In rice areas with shallow aquifers, an evaluation of alternative irrigation strategies should include the interactions between irrigation and groundwater recharge and reuse, which influence the overall irrigation efficiency. A modelling system composed of three sub-models within a MATLAB framework (a physically based, semi-distributed agro-hydrological model and two empirical models, the former for the channel network percolation and the latter for the groundwater level) was applied to a 1000 ha rice district in the Padana Plain, Italy. The calibrated framework estimates the daily time series of the water supply needed and of the groundwater level for a given irrigation management, based on the inputs provided (agro-meteorology, crop data, soil data, irrigation practices, groundwater table depth upstream of the study area). Five irrigation management strategies, relevant to the area, were compared: (i) wet seeding and continuous flooding (WFL), (ii) wet seeding and alternate wetting and drying (AWD), (iii) dry seeding and delayed flooding (DFL), (iv) dry seeding and fixed-turn irrigation FTI), (v) early dry seeding and delayed flooding (DFLearly). Due to economic advantages, dry-seeded techniques (DFL, FTI) are replacing the traditional WFL in northern Italy. Simulations show that dry seeding leads to a drastic decrease of the water table in April/May, reducing the overall irrigation efficiency of the area, and that DFL (widely adopted in the area) also causes a spike in rice irrigation needs in June when other crops increase their water demand, exposing the area to water scarcity. All the cited management strategies are assessed in the paper and AWD turned out to couple smaller irrigation needs (from June onwards) compared to continuous flooding techniques with a maintenance of the groundwater recharge, especially in the first part of the irrigation season, thus being a recommendable rice management alternative for the study area.

Rice‑growing regions underlain by shallow aquifers require irrigation strategies that simultaneously satisfy crop water demand, sustain groundwater recharge, and ensure an appropriate response to the increasingly frequent occurrence of water scarcity in many geographical areas. Traditional approaches, typically based on field‑scale experiments or conceptual water‑balance models, struggle to represent the complex interactions among irrigation practices, soil-water-crop processes, and groundwater dynamics at broader spatial scales.To address this limitation, we developed QGIS‑SWAP‑Paddy, a novel GIS‑integrated modelling framework for simulating lowland agricultural systems across multiple spatial scales. The framework couples a semi-distributed SWAP (https://www.swap.wur.nl/) based agro‑hydrological model with a channel‑network module embedded within the QGIS environment. Simulations are driven by thematic maps and information stored in a GeoPackage database, including soil data, land use, irrigation management, groundwater depth, and agro‑meteorological conditions. This architecture enables the systematic integration of diverse inputs — maps, time series, field measurements, parameter estimates for soil hydraulic properties, crop development, and alternative irrigation management — into a coherent modelling workflow.The framework has been applied to the Lomellina region in northern Italy, located in the largest rice‑growing district in Europe. After calibration and validation, initial applications of QGIS-SWAP-Paddy highlight its capacity to support scenario analyses for contrasting irrigation strategies, including wet seeding with continuous flooding, dry seeding with delayed flooding, and Alternate Wetting and Drying (AWD). The system is designed to be integrated with external models — most notably MODFLOW (https://www.usgs.gov/software/modflow-6-usgs-modular-hydrologic-model) — to simulate groundwater flow and quantify the impacts of irrigation management on aquifer dynamics. This coupling, currently being developed within the PROMEDRICE project (https://promedrice.org/; PRIMA-Section2-2022), will enable comprehensive assessments of water‑reuse mechanisms and groundwater sustainability in rice‑based irrigation systems.QGIS‑SWAP‑Paddy produces both aggregated outputs (e.g., time series of irrigation requirements and deep percolation at domain or sub‑domain scale) and spatially explicit outputs such as maps of first‑aquifer recharge, representing a powerful tool for scientific research and operational decision‑support in lowland irrigated agricultural areas.

Italy is the leading rice producer in Europe, with 92% of its production concentrated in the Piedmont–Lombardy rice basin in Northern Italy. To increase yields, farmers use fertilizers, which can potentially lead to nitrate contamination of groundwater. However, there is limited research in Italy on the extent to which rice production contributes to nitrate pollution. Assessing the environmental impact of rice production is therefore crucial, as water pollution is a major issue for the sustainability of rice-growing regions. This is particularly important as the protection of water from nitrate pollution caused by agricultural activities is a key aspect of European Union legislation, specifically the Nitrates Directive 91/676/EEC. This study focuses on the Lomellina area, a sub-region of the Piedmont–Lombardy rice basin, located on the left bank of the Po River and along the Ticino River. This area has 90.000 ha of irrigated crops out of a total agricultural area of 125,000 ha, of which 70% is devoted to rice cropping. Our objectives were to identify the factors controlling nitrate concentrations and to assess groundwater vulnerability based on the relationship between observed concentrations and statistically significant explanatory variables. Nitrate concentrations were measured in 17 groundwater samples collected from shallow monitoring wells in June 2024 across the Lomellina area. The factors initially considered for deriving the associated explanatory variables to be used in the analysis were: topography, groundwater recharge and table depth, presence of irrigation canals, vertical hydraulic conductivity, and land use. Generalized linear regression (GLR) models were used to assess the relationships between nitrate concentrations and twelve explanatory variables. Covariate selection was done based on Akaike's Information Criterion corrected (AICc) and adjusted R2 values. The best GLR model fit (AICc=140.7 and adjusted R2= 0.73) showed that the most important covariates are: topography, slope, groundwater table depth, vertical hydraulic conductivity, and distance to rice fields. These covariates were statistically significant (at a 0.05 level) except for vertical hydraulic conductivity. Nitrate concentration and slope were negatively correlated, while the other covariates showed positive correlation. Next steps will include investigating and addressing spatial autocorrelation, and build a predictive model and explore the use of machine learning techniques. An improved understanding of the spatially varying relation between nitrate concentrations and influencing factors could be used to produce reliable groundwater vulnerability maps and help to assess the environmental impact of rice cultivation. Our approach highlights the importance of local variability and contributes to discussion on the regional-scale impacts. This study was carried out in the context of the PROMEDRICE project (https://promedrice.org/; PRIMA-Section2–2022) funded, for the Italian partners, by MUR (Italian Ministry of University and Research).

Agricultural Managed Aquifer Recharge (Ag-MAR) has emerged as a sustainable water management technique that utilizes farmland, particularly during fallow periods, to intentionally flood fields and replenish underground aquifers, thereby storing water for future use. Quantifying the benefits of Ag-MAR and evaluating its cost-effectiveness remains challenging due to strong nonlinear interactions between surface–subsurface processes, which generally require numerical modeling approaches that are still poorly established for Ag-MAR systems. This study presents the initial development of a numerical modeling framework to evaluate the effectiveness of different Ag-MAR practices in the Lomellina area (1,250 km2) in Northern Italy, Europe’s largest rice-producing district. In this region, excess surface water is typically available during the winter months (November–February). Although winter flooding of rice fields has been promoted in recent years by the EU Rural Development Programme (CAP) for agronomic and environmental purposes, its hydrological benefits remain largely unquantified. Ag-MAR in the region may occur through deliberate field flooding or infiltration from irrigation canals, yet the relative importance of these pathways and their long-term impacts on groundwater resources are still unknown. The proposed framework couples (a) recharge rates calculated using QGIS-SWAP-Paddy, a semi-distributed version of the SWAP model (https://www.swap.alterra.nl/), implementing also the irrigation/drainage network, and (b) groundwater flow rates calculated using the MODFLOW-6 model (https://www.usgs.gov/software/modflow-6-usgs-modular-hydrologic-model). QGIS-SWAP-Paddy provides transient net percolation rates from agricultural fields and the irrigation canal network, accounting for land-use, soil variability, and irrigation practices throughout the year. MODFLOW-6 uses QGIS-SWAP-Paddy’s percolation rates to simulate seasonal and interannual groundwater dynamics across the domain, including interactions with major rivers (Po, Ticino, and Sesia) and minor surface water bodies. Due to the characteristics of the hydrogeological system, namely, the flooding of vast rice-growing areas and the presence of very shallow groundwater levels, recharge in QGIS-SWAP-Paddy depends on groundwater levels, while the groundwater level simulated by MODFLOW-6 depends on percolation rates estimated in QGIS-SWAP-Paddy. Currently, the two models have been calibrated independently. QGIS-SWAP-Paddy has been calibrated using irrigation discharge data and a reference groundwater level map, interpolated using geostatistical methods, as the lower boundary condition. Conversely, the groundwater flow model, using the recharge rates produced by the QGIS-SWAP-Paddy model as input, was calibrated against observed groundwater levels in various wells from 2018–2020, successfully reproducing observed seasonal fluctuations. The next step is to develop a code that allows the two models to be explicitly coupled monthly to improve the estimation of both percolation rates and the groundwater balance. If necessary, the calibration and validation of the integrated model will therefore be repeated, considering the irrigation discharges delivered to the district and the groundwater levels observed during the period 2018–2020. Once this step has been completed, the model will be ready to simulate Ag-MAR scenarios. This study was carried out in the context of the PROMEDRICE project (https://promedrice.org/; PRIMA-Section2–2022) funded, for the Italian partners, by MUR (Italian Ministry of University and Research).

The main goals of agro-hydrological models are to estimate water fluxes in the soil-plant-atmosphere continuum (SPAC) and to support management and planning of the water resource over specific areas (a field, a farm, an irrigation district, a river catchment, etc.). Hydrological models are ‘physically-based’ (e.g., SWAP, HYDRUS, FLOWS) if they rely on an accurate mathematical description of processes taking place in the physical system, or are ‘conceptual’ (e.g., SWAT/SWAT+, DSSAT, IDRAGRA) when they are based on a simplified schematization of the physical system and the processes involved. The Richards’ equation (Richards, 1931), modified to include root water extraction, is applied by physically-based models to compute soil water movements, requiring the knowledge of the soil water retention, θ(h), and unsaturated conductivity, K(θ), curves, and the use of quite complex and computationally demanding numerical schemes to be solved. On the contrary, conceptual models are based on a bucket/cascade bucket approach to describe water movement into the soil.Conceptual models are often applied for irrigation planning over large areas in a spatially distributed mode, since they require less data and a lower computational effort than physically-based models. However, when considering rice areas, the strong link between irrigation, soil characteristics and groundwater dynamics dictates the need of a rigorous way to compute soil water fluxes, from which the irrigation efficiency depends. This suggests the application of physically-based models or the development of a novel conceptual approach able to describe the effect of compacted soil layers and shallow groundwater conditions on the percolation/capillary rise dynamics.This work presents a simplified soil water balance model for rice areas based on the Darcy equation. This approach allows the quantification of vertical fluxes based on a limited number of data that can be retrieved from existent data sources (depth and thickness of the less conductive layer within the soil profile, saturated soil hydraulic conductivity of this layer, ponding water level inside the paddy field and depth of the groundwater table). The new approach was validated comparing the water fluxes obtained with those achieved through a semi-distributed/mechanistic agro-hydrological model based on SWAP (https://www.swap.alterra.nl/) applied to a pilot rice district of about 1.000 ha in northern Italy (San Giorgio di Lomellina, Pavia).This research was developed in the context of the MEDWATERICE (PRIMA Section2-2018) and RISWAGEST projects (Regione Lombardia, RDP 2014-20).

The implementation of adaptation strategies is becoming increasingly important to mitigate climate-related risks and water resource overuse in agriculture. When considering large spatial domains, the assessment of alternative irrigation techniques can be carried out using a modelling approach, useful to take into consideration all the relevant processes in complex agro-ecosystems.In ‘fully distributed’ models, the domain of interest is divided into simulation units, each characterized by a unique set of parameters and inputs, by using a regular grid. In ‘semi-distributed’ models, simulation units correspond with spatial units of different size and shape but homogeneous in terms of parameters and inputs. Moreover, if the description of processes is based on simplified schematization of the physical system and equations, models are referred as ‘conceptual’, whereas if an accurate physical-mathematical description is adopted, they are considered as ‘physically based’. Because of their complexity and computational requirements, ‘physically based’ models are often applied in a ‘semi-distributed’ manner when describing large territories.A framework is currently under development to directly link a file-based vector database (GeoPackage), describing the main features of an agricultural area, and ‘physically based’ simulations carried out by the SWAP model (https://www.swap.alterra.nl/). The framework, written in Python, runs within the QGIS environment. It requires the user to define seven basic themes: I) a district domain, II) soil types, III) land uses, IV) irrigation water distribution areas, V) homogeneous groundwater depth polygons or groundwater level measuring stations, VI) homogeneous agro-meteorological polygons or agro-meteorological stations, and VII) a DTM raster layer. From the intersection of the layers considered, a number of polygons are generated. Next, the polygons are post-processed based on of the following options: a) aggregate all polygons characterized by the same value of the input themes, b) maintain all the polygons obtained through the intersection operation, or c) aggregate polygons based on a critical distance (meters). This last option is useful to limit the number of polygons and reduce the computational effort. In the case of multiple groundwater level or agro-meteorological measuring stations, the framework calculates the values of the variables to be assigned to each polygon through the ‘Inverse Distance Weighting’ (IDW) algorithm. Finally, the framework links each unit to its parameter set, transferring the information stored in the database into the SWAP input files. Simulation results are saved in a tabular format that allows them to be analyzed according to different aggregations (by land use, soil type, etc.) and to produce time series graphs or vector maps.The application of the tool for the estimation of the irrigation requirements and the percolation fluxes of the Lomellina region (northern Italy) under the current and alternative irrigation strategies will be presented and discussed. The study area, located on the left bank of the Po River, covers more than 125,000 hectares mainly cropped with rice. In more recent years, this area is experiencing water shortages and a reduction in aquifer levels.