Evapotranspiration (ET) is a key variable in the hydrological cycle and it directly impacts the surface balance and its accurate assessment is essential for a correct water management. ET is difficult to measure, since the existing methods for its direct estimate, such as the weighing lysimeter or the eddy-covariance system, are often expensive and require well-trained research personnel. To overcome this limit, different authors developed experimental models for indirect estimation of ET. However, since the accuracy of ET prediction is crucial from different points of view, the continuous search for more and more precise modeling approaches is encouraged. In light of this, the aim of the present work is to test the efficiency in predicting ET fluxes in a newly introduced physical-based model, named Prospero, which is based on the ability to compute the ET using a multi-layer canopy model, solving the energy balance both for the sunlight and shadow vegetation, extending the recently developed Schymanski and Or method to canopy level. Additionally, Prospero is able to compute the actual ET using a Jarvis-like model. The model is integrated as a component in the hydrological modelling system GEOframe. Its estimates were validated against observed data from five Eddy covariance (EC) sites with different climatic conditions and the same vegetation cover. Then, its performances were compared with those of two already consolidated models, the Priestley–Taylor model and Penman FAO model, using four goodness-of-fit indices. Subsequently a calibration of the three methods has been carried out using LUCA calibration within GEOframe, with the purpose of prediction errors. The results showed that Prospero is more accurate and precise with respect to the other two models, even if no calibrations were performed, with better performances in dry climatic conditions. In addition, Prospero model turned to be the least affected by the calibration procedure and, therefore, it can be effectively also used in a context of data scarcity.

Many efforts have been made in recent decades to formulate strategies for improving the efficiency of water distribution systems (WDS), led by the socio-demographic evolution of modern society and the climate change scenario. The improvement of WDS management is a complex task that can be addressed by providing services to maximize revenues while ensuring that the quality standards required by national and international regulations are upheld. These two objectives can be fulfilled by utilizing optimized techniques for the operational and maintenance strategies of WDS. This paper proposes a methodology for assisting engineers in identifying water leakages in WDS, thus providing an effective procedure for ensuring high level hydraulic network functionality. The proposed approach is based on an inverse analysis of measured flow rates and pressure data, and consists of three steps: The analysis of measurements to select the most suitable period for leakage identification, the localization of the best measurement points based on a correlation analysis, and leakage identification with a hybrid optimization that combines the exploration capability of the differential evolution algorithm with the rapid convergence of particle swarm optimization. The proposed procedure is validated on a reference hydraulic network, known as the Apulian network.

At the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non‐interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post‐processed within the Consortium for Small‐scale Modeling‐Limited Area Model Italia (COSMO‐LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium‐Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC‐based post‐processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended‐resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi‐layer maps.

This work presents a software package for the interpolation of climatological variables, such as temperature and precipitation, using kriging techniques. The purposes of the paper are (1) to present a geostatistical software that is easy to use and easy to plug in to a hydrological model; (2) to provide a practical example of an accurately designed software from the perspective of reproducible research; and (3) to demonstrate the goodness of the results of the software and so have a reliable alternative to other, more traditional tools. A total of 11 types of theoretical semivariograms and four types of kriging were implemented and gathered into Object Modeling System-compliant components. The package provides real-time optimization for semivariogram and kriging parameters. The software was tested using a year's worth of hourly temperature readings and a rain storm event (11 h) recorded in 2008 and retrieved from 97 meteorological stations in the Isarco River basin, Italy. For both the variables, good interpolation results were obtained and then compared to the results from the R package gstat.

This contribution illustrates the GEOframe Soil Plant Atmosphere Continuum Estimator (GEOSPACE). It is the ecohydrological model of the GEOframe system and it wants to simulate the soil-vegetation-atmosphere interactions to study and analyze the complex processes that occur in the Earth Critical Zone (CZ). The CZ is defined as the “heterogeneous, near surface environment in which complex interactions involving rock, soil, water, air, and living organism regulate the natural habitat and determine the availability of life-sustaining resources”.GEOSPACE is a coupled model in which the three major components are WHETGEO, GEO-ET and BrokerGEO. WHETGEO, Water Heat and Transport in GEOframe, (Tubini N. and Rigon R., 2021), solves the conservative form of Richardson-Richards equation using the Newton-Casulli-Zanolli algorithm (Casulli V. and Zanolli P., 2010) that guarantees the convergence at any time step, and the proper transition from unsaturated condition to saturated one. Besides it deals seamlessly the surface water ponding. WHETGEO also implements the numerical solution shown in Casulli and Zanolli (2005) to solve the advection-dispersion equation and describe the solute transport. GEO-ET, EvapoTranspiration in GEOframe, computes evapotranspiration according to three different formulations, the Priestley-Taylor model, Penman FAO model and GEOframe-Prospero model (Bottazzi, 2020), by considering Jarvis model (Macfarlane et al., 2004) and Ball-Berry-Leuning model (Lin et al., 2015) to compute environmental and water stress factors. BrokerGEO is the coupler component that allow the exchange of data between the other two components in memory and considers the root water uptake for the computation of the actual evapotranspiration. The GEOSPACE model was tested with the lysimeter of the “Spike II” experiment (Nehemy et al., 2019; Benettin et al., 2021) of the Ecole Polytechnic Federal de Lausanne. The analysis we carried out with GEOSPACE concern the flux partitioning of precipitation and irrigation water into evaporation and transpiration; the soil water and groundwater storage; the transport of water stable isotopes through the soil. In this research we present them and show how GEOSPACE can be used to test hypotheses on the links between the plant water status and its isotopic signatures.GEOSPACE is developed in Java using the Object-Oriented programming paradigm and it is completely open source, available on the GEOframe GitHub website. The code organization and its functionalities besides solving the hydrological issues are designed according to principle of open science to be inspectable and verified by third parties.

The Digital Earth (DE) metaphor is very useful for both end users and for hydrological modellers. However, in literature it can promote the erroneous view of models as a commodity, that is a basic good used in commerce that is interchangeable with other goods of the same type, without any warning about the fact that some models work better than others, some models just work while others can be simply wrong. These distinctions are at the core of doing good science. This opinion contribution, on the one hand, tries to accept the challenge of adopting models as commodities but, on the other, it wants to show that this acceptance comes with some consequences as to how models must be structured.  The first reuirement is that Digital eARth Twin Hydrology system (called DARTH) need to be Open Source and built with Open Science rules in mind. We analyse different categories of models, with the view of making them part of a . We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate infrastructure that provides some basic services for connection to input data and allows for a modelling-by-components strategy, which, we  argue, is the right one for accomplishing the requirements of the DE. The need to tie predictions to an estimated confidence interval is also supported. Finally, it is argued that DARTHs must promote a new participatory way of doing hydrological science, where researchers can contribute cooperatively to characterize and control model outcomes in various territories. Furthermore, this has consequences for the engineering of the systems.