrated hydrograph (Mahaut & Andrieu, 2019; White & Greer, 2006). Even so, urban storm drainage characteristics remain uncertain with state-of-the-art modeling techniques require field measurements for detrimental effects assessment. For instance, catchment studies conducted in East Africa evaluated the impact of land use and land cover changes on discharge, surface, and subsurface runoff, suggesting that forest cover loss is accompanied by stream discharges and surface runoff increase (Guzha, Rufino, Okoth, Jacobs, & Nobrega, 2018). With this context, the main objectives of this study were to develop and apply a procedure for coupling urbanization to changes in the hydrological properties of typical karstic Mediterranean mountainous terrains. This research aimed to develop measurements and modeling techniques applicable to mountainous and hilly towns that are located on karstic terrains. For the case study site, the Shiloh basin urbanization processes on runoff patterns and groundwater recharge were evaluated.
2. Main framework 2.1. Geographical framework The study region (Samaria mountain rim) is a developed karstic terrain with an altitude range of less than 100 m Above Sea Level (ASL) along the western contact with the coastal plain and up to 900 m ASL along the eastern mountain rims (Frumkin & Bason, 1996). Average annual rainfall is approximately 500 mm and while the main soil type in the region is terra-rosa, rendzina soils commonly developed over chalk and marl lithologies. The Shiloh basin (Fig. 1) covers an area of about 400 km2, which is approximately 20% of the contributing area to the Yarkon River that when flooded may pose severe consequences to adjacent populated regions. The Shiloh basin land cover change is considered to have the largest impact on the runoff intensification and is currently related to urbanization and vegetation removal (Ohana et al., 2013). At present, several major urban development programs are in progress in the Shiloh basin, including two new cities, Rawabi (Palestinian) and Leshem (Israeli). The variety of building patterns consist of the inherent differences between traditional Palestinian villages and modern Israeli and Palestinian urban settings (ANUPU, 2010). The shift from single houses surrounded by open land to apartment blocks and large public buildings of various types is expected to produce urban runoff. While karstic rural landscape is almost entirely permeable, the percent of the impervious area ranges from 15% to 45% in the different urban patterns (Inbar & Gimburg, 2010; Shahin, 2006). Our research hypothesis suggests that the rapid shift from rural to urban land cover is increasing the runoff amount produced and depriving groundwater recharge. Flooding of the Shiloh stream bridge passing on road 446 between Rafaat to Beit Arye (Fig. 1) is a vivid demonstration of the runoff pattern change. After almost a decade without over-road flooding, on February 2nd, 2012 the bridge was flooded for the first time (Fig. 2). Since then, apart from 2016 to 2017, similar events have occurred during all the following winters. 2
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Fig. 1. Study site location and sub-basins extent (polygons). The figure was prepared by AutoCAD, whereas the source for basin margins and stream route as well as the settlement layers is the Israel National GIS Inventory, (Govmap, 2018). Meteorological and hydrologic station locations and the Thiessen polygons were manually inserted, and contours were extracted from the ASTER Digital Elevation Model (DEM).
For this purpose, the precipitation database of 13 weather stations was integrated into a precipitation distribution of six Thiessen cells (Fig. 1). By differentiating the accumulated runoff from the measured runoff at the basin outlet, a regional runoff coefficient was calculated (Eq. (1)). The land use classification was repeated for each decade since the 1970s, since this time gap was found to be nominal for monitoring the urban development in this region. Once a comparative land use assessment was attained from the Landsat series, the various classes were integrated to rural – permeable, altered – semi-permeable, and urban – impervious regions. The integration of the various data processing types and modeling finally yielded a spatial-temporal illustration of the basin’s hydrological response due to urbanization processes (Fig. 3).
Fig. 2. Photo of flood event from February 2, 2012 (Photo taken by Yaakov Anker).
2.2. Research methodology 3. Materials and methods This study integrates orbital remote sensing data with on-going climate data monitoring and field research, in order to estimate the urbanization effect on mountainous karstic terrain hydrology. For correct modeling of complex scenarios, variety of data sources needed to be integrated through several processing stages. Orbital remote sensing is utilized to supply a Digital Terrain Model (DTM) that is then used as a DEM, which is required for the hydrological modeling. Once the hydrological modeling supplies the basin boundaries and flow paths, a spectral data set is used for land use classification (Fig. 3). In previous work, the city of Ariel was used to characterize infiltration losses of urbanized, semi-urbanized, and rural mountainous karstic areas It was fond that the Runoff coefficient of a highly urbanized area (˜50% pavement) is 23%, mildly urbanized (˜25% pavement) is 12%, and mostly rural is 7% (Inbar & Gimburg, 2010). In addition to the runoff coefficients attained in previous research, through a spatial hydrological modeling of the Shiloh basin an upscaled evaluation of the urbanization hydrological effect was evaluated.
3.1. Datasets Runoff coefficient and lost surface water estimations were calculated using the following: 1 Israel Meteorological Survey – Annual and average monthly rainfall and evapotranspiration series from 1978 to 2018. Averaged precipitation and evapotranspiration values were assigned to the Shiloh sub-basin catchments (Table 1 and Fig. 1). 2 Israeli Hydrological Survey – Daily and annual runoff series measured at several of their hydrological stations, located at the basin outlet from the mountainous catchment area (Fig. 1). 3 Landsat datasets (Table 2) – Surface permeability evaluation is based on land-use classification starting from 1978 (Landsat 3) and up to 2018 (Landsat 8). For each decade, a representative image was obtained through the following criteria: uniform spatial coverage 3
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Fig. 3. Conceptual working process applied for the assessment of urbanization effect on the Shiloh basin hydrological regime.
3.2. Analytical tools and procedures
Table 1 Meteorological stations used for precipitation estimations. Name
Number
Sub-basin area, km2
Itamar Itamar hill 777 Itamar man Gittit Eli Eli Pilgei Maim Shiloh Ofra Ari'el college Talmon Elqana Enat Be'erot Yizhak Total area of the Shiloh catchment basin
241,260 241,265 241,180 241,296 241,485 241,460 241,492 241,933 241,414 242,010 241,381 135,496 136,580
10.1
The multi-year runoff coefficient estimation was calculated for the time period, 1978 to 2018. This estimation is based on annual rainfall series over the Shiloh basin and on annual runoff series. Annual rainfall volumes were calculated as a sub-basin precipitation sum on the catchment area. Annual rainfall thickness (mm) was calculated as Thiessen Polygon averaged values (Rhynsburger, 1973) that were normalized according to the sub-basin catchment area. Runoff maximal flow rates were measured during discrete storm events and divided into 5 groups: 1: 0.1–0.4 m3/s; 2: 0.4–0.8 m3/s; 3: 0.8–2.0 m3/s; 4: 2.0–10.0 m3/s; and 5: 10.0–20.0 m3/s, respectively. For each hydrogeological year, a percentage ratio of the number of storm events in each group out of the total amount of events was calculated. The discrete storm event runoff maximal flow rates classification reflects the probability of these flow rates being present in a given event. The first group includes very low flow rates not exceeding 0.4 m3/s that flow along the river bed, moistening the sub-floor deposits, and often do not reach the foothill plain. The second group includes flow rates of up to 0.8 m3/s that also flow along the river bed, moistening the sub-floor deposits and probably reach the groundwater level, especially when events are longer than a day. The third group is characterized by average flow rate values of up to two m3/s. Runoff flows through the river bed and partially through the river flood plain. Slight damage to flood plain vegetation is possible, with surface runoff reaching the foothill plain and replenishing the groundwater. The fourth group is characterized by high flow rate values of 10 m3/ s. In this case, the surface runoff can cause significant damage to floodplain vegetation, entailing the erosion of the channel and containing sediment materials. Often it causes agricultural land flooding along the stream floodplain. The fifth group includes events with extremely high flow rates, reaching 20 m3/s or more. These are catastrophic floods causing flooding not only of the floodplain, but also of roads as shown in Fig. 2. Surface runoff is characterized by a high content of mud and stones, like mudslides. The passing of the flood leads to significant erosion effects and changes the river bed profile. The runoff coefficient (C) is a dimensionless coefficient relating the amount of runoff to the amount of precipitation and was calculated according to Eq. (1) (Critchley & Siegert, 1991).
5.7 72.3 43.7 65.7 85.8 26.5 47.6 3 4.4 364.8
Table 2 Landsat datasets used for classification. Year
Dataset
1987 1989 1998 2009 2018
LM03_L1TP_187038_19781219_20180421_01_T2 LM04_L1TP_174038_19890710_20180325_01_T2 LT051740381998092901T1-SC20181103101622 LT051740382009062301T1-SC20181103232727 LC081740382018042901T1-SC20180622094203
(Fig. 4) and acquisition during the summer months and during midday and also without cloud coverage. All images withstanding these criteria were then comparatively evaluated for Signal to Noise Ratio (SNR) and general clarity. Finally, for each decade, a couple of images underwent spectral classification, with an additional image processed for validation in case of variability between the results. Since the spectral configuration of each Landsat generation varies only a little, four correlative bands were used for the spectral classification In Landsat third generation bands 4,5,6 & 7 were used; in the fourth & fifth generations bands 1, 2, 3 & 4 were used and in the eighth generation bands 3, 4, 5 & 6 were used.
4
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Fig. 4. The Landsat (03rd–08th generations) image granola, typical coverage, and the Shiloh basin subset parameter used as subset for land use classification.
C = Annual Runoff (MCM)/Annual Precipitation (MCM)
topographic convergence, flow direction, and, finally, channel network and drainage basins. By manually including all of the sub-basins in one polygon, the entire Shiloh drainage basin was delineated. After creating the basin parameter, the sequential Landsat datasets were loaded to the project. For each dataset, four equivalent bands were chosen for spectral classification. The image was then spatially resampled according to Shiloh basin polygon subsets. The spectral classification was carried out by the Q-GIS Semi-automatic Classification Plugin, through the following steps:
(1)
Estimation of multi-year infiltration losses was based on the calculated water balance of the Shiloh catchment basin (Eq. (2)). R = I – (E + Q)
(2)
Where: R – Infiltration losses into the soil; I – Precipitation; E – Evapotranspiration; Q – Runoff. Infiltration losses were calculated for days with runoff events that occurred between October and March. Based on these calculations, multi-year monthly average infiltration losses were estimated, and multi-year average monthly evapotranspiration was calculated by the Penman-Monteith Eq. (3), where Δ represents the slope of the saturation vapor pressure temperature relationship, Rn is the net radiation, G is the soil heat flux, (es - ea) represents the vapor pressure deficit of the air, γ a is the mean air density at constant pressure, cp is the specific heat of the air, γ is the psychrometric constant, and rs and ra are the (bulk) surface and aerodynamic resistances (Sabbah, 2004).
λET =
Δ (Rn-G) + pa c p
1 Setting a band set of four equivalent bands (1: ˜550 nm, 2: ˜650 nm, 3: ˜750 nm & 4: ˜950 nm) from each Landsat satellite generation. 2 Clustering of spectral backgrounds using a k-means algorithm with spectral signature seed from each set in multi-dimensional mode and maximal 20 classes in 10 iterations. 3 Evaluating the extent of the various classes by Q-GIS Raster Layer Unique Value Report, this supplies a statistical illustration of the clusters. 4 Creating raster clusters to shape layer transformation, in order to attain the land use classification as a vector layer. 5 Adding equivalent land use classes into permeability classes, based on the figures attained in the hydrological modeling and the previous study (Inbar & Gimburg, 2010). 6 Once the vector mapping of the various permeability surfaces was aligned and tests over the RS platform, additional cross-validation of classes was carried out in a field survey that compared the mapping to the accentual surface.
(es − ea ) ra
( ) r
Δ+γ 1+ rs
a
(3)
The Q-GIS 3.4 platform was used for evaluating the study area surface permeability, where the first step consisted of selecting the datasets for analysis (Section 2.2). Creating the basin polygon included hydrological modeling over the ASTER DEM (dh15 m) dataset. The model included GRASS-TERRAFLOW analysis that supplied stream 5
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Fig. 5. Shiloh basin permeability classification; green indicates permeable rural area, light blue semi-permeable altered area, and blue impermeable urban.
4. Results and discussion
natural topsoil was tempered by anthropogenic activity (Fig. 6). The statistical evaluation (Fig. 6) suggests that urban surface coverage increased mostly during the last two decades. Thus, today they are characterized by approximately the same coverage as natural rural area. In addition, the altered rural area has, since the last decade, covered most of the Shiloh basin area. These observations might relate to the increase in runoff coefficient during the last two decades, although the rainfall amounts are smaller than during the 80 s and 90 s.
4.1. Change in permeable surface area along the research term From the classification, it is evident that the rural area extent is gradually declining over the years (Fig. 5). Moreover, the extent of urban areas has increased mostly since the new millennium. The rural natural altered areas are transitional areas between impermeable urban surfaces and rural surfaces that produce nearly no runoff. Rural areas that underwent some sort of anthropogenic alteration demonstrate lower roughness than rural terrains, owing to which they produce more runoff. The statistical evaluation shows that in 1978 most of the basin was rural and in 1989 some rural area was degraded, although not urbanized. However, since 1998 there was a rapid increase in urbanization. Beyond this alteration of natural urban areas to open areas, their
4.2. Multi-year change in runoff coefficient of the Shiloh basin Annual precipitation in the study period varied from maximal 1175 mm in 1991/92 to minimal 302 mm in 1998/99. Annual surface runoff volumes vary from almost zero to 34 million cubic meters and depend mainly on the total volume and mode of precipitation. Fig. 6 6
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precipitation (Fig. 7), most runoff events are back to class 1, during the last winter (2019) precipitation amounts more than doubles and there are runoff events of all categories (Fig. 8). The Shiloh catchment basin runoff coefficient, expressed in percentage from annual precipitation amount, is shown in Fig. 9. It seldom exceeds 1% and only during a few abnormal years has it reached and passed this mark. The period from 1991 to 1993 saw the highest recorded precipitation amounts reaching 1200 mm (Fig. 7) and a maximal annual runoff coefficient value of 8% (Fig. 9). The last winter supplied the evident for runoff intensification as while rain amounts were slightly lower than during the winter of 1979–1980, the runoff coefficient is twice as big. The correlation between runoff coefficient and precipitation for decade periods is described by polynomial dependencies with a standard deviation ranging from 0.791 to 0.639. For the 1988–98 and 2008–18 periods, the correlation is stronger than the other decades (Fig. 10).
Fig. 6. Statistical analysis of the Shiloh basin time domain change in surface permeability.
4.3. Infiltration losses The averaged infiltration loss values varied from 0.5 MCM in October to 7.4 MCM in February. Maximal multi-year average monthly infiltration loss values reach 29 MCM in January and minimal values (−1.5 MCM) in October. Negative infiltration loss values indicate that in certain periods, the water balance output exceeds the input, probably owing to drainage of the soil section in the catchment basin (Fig. 11). Maximal infiltration loss is typical of surface runoff events with a duration of three days or more. 4.4. Integration of runoff evaluations and urbanization process estimation The tendency of annual precipitation decrease during the last decade is evident when averaging annual precipitation calculated for ten-year periods (Fig. 12). Nonetheless, during the last two decades, more runoff is produced, which is supported by an increase in runoff coefficient since 1998. The integration of these observations with the change in surface permeability (Fig. 6), suggests that the Shiloh basin urbanization process encourages runoff accumulation in the form of floods, which also significantly reduces the groundwater recharge. The notion that flood intensity and reoccurrence are intensifying have motivated the regional drainage authorities to utilize an abandoned query as a detention and infiltration reservoir near Einat (Fig. 1), which currently compensate for the basin's anthropogenic alteration.
Fig. 7. Annual surface runoff volume and annual precipitation.
illustrates the relationship between the annual volume of runoff and annual precipitation in the period from 1978 to 2018 (Fig. 7). When scrutinizing the Shiloh catchment average annual rainfall amounts during the last three decades, there is an evident decreasing trend (Fig. 7). Although since the start of the millennium the annual precipitation average is between 400 mm to 600 mm, the number of runoff events with extreme maximal surface runoff flow rates is increasing (Fig. 8). The classification of maximum flow rates into five ranges (Section 3.2) implies that although the annual runoff volume is decreasing from 2003 and up to 2012 most runoff events were categorized as class 1, since then they are categorized as class 2 (Fig. 8). Although during 2017 and 2018, probably owing to decrease in annual
5. Conclusions The Shiloh basin provides a regional scale model for rainfall regime trends along Israel's central mountain rim, which during the last decades is decreasing owing to the global climate change (Ziv, Saaroni, Pargament, Harpaz, & Alpert, 2014). The decreasing trend in the rainfall regime has a potential impact on sensitive Mediterranean environments experiencing rapid urbanization processes (Garcia Nieto, Geijzendorffer, Francesc Baró, Alberte, & Cramer, 2018). Although the regional annual precipitation trend is decreasing, the annual runoff coefficient has a tendency to increase, in particular over the last two decades (1998–2019). The average annual precipitation calculated for ten-year periods decreased from 265.4 mm/year in 1978–1988 to 185.8 mm/year in 2008-2018. Annual runoff coefficient exceeded 1% of the annual precipitation average only during the abnormal winter of 1991–1992 and again during the last winter that had about the same precipitation amounts as in the winter of 1979–1980 (Fig. 12). In addition, although the winter of 2018–2019 experienced the largest volume of precipitation in two decades, the annual average trend-line slope is negative (Fig. 7). These observations suggest a climate change trend of dryer winters that ought to produce less runoff, although, probably owing to rapid
Fig. 8. Percentage of maximum flow rate classes of discrete storm events since 2003. 7
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Fig. 9. Annual runoff coefficients.
Fig. 10. Correlation between runoff coefficient and precipitation for ten-year periods.
Fig. 12. Average ten-year period Rainfall and Runoff coefficient calculated from 1978 to 2018. Fig. 11. Multi-Year infiltration losses into the soil calculated according to the water balance for the period from October to March.
values are typical for surface runoff events lasting three days or more. The permeable surface in the basin is constantly declining, even more rapidly during the last two decades (Figs. 5 and 6). The anthropogenic impact on the Shiloh basin, as representative of developing mountainous karstic terrains, encourages runoff accumulation in the form of floods and significantly reduces the groundwater recharge. It is suggested that in order to prevent flooding and groundwater recharge reduction, the urban areas should be planned with runoff infiltration zones in a way that will prevent their hydrological effect on the adjacent natural drainage systems. In addition to flood mitigation and groundwater recharge, the vacant land used as runoff absorbing strata provides social benefits for the surrounding community (Kim, 2018). This notion has led in recent years to the "Sponge City"
urbanization, during the last two decades runoff amounts seem to be increasing (Fig. 12). A combined climate change effect with a rapid urbanization scenario is common in other regions. This should be mitigated by domestic runoff absorbing facilities (Tavakol-Davani et al., 2016) and designated road infrastructure (Cheng & Wang, 2018), The numerical correlations between runoff coefficient and precipitation for ten-year periods were described by polynomial dependences with a standard deviation ranging from 0.791 to 0.639. Maximal values of multi-year averaged month infiltration losses reached 28.8MCM and were noted in January. Maximum infiltration 8
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methodology aimed to preserve a zero runoff signature of urbanized areas, which is becoming common practice among China's urban planners and planning authorities (Du et al., 2019).
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Sustainable Cities and Society, 27, 398–406. https://doi.org/10.1016/j.scs.2016.08.008. Ne’eman, N. (2016). Modeling urban runoff, high-resolution adaptation of the SCS-CN/ ModClark model for urban mountainous watershed (the city of Ariel). Master of Science in Environmental Engineering thesis, Tel Aviv University140. Ohana, N., Krnieli, A., Egozi, R., & Peeters, A. (2013). Effects of land-cover change on rainfall-runoff relationships: A case study of the Yarkon-Ayalon watershed, Israel. In: Proc. of SPIE, Vol. 87951, 1–9. Rhynsburger, D. (1973). Analytic delineation of Thiessen polygons. Geographical Analysis, 5(2), 133–144. Rojay, B., Heimann, A., & Toprak, V. (2001). Neotectonic and volcanic characteristics of the Karasu Fault Zone (Anatolia, Turkey): The transition zone between the Dead Sea Transform and the East Anatolian Fault Zone. Geodinamica Acta, 14(1–3). Sabbah, W. W. (2004). Developing a GIS and hydrological modeling approach for sustainable water resources management in the west bank – Palestine. Department of Brigham Young University. Sala, M., & Inbar, M. (1992). Some hydrologic effects of urbanization in Catalan rivers. Catena, 19, 363–378. Shaad, K., Ninsalam, Y., Padawangi, R., & Burlando, P. (2016). Towards high resolution and cost-effective terrain mapping for urban hydrodynamic modelling in densely settled river-corridors. Sustainable Cities and Society, 20, 168–179. https://doi.org/10. 1016/j.scs.2015.09.005. Shahin, K. (2006). The impact of urbanization of the hydrological and hydrochemical characteristics of stormwater: A nested approach, Ramallah, West Bank. Doctoral Thesis. Hebrew University164. Stephenson, D. (1994). Comparison of the water balance for an undeveloped and suburban catchment. Hydrological Sciences, 39, 295–307. https://doi.org/10.1080/ 02626669409492751. Tavakol-Davani, H., Goharian, E., Hansen, C. H., Tavakol-Davani, H., Apul, D., & Burian, S. J. (2016). How does climate change affect combined sewer overflow in a system benefiting from rainwater harvesting systems? Sustainable Cities and Society, 27, 430–438. https://doi.org/10.1016/j.scs.2016.07.003. Wang, R., & Cheng, J. S. (2002). An approach for evaluating the hydrological effects of urbanization and its application. Hydrological Processes, 16, 1403–1418. https://doi. org/10.1002/hyp.350. White, M. D., & Greer, K. A. (2006). The effects of watershed urbanization in the stream hydrology, and riparian vegetation of Los Penasquitos creek, California. Landscape and Urban Planning, 74(2), 125–138. https://doi.org/10.1016/j.landurbplan.2004. 11.015. Yamashita, S., Watanabe, R., & Shimatani, Y. (2016). Smart adaptation activities and measures against urban flood disasters. Sustainable Cities and Society, 27, 175–184. https://doi.org/10.1016/j.scs.2016.06.027. Ziegel, A., & Inbar, M. (2008). Urban impact on the runoff/rainfall relationship in the Matzleva Valley stream, Jerusalem. In M. Billig (Vol. Ed.), The Judea and Samaria research studies: Vol. 17, (pp. 319–328). Ariel Academic Press (in Hebrew, with English abstract). Ziegel, A. (2007). Finding the runoff coefficient in urban environmentDoctoral thesis. Univ. of Haifa (in Hebrew, with English abstract). . Ziv, B., Saaroni, H., Pargament, R., Harpaz, T., & Alpert, P. (2014). Trends in rainfall regime over Israel, 1975–2010, and their relationship to large-scale variability. Regional Environmental Change, 14, 1751–1764.
Acknowledgments The study was financed by the Israel Ministry of Science and Technology research grant # 68016 and the runoff ground measurements and part of the chemical analysis were given by the Israel hydrological survey. References Abbott, J. (1978). Testing of several runoff models on an urban watershed (No. TP-59). US Army Corps of Engineers, Hydrologic Engineering Center (HEC). AlHirsh, I., Battisti, C., & Schirone, B. (2016). Threat analysis for a network of sites in West Bank (Palestine): An expert-based evaluation supported by grey literature and local knowledge. Journal for Nature Conservation, 31, 61–70. https://doi.org/10. 1016/j.jnc.2016.03.005. Anker, Y., Tal, M., Nisnevitch, M., Gimburg, A., & Inbar, M. (2013). Definition of urban runoff bio-chemical properties and development of suitable treatment methodology. In M. Billig (Vol. Ed.), The Judea and Samaria research studies: Vol. 22, (pp. 349–368). Ariel Academic Press (in Hebrew, with English abstract). Asaf, L., Nativ, R., Shain, D., Hassan, M., & Geyer, S. (2004). Controls on the chemical and isotopic compositions of urban storm-water in a semiarid zone. Journal of Hydrology, 294(4), 270–293. https://doi.org/10.1016/j.jhydrol.2004.02.010. ANUPU (2010). Environmental impact assessment for the New City of Rawabi. Palestinian National Authority Environmental Quality Authority137. https://www3.opic.gov/ environment/eia/amal_rawabi/RawabiEIAFinal-KCR.pdf. Ben Zvi, A. (1970). On the relationship between rainfall and surface runoff on laboratory watersheds, Ph.D. Thesis. The University of Illinois137. Ben-Zvi, A. (1984). Runoff peaks from Two Dimensional Laboratory Watersheds. Journal of Hydrology, 68, 115–139. Beven, K. (2002). Towards an alternative blueprint for a physically based digitally simulated hydrologic response modeling system. Hydrological Processes, 16, 189–206. https://doi.org/10.1002/hyp.343. Bichai, F., & Ashbolt, N. (2017). Public health and water quality management in lowexposure stormwater schemes: A critical review of regulatory frameworks and path forward. Sustainable Cities and Society, 28, 453–465. https://doi.org/10.1016/j.scs. 2016.09.003. Burmil, S., Shamir, U., & Carmon, N. (2003). Urban runoff in residential neighborhoods. Haifa: Technion Center for Urban and Regional Studies and the Grand Water Research Institute118 (In Hebrew). Cheng, Y., & Wang, R. (2018). A novel stormwater management system for urban roads in China based on local conditions. Sustainable Cities and Society, 39, 163–171. https:// doi.org/10.1016/j.scs.2017.09.001 (March 2017). Critchley, W., & Siegert, K. (1991). Water harvesting. Retrieved fromRome: FAO - UN. http://www.fao.org/3/u3160e/u3160e00.htm#Contents. Du, S., Wang, C., Shen, J., Wen, J., Gao, J., Wu, J., ... Xu, H. (2019). Mapping the capacity of concave green land in mitigating urban pluvial floods and its beneficiaries. Sustainable Cities and Society, 44, 774–782. https://doi.org/10.1016/j.scs.2018.11. 003 (May 2018). Dunne, T., & Leopold, L. B. (1978). Water in environmental planning. New York: Freeman818. Fritz, A., Lange, J., Leibundgut, C.h, Husary, S., Ghanem, M., Grodek, T., & Lekach, J. (2005). Simulation of the effects of change in land use on urban runoff in the Middle Eastern City Ramallah. Geophysical research abstracts Abstract EGU05-A-09299. Frumkin, A., & Bason, U. (1996). Ariel Cave-survey and ground penetrating radar study. In Y. Eshel (Vol. Ed.), The Judea and Samaria research studies: Vol. 6, (pp. 321–328). Ariel Academic Press (in Hebrew, with English abstract). Garcia Nieto, A. P., Geijzendorffer, I., Francesc Baró, P. R., Alberte, B., & Cramer, W. (2018). Impacts of urbanization around Mediterranean cities: Changes in ecosystem service supply. Ecological Indicators, 91, 589–606. Govmap (2018). Israel national GIS inventory. https://www.govmap.gov.il/. Guzha, A. C., Rufino, M. C., Okoth, S., Jacobs, S., & Nobrega, R. L. B. (2018). Impacts of land use and land cover change on surface runoff, discharge and low flows: Evidence from East Africa. Journal of Hydrology Regional Studies, 15, 49–67. https://doi.org/10. 1016/j.ejrh.2017.11.005. Hollis, G. E. (1988). Rain, roads, roofs and runoff: Hydrology in cities. Geography, 73, 9–18. Inbar, M., & Gimburg, A. (2010). Transmission losses in urban storm runoff in a karstic mountain environment—The city of Ariel. In M. Billig (Vol. Ed.), The Judea and Samaria research studies: Vol. 19, (pp. 313–321). Ariel Academic Press (in Hebrew, with English abstract).
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Effect of rapid urbanization on Mediterranean karstic mountainous drainage basins
T
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Yaakov Ankera, , Vladimir Mirlasb, Alexander Gimburgb, Michael Zilberbrandb, Faina Nakonechnya, Itzhak Meirc, Moshe Inbard a
Department of Chemical Engineering and Materials & Biotechnology, Ariel University, Ariel, Israel Department of Environmental Research, Eastern Israel R&D Center, Ariel, Israel c The Municipal Environmental Associations of Judea and Samaria, Ariel, Israel d Department of Geography and Environmental Studies, University of Haifa, Haifa, Israel b
A R T I C LE I N FO
A B S T R A C T
Keywords: Urbanization Hydrology Runoff coefficient Israel Catchment basin Karstic terrains Remote sensing
Transformation of permeable natural areas into impervious surfaces (e.g. roads, parking lots, and buildings) is altering the watershed response to precipitation, generating bigger runoff volumes with increased peak discharges. The runoff travels faster to the watershed outlet, accumulates, and might cause flooding. Urban areas are affected by flooding, with consequences that might result in severe economic damage and even hazard to life. The paper will estimate the effect of rapid urbanization on runoff and on groundwater recharge. The model includes the Shiloh catchment basin located in a karstic mountainous terrain in Israel for a time period of rapid development from 1978 to 2018. Evaluation of surface permeability was based on land-use classifications from Landsat datasets using the Q-GIS 3.4 platform. Multiyear runoff coefficient estimation was calculated based on annual runoff and rainfall series. The evaluation suggests natural permeable areas were constantly declining from about 400 km2 (nearby 100% of the Shiloh basin) in 1978 to about 100 km2 in 2018 and that nonpermeable areas increased to 95 km2 and semi-permeable areas to 250 km2. Despite annual precipitation decrease during the last two decades, the annual runoff coefficient has a pronounced tendency to increase. This research suggests that the anthropogenic impact on the Shiloh basin and, hence, similar watersheds encourages runoff accumulation, resulting in extreme flood events recurrence while significantly reducing the groundwater recharge.
1. Introduction One consequence of urbanization is the proliferation of impervious surfaces such as roads, sidewalks, parking lots and buildings, which cover previously natural permeable areas. Another outcome is the modification of natural flow paths into paved gutters, storm sewers, or other elements of artificial drainage. Urbanization changes the overall watershed’s response to precipitation such that developed urban watersheds generate increased runoff volumes, with increased peak discharges. Runoff that travels faster to the watershed’s outlet might cause flooding once the watercourse slope moderates. The increase in flooding magnitude and reoccurrence affects expanding urban areas with consequences that might result in severe economic damage and even hazard to life (Shaad, Ninsalam, Padawangi, & Burlando, 2016). The surplus runoff, due to relatively impervious urban areas, is conducted either by the natural conveying system or by artificial systems to concentration points where runoff mitigation procedures may
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be applied (Hollis, 1988; Marsalek, 1993). Urban hydrology characterization (e.g. Dunne & Leopold, 1978; Lazaro, 1979; Leopold, 1968) has yielded empirical and computer modeling of urban runoff, primarily to assist engineers in designing stormwater drain facilities. Some of these models have achieved widespread usage, especially in the United States (Abbott, 1978). Miller and Hutchins (2017) reviewed the combined impact of urbanization and climate change on inland urban catchments in the United Kingdom (UK). They found high spatiotemporal variability and uncertainty in climate change and population growth, suggesting that the UK needs to design future climate-proof cities to prevent flooding and water quality deterioration. Urban runoff quality can degrade as the urbanized surrounding might induce microbial contaminants, elevated concentration of nutrients, and suspended solids. It can also carry inorganic and organic chemicals, hydrocarbons, pesticides, heavymetals, and potential endocrine disruptors. Nevertheless, these potentially hazardous contaminants are usually at low levels due to a high dilution factor (Bichai
Corresponding author. E-mail address: [email protected] (Y. Anker).
https://doi.org/10.1016/j.scs.2019.101704 Received 11 February 2019; Received in revised form 24 June 2019; Accepted 5 July 2019 Available online 20 July 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.
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created by urban development affect hydrological processes in the following ways:
& Ashbolt, 2017). Modeling the effect of urban development on runoff quality is currently indecisive, owing to urban densification and expansion quantification, population distribution, and high-resolution rainfall intensity and frequency changes (Anker, Tal, Nisnevitch, Gimburg, & Inbar, 2013; Asaf, Nativ, Shain, Hassan, & Geyer, 2004). Our study region is located at a triple junction of the Arabian Plate (western Asia), African Plate, and Anatolian Block, and is lengthwise the Arabian and African plates’ bounding fault (Rojay, Heimann, & Toprak, 2001). While this region is still considered rural, in recent years, surface water quantity and quality issues may jeopardize political trends, motivating region-wide urban development (AlHirsh, Battisti, & Schirone, 2016). An initial study of the urbanization effect along the coastline (the Tel Aviv area) already indicated that constructed water harvesting is essential to prevent runoff intensification, which otherwise may over-flood drainage, leading to ecologic and economic hazards (Nachshon, Netzer, & Livshitz, 2016). Urban runoff research in Israel began in the early 1970s (Ben Zvi, 1970), aiming mostly at runoff coefficient estimation for drainage system planning. Rainfall-runoff-infiltration models were later used for discharge and total urban runoff volume estimation, and for urban drainage design and for watershed management. Water volume modeling is used for downstream flooding prevention by water harvesting systems and detention reservoirs that should infiltrate surplus runoff to groundwater or repurpose it for municipal use reclamation (ANUPU, 2010; Beven, 2002). In recent years, high resolution mapping has enabled hydrodynamic modeling of highly and rapidly populated areas (Shaad et al., 2016) as well as of heterogeneous areas such as mountainous karstic terrains (Ne’eman, 2016). Urban watershed characteristics such as land-use heterogeneity and small-scale topographic change affect flow directions and velocities and should be considered in any hydrological model (McCuen, 1998). Preliminary research evaluating these parameters in the runoff coefficient demonstrated that for a city on the Israeli coastal plain (e.g. Ashdod), the averaged runoff yield is about 160,000 cubic meters/sq km, with a runoff coefficient of 0.22 (Asaf et al., 2004). A study of the mountainous Haifa region (i.e. the Mt. Carmel basins) indicated that the average runoff coefficient values range from 0.18 to 0.32, increasing with rain amount and impervious area percentage (Ziegel, 2007). A pilot study in the Jerusalem area, like Haifa a large city located on mountainous karstic terrain, demonstrated that for large storms the runoff coefficient reaches a value of 0.25 (Ziegel & Inbar, 2008). Since Ben-Zvi (1984), most projects carried out in Israel studied the urbanization impact of coastal cities and the impact on the coastal aquifer. For example, Burmil, Shamir, and Carmon (2003) developed a neighborhood scale (for the city of Rishon LeZion) hydrological model based on an empirical study. Preliminary studies of mountain ranges applied methods that were initially developed for different regions. For example, rational method exponent values (CIA) were calculated for several catchments in Haifa and Yokneam (Ziegel, 2007), and the ZIN model, originally developed for the Negev Desert, was also aligned for regional urban settings (e.g. Fritz et al., 2005; Lange & Guwang, 2004). The KINEROS2 model of the Yarkon-Ayalon watershed is the first to emphasize the harsh effect of the study area (Shiloh basin, Fig. 1) urbanization processes on runoff accumulation (Ohana et al., 2013). Another work carried out in the city of Ramallah, which is in the study area (Fig. 1), indicated that the rainwater harvesting effect and different land use scenarios could mitigate urban runoff (Shahin, 2006). As an initial research initiative, a modeling strategy and urban runoff chemical evaluation were performed for the city of Ariel (Anker et al., 2013; Inbar & Gimburg, 2010), and long-term studies including different urban patterns, lithology, and catchment size were used to improve urban mountainous karstic terrain hydrological modeling. This detailed study revealed that while the rural regions in Ariel had a runoff coefficient that did not exceed 0.1, the urban regions were in the range of 0.3 to 0.4 (Ne’eman, 2016). Integration of these early works suggests that impervious surfaces
1 Increase of urban basin runoff total volume, especially when compared to similar non-urban physiographic areas (Stephenson, 1994; White & Greer, 2006; Yamashita, Watanabe, & Shimatani, 2016). 2 Increase of peak discharge of runoff draining to urban channels (Kendall et al., 2005; Leopold, 1968; Mahaut & Andrieu, 2019). 3 Reduction of the lag time between rainfall and runoff events (Sala & Inbar, 1992; Wang & Cheng, 2002; Yamashita et al., 2016). 4 Reduction of the recurrence interval of extreme flood events (Lazaro, 1979). 5 Increase in the number of runoff episodes in a storm integrated hydrograph (Mahaut & Andrieu, 2019; White & Greer, 2006). Even so, urban storm drainage characteristics remain uncertain with state-of-the-art modeling techniques require field measurements for detrimental effects assessment. For instance, catchment studies conducted in East Africa evaluated the impact of land use and land cover changes on discharge, surface, and subsurface runoff, suggesting that forest cover loss is accompanied by stream discharges and surface runoff increase (Guzha, Rufino, Okoth, Jacobs, & Nobrega, 2018). With this context, the main objectives of this study were to develop and apply a procedure for coupling urbanization to changes in the hydrological properties of typical karstic Mediterranean mountainous terrains. This research aimed to develop measurements and modeling techniques applicable to mountainous and hilly towns that are located on karstic terrains. For the case study site, the Shiloh basin urbanization processes on runoff patterns and groundwater recharge were evaluated.
2. Main framework 2.1. Geographical framework The study region (Samaria mountain rim) is a developed karstic terrain with an altitude range of less than 100 m Above Sea Level (ASL) along the western contact with the coastal plain and up to 900 m ASL along the eastern mountain rims (Frumkin & Bason, 1996). Average annual rainfall is approximately 500 mm and while the main soil type in the region is terra-rosa, rendzina soils commonly developed over chalk and marl lithologies. The Shiloh basin (Fig. 1) covers an area of about 400 km2, which is approximately 20% of the contributing area to the Yarkon River that when flooded may pose severe consequences to adjacent populated regions. The Shiloh basin land cover change is considered to have the largest impact on the runoff intensification and is currently related to urbanization and vegetation removal (Ohana et al., 2013). At present, several major urban development programs are in progress in the Shiloh basin, including two new cities, Rawabi (Palestinian) and Leshem (Israeli). The variety of building patterns consist of the inherent differences between traditional Palestinian villages and modern Israeli and Palestinian urban settings (ANUPU, 2010). The shift from single houses surrounded by open land to apartment blocks and large public buildings of various types is expected to produce urban runoff. While karstic rural landscape is almost entirely permeable, the percent of the impervious area ranges from 15% to 45% in the different urban patterns (Inbar & Gimburg, 2010; Shahin, 2006). Our research hypothesis suggests that the rapid shift from rural to urban land cover is increasing the runoff amount produced and depriving groundwater recharge. Flooding of the Shiloh stream bridge passing on road 446 between Rafaat to Beit Arye (Fig. 1) is a vivid demonstration of the runoff pattern change. After almost a decade without over-road flooding, on February 2nd, 2012 the bridge was flooded for the first time (Fig. 2). Since then, apart from 2016 to 2017, similar events have occurred during all the following winters. 2
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Fig. 1. Study site location and sub-basins extent (polygons). The figure was prepared by AutoCAD, whereas the source for basin margins and stream route as well as the settlement layers is the Israel National GIS Inventory, (Govmap, 2018). Meteorological and hydrologic station locations and the Thiessen polygons were manually inserted, and contours were extracted from the ASTER Digital Elevation Model (DEM).
For this purpose, the precipitation database of 13 weather stations was integrated into a precipitation distribution of six Thiessen cells (Fig. 1). By differentiating the accumulated runoff from the measured runoff at the basin outlet, a regional runoff coefficient was calculated (Eq. (1)). The land use classification was repeated for each decade since the 1970s, since this time gap was found to be nominal for monitoring the urban development in this region. Once a comparative land use assessment was attained from the Landsat series, the various classes were integrated to rural – permeable, altered – semi-permeable, and urban – impervious regions. The integration of the various data processing types and modeling finally yielded a spatial-temporal illustration of the basin’s hydrological response due to urbanization processes (Fig. 3).
Fig. 2. Photo of flood event from February 2, 2012 (Photo taken by Yaakov Anker).
2.2. Research methodology 3. Materials and methods This study integrates orbital remote sensing data with on-going climate data monitoring and field research, in order to estimate the urbanization effect on mountainous karstic terrain hydrology. For correct modeling of complex scenarios, variety of data sources needed to be integrated through several processing stages. Orbital remote sensing is utilized to supply a Digital Terrain Model (DTM) that is then used as a DEM, which is required for the hydrological modeling. Once the hydrological modeling supplies the basin boundaries and flow paths, a spectral data set is used for land use classification (Fig. 3). In previous work, the city of Ariel was used to characterize infiltration losses of urbanized, semi-urbanized, and rural mountainous karstic areas It was fond that the Runoff coefficient of a highly urbanized area (˜50% pavement) is 23%, mildly urbanized (˜25% pavement) is 12%, and mostly rural is 7% (Inbar & Gimburg, 2010). In addition to the runoff coefficients attained in previous research, through a spatial hydrological modeling of the Shiloh basin an upscaled evaluation of the urbanization hydrological effect was evaluated.
3.1. Datasets Runoff coefficient and lost surface water estimations were calculated using the following: 1 Israel Meteorological Survey – Annual and average monthly rainfall and evapotranspiration series from 1978 to 2018. Averaged precipitation and evapotranspiration values were assigned to the Shiloh sub-basin catchments (Table 1 and Fig. 1). 2 Israeli Hydrological Survey – Daily and annual runoff series measured at several of their hydrological stations, located at the basin outlet from the mountainous catchment area (Fig. 1). 3 Landsat datasets (Table 2) – Surface permeability evaluation is based on land-use classification starting from 1978 (Landsat 3) and up to 2018 (Landsat 8). For each decade, a representative image was obtained through the following criteria: uniform spatial coverage 3
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Fig. 3. Conceptual working process applied for the assessment of urbanization effect on the Shiloh basin hydrological regime.
3.2. Analytical tools and procedures
Table 1 Meteorological stations used for precipitation estimations. Name
Number
Sub-basin area, km2
Itamar Itamar hill 777 Itamar man Gittit Eli Eli Pilgei Maim Shiloh Ofra Ari'el college Talmon Elqana Enat Be'erot Yizhak Total area of the Shiloh catchment basin
241,260 241,265 241,180 241,296 241,485 241,460 241,492 241,933 241,414 242,010 241,381 135,496 136,580
10.1
The multi-year runoff coefficient estimation was calculated for the time period, 1978 to 2018. This estimation is based on annual rainfall series over the Shiloh basin and on annual runoff series. Annual rainfall volumes were calculated as a sub-basin precipitation sum on the catchment area. Annual rainfall thickness (mm) was calculated as Thiessen Polygon averaged values (Rhynsburger, 1973) that were normalized according to the sub-basin catchment area. Runoff maximal flow rates were measured during discrete storm events and divided into 5 groups: 1: 0.1–0.4 m3/s; 2: 0.4–0.8 m3/s; 3: 0.8–2.0 m3/s; 4: 2.0–10.0 m3/s; and 5: 10.0–20.0 m3/s, respectively. For each hydrogeological year, a percentage ratio of the number of storm events in each group out of the total amount of events was calculated. The discrete storm event runoff maximal flow rates classification reflects the probability of these flow rates being present in a given event. The first group includes very low flow rates not exceeding 0.4 m3/s that flow along the river bed, moistening the sub-floor deposits, and often do not reach the foothill plain. The second group includes flow rates of up to 0.8 m3/s that also flow along the river bed, moistening the sub-floor deposits and probably reach the groundwater level, especially when events are longer than a day. The third group is characterized by average flow rate values of up to two m3/s. Runoff flows through the river bed and partially through the river flood plain. Slight damage to flood plain vegetation is possible, with surface runoff reaching the foothill plain and replenishing the groundwater. The fourth group is characterized by high flow rate values of 10 m3/ s. In this case, the surface runoff can cause significant damage to floodplain vegetation, entailing the erosion of the channel and containing sediment materials. Often it causes agricultural land flooding along the stream floodplain. The fifth group includes events with extremely high flow rates, reaching 20 m3/s or more. These are catastrophic floods causing flooding not only of the floodplain, but also of roads as shown in Fig. 2. Surface runoff is characterized by a high content of mud and stones, like mudslides. The passing of the flood leads to significant erosion effects and changes the river bed profile. The runoff coefficient (C) is a dimensionless coefficient relating the amount of runoff to the amount of precipitation and was calculated according to Eq. (1) (Critchley & Siegert, 1991).
5.7 72.3 43.7 65.7 85.8 26.5 47.6 3 4.4 364.8
Table 2 Landsat datasets used for classification. Year
Dataset
1987 1989 1998 2009 2018
LM03_L1TP_187038_19781219_20180421_01_T2 LM04_L1TP_174038_19890710_20180325_01_T2 LT051740381998092901T1-SC20181103101622 LT051740382009062301T1-SC20181103232727 LC081740382018042901T1-SC20180622094203
(Fig. 4) and acquisition during the summer months and during midday and also without cloud coverage. All images withstanding these criteria were then comparatively evaluated for Signal to Noise Ratio (SNR) and general clarity. Finally, for each decade, a couple of images underwent spectral classification, with an additional image processed for validation in case of variability between the results. Since the spectral configuration of each Landsat generation varies only a little, four correlative bands were used for the spectral classification In Landsat third generation bands 4,5,6 & 7 were used; in the fourth & fifth generations bands 1, 2, 3 & 4 were used and in the eighth generation bands 3, 4, 5 & 6 were used.
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Fig. 4. The Landsat (03rd–08th generations) image granola, typical coverage, and the Shiloh basin subset parameter used as subset for land use classification.
C = Annual Runoff (MCM)/Annual Precipitation (MCM)
topographic convergence, flow direction, and, finally, channel network and drainage basins. By manually including all of the sub-basins in one polygon, the entire Shiloh drainage basin was delineated. After creating the basin parameter, the sequential Landsat datasets were loaded to the project. For each dataset, four equivalent bands were chosen for spectral classification. The image was then spatially resampled according to Shiloh basin polygon subsets. The spectral classification was carried out by the Q-GIS Semi-automatic Classification Plugin, through the following steps:
(1)
Estimation of multi-year infiltration losses was based on the calculated water balance of the Shiloh catchment basin (Eq. (2)). R = I – (E + Q)
(2)
Where: R – Infiltration losses into the soil; I – Precipitation; E – Evapotranspiration; Q – Runoff. Infiltration losses were calculated for days with runoff events that occurred between October and March. Based on these calculations, multi-year monthly average infiltration losses were estimated, and multi-year average monthly evapotranspiration was calculated by the Penman-Monteith Eq. (3), where Δ represents the slope of the saturation vapor pressure temperature relationship, Rn is the net radiation, G is the soil heat flux, (es - ea) represents the vapor pressure deficit of the air, γ a is the mean air density at constant pressure, cp is the specific heat of the air, γ is the psychrometric constant, and rs and ra are the (bulk) surface and aerodynamic resistances (Sabbah, 2004).
λET =
Δ (Rn-G) + pa c p
1 Setting a band set of four equivalent bands (1: ˜550 nm, 2: ˜650 nm, 3: ˜750 nm & 4: ˜950 nm) from each Landsat satellite generation. 2 Clustering of spectral backgrounds using a k-means algorithm with spectral signature seed from each set in multi-dimensional mode and maximal 20 classes in 10 iterations. 3 Evaluating the extent of the various classes by Q-GIS Raster Layer Unique Value Report, this supplies a statistical illustration of the clusters. 4 Creating raster clusters to shape layer transformation, in order to attain the land use classification as a vector layer. 5 Adding equivalent land use classes into permeability classes, based on the figures attained in the hydrological modeling and the previous study (Inbar & Gimburg, 2010). 6 Once the vector mapping of the various permeability surfaces was aligned and tests over the RS platform, additional cross-validation of classes was carried out in a field survey that compared the mapping to the accentual surface.
(es − ea ) ra
( ) r
Δ+γ 1+ rs
a
(3)
The Q-GIS 3.4 platform was used for evaluating the study area surface permeability, where the first step consisted of selecting the datasets for analysis (Section 2.2). Creating the basin polygon included hydrological modeling over the ASTER DEM (dh15 m) dataset. The model included GRASS-TERRAFLOW analysis that supplied stream 5
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Fig. 5. Shiloh basin permeability classification; green indicates permeable rural area, light blue semi-permeable altered area, and blue impermeable urban.
4. Results and discussion
natural topsoil was tempered by anthropogenic activity (Fig. 6). The statistical evaluation (Fig. 6) suggests that urban surface coverage increased mostly during the last two decades. Thus, today they are characterized by approximately the same coverage as natural rural area. In addition, the altered rural area has, since the last decade, covered most of the Shiloh basin area. These observations might relate to the increase in runoff coefficient during the last two decades, although the rainfall amounts are smaller than during the 80 s and 90 s.
4.1. Change in permeable surface area along the research term From the classification, it is evident that the rural area extent is gradually declining over the years (Fig. 5). Moreover, the extent of urban areas has increased mostly since the new millennium. The rural natural altered areas are transitional areas between impermeable urban surfaces and rural surfaces that produce nearly no runoff. Rural areas that underwent some sort of anthropogenic alteration demonstrate lower roughness than rural terrains, owing to which they produce more runoff. The statistical evaluation shows that in 1978 most of the basin was rural and in 1989 some rural area was degraded, although not urbanized. However, since 1998 there was a rapid increase in urbanization. Beyond this alteration of natural urban areas to open areas, their
4.2. Multi-year change in runoff coefficient of the Shiloh basin Annual precipitation in the study period varied from maximal 1175 mm in 1991/92 to minimal 302 mm in 1998/99. Annual surface runoff volumes vary from almost zero to 34 million cubic meters and depend mainly on the total volume and mode of precipitation. Fig. 6 6
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precipitation (Fig. 7), most runoff events are back to class 1, during the last winter (2019) precipitation amounts more than doubles and there are runoff events of all categories (Fig. 8). The Shiloh catchment basin runoff coefficient, expressed in percentage from annual precipitation amount, is shown in Fig. 9. It seldom exceeds 1% and only during a few abnormal years has it reached and passed this mark. The period from 1991 to 1993 saw the highest recorded precipitation amounts reaching 1200 mm (Fig. 7) and a maximal annual runoff coefficient value of 8% (Fig. 9). The last winter supplied the evident for runoff intensification as while rain amounts were slightly lower than during the winter of 1979–1980, the runoff coefficient is twice as big. The correlation between runoff coefficient and precipitation for decade periods is described by polynomial dependencies with a standard deviation ranging from 0.791 to 0.639. For the 1988–98 and 2008–18 periods, the correlation is stronger than the other decades (Fig. 10).
Fig. 6. Statistical analysis of the Shiloh basin time domain change in surface permeability.
4.3. Infiltration losses The averaged infiltration loss values varied from 0.5 MCM in October to 7.4 MCM in February. Maximal multi-year average monthly infiltration loss values reach 29 MCM in January and minimal values (−1.5 MCM) in October. Negative infiltration loss values indicate that in certain periods, the water balance output exceeds the input, probably owing to drainage of the soil section in the catchment basin (Fig. 11). Maximal infiltration loss is typical of surface runoff events with a duration of three days or more. 4.4. Integration of runoff evaluations and urbanization process estimation The tendency of annual precipitation decrease during the last decade is evident when averaging annual precipitation calculated for ten-year periods (Fig. 12). Nonetheless, during the last two decades, more runoff is produced, which is supported by an increase in runoff coefficient since 1998. The integration of these observations with the change in surface permeability (Fig. 6), suggests that the Shiloh basin urbanization process encourages runoff accumulation in the form of floods, which also significantly reduces the groundwater recharge. The notion that flood intensity and reoccurrence are intensifying have motivated the regional drainage authorities to utilize an abandoned query as a detention and infiltration reservoir near Einat (Fig. 1), which currently compensate for the basin's anthropogenic alteration.
Fig. 7. Annual surface runoff volume and annual precipitation.
illustrates the relationship between the annual volume of runoff and annual precipitation in the period from 1978 to 2018 (Fig. 7). When scrutinizing the Shiloh catchment average annual rainfall amounts during the last three decades, there is an evident decreasing trend (Fig. 7). Although since the start of the millennium the annual precipitation average is between 400 mm to 600 mm, the number of runoff events with extreme maximal surface runoff flow rates is increasing (Fig. 8). The classification of maximum flow rates into five ranges (Section 3.2) implies that although the annual runoff volume is decreasing from 2003 and up to 2012 most runoff events were categorized as class 1, since then they are categorized as class 2 (Fig. 8). Although during 2017 and 2018, probably owing to decrease in annual
5. Conclusions The Shiloh basin provides a regional scale model for rainfall regime trends along Israel's central mountain rim, which during the last decades is decreasing owing to the global climate change (Ziv, Saaroni, Pargament, Harpaz, & Alpert, 2014). The decreasing trend in the rainfall regime has a potential impact on sensitive Mediterranean environments experiencing rapid urbanization processes (Garcia Nieto, Geijzendorffer, Francesc Baró, Alberte, & Cramer, 2018). Although the regional annual precipitation trend is decreasing, the annual runoff coefficient has a tendency to increase, in particular over the last two decades (1998–2019). The average annual precipitation calculated for ten-year periods decreased from 265.4 mm/year in 1978–1988 to 185.8 mm/year in 2008-2018. Annual runoff coefficient exceeded 1% of the annual precipitation average only during the abnormal winter of 1991–1992 and again during the last winter that had about the same precipitation amounts as in the winter of 1979–1980 (Fig. 12). In addition, although the winter of 2018–2019 experienced the largest volume of precipitation in two decades, the annual average trend-line slope is negative (Fig. 7). These observations suggest a climate change trend of dryer winters that ought to produce less runoff, although, probably owing to rapid
Fig. 8. Percentage of maximum flow rate classes of discrete storm events since 2003. 7
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Fig. 9. Annual runoff coefficients.
Fig. 10. Correlation between runoff coefficient and precipitation for ten-year periods.
Fig. 12. Average ten-year period Rainfall and Runoff coefficient calculated from 1978 to 2018. Fig. 11. Multi-Year infiltration losses into the soil calculated according to the water balance for the period from October to March.
values are typical for surface runoff events lasting three days or more. The permeable surface in the basin is constantly declining, even more rapidly during the last two decades (Figs. 5 and 6). The anthropogenic impact on the Shiloh basin, as representative of developing mountainous karstic terrains, encourages runoff accumulation in the form of floods and significantly reduces the groundwater recharge. It is suggested that in order to prevent flooding and groundwater recharge reduction, the urban areas should be planned with runoff infiltration zones in a way that will prevent their hydrological effect on the adjacent natural drainage systems. In addition to flood mitigation and groundwater recharge, the vacant land used as runoff absorbing strata provides social benefits for the surrounding community (Kim, 2018). This notion has led in recent years to the "Sponge City"
urbanization, during the last two decades runoff amounts seem to be increasing (Fig. 12). A combined climate change effect with a rapid urbanization scenario is common in other regions. This should be mitigated by domestic runoff absorbing facilities (Tavakol-Davani et al., 2016) and designated road infrastructure (Cheng & Wang, 2018), The numerical correlations between runoff coefficient and precipitation for ten-year periods were described by polynomial dependences with a standard deviation ranging from 0.791 to 0.639. Maximal values of multi-year averaged month infiltration losses reached 28.8MCM and were noted in January. Maximum infiltration 8
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methodology aimed to preserve a zero runoff signature of urbanized areas, which is becoming common practice among China's urban planners and planning authorities (Du et al., 2019).
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