Vineyards represent complex Mediterranean agrosystems that deliver significant ecosystem services to society. Yet, many vine-growers still need to assimilate the importance of crop and soil management to the conservation of soil and water resources. The main objective of this study was to evaluate water use and the water balance terms in rainfed and irrigated vineyards in Italy and Portugal, respectively, in both cases aiming at the sustainability of natural resources use. The SIMDualKc model is used for both sites after calibration and validation by fitting soil water content measurements. The Italian case study focused on the impacts of inter-row conservation management in hillslope vineyards while the Portuguese case study analyzed irrigation water management under scarcity in flat vineyards. For the Italian vineyards, the model results focused on the evapotranspiration fluxes and their partition, control of surface runoff, and soil water recharge provided by the inter-row soil management using cover crops. Model results of the Portuguese case study showed the need for improving irrigation water use and the terms of water balance, namely referring to percolation and soil water evaporation. Both case studies further demonstrated the advantages of using computational tools to better cope with climate variability in the Mediterranean region and made evident the benefits of improved crop and soil management practices in counteracting land degradation and valuing the use and conservation of natural resources.

The southern Iberian Peninsula is characterized by evergreen oak woodlands (locally known as montado), which constitute an important savanna-type agro-silvo-pastoral ecosystem. This ecosystem is facing a progressive decline for several reasons, with the foremost being overgrazing. Better management tools are necessary to accurately quantify the systems’ carrying capacity and the sustainable stocking rates that prevent land degradation. The purpose of this study was to determine whether the MOHID-Land model could adequately simulate soil water dynamics and pasture growth in the montado ecosystem. The study area was located in the Alentejo region of southern Portugal. The model successfully simulated soil water contents and aboveground biomass during the 2010–2011 and 2011–2012 growing seasons, producing acceptable errors of the estimates (0.015 ≤ RMSE ≤ 0.026 cm³ cm⁻³; 279 ≤ RMSE ≤ 1286.5 kg ha⁻¹), and relatively high modeling efficiencies (0.481 ≤ EF ≤ 0.882). The model was further used to simulate the same variables for a longer period (1979/2009 seasons), to account for the effect of climate variability on model estimates. Water balance and dry biomass estimates were found to be significantly different between rainfed and irrigated pastures, as well as between the ten driest and ten wettest seasons, with the model responding well to climate variability. The results showed the potential of using the MOHID-Land model for improving pasture management in the montado ecosystem.

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

Hydrological models are increasingly used for studying watershed behavior and its response to past and future events. The main objective of this study was to conduct a sensitivity analysis of the MOHID-Land model and identify the most relevant parameters/processes influencing river flow generation. MOHID-Land is a complex, physically based, three-dimensional model used for catchment-scale applications. A reference simulation was implemented in the Ulla River watershed, northwestern Spain. The sensitivity analysis focused on sixteen parameters/processes influencing water dynamics at that scale. River flow generation was influenced by the resolution of the simulation grid, soil water infiltration, and crop evapotranspiration. Baseflow was affected by soil hydraulic properties, the depth of the soil profile, and the dimensions of the river cross-sections. Peak flows were mostly constrained by Manning’s coefficient in the river network, as well as the dimensions of the river cross-sections. The MOHID-Land model was then used to simulate daily streamflow during a 10-year period (2008−2017). Model simulations were compared against measured data at four hydrometric stations characterizing the natural flow regime of the Ulla River, resulting in coefficients of determination (R²) from 0.56 to 0.85; ratios of the standard deviation of the root mean square error to observation (RSR) between 0.4 and 0.67, and Nash and Sutcliffe model efficiency (NSE) values ranging from 0.55 to 0.84. The MOHID-Land model thus has the capacity to reproduce watershed behavior at a daily scale with reliable accuracy, constituting a powerful tool to improve water governance at the watershed scale.

Hydrological modeling is nowadays critical for evaluating the status, past trends, and future perspectives of water availability at the global, regional, and local scales. The Iberian Peninsula is registering more frequent and severe droughts and water scarcity caused not only by extreme meteorological events, but also by increased demand for water for urban, industrial, and agricultural supplies. Better simulation models are thus needed for accurately quantifying the availability of local water resources. In this study, the natural flow regime in different watersheds of the Iberian Peninsula was simulated using the process-based, fully distributed, MOHID-Land model from 1979 to 2013. Streamflow results were compared with measurements at 73 hydrometric stations not influenced by reservoirs, and with the data available in the management plans of each hydrographic region. The results showed a high dispersion of the goodness-of-fit indicators, with the coefficient of determination (R²) ranging between 0 and 0.91, and the modeling efficiency (NSE) being higher than 0.35 at only 22 (calibration) and 28 (validation) hydrometric stations. Considering the scale of application, results were acceptable but evidenced the difficulties in simulating streamflow in watersheds using a coarse resolution. As such, this paper further deals with the difficulties and challenges of the adopted modeling approach. Nevertheless, this study constitutes a further step towards the more accurate assessment of water resources availability at the Iberian Peninsula scale using process-based modeling.

The success of an irrigation decision support system (DSS) much depends on the reliability of the information provided to farmers. Remote sensing data can expectably help validate that information at the field scale. In this study, the MOHID-Land model, the core engine of the IrrigaSys DSS, was used to simulate the soil water balance in an irrigated vineyard located in southern Portugal during three growing seasons. Modeled actual basal crop coefficients and transpiration rates were then compared with the corresponding estimates derived from the normalized difference vegetation index (NDVI) computed from Sentinel-2 imagery. On one hand, the hydrological model was able to successfully estimate the soil water balance during the monitored seasons, exposing the need for improved irrigation schedules to minimize percolation losses. On the other hand, remote sensing products found correspondence with model outputs despite the conceptual differences between both approaches. With the necessary precautions, those products can be used to complement the information provided to farmers for irrigation of vine crop, further contributing to the regular validation of model estimates in the absence of field datasets.