Parameter sensitivity analysis plays a critical role in efficiently determining main parameters, enhancing the effectiveness of the estimation of parameters and uncertainty quantification in hydrologic modeling. In this paper, we demonstrate an uncertainty and sensitivity analysis technique for the holistic Soil and Water Assessment Tool (SWAT+) model coupled with new gwflow module, spatially distributed, physically based groundwater flow modeling. The 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 interactions (Nanticoke River, Delaware, Maryland), groundwater pumping for irrigation (Cache River, Missouri, Arkansas) and mountain snowmelt (Arkansas Headwaters, Colorado). The main parameters of the coupled SWAT+gwflow model are estimated utilizing the parameter estimation software PEST. The monthly streamflow of holistic SWAT+gwflow is evaluated based on the 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 and percolation coefficient (for soil water processes). The presence of gwflow parameters permits the recognition of all key parameters in the surface and/or subsurface flow processes, with results substantially differing if the base SWAT+ models are utilized.

The Soil and Water Assessment Tool (SWAT) model is commonly used to predict the impacts of agricultural practices on water quality and quantity. Although widely applied, the data framework used to drive SWAT in the United States (U.S.) is fragmented and inconsistent, varying by user and model interface. This research describes the development of the National Agroecosystems Model (NAM), which provides a unified field to national scale modeling computational framework for research and decision support by using the latest SWAT platform, dubbed SWAT+. NAM has sufficient detail to capture field‐level processes and management actions and spans the full extent of the contiguous U.S. NAM contains 7 million computational units, 4 million of which represent specific cultivated fields. It contains 3 million individually identifiable stream segments and more than 5,000 reservoirs. NAM is intended to serve as a reasonable base framework to be refined for specific applications. This work describes the individual data sources, assumptions, and the processing steps for their inclusion. NAM is constructed with 2,121 individual SWAT+ models which can be executed in a parallel hierarchical structure to dramatically improve runtime. This framework was tested in a case study of the Little River Watershed, Tifton, GA. NAM is developed using only publicly available data sources such that subsets of it can be shared to support research with other government agencies, universities, and others in the public domain.

In this paper we focus on the model setup scheme for medium-size watershed with high resolution, multi-site calibration, and present results on the possible changes of the Minija River in flow, sediment load, total nitrogen (TN), and total phosphorus (TP) load in the near-term (up to 2050) and long-term (up to 2099) in the light of climate change (RCP 4.5 and RCP 8.5 scenarios) under business-as-usual conditions. The SWAT model for the Minija River basin was setup by using the developed Matlab (SWAT-LAB) scripts for a highly customized watershed configuration that addresses the specific needs of the project objective. We performed the watershed delineation by combining sub-basin and hillslope discretization schemes. We defined the HRUs by aggregating the topographic, land use, soil, and administrative unit features of the area. A multisite manual calibration approach was adopted to calibrate and validate the model, achieving good to satisfactory results across different sub-basins of the area for flow, sediments and nutrient loads (TP and TN). After completing the climate change scenario calculations, we found that a net decrease of flow (up to 35%), TN (up to 34%), and TP (up to 50%) loads are projected under both scenarios. Furthermore, we explored the changes in the streamflow composition and provide new insight on the reason of projected nutrient load decrease.

To effectively guide agricultural management planning strategies and policy, it is important to simulate water quantity and quality patterns and to quantify the impact of land use and climate change on soil functions, soil health, and hydrological and other underlying processes. Environmental models that depict alterations in surface and groundwater quality and quantity at the catchment scale require substantial input, particularly concerning movement and retention in the unsaturated zone. Over the past few decades, numerous soil information sources, containing structured data on diverse basic and advanced soil parameters, alongside innovative solutions to estimate missing soil data, have become increasingly available. This study aims to (i) catalogue open-source soil datasets and pedotransfer functions (PTFs) applicable in simulation studies across European catchments; (ii) evaluate the performance of selected PTFs; and (iii) present compiled R scripts proposing estimation solutions to address soil physical, hydraulic, and chemical data needs and gaps in catchment-scale environmental modelling in Europe. Our focus encompassed basic soil properties, bulk density, porosity, albedo, soil erodibility factor, field capacity, wilting point, available water capacity, saturated hydraulic conductivity, and phosphorus content. We aim to recommend widely supported data sources and pioneering prediction methods that maintain physical consistency and present them through streamlined workflows.

During recent years plastics became one of the focuses of EU policy. A harmonisation and comparability of microplastics monitoring results across Europe is needed. The complexity of microplastic data makes it necessary to develop a specific, tailor-made database rather than adapting and modifying one of the existing databases. To meet this demand, we present a publicly accessible, flexible, and extendable structured relational database for particle-based microplastic data. The developed relational database is adaptive and meets the specific demands of microplastics, e.g., a large variety of sampling, processing and analytical methods, many types of plastics, and a very wide size spectrum ranging from micrometres to millimetres. In this paper we discuss the development of the database, data entry specifics, sample analysis methods, microplastics data manipulation and quality assurance, and database integration and accessibility.