With the aim of developing a fully coupled atmosphere‐hydrology model system, the Weather Research and Forecasting (WRF) model was enhanced by integrating a new set of hydrologic physics parameterizations accounting for lateral water flow occurring at the land surface. The WRF‐Hydro modeling system was applied for a 3 year long simulation in the Crati River Basin (Southern Italy), where output from the fully coupled WRF/WRF‐Hydro was compared to that provided by original WRF model. Prior to performing coupled land‐atmosphere simulations, the stand‐alone hydrological model (“uncoupled” WRF‐Hydro) was calibrated through an automated procedure and validated using observed meteorological forcing and streamflow data, achieving a Nash‐Sutcliffe Efficiency value of 0.80 for 1 year of simulation. Precipitation, runoff, soil moisture, deep drainage, and land surface heat fluxes were compared between WRF‐only and WRF/WRF‐Hydro simulations and validated additionally with ground‐based observations, a FLUXNET site, and MODIS‐derived LST. Since the main rain events in the study area are mostly dependent on the interactions between the atmosphere and the surrounding Mediterranean Sea, changes in precipitation between modeling experiments were modest. However, redistribution and reinfiltration of local infiltration excess produced higher soil moisture content, lower overall surface runoff, and higher drainage in the fully coupled model. Higher soil moisture values in WRF/WRF‐Hydro slightly influenced precipitation and also increased latent heat fluxes. Overall, the fully coupled model tended to show better performance with respect to observed precipitation while allowing more water to circulate in the modeled regional water cycle thus, ultimately, modifying long‐term hydrological processes at the land surface.

Simulations from 13 highly resolved regional climate models run within the Coordinated Downscaling Experiment initiative at 0.11° resolution with boundary forcings from five different Coupled Model Intercomparison Project Phase 5 global models are employed to derive future climate change signal for the Greater Alpine Region (GAR) and four smaller investigation areas. Evaluation statistics include mean temperature and precipitation, frequency of days with precipitation over 1 mm and over 15 mm, 90% quantile of the frequency distribution, and maximum number of consecutive dry days. The evaluation for the period from 1971 to 2000 indicates that the models reproduce spatial seasonal precipitation patterns. In general, the simulations underestimate the seasonal mean temperature and overestimate the mean precipitation values. In GAR the ensemble seasonal mean temperature bias ranges from −0.8 to −1.9°C. The bias in precipitation varies between +14.8% in summer and +41.6% in the winter season. Larger errors are found for other statistics and in the investigated regions. In general, no significant gains in the quality of reproduction of the observed precipitation and temperature statistics compared to previous experiments can be identified. The temperature calculations for 2071–2100 related to the period from 1971 to 2000 in the GAR area show ensemble mean increases in the seasonal mean 2 m temperature of 2.5°C in fall and winter, 2.4°C in summer, and 1.9°C in spring. In the same area, precipitation is simulated to increase up to 12.3% in winter and 5.7% in spring. Only minor changes of the ensemble mean are predicted with +2.3% in fall and −1.7% in summer.

The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases, and energy. Nonlinear feedback and scale-dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local-area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identically calibrated parameter settings for the land surface model (Noah-Multiparametrization; Noah-MP). The simulations are evaluated based on extensive observations at the Terrestrial Environmental Observatories (TERENO) Pre-Alpine Observatory for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a 5-month period (June–October 2016). The sensitivity of seven land surface parameters is tested using the Latin-Hypercube–One-factor-At-a-Time (LH-OAT) method, and six sensitive parameters are subsequently optimized for six different subcatchments, using the model-independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash–Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of the classic WRF and fully coupled WRF-Hydro models, both using the calibrated parameters from the offline model, shows only tiny alterations for radiation and precipitation but considerable changes for moisture and heat fluxes. By comparison with TERENO Pre-Alpine Observatory measurements, the fully coupled model slightly outperforms the classic WRF model with respect to evapotranspiration, sensible and ground heat flux, the near-surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation, whereas soil moisture and precipitation change randomly.

The Hargreaves equation (HE) is the most reliable and diffused temperature-based method for the estimate of the reference crop evapotranspiration ${\mathrm{ET}}_o$ in areas with limited meteorological data. Several studies proposed its further enhancement by adopting correction equations for recalibrating its parameters, specifically the empirical Hargreaves coefficient, $C=0.0023$ . In this paper, a local recalibration for southern Italy is shown, taking as a reference the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith (PM) equation applied over 137 (34 coastal and 103 inland) meteorological stations, allowing the estimation of 357,920 daily ${\mathrm{ET}}_o$ values in the period 1994–2011. Through a regression analysis, the adjusted Hargreaves coefficients (AHC), minimizing the errors for each individual station between the HE and the FAO Penman-Monteith equation, and providing better performances than the original HE applied at the monthly scale, were correlated to the available daily meteorological data; reliable regionalization equations of the AHCs were found determined by either the average daily temperature range $\overline{\mathrm{\Delta }T}$ or the ratio between the average daily temperature $\overline{T}$ and $\overline{\mathrm{\Delta }T}$ , with similar coefficients to analogous relationships proposed in the literature. Specifically, the $\overline{\mathrm{\Delta }T}$ dependent model proved to be very reliable over the whole study area for all climate types, from the shore up to 1,928 m. The improvement in the performances of the regional model with respect to the original HE was on average 22%, and it also proved able to simulate the yearly cumulative ${\mathrm{ET}}_o$ evolution in several meteorological stations. The strength of the model was positively tested through a jackknife resampling procedure and a sensitivity analysis, taking into account possible trends for the average daily temperature range.