Conceptual hydrological models are preferable for real-time flood forecasting, among which the Xin'anjiang (XAJ) model has been widely applied in humid and semi-humid regions of China. Although the relatively simple mass balance scheme ensures a good performance of runoff simulation during flood events, the model still has some defects. Previous studies have confirmed the importance of evapotranspiration (ET) and soil moisture content (SMC) in runoff simulation. In order to add more constraints to the original XAJ model, an energy balance scheme suitable for the XAJ model was developed and coupled with the original mass balance scheme of the XAJ model. The detailed parameterizations of the improved model, XAJ-EB, are presented in the first part of this paper. XAJ-EB employs various meteorological forcing and remote sensing data as input, simulating ET and runoff yield using a more physically based mass–energy balance scheme. In particular, the energy balance is solved by determining the representative equilibrium temperature (RET), which is comparable to land surface temperature (LST). The XAJ-EB was evaluated in the Lushui catchment situated in the middle reach of the Yangtze River basin for the period between 2004 and 2007. Validation using ground-measured runoff data proves that the XAJ-EB is capable of reproducing runoff comparable to the original XAJ model. Additionally, RET simulated by XAJ-EB agreed well with moderate resolution imaging spectroradiometer (MODIS)-retrieved LST, which further confirms that the model is able to simulate the mass–energy balance since LST reflects the interactions among various processes. The validation results prove that the XAJ-EB model has superior performance compared with the XAJ model and also extends its applicability.

Distributed hydrological models of energy and mass balance need as inputs many soil and vegetation parameters, which are usually difficult to define. This paper will try to approach this problem by performing a pixel to pixel calibration procedure of soil hydraulic and vegetation parameters based on satellite land surface temperature data as a complementary method to the traditional calibration with ground discharge measurements at river control cross sections. These analyses are performed for the upper Po River basin (Italy) closed at the river cross section of Ponte della Becca with a total catchment area of about 38 000 km2, for a calibration period from 2000 to 2003, and a validation period from 2004 to 2010. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data and a distributed hydrological model, Flash-Flood Event-Based Spatially Distributed Rainfall-Runoff Transformation Energy Water Balance model (FEST-EWB), that solves the system of energy and mass balance equations as a function of the representative equilibrium temperature will be used. This equilibrium surface temperature is comparable to the land surface temperature as retrieved from operational remote sensing data. Results suggest that a combined calibration based on satellite land surface temperature and ground discharge is needed to correctly reproduce volume discharge and also spatially distributed maps of representative equilibrium temperature and evapotranspiration. Improvements of about 10 mm/8 days are obtained on evapotranspiration from the model calibrated with Q and land surface temperature (LST) respect to the calibration based only on discharge.

The feasibility of combining remotely sensed land surface temperature data (LST) and an energy–water balance model for improving evapotranspiration estimates over time distributed in space in the Capitanata irrigation consortium is analysed. The energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parametres with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration. The FEST-EWB model was run at 30 m of spatial resolution for the period between 2013 and 2018. Absolute errors of 2.5 °C were obtained for LST estimates against satellite data; while RMSE around 0.06 and 40 Wm⁻² are found for ground measured soil moisture and latent heat flux, respectively.

The agricultural sector, the largest and least efficient water user, is facing important challenges in sustaining food production and careful water use. The objective of this study is to improve farm and irrigation district water use efficiency by developing an operational procedure for smart irrigation and optimizing the exact water use and relative water productivity. The SIM (smart irrigation monitoring and forecasting) optimization irrigation strategy, based on soil moisture (SM) and crop stress thresholds, was implemented in the Chiese (North Italy) and Capitanata (South Italy) Irrigation Consortia. The system is based on the energy–water balance model FEST-EWB (Flashflood Event-based Spatially distributed rainfall runoff Transformation Energy–Water Balance model), which was pixelwise calibrated with remotely sensed land surface temperature (LST), with mean areal absolute errors of approximately 3 °C, and then validated against local measured SM and latent heat flux (LE) with RMSE values of approximately 0.07 and 40 Wm⁻², respectively. The effect of the optimization strategy was evaluated on the reductions in irrigation volume and on the different timing, from approximately 500 mm over the crop season in the Capitanata area to approximately 1000 mm in the Chiese district, as well as on cumulated drainage and ET fluxes. The irrigation water use efficiency (IWUE) indicator appears to be higher when applying the SIM strategy than when applying the traditional irrigation strategy: greater than 35% for the tomato fields in southern Italy and 80% for maize fields in northern Italy.

Remote sensing data of canopy cover and leaf area index are used together with the AquaCrop model to optimize irrigation water use efficiency for tomato and maize fields across Italy, which differ in climate, soil types and irrigation technique. An optimization irrigation strategy, “SIM strategy”, is developed based on crop stress thresholds and then applied to all the analyzed fields in different crop seasons, evaluating the effect not only on irrigation volume and number of irrigations but also on crop yield and canopy cover, and on the drainage flux which represents the main water loss. Irrigation volume reduction is found to be between 200 and 1000 mm, mainly depending on the different soil types within the climate, irrigation technique and crop type. This is directly related to the drainage flux reduction which is of a similar entity. The SIM strategy efficiency has then been quantified by different indicators, such as the irrigation water use efficiency (IWUE) which is higher than with the observed irrigations (around 35% for tomato fields in Southern Italy, between 30 and 80% for maize in Northern Italy), and the percolation deficit and irrigation efficiency. The AquaCrop model has been previously calibrated against canopy cover and leaf area index (LAI) data, producing errors between 0.7 and 5%, while absolute mean errors (MAE) between 0.015 and 0.04 are obtained for soil moisture (SM). The validation of the AquaCrop model has been performed against evapotranspiration (ET) ground-measured data and crop yields producing MAE values ranging from 0.3 to 0.9 mm/day, and 0.9 ton/ha for maize and 10 ton/ha for tomatoes, respectively.

A new methodology is proposed for the calibration of distributed hydrological models at the basin scale by constraining an internal model variable using satellite data of land surface temperature (LST). The model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature that governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is compared to operational satellite LST, while calibrating soil hydraulic parameters and vegetation variables differently in each pixel, minimizing the errors. This procedure is compared to the traditional calibration using only discharge measurements. The distributed energy water balance model, Flash-flood Event-based Spatially-distributed rainfall–runoff Transformation – Energy Water Balance model (FEST-EWB), is used to test this approach. This methodology is applied to the Upper Yangtze River basin (China) using MODIS LST retrieved from satellite data in the framework of the NRSCC-ESA DRAGON-2 Programme. The calibration procedure based on LST seems to outperform the calibration based on discharge, with lower relative error and higher Nash-Sutcliffe efficiency index on cumulated volume.