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Natural/Small Water Retention Measures (NSWRMs) can help to mitigate conflicts among agricultural, human and environmental water uses. They can significantly contribute to an improved water quality and an increased resilience against floods and droughts. Although potentials for water and nutrient retention are well studied, knowledge on the effectiveness of different measures is still lacking. The EU Horizon2020 project OPTAIN aims to identify efficient techniques for the retention and reuse of water and nutrients in small agricultural catchments and optimize spatial allocation and combination of NSWRMs based on environmental and economic sustainability indicators.The presentation will focus on the advancements in process-based modeling of NSWRMs using the SWAT+ model. In OPTAIN, we established a modeling protocol aiming to harmonize the SWAT+ model setup process to develop reproducible and transparent model setups and to implement a comprehensive, multi-objective and process-oriented calibration strategy involving the use of both soft and hard data. The newly developed COntiguous object COnnectivity Approach (COCOA) is able to represent features in the landscape at the field scale allowing for a more accurate representation of the measures in the model setup. The second advancement concerns a more realistic simulation of long-term dynamics of crops and a more plausible way of planning farm management operations. Preliminary results for two catchments (Upper Zgłowiączka, Poland and Schwarzer Schöps, Germany) illustrate the implementation of the modeling protocol. We demonstrate the reporting of the simulated catchment water balance and dominant runoff processes and show how selected NSWRMs perform under current and future climate., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Parameter Sensitivity analysis plays a critical role in efficiently determining main parameters, enhancing the effectiveness of estimation of parameters, and uncertainty quantification in hydrologic modeling. In this paper, we demonstrate uncertainty and sensitivity analysis technique for the holistic SWAT+ model, coupled with new gwflow module, spatially distributed, physically based groundwater flow modeling. Main calculated groundwater inflows and outflows include boundary exchange, pumping, saturation excess flow, groundwater–surface water exchange, recharge, groundwater–lake exchange, and tile drainage outflow. We present the method for four watersheds located in different areas of the United States for 16 years (2000–2015), emphasizing regions of extensive tile drainage (Winnebago River, Minnesota, Iowa), intensive surface–groundwater interaction (Nanticoke River, Delaware, Maryland), groundwater pumping for irrigation (Cache River, Missouri, Arkansas), and mountain snowmelt (Arkansas Headwaters, Colorado). The main parameters of coupled SWAT+gwflow model are estimated utilizing the parameter estimation software (PEST). The monthly streamflow of holistic SWAT+gwflow is evaluated based Nash–Sutcliffe efficiency index (NSE), percentage bias (PBIAS), determination coefficient (R2), and Kling–Gupta efficiency coefficient (KGE), whereas groundwater head is evaluated using mean absolute error (MAE). The Morris method is employed to identify the key parameters influencing hydrological fluxes. Furthermore, the iterative ensemble smoother (iES) is utilized as a technique for Uncertainty Quantification (UQ) and Parameter Estimation (PE) and to decrease the computational cost owing to the large number of parameters. Depending on the watershed, key identified selected parameters include aquifer specific yield, aquifer hydraulic conductivity, recharge delay, streambed thickness, streambed hydraulic conductivity, area of groundwater inflow to tile, depth of tiles below ground surface, hydraulic conductivity of the drain perimeter, river depth (for groundwater flow processes); runoff curve number (for surface runoff processes); plant uptake compensation factor, soil evaporation compensation factor (for Potential and actual evapotranspiration processes); soil available water capacity, percolation coefficient (for Soil water processes). The presence of gwflow parameters permits for the recognition of all key parameters in the surface/subsurface flow processes, with results substantially differing if the base SWAT+ models are utilized.

The H2020 OPTAIN project involves both, catchment-, and field-scale modelling of the transport of water and nutrients. The catchment-scale modelling is performed at fourteen case study catchments across Europe using the SWAT+ model. At seven OPTAIN case studies, field-scale modelling is applied using the SWAP model. The aim of the SWAP modelling is to provide data on soil water balance elements using a more detailed (at field-scale) soil hydrological model and to cross-validate this data with the relevant fields in SWAT+. As the official manual from the SWAP model developers is rather detailed and complex, the OPTAIN SWAP modelling protocol focuses on practical issues, without overwhelming the modellers with information unnecessary for their case-studies. It also describes new tools, such as rswap, developed within the OPTAIN project for reference data quality check, model calibration and visualisation of the model results., Cite as: Farkas, C., Shore, M., Cuceloglu, G., Czelnai, L., Nemes, A., Szabó, B., Čerkasova, N., Idzelyté, R., Weiland, S. (2022): OPTAIN Modelling Protocols. Part 2. SWAP modelling protocol. Deliverable D4.2/b EU Horizon 2020 OPTAIN Project, Grant agreement No. 862756. DOI: 10.5281/zenodo.7907115

This SWAT+ modelling protocol was designed for guiding model setup development and model calibration in 14 European case studysites participating in the modelling component of the EU funded research and innovation project OPtimal strategies to retAIN and re-use water and nutrients in small agricultural catchments across different soil-climatic regions in Europe (OPTAIN). These 14 case studiesare small agricultural catchments (ranging in size from 21 to 254 km2 ) located in three biogeographical regions of Europe and 12 different countries. The main topic of OPTAIN are Natural/Small Water Retention Measures, which are a relatively new concept. These are small and multi-functional measures for the retention/management of water and nutrients in the landscape, thus addressing drought/flood control, management of water quality problems, climate change adaptation, biodiversity restoration, etc.

Deliverable report D4.1 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) Description of the local conditions in case study areas important for NSWRM implementation. Summary The OPTAIN project aims to identify efficient measures for the retention and reuse of water and nutrients (NSWRM - Natural/Small Water Retention Measures) in small agricultural catchments based on empirical data and scale-adapted integrated modelling approaches. Task 4.1 of the project focused on the analysis of local conditions that are important for model implementation of NSWRM and scenario design. This deliverable reports about the activities of task 4.1, which were completed in three steps: 1) issue identification, 2) possible measure selection and 3) analysis of the possibility of model implementation. Each lead of an OPTAIN case study identified and analysed its major case-study specific issues, and determined the needs for water retention within the catchment based on a questionnaire, public European wide datasets as well as local national datasets (if available). Moreover, the need for water quality improvement has been summarized and the existing yield gap was analysed. The leads of task 4.1 developed a detailed questionnaire which served a dual purpose: 1) the questions and the given answers provided a data/requirement screening, and 2) case study leads and the OPTAIN project consortium gained knowledge and an overview of the local conditions that are important to consider when developing a hydrological and water quality model and analysing the scenario results. Based on the questionnaire results, a preliminary overview about NSWRM implementation in OPTAINs model setups was derived. The final result of this analysis is presented as a matrix, where measure implementation possibility in each case study is assessed. Due to the foreseen data demanding modelling tasks of the OPTAIN project, the final selection of the modelled measures might differ, as new data sources are identified or new measurements are gathered.

Deliverable report D6.1 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) Description of the methods and tools for the harmonised reporting of project outputs. Summary We analysed standards for the common working environment (CWE) and established a platform for project partners' harmonised reporting of project outputs. The results of OPTAINs work packages addressing specific issues of the project (actor platform, measures and indicators, data mining, modelling, optimisation) are assimilated in the following way: Basic project integration is handled through the coordination of all elements of the project. These elements include the project management structure as defined by OPTAIN’s Grant Agreement and Consortium Agreement, Data Management Plan (D8.1), Knowledge Management Plan (MS24), Communication and dissemination strategy (D7.2), and data sharing standards. Cross WP and cross catchment analysis are enabled by harmonising tools, data, and process workflows. Following the ISO 21500:2012 Guidance on Project Management, the Icam DEFinition for Function Modeling (IDEF0) is used as a tool to enable such harmonisation, where database will provide possibilities for cross-Case Study analysis. Exchange of modelling and communication with the public will be coordinated by following the developed protocols, metadata standards via the data-sharing platforms. Selected exchange tools and platforms follow the EU rules on open access to all scientific peer-reviewed publications and research data (Article 29.2 of the Model Grant Agreement) as well as the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. In addition, custom tools were created to semi-automate the generation of metadata files to ease project data and result dissemination. Providing the information base for the OPTAIN Learning Environment is enabled by ensuring that the OPTAIN project consortium utilises the established CWE platform and stores the data (or information) in a joint project database. Since the data, information, resources, and project results are harmonised across all the case studies and work packages, the Learning Environment can utilise these tools or use any database views to produce the necessary data or information. For example, Learning Platforms could utilise Application Programming Interfaces (APIs) to extract the needed data in the required format. The established CWE provides an understanding of the technical and functional background of the knowledge produced in the project and enables communication between project partners and stakeholders at different management levels and provides easy sharing of project results and recommendations. From a technical standpoint, the CWE will bring together data that is shared among partners using a cloud and/or Structured Query Language (SQL) server. Version control of the created tools and models will be handled by utilising the Git software. Data sharing with the public will be performed through Zenodo, a general-purpose open-access repository developed under the European OpenAIRE. Metadata standards will be ensured by creating the metadata Extensible Markup Language (XML) files for every produced geospatial dataset.

The aim of the study is to assess a different approach of SWAT model setup with enhanced HRU definition procedure. The method uses a set of GIS process models based on ArcPy and customizable MATLAB scripts (hence, SWAT-LAB) to produce hydrological response units (HRUs), as defined in SWAT, from a combination of topographic, soil, landuse and administrative unit rasterized datasets. Moreover, the applied approach uses a combination of subbasin and hillslope discretization procedures, with the aim of creating a model setup, which is flexible, can be used for large scale models with substantial amount of subbasins and HRUs. The model input data set assembled, therefore, produce a physically more sound model setup than the subbasin delineation alone would have at an acceptably low increase of the number of land elements (subbasins and HRUs) within the watershed. This method was used to setup, run and calibrate a large-scale transboundary Nemunas River model, consisting of eleven sub-models and link them from upstream to downstream. Nemunas River is the major contributory that discharges into the Curonian Lagoon, which is the largest European coastal lagoon. Nemunas River basin is shared by Belarus, Lithuania, Poland and the Russian Federation Kaliningrad Oblast. The river basin is under nutrient load pressure from different sources in the riparian countries, nevertheless, the burden of improving the water quality of the river falls mainly on Lithuania (as defined by the HELCOM Nutrient Reduction Scheme). Currently, a good measure of confidence was achieved in several calibrated sub-models, representing upstream sub-watershed of the Nemunas River basin (48% of the total watershed area), which are situated outside the territory of Lithuania, for hydrology, sediments and nutrients (TN and TP), thus providing insight of the possible hydrology change and nutrient loads to Lithuania from neighboring countries. This ongoing research will further develop the modeling system and use it for assessing the hydrology, sediments and nutrients of the entire Nemunas River watershed under different land management and climate change scenarios, as well as assessing the best management practices for nutrient load reduction.

Deliverable report D2.1 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) To gather and organise the knowledge collected and created by OPTAIN on NSWRM, the Work Package 2 developed a catalogue of NSWRM. It presents each NSWRM considered in the project and contains all the associated information. The objective of D2.1 is to make qualitative and quantitative information on each specific measure available to various stakeholder groups, like all end users and NSWRM implementers. It enables to allow them to select, design and implement one or more NSWRM on their farm (or territory), or extract specific data and information they need for supporting the development and promotion of NSWRM use. Summary During the first reporting period of the H2020 project OPTAIN, the task 2.1 partners focused on framing the concept of Natural Small Water Retention Measures (NSWRM) and on identifying and documenting existing and underutilised NSWRM in all 14 case studies (CS) of the project. Therefore, a systematic approach was developed involving all relevant stakeholder groups in Multi Actor Reference Groups (MARG), comprising the following steps: identification of existing or potentially suitable measures, prioritisation of measures with a high potential in the local context of the different case studies, selection of a set of 3 to 7 measures per case study, which are relevant for the case studies and the OPTAIN project. Once the NSWRM have been selected, all case study implementers started to collect data on their individual measures and to document them by using the World Overview of Conservation Approaches and Technologies (WOCAT) questionnaire on Sustainable Land Management (SLM) Technologies, thus generating a standardised factsheet of each measure. All entered data on the WOCAT SLM database will then be linked to the Natural Water Retention Measure (NWRM) platform. The OPTAIN catalogue will be accessible from both websites as well as through the project’s own “Learning Environment” which will include a section dedicated to OPTAINs catalogue of NSWRM. Overall, the prioritization in the 14 case studies resulted in 66 selected NSWRM. The case study teams started documenting these selected NSWRM with the World Overview of Conservation Approaches and Technologies (WOCAT) Technology questionnaire, including description and classification, technical specification, implementation inputs and costs, natural and human environment, as well as ecological, socio-economic and socio-cultural impacts. To help in this process, a two-day virtual WOCAT training for all case study teams was organised and conducted by the task 2.1 team. In parallel, the task 2.1 partners conducted an analysis of the commonalities and differences between both WOCAT and NWRM.eu databases to provide an integrated view. The result of this analysis was d that there are only a few differences and a smooth integration could be possible. All entered data on the WOCAT global SLM database (https://qcat.wocat.net) will thus be linked to the NWRM platform (http://nwrm.eu/).

Deliverable report D2.2 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756). List of tailored and case-specific performance indicators (environmental and socio-economical) that help to evaluate the effectiveness of NSWRM monitored (existing measures) and modelled (potential future measures) in the OPTAIN case studies as well as used to establish a common language across project members and activities and to facilitate the knowledge sharing with stakeholders and the wider dissemination of project results. Summary: The content of this deliverable addresses the activities of the task 2.2 “Identification of performance indicators for the selected NSWRM” within the H2020 project OPTAIN. The core purpose of the task is to develop a customized set of indicators that allow assessing the effectiveness of selected (either existing or potential future) Natural/Small Water Retention Measures (NSWRMs) in and across the OPTAIN case studies (CS). The relevance of specific NSWRMs to face local challenges, their multifunctional nature and the manifold impacts they may have in the territory require identifying key elements that easily resume such features, while being flexible and adaptable enough to be used in different contexts. Therefore, task 2.2 elaborated a pathway to produce a list of Performance Indicators (PI), to set the focus for model parametrisation at different scales as well as to ensure an appropriate model setup and utilisation of modelling outcomes (WP4, WP5). For this reason, the screening, selection and tailoring of the most relevant indicators, to be used as PI, have been conducted from both the environmental (EPIs) and socio-economic (SPIs) points of view. The selection process was built at the interface between science and society, in a fruitful process of knowledge co-creation and sharing. As such, agreed lists of indicators can be used to support the harmonized approach of OPTAIN by establishing a common language across project members and activities, favour the understanding and the comparison of modelling results across CS, facilitating the dialogue with stakeholders and the wider dissemination of project results. The methodology followed to outline the customized list of indicators, to be used as PIs, was based upon the initial contribution of scientific / academic partners’ expertise to compile all the potential or candidate indicators and preselect the most relevant ones for the selected NSWRMs. As a result, we ended up with short lists of both environmental (25) and socio-economic (17) indicators that cover the most relevant issues of the OPTAIN case studies. In the second instance a participative approach involved local research teams and stakeholders in the valuation, adjustment and prioritisation of the most important indicators, also owing to the intensive consultation with OPTAINs Multi-Actor-Reference Groups (MARG). Based on the feedback obtained, the task 2.2 partners conducted an analysis of the commonalities and differences between CSs and scales. This allowed drawing the conclusion that, despite CS are experiencing diverse challenges, the most important issues covered by the selected indicators, and priorities given are very similar. Finally, for comparison purposes across CSs, a common set of PIs is proposed, including first discussions on the best way to represent selected PIs based on monitoring and/or modelling results to be available in CS.

Deliverable report D3.1 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756). This report describes the preparation of the bias-corrected RCM simulation data for all case studies as an input to the OPTAIN modeling approaches. Summary: The objective of OPTAINs task 3.2 was to provide bias-corrected regional climate model (RCM) simulation data for all case studies as input to the OPTAIN modelling approaches. For this purpose, we used a common climate database - RCM simulations from the EURO-CORDEX project and the Representative Concentration Pathway (RCP) scenarios 2.6, 4.5 and 8.5. Bias correction was done using ERA5-Land reanalysis data with non-parametric empirical quantile mapping. Moreover, for the field scale modelling the interpolation of gridded bias-corrected climate model simulations to the location of the modelling sites was made using universal kriging. This deliverable D3.1 of the OPTAIN project reports about the procedure of creating the bias-corrected RCM simulation data and provides all necessary background information. The report starts with an introduction, followed by a description of the materials and method, where the process of preparing bias-corrected RCM simulation data is explained in six subchapters, namely: (1) required variables, (2) selection of reference data for bias correction, (3) selection of domains, (4) selection of EURO-CORDEX RCM simulations, (5) bias correction and interpolation of climate simulations and (6) evaluation and analysis. Finally, the last chapter presents an analysis of the results of the bias-correction procedure and the ensemble of RCM simulations. Together with this report, the dataset on climate scenarios for integrated modelling of the OPTAIN case studies was made publicly accessible on ZENODO: [DOI LINK] as a part of the OPTAIN project repository: [ZENODO Community LINK].