The uncertainty in a hydrological model, due to its structure or implemented input parameters, affects the accuracy of simulations that are usually used for important applications such as drought predictions, flood risk assessment, irrigation scheduling, ground water recharge and contamination. Several models describing soil infiltration processes have been developed. Some are analytical, while others implement numerical solutions of the Richards’ equation. The objective of this work was to assess the impact of infiltration process modeling on soil water content simulations. For this study, different infiltration models were included within FEST-WB (Flash Flood Event-based Spatially-distributed rainfall-runoff Transformations-Water Balance) distributed hydrological model (SCS-CN, Green and Ampt, Philip and Ross solution). Performances of implemented infiltration models in simulating soil water content were evaluated against observations acquired in the experimental site located in a maize field in northern Italy. Soil water content was monitored together with continuous measurements of meteorological data. A sensitivity analysis was performed to assess the most important parameters governing infiltration process in the different models tested. A comparison of soil water content simulations show that Ross solution allowed the description of soil moisture variation along the vertical, but simpler lumped models provide sufficient accuracy when properly calibrated.

A comparative assessment of the estimates of the saturated hydraulic conductivity of two anthropogenic soils and their impact on hydrological model simulations | Mouna Feki, Giovanni Ravazzani, Stefano Barontini, Alessandro Ceppi, Marco Mancini | Agricultural Journals

Integrated water resources management at the catchment scale, considering the full water cycle as manageable, is a primary approach to improve water use efficiency and promote sustainable water management solutions. To this purpose, advanced modelling tools are required to quantify the physical and economic effects of alternative land management options. This work presents an application of a spatially distributed physically based hydrological model to the Bonis experimental watershed located in the mountain area of Sila Greca (southern Italy). Different infiltration models were tested to better reproduce discharge observations at basin outlet. The model will be used for evaluating different land use/management scenarios, combined with climate change forcing, to quantify the effect of alternative management options on the land-water cycle. This work is part of the INNOMED project (Innovative Options for Integrated Water Resources Management in the Mediterranean) funded by ERA-NET COFUND WATERWORKS 2015 call.

The concept of integrated forest management offers a framework for understanding how forest ecosystem services interact with efforts to conserve natural resources. Forests face various disturbances stemming from human activities, management approaches, and shifts in climate patterns. This study aims to explore how forested watersheds respond to diverse silvicultural practices amidst changing climate conditions. The research is centered in the Bonis catchment, situated in the mountainous region of Sila Greca (latitude 39°25′15″ N, longitude 16°12′38″ W) within Southern Italy’s Calabria region. Nearly 93% of the catchment area is cloaked in a forest dominated by approximately 50-year-old Pinus laricio Poiret stands. To model the catchment’s response to various climate and management scenarios, the FEST-FOREST eco-hydrological model, which is distributed and based on physical principles, has been employed. This model accounts for the dynamic interactions between vegetation and the watershed’s hydrological processes. The monitoring of the basin has been ongoing since 1986, with runoff measurements collected at the catchment outlet using dedicated gauging structures. These data have been utilized to calibrate and validate the model, ensuring its accuracy in simulating future scenarios. These simulation results offer stakeholders some qualitative and scientifically based recommendations for the sustainable management of the catchment. In fact, thinning intensity affects hydrological processes, with a 50% stand density reduction identified as a threshold for significant impact on processes like rainfall partitioning and evapotranspiration. Under heavy thinning scenarios, runoff can change by over 60%, and the impact decreases with larger thinning intervals. Furthermore, different climate scenarios influence stem yield levels, with higher production under RCP 4.5 and RCP 8.5 compared to the base climate scenario. In particular, the RCP 8.5 scenario produces the highest yield due to better forest growth under different climate scenarios. This implies the idea that in regions with a Mediterranean climate and coniferous forests, amidst climate change, meticulous forest management involving precisely calibrated thinning schedules and intensities, tailored to unique biotic and abiotic factors, could potentially enhance carbon sequestration while positively influencing runoff rates.

In this paper, the FEST-FOREST model is presented. A FOREST module is written in the FORTRAN-90 programming language, and was included in the FEST-WB distributed hydrological model delivering the FEST-FOREST model. FEST-FOREST is a process-based dynamic model allowing the simulation at daily basis of gross primary production (GPP) and net primary production (NPP) together with the carbon allocation of a homogeneous population of trees (same age, same species). The model was implemented based on different equations from literature, commonly used in Eco-hydrological models. This model was developed within the framework of the INNOMED project co-funded under the ERA-NET WaterWorks2015 Call of the European Commission. The aim behind the implementation of the model was to simulate in a simplified mode the forest growth under different climate change and management scenarios, together with the impact on the water balance at the catchment. On a first application of the model, the results are considered very promising when compared to field measured data.