Wastewater matters: incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08)

Geosci. Model Dev., 18, 3735–3754, 2025 https://doi.org/10.5194/gmd-18-3735-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Development and technical paper Wastewater matters: incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08) Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil Water Security Research Group, Biodiversity and Natural Resources Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria Correspondence: Dor Fridman ([email protected]) Received: 23 July 2024 – Discussion started: 2 August 2024 Revised: 28 February 2025 – Accepted: 7 March 2025 – Published: 23 June 2025 Abstract. Wastewater treatment and reuse are becoming increasingly critical for enhancing water use efficiency and ensuring reliable water availability. Wastewater also significantly influences hydrological dynamics within urban watersheds. Although hydrological modeling has advanced to incorporate human–water interactions, large-scale and multiresolution models often lack the comprehensive integration of wastewater treatment and reuse processes. This paper presents the new wastewater treatment and reuse module as part of the hydrological Community Water Model (CWatM) and demonstrates its capabilities and advantages in an urban watershed with intermittent flows. Incorporating wastewater into the model improves model performance by better representing low and peak flows during the respective dry and wet seasons. It allows for the representation of sectoral wastewater reuse, the exploration of different measures to increase wastewater reuse, and the examination of the effects of wastewater reuse on the water stress level. Modeling wastewater treatment and reuse is particularly relevant in regions with semi-arid or arid climates, rapid urbanization, or active policies promoting water reuse. The wastewater treatment and reuse module could be upscaled by minimizing the data requirements via simplified workflows. Combined with the availability of recent datasets on wastewater treatment plants and processes, a global application of the module is feasible. As current developments focus on water quantity, the water quality dimension of wastewater treatment remains a limitation. This opens prospects for incorporating water quality into the model and developing global input data for wastewater treatment and reuse. 1 Introduction Over the last few decades, hydrological modeling has developed to account for the human–water interface (Wada et al., 2017). Recent developments in this field have focused on developing higher-resolution global hydrological models (GHMs) by increasing models’ spatial resolution, adjusting their datasets, and including a variety of water management options (Abeshu et al., 2023; Hoch et al., 2023; Burek et al., 2020; Hanasaki et al., 2022). Increasing human interventions in the water cycle and higher-spatial-resolution modeling have emphasized the need to include water management as an integral part of hydrological models (Hanasaki et al., 2022). Some large-scale hydrological models (LHMs) already account for water management aspects, like water withdrawal and consumption, irrigation management, reservoir operations, water transfers, and desalination (Wada et al., 2017). Wastewater treatment and reuse are other management options that are increasingly important in many regions. Currently, treated wastewater is estimated to be 188 km3yr−1 globally, which is around 52 % of the effluents generated. Further, approximately 22 % (of treated wastewater) is estimated to be reused (Jones et al., 2021). Thebo et al. (2017) found that around 35.9×106ha of irrigated cropland is supported by rivers dominated by wastewater from upstream urban areas, and Van Vliet et al. (2021) indicated that expansion of treated wastewater use from 1.6×109to 4.0×109m3 per month could strongly reduce water scarcity levels worldwide. Specifically, wastewater reuse is a valuable water source for industrial and irrigation applications in water-stressed regions. For example, Israel reuses around 88 % of its treated Published by Copernicus Publications on behalf of the European Geosciences Union. 3736 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM wastewater, mainly for use in the agricultural sector, where it satisfies about 45 % of the agricultural water withdrawals (Fridman et al., 2021). Treated wastewater is also used for irrigation in southern European, Mediterranean, and North African countries (Angelakis et al., 1999; Bixio et al., 2006). While accepting exacerbated stress on freshwater resources, the European Parliament is working to improve the quality of wastewater treatment in the European Union, aiming to increase wastewater reuse (European Parliament, 2024). It follows that prospects of increased utilization of this resource are plausible. Wastewater collection, treatment, and reuse are relevant processes for the hydrological modeling of urban catchments and complex water resource systems, and these processes are included in different small-scale models (Salvadore et al., 2015). Large-scale hydrological models often neglect wastewater treatment and reuse. However, to some extent, few models include wastewater treatment effects on water quality. The Soil and Water Assessment Tool (SWAT) includes septic tanks as an on-site treatment option. It simulates the percolation of wastewater into soils, the interaction between pollutants and the soil media, and bacteria buildup and nutrient uptake (Neitsch et al., 2011). Another example is DynQual, a global water quality model coupled with the PCR-GLOBWB2 hydrological model (Jones et al., 2023). The model includes wastewater treatment processes in water quality simulations while also simplifying wastewater treatment and reuse management. Namely, in DynQual, wastewater is generated, collected, treated, and discharged locally (in a single grid cell). While these are significant developments, they only partially capture the complex dynamics between human activities and hydrological processes occurring in urbanized catchments or otherwise complex water resource systems. This paper introduces a recently developed, customizable wastewater treatment and reuse module as part of the Community Water Model (CWatM), allowing various modes of simulating wastewater treatment and reuse processes. CWatM is a versatile, fully distributed, modular, and opensource hydrological model that simulates natural and anthropogenically affected hydrological processes at a daily time step and multiple spatial resolutions ranging from 0.5° to 30 arcsec (Burek et al., 2020). CWatM has extensive and publicly available documentation of the source code, the model structure, and model training and tutorials (https://cwatm. iiasa.ac.at/, last access: last access: 11 July 2024). The development of the wastewater treatment and reuse fits with the modularity and flexibility of CWatM by providing various modus operandi to enable the simulation of wastewater treatment and reuse on global (0.5°), regional (5 arcmin), and local (up to 30 arcsec) scales. This paper aims to introduce this module using a high-resolution (around 1 km2) case study of an urbanized river basin in a relatively dry climate (the Ayalon Basin in Israel). The rest of the article is organized as follows: Sect. 2 describes the model development; Sect. 3 covers the case study, input data, and scenarios; and Sect. 4 presents the results, followed by discussion and conclusions in Sects. 5 and 6, respectively. 2 Module development and description 2.1 The Community Water Model (CWatM) CWatM is a large-scale, distributed hydrological model suitable for implementation at global and regional scales (Burek et al., 2020). It is implemented in the Python programming language and is a fully open-source model (https://cwatm. iiasa.ac.at, last access: 11 July 2024). CWatM simulates the main hydrological processes and covers some aspects of the human–water interface. This paper presents the recently developed wastewater treatment module to enhance CWatM’s capacity to address human water management. The model is applied to the relatively water-scarce Ayalon Basin in Israel. It uses a spatial resolution of 30 arcsec (∼1 km2grid) in a geographic coordinate system (WGS84). Groundwater is simulated by the coupled CWatM–MODFLOW6 model (Guillaumot et al., 2022) at a spatial resolution of 500 m using the UTM36N coordinate system. 2.2 Developing the wastewater treatment and reuse module (WTRM) The wastewater treatment and reuse module (WTRM) enhances the capacity of CWatM to simulate the human–water interface at high spatial resolution. It introduces wastewater collection, treatment, disposal, and reuse to CWatM. Largescale modeling shall utilize the basic setup of the WTRM, for which sufficient data are available globally. Case studies with higher data availability may benefit from optional advanced functions. The following section distinguishes between basic and advanced (optional) model processes. Figure 1a demonstrates the WTRM workflow, split into the following three sub-processes: (1) pre-treatment, (2) treatment, and (3) posttreatment. It also differentiates between the CWatM existing (gray boxes) and newly added (green boxes) features. 2.2.1 Pre-treatment: wastewater generation and collection Wastewater generation in CWatM is represented by nonirrigation return flows, which are a function of water availability and sectoral allocation scheme, and the ratio between the consumptive and total water withdrawal. The wastewater module estimates domestic and industrial wastewater generation (EffDom and EffInd, respectively) by multiplying the nonirrigation return flows by the relative sectoral water demand. The next step is to collect and supply wastewater to wastewater treatment plants (WWTPs) (see Eq. 1). Geosci. Model Dev., 18, 3735–3754, 2025 https://doi.org/10.5194/gmd-18-3735-2025 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM 3737 Figure 1. (a) The new features in WTRM (green boxes and arrows) and their interactions with the existing features of CWatM (gray boxes and arrows). (b) Water balance for the intensive and extensive wastewater treatment systems. Equation (1): calculating WWTP influents in WTRM. WWTP influents in WTRM are calculated as follows: Inflowj,t =X l∈jEffDom l,t ×DDom j+EffInd l,t ×DInd j×Csl +Rfl×α, (1) where jand trepresent a simulated WWTP and the time step, respectively, and lindicates a grid cell. Table 4 describes all of the WTRM variables, data sources, and default values. WWTP service areas (or collection areas) are model input defining the linkages between the location of wastewater generation (individual grid cells, denoted by l) and wastewater treatment plants (denoted by j) – namely, that the wastewater from all grid cells in a collection area https://doi.org/10.5194/gmd-18-3735-2025 Geosci. Model Dev., 18, 3735–3754, 2025 3738 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM is treated at the associated WWTP (see Fig. S10). Wastewater collection is also a function of the sewer connection rate (Csl), where a value of 1 indicates that all wastewater is collected and sent to a WWTP. Moreover, it can include urban runoff (Rfl) due to leakage or integration of the urban stormwater and wastewater systems. The αcoefficient defines the system integration level and ranges from 0 (no integration) to 1 (complete system integration). The total wastewater collected in all grid cells lassociated with a WWTP jis registered as the treatment plant’s inflow. Modeling sector-specific wastewater treatment at a WWTP (e.g., treatment of only industrial wastewater) is an advanced model functionality and currently does not fit a global application. It uses a Boolean variable (e.g., DDom), which equals 1 if the treatment plant receives a specific wastewater stream (e.g., domestic). A default value of 1 for both sectors is set in place in the case of missing data. 2.2.2 Treatment: influent, evaporation, and effluent Simulated WWTPs must have the following basic features: location, start year of operation, daily treatment capacity, treatment period (days), and outflow location. Currently, the module supports two optional wastewater treatment technologies associated with the treatment period. The two options are intensive and extensive treatment plants, as described in points 4 and 5 in Fig. 1b, respectively. Intensive treatment refers to conventional wastewater treatment technology and is characterized by a low residence time and low area requirements. It treats water to secondary or tertiary levels over less than 24 h (Pescod, 1992). CWatM uses a daily time step, so the intensive treatment plant’s treatment period is set to 1 d. Any WWTP with a longer treatment period (i.e., ≥2 d) would be classified as extensive. Extensive treatment refers to natural biological systems that consist of a short primary treatment in a relatively deep anaerobic pond which is followed by a longer residence time (20–40 d) in a shallow facultative pond for secondary treatment (Pescod, 1992). An advanced model feature enables the exceedance of the WWTP daily capacity by temporarily reducing the hydrological retention time (HRT). This feature is enabled by setting a treatment-plant-specific minimally allowed HRT, providing WWTPs some buffer to handle days with extreme inflows, e.g., due to rain events. Another advanced option is to simulate WWTP closure or upgrades by providing an end year of operation for a WWTP instance. The main flows within the treatment section are influent, evaporation, and effluent, as described below. Influent inflows According to the basic model setup, excess wastewater beyond the plant’s daily treatment capacity is discharged to a predefined outflow location (see Table 4). However, the model holds advanced modeling capabilities, enabling WWTPs to accept larger inflows to handle temporal fluctuations (e.g., due to significant rain events). Inflows higher than the designed capacity shorten the hydrological retention time (HRT or residence time), resulting in less effective wastewater treatment. The designed retention time is calculated as HRTDesign j=Volumej/InflowDesign j, where Volumej is the volume of WWTP jand InflowDesign jis the daily treatment capacity of WWTP j(Pescod, 1992). The daily treatment capacity and time (or designed HRT) are model inputs (see Table 4). The minimally allowed HRT (days) parameter allows treatment plants to maintain higher inflows than their designed capacities. It expresses the lowest operational hydraulic retention time that a treatment plant can withstand before it refuses inflows. Following the calculation of the hydraulic retention time, the maximum daily capacity can be calculated as follows: Inflowmax =Volume/HRTmin, where Volume is fixed. For example, a minimally allowed HRT of 0.8 d implies an increase of 25 % in the operational daily capacity for a designed treatment time of 1 d. Evaporation Water surface evaporation is calculated by multiplying the potential open-water evaporation rate by the treatment pools’ estimated surface area, and the pools’ live storage volume limits it. Calculating the surface area of the treatment pools is different for intensive and extensive systems. The surface area of an intensive WWTP is defined as the ratio between the plant volume and the pool depth. For that purpose, a simplified representation of a WWTP treatment pool is adopted based on a clarifier design (used during both primary and secondary treatment; Pescod, 1992), and the pool depth is estimated to be 6 m (WEF, 2005; see Fig. B1). Extensive systems are modeled as natural biological treatment ponds, alternately filling up and treating water. These processes consist of a relatively short anaerobic treatment in deeper ponds that is followed by a long-term (20–40 d) residence in facultative shallow ponds (see Fig. 1b; also refer to Pescod, 1992). Unlike intensive systems, treatment ponds in extensive systems may remain empty for long periods. As evaporation is simulated at the pond level, it considers only ponds with positive water storage. Equation (2): calculation of the surface area of extensive treatment systems. The surface area of extensive treatment systems is calculated as follows: Asj=1 Depthj ×VolCapj×TreatTimej TreatPoolj−1.(2) The surface area of each treatment pool is calculated by dividing the pool’s volume by its depth (see Eq. 2; Depth is currently set to 1.5 m, as the depth of a facultative pond; Geosci. Model Dev., 18, 3735–3754, 2025 https://doi.org/10.5194/gmd-18-3735-2025 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM 3739 Pescod, 1992). Each pool’s volume is derived by multiplying the daily capacity (VolCap) by the pool’s filling time. The latter is a function of the designed treatment time (TreatTime) and a predefined number of treatment pools (TreatPool; currently set to two; Pescod, 1992). Although evaporative losses are generally small (see Fig. 4), we allow modelers to change these default technical values to their estimates (see Appendix B). Effluents Treated wastewater (effluent) is discharged into a natural waterbody or sent to reservoirs for reuse. The timing of effluent release differs between intensive and extensive systems. Figure 1b shows the main differences between these two types of systems. In intensive systems, influents remain in the treatment plant throughout the predefined treatment time. For example, for a treatment time of one time step, the effluent volume at time tequals the influent volume minus the evaporation of time t – 1. Extensive systems differentiate between two types of treatment ponds. At each time, one treatment pond receives all inflows; the other pond is either full or empty. Ponds that do not receive inflows and are not empty are considered “active”, i.e., wastewater treatment occurs. Effluents are released from active ponds under either of the following conditions: (a) a predefined treatment time has passed since the active pond stopped receiving inflows or (b) all pools are at full capacity and more influents should be added into the system. In the latter case, the effluents always originated from the active pond that had gone through the longest treatment time, although they may not be fully treated. 2.2.3 Post-treatment The basic module has two post-treatment options: river discharge and reuse. Direct reuse (e.g., for irrigation, industrial, and potable applications) is possible using the CWatM reservoirs and water demand routines. This option requires data on the linkages between WWTPs and reservoirs, representing existing or planned water conveyance systems. The routine iterates over the list of WWTP-reservoir links and attempts to send treated wastewater to associated reservoirs. In the case of multiple recipient reservoirs, the water is split in proportion to the reservoirs’ remaining storage (calculated as remainingStoragei,t =totalVolumei−liveStoragei,t ). Excess water is discharged to predefined overflow locations if all related reservoirs are full. Discharge into streams/rivers is the default behavior if no reservoir is associated by a treatment plant. Finally, untreated wastewater is discharged if a plant’s inflows exceed the plant’s peak capacity (see the minimally allowed HRT in Sect. 2.2.2). Treated wastewater can be managed in a separate reuse system by establishing a set of artificial, off-stream (type4) storage reservoirs. A type-4 reservoir is not connected to the river network and, thus, has no channel-related inflows or outflows. Instead, water inputs include water/wastewater pumping, whereas water outputs are evaporation and pumping. The model combines the two approaches mentioned above, as each WWTP can be linked to one or more reservoirs or can discharge its water directly into a river channel. Indirect reuse can be simulated by releasing the water into a channel; upstream to a lift area, where river water is abstracted and used; or into a reservoir linked to the river network, where effluents are mixed with fresh water. The module is designed to allow interbasin transfers of wastewater or treated wastewater, although this advanced option is not required in the case of a global model. Interbasin transfer of treated wastewater aims to account for cases in which the reuse areas extend beyond the borders of the simulated river basin. In that case, WWTP-specific export-share parameters indicate the daily fixed percentage of treated wastewater transferred for reuse in other basins. Similarly, the interbasin transfer of untreated wastewater represents cases in which treated wastewater collected in one basin is treated in another. This occurs automatically if a defined service area is not associated with any WWTP within the simulated basin. 3 Case study application Israel is located on the eastern coast of the Mediterranean between the latitudes of 29 and 34° N and along the 35° E longitude. Its central coastal and northern regions are governed by a Mediterranean climate (hot and dry summer), its eastern areas are arid due to the rain shadow from its central mountain range, and the southern regions experience a semito hyperarid climate due to their vicinity to the world’s desert belt. During the 1960s, Israel initiated a country-wide water conveyance system (the “National Water Carrier”) to transfer water southwards from the northern Sea of Galilee, allowing rural development and large-scale irrigation in the semiarid Negev region (Tal, 2006). Presently, Israel’s water system is intensively managed and relies primarily on seawater desalination, treated wastewater reuse, and groundwater abstraction. Although it is a nationally managed system, significant regional differences exist in sectoral water provision (Fridman et al., 2021). The Ayalon Basin is located in Israel and the West Bank and stretches 815 km2between the western slopes of the Judaean Mountains and the Mediterranean coastal zone. A few kilometers inland, the Ayalon River spills into the Yarkon (see Fig. 2). Ayalon is an urbanized river basin partially overlaying the Tel Aviv metropolitan area downstream and the city of Modi’in-Maccabim-Re’ut in its middle segment. Downstream urban areas result in considerable water demand, vast runoff from sealed areas, and a high rate of wastewater generation. Upstream, the landscape of the Ayhttps://doi.org/10.5194/gmd-18-3735-2025 Geosci. Model Dev., 18, 3735–3754, 2025 3740 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM alon Basin is predominantly a rural mosaic of open areas and small settlements. Patches of irrigated agriculture and forests are primarily found in the southeastern parts of the basin. Ayalon is a seasonal river originating in the southeastern part of the basin. An artificial “horseshoe-shaped” reservoir (“Mishmar Ayalon”) regulates its flows and maintains relatively fast groundwater recharge. Five main tributaries drain the remaining basin and feed the Ayalon River downstream. An artificial, cemented canal collects the river water before crossing densely populated urban areas downstream. 3.1 Data sources CWatM provides global datasets at a 0.5° and 5 arcmin resolution, as described in Burek et al. (2020). This high-resolution analysis better combines global and local data sources to represent the case study hydrologic processes and human–hydrologic interactions (Hanasaki et al., 2022). Table 1 provides an overview of both the global (e.g., meteorological forcings, soil characteristics, topography, and the river network) and the local (e.g., wastewater treatment and reuse, reservoir networks, aquifer properties, land cover maps, seawater desalination, and water demand) datasets. A complete documentation of the dataset associated with this publication is available at https://doi.org/10.5281/zenodo.12752966 (Fridman et al., 2025). 3.1.1 Groundwater basins and aquifers This case study uses the coupled CWatM–MODFLOW6 model to account for the interface between surface and groundwater hydrology and groundwater dynamics (Guillaumot et al., 2022). The Ayalon Basin lies above two principal groundwater aquifers. The Western Mountain Aquifer is part of the larger Yarkon-Taninim Aquifer system and has two partially separated sub-aquifers reaching a thickness of 600 m. It comprises carbonate sedimentary rocks and has a relatively high but non-homogenous hydraulic conductivity (Wollmann et al., 2009). The slopes of the western Judaean Mountains function as recharge zones, and the top layers in the western foothills are made of chalk and marl and act as an aquitard, confining the Western Mountain Aquifer (see Fig. A1). To the west, the relatively shallow coastal aquifer (thickness up to 200 m) mixes a sandstone aquifer with a clay lens, resulting in varying hydraulic conductivity (Melloul et al., 2006). Data on groundwater abstraction volumes, locations, and the water table changes were unavailable. 3.1.2 Reservoirs We manually identified and digitized reservoirs in the Ayalon Basin using multiple data sources, including georeferenced aerial photography, visual inspection of satellite imagery, fieldwork, and interviews with local water management experts. The biggest reservoir in the Ayalon Basin is Mishmar Ayalon (7.5×106m3; Fig. 2), a seasonal water storage fed by the upstream section of the Ayalon River that regulates downstream flows. The Natuf reservoir is located at a former quarry site northeast of the basin (4.3×106m3) and contributes to groundwater recharge. Four smaller reservoirs constitute the wastewater irrigation infrastructure and have a total designed storage of 634 200 m3. This reuse system extends beyond the basin’s borders, and we account for this by exporting a fraction of the treated wastewater. 3.1.3 Wastewater in the Ayalon Basin Two primary WWTPs collect the wastewater generated in the main cities, and small-scale treatment plants collect that generated by the rural sector. The Shafdan WWTP treats all wastewater generated in the Tel Aviv metropolitan area in the adjacent Sorek River basin, which is outside the scope of this analysis. Later, this water is exported to northwestern Negev for irrigation purposes (Fridman et al., 2021). The Ayalon WWTP is the most significant facility in the basin, with a daily capacity of 81 000 m3. It collects treated wastewater from the cities of Lod and Modi’in-Maccabim-Re’ut (see Fig. 2) as well as their surrounding areas. An extensive treatment plant has existed since 1995, but development and population growth have exceeded its capacity, increasing sewer discharge frequency into the stream. An intensive activated sludge treatment plant with a daily capacity of 54 000 m3 started operating in 2003. However, on some occasions, daily inflow exceeded daily capacity by over 1.5 times (see Table S4). Almost 10 small-scale wastewater treatment plants in the Ayalon Basin are treating sewage at a settlement scale with a total daily capacity of 12 298 m3. 3.2 Setting calibration scenarios and model parameters In this analysis, we simulate the Ayalon Basin hydrology and wastewater treatment and reuse under three scenarios, aiming to explore the effects of the wastewater treatment module’s different modes of operation on model calibration and basinscale water resource management. In the first scenario (S0), we disable the wastewater treatment and reuse module. The second (S1) and third (S2) include wastewater treatment and reuse without and with urban runoff collection, respectively. The share of urban runoff flowing into the sewers is set as a calibration parameter in S2. In this case study, we defined sectoral water allocations to limit wastewater reuse to irrigation, with limited use for livestock purposes. Additional calibration parameters are associated with the evapotranspiration rates of irrigated cropland and grassland, soil depth adjustment, the within-grid-cell soil moisture spatial distribution, the soil hydraulic conductivity and water content at saturation, Manning’s roughness coefficient, the riverbed exchange rate, the urban evaporation coefficient, and the urban infiltration coefficient. The emphasis on the urban landscape Geosci. Model Dev., 18, 3735–3754, 2025 https://doi.org/10.5194/gmd-18-3735-2025 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM 3741 Table 1. Model inputs from global and local datasets. Unless explicitly indicated, all datasets were resampled to 30 arcsec or converted to a raster format. Input data Spatial Temporal Data sources and comments on data processing resolution resolution Global datasets Meteorological forcing 0.5° grid Daily ISIMIP 3a and GSWP3-W5E5 (Lange et al., 2022) Spatiotemporal precipitation and temperature for downscaling 30 arcsec grid Multiyear monthly average WorldClim (Fick and Hijmans, 2017) Soil 30 arcsec grid Fixed value Dai et al. (2019) and Shangguan et al. (2017) Topography 3 arcsec grid Fixed value MERIT digital elevation model (Yamazaki et al., 2017) River network properties – flow direction map 30 arcsec grid Fixed value MERIT Hydro IHU (Eilander et al., 2020) Local/modified datasets Land cover maps 500 m grid Annual Data were sourced from MODIS global land cover between 2001 and 2019 (Friedl and Sulla-Menashe, 2019), OpenStreetMap (urban areas, water, and green spaces; available at https://www.openstreetmap.org, last access: 5 June 2022), the Ministry of Agriculture and Rural Development (MOAG, 2022; cultivated land), and Hamaarag (2017; forest map) Municipal and industrial water demand Local government borders, polygons Annual Data were sourced from the Israel Central Bureau of Statistics (ICBS, 2022) and the Palestinian Central Bureau of Statistics (PCBS, 2022b). A random forest regression imputed missing data for different localities and specific years. Wastewater treatment plant location Point data Fixed value A national dataset was compiled mainly relying on a report by the Israel National Reserve Authority (INRA, 2016) and data from PCBS (2022a). Wastewater treatment plant discharge points (e.g., due to overflow) are fixed to the WWTP location. Wastewater attributes and technical data Tabular format Annual A national dataset was compiled mainly relying on a report by the Israel National Reserve Authority (INRA, 2016) and data from PCBS (2022a). Attributes include wastewater treatment levels and years of operation. Wastewater collection systems Local government borders, polygons Fixed value A national dataset was compiled mainly relying on a report by the Israel National Reserve Authority (INRA, 2016) and data from PCBS (2022a). The data for the wastewater collection systems include service areas, the connection rate, and wastewater generation coefficients. Desalination National value Annual Data include the annual desalination capacity between 2005 and 2019 (Israeli Government portal, 2022). A basin-scale desalination is allocated in proportion to the relative domestic water demand. For example, the national supply of desalinated seawater in 2005 and 2015 was 20×106and 503.4×106m3, respectively. In the same years, the Ayalon desalinated seawater supply was estimated to be 3.4×106and 88 ×106m3. Reservoirs Digitized polygons and attributes Fixed value Reservoirs were manually identified and digitized based on aerial photography and satellite imagery. Depth and volume were assumed based on fieldwork and engagement with water managers. The link between WWTPs and reservoirs is based on INRA (2016). Aquifer delineation Digitized polygons Fixed value Israel Hydrological Services (2014) Aquifer properties – coastal aquifer Digitized polygons Fixed value Melloul et al. (2006) Aquifer properties include porosity and permeability. Aquifer properties – mountain aquifer Digitized polygons Fixed value Wollmann et al. (2009) Aquifer properties include porosity and permeability. https://doi.org/10.5194/gmd-18-3735-2025 Geosci. Model Dev., 18, 3735–3754, 2025 3742 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM Figure 2. The Ayalon Basin case study: land cover and significant water features. Partially uses data from © OpenStreetMap contributors 2022. Distributed under the Open Data Commons Open Database License (ODbL) v1.0. The marked reservoirs are as follows: (1) Ayalon, (2) Mishmar Ayalon, (3) Ta’oz, (4) Mesilat Zion, and (5) Matsli’ah. Publisher’s remark: please note that the above figure contains disputed territories. is due to the relatively high share of built-up areas in the Ayalon Basin (see Fig. 2). We set three more wastewater reuse scenarios apart from the calibration scenarios by expanding the irrigated agriculture area (by 2.5 %) and increasing storage volume (by 5 %) for two reservoirs for which command areas are defined: Ayalon and Matsli’ah. One scenario includes expansion and increased storage, and each of the other scenarios includes expansion or increased storage. 4 Results 4.1 Model validation We have calibrated the Ayalon case study against the daily average discharge at the Ayalon-Ezra gauging station (32.04° N, 34.794° E; Fig. 2) over the period from 1 August 2001 to 30 July 2006, whereas validation was carried out over the period from 1 August 2007 to 31 December 2019. We further compared the simulated evapotranspiration with multiple satellite-derived products (Fig. S7; Mu et al., 2014; Reichle et al., 2022; Rodell et al., 2004) and the simulated monthly influent flows into the Ayalon WWTP with observed data between 2016 and 2019 (Fig. 5; Ayalon Cities Association, 2020, 2021, 2022, 2023). We measure model performance using the Kling–Gupta efficiency (KGE) and Nash– Sutcliffe efficiency (NSE) coefficients (Moriasi et al., 2015). The S2 (wastewater and urban runoff collection) scenario generated the best-performing model (KGE =0.76 and NSE =0.72 during training), followed by S1 (wastewater without urban runoff collection; KGE =0.27 and NSE = 0.61), and S0 (no wastewater; KGE = −0.4 and NSE = 0.57). Model performance was lower during the validation periods across all scenarios. During the validation periods, the complete implementation in scenario S2 also resulted in the best-performing model (KGE2006−2013 =0.69 and KGE2014−2019 =0.55). Over the complete simulation period (1995–2019), the mean observed discharge at the outlet was 0.81 m3s−1, and it was best matched by the simulated discharge in scenario S2 (0.87 m3s−1; see Table 2). The full implementation scenario (S2) best matches the observed discharge during most days in the dry (April–September) and the wet season, as demonstrated in Fig. 3b. Sometimes, the model overestimates discharge or simulates flow events during the dry period (e.g., late April 2003; see Fig. 3c). This overestimation is often associated with a mismatch between forcing data (e.g., precipitation) and actual precipitation (see Fig. S6 and Table S1 in the Supplement). The S2 scenario performs well and captures peak events better when comGeosci. Model Dev., 18, 3735–3754, 2025 https://doi.org/10.5194/gmd-18-3735-2025 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM 3743 pared to the alternative modes of operation. For example, it overestimated the discharge in a peak flow event at the end of February 2003, whereas others underestimated the discharge by over 50 % (see Fig. 3a and b). The simulations were compared with different remotesensing-derived evapotranspiration (RS-ET) time series. All scenarios can capture seasonal dynamics but overestimate ET during early spring (around March–April, except SMAP; see Fig. S7). The “no wastewater” (S0) scenario highly overestimates the ET, whereas the other two (S1 and S2) scenarios better align with the RS-ET data, particularly after 2015. There are differences between the RS-ET datasets associated with process, forcings, and parameterization errors (Zhang et al., 2016); some are shown in Table S2. GLDAS-2.1 shows the lowest KGE across scenarios, whereas SMAP indicates the highest (see Table S3). These findings are consistent with an intercomparison of RS-ET datasets (Kim et al., 2023). Furthermore, activating additional features of the wastewater module improves the simulated ET trends compared with RS-ET datasets. The average KGE values are −0.68 (S0), −0.27 (S1), and −0.17 (S2). Modeling the intermittent Ayalon River case study is challenging, mainly due to its arid climate and small basin area. Under these conditions, even a small deviation in the absolute simulated discharge results in a high relative error. It follows that diverting return flows (i.e., sewage) away from the river was a crucial step in the Ayalon model calibration. Introducing wastewater treatment and reuse into CWatM enables the simulation of actual water dynamics in the Ayalon Basin, resulting in a better-performing model. The respective KGE values of scenarios S0, S1, and S2 between 1995 and 2019 are −0.75, 0.17, and 0.66, and the percentage differences between the simulated and observed average discharge are 162 %, 62 %, and 7 %, respectively (see Table 2). Similar improvement is also shown when comparing simulated and observed discharge between 1995 and 2019 (see Figs. S1– S3 in the Supplement). The improvement due to including the wastewater treatment and reuse module (scenario S1) is associated with reducing the dry season’s baseflow from an average of 0.07 to 0.06 m3s−1. The effects of urban runoff collection were mainly evident in the wet season’s discharge, which was reduced from an average of 2.53 m3s−1(scenario S1) to 1.68 m3s−1(scenario S2). The collection of urban runoff into the sewers reduces flows downstream to urban areas and fits, to some extent, the inflow dynamics into the Ayalon WWTP (see Fig. 5). 4.2 Component and flows of the wastewater module The wastewater flows between different model components are illustrated in Fig. 4 using the water circle concept. A water circle is a simplified depiction of the water cycle within a specific region, component, and time frame. It illustrates the water balance by linking inputs, outputs, and changes in storage while also representing various water sources and uses (Smilovic et al., 2024). Figure 4 presents the wastewater reuse water balance in the Ayalon Basin between 2001 and 2006, totaling 209×106m3yr−1(inputs +outputs +change in storage). Inflows to wastewater treatment plants primarily originated from non-irrigation return flows (labeled as 1 in Fig. 4), consisting mainly of domestic sewage mixed with urban runoff, especially in dual-purpose urban drainage systems. These inflows are based on existing model routines (e.g., water demand and soil; see Fig. 1a) and amount to 104 ×106m3. In the Ayalon Basin case study, the largest share (almost 70 %) of the influents is treated in the Shafdan WWTP outside of the basin of interest (labeled as 2 in Fig 4), while approximately 14 % is sent to reservoirs for reuse, although actual reuse is lower (labeled as 4 in Fig. 4). The gap between the volume of wastewater sent to reservoirs and the actual reuse is associated with evaporation, outflows, and leakage losses (prominent in one of the reservoirs; see Fig. S4). The remaining share includes the discharge of treated wastewater (4 %) and raw sewage (8 %; labeled as 4 in Fig. 4). Evaporative loss from WWTPs is marginal (<4 %) and is represented by one of the unlabeled wedges on the wastewater circle (labeled as 5 in Fig. 4). The annual average wastewater reuse in the Ayalon Basin (2.3×106m3) accounts for almost 10 % of the basins’ irrigation withdrawal (25 ×106m3). In addition, around 71 ×106m3of the wastewater generated in the Tel Aviv metropolitan area (see Fig. S10) ia treated in the Shafdan WWTP (in the Sorek River basin) and reused for irrigation in southern Israel (Fridman et al., 2021). 4.3 Modeling wastewater and urban stormwater collection systems CWatM includes two main hydrological processes for urban areas: return flows (e.g., sewage generation) and urban runoff. These flows are managed by either separated or combined collection and drainage systems. In Israel, two systems are operated separately to collect urban wastewater and stormwater. However, stormwater frequently leaks into the sewers due to illegal urban drainage connections. The runoff collection coefficient allows the user to control the magnitude of system integration. One combined system would have a coefficient of 1, implying that all urban runoff flows into the sewers collection system, whereas a coefficient of 0 suggests two completely separated systems. The calibrated model ended up with a coefficient of 0.78, implying that 78 % of urban runoff flows into the sewers. The advantages of the runoff collection coefficient are shown in Fig. 5, which compares the monthly inflows to the Ayalon WWTP against the simulated inflows with (S2) and without (S1) urban runoff collection. On average, between 2016 and 2019, the Ayalon WWTP accepted 1780 ± 86 ×103m3of sewage every month. The average inflows in the scenarios without and with urban runoff collection are 1562 ±119 and 1699 ±203 ×103m3per month, respechttps://doi.org/10.5194/gmd-18-3735-2025 Geosci. Model Dev., 18, 3735–3754, 2025 3750 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM Appendix A Figure A1 describes the vertical and lateral permeability of the Yarkon-Taninim (YARTAN) and coastal aquifers in Israel. The coastal aquifer forms a relatively narrow stripe stretching north to the south. Next, the Western Mountain Aquifer is located towards the east, showing a relatively diverse permeability. The YARTAN groundwater basin includes the Western Mountain Aquifer but extends far beyond the borders of the Ayalon Basin. Figure A1. Vertical and lateral permeability in the YARTAN and coastal aquifers in the Ayalon Basin and its surroundings. Appendix B The treatment pool depth in an intensive WWTP represents the depth of a clarifier through which sewage flows at different treatment stages. The ratios between the clarifier’s depth and diameter are relatively fixed to optimize the biological treatment of sewage (e.g., biofilm development). A standard design for a clarifier is a relatively deep pool with a sloped bottom, as demonstrated in Fig. B1. In the WTRM, the pool depth is only used to calculate the water surface area and simulate evaporative losses; therefore, we find a simplified representation of the treatment pool with a flat bottom sufficient. In Fig. B1, we convert the sloped-bottom clarifier dimensions (WEF, 2005) to the equivalent pool depth in a flat clarifier, maintaining the pool’s volume. This results in an approximate depth of 6.6 m, which, based on data collected for the Ayalon case study, was rounded to 6 m. We allow modelers to change the pool depth of either intensive, extensive, or both treatment systems using the following settings in the settings file: “pooldepth_intensive” and “pooldepth_extensive”. The respective default settings are hard coded as 6 and 1.5 m, as described in this paper. In addition, to calculate the evaporation from extensive WWTPs, we allow users to change the default value of two treatment pools by adding the “poolsExtensive” to the settings file. Geosci. Model Dev., 18, 3735–3754, 2025 https://doi.org/10.5194/gmd-18-3735-2025 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM 3751 Figure B1. A simplified approach to estimate the wastewater treatment pool depth in an intensive WWTP. Code and data availability. The CWatM code is provided via a GitHub repository (https://github.com/ iiasa/CWatM, Smilovic, 2025), and the model version used for this study (CWatM-Israel v1.08) (https://doi.org/10.5281/zenodo.10044318, Burek et al., 2023) is available from https://doi.org/10.5281/zenodo.13990296 (Fridman, 2024). CWatM’s documentation and tutorials are available from https://cwatm.iiasa.ac.at/ (IIASA, 2025). The input data used for this publication, including model settings and initial conditions files, can be downloaded from https://doi.org/10.5281/zenodo.12752966 (Fridman et al., 2025). Supplement. The supplement related to this article is available online at https://doi.org/10.5194/gmd-18-3735-2025-supplement. Author contributions. DF, MS, and PB developed the module code (wastewater treatment and reuse) and prepared the model input and observation datasets. DF, MS, and PB prepared model inputs. DF performed the simulations and the associated post-processing and prepared the paper with contributions from MS, PB, ST, and TK. MS, ST, and TK acquired the funding and undertook project administration and supervision. Competing interests. The contact author has declared that none of the authors has competing interests. Disclaimer. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the funding organizations. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Acknowledgements. The authors would like to thank the reviewers for their constructive comments. Financial support. This research has received support from the SOS-Water project (grant no. 101059264), funded under the European Union’s Horizon Europe Research and Innovation program, and the GATWIP project, funded by the Innovation and Bridging Grants Fund of IIASA. The work of Dor Fridman was partially funded by the IIASA-Israel postdoctoral program. Review statement. This paper was edited by Thomas B. Wild and reviewed by Huy Dang and two anonymous referees. https://doi.org/10.5194/gmd-18-3735-2025 Geosci. Model Dev., 18, 3735–3754, 2025 3752 D. Fridman et al.: Wastewater matters: incorporating wastewater treatment and reuse into CWatM References Abeshu, G. W., Tian, F., Wild, T., Zhao, M., Turner, S., Chowdhury, A. F. M. K., Vernon, C. R., Hu, H., Zhuang, Y., Hejazi, M., and Li, H.-Y.: Enhancing the representation of water management in global hydrological models, Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, 2023. Angelakis, A., Marecos Do Monter, M. H. 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