The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

Optimal control of reservoirs is a challenging task due to conflicting objectives, complex system structure, and uncertainties in the system. Real time control decisions suffer from streamflow forecast uncertainty. This study aims to use Probabilistic Streamflow Forecasts (PSFs) having a lead-time up to 48 h as input for the recurrent reservoir operation problem. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). Deterministic Streamflow Forecasts (DSFs) are provided by applying random perturbations on perfect data. PSFs are synthetically generated from DSFs by a new approach which explicitly presents dynamic uncertainty evolution. We assessed different variables in the generation of stochasticity and compared the results using different scenarios. The developed real-time hourly flood control was applied to a test case which had limited reservoir storage and restricted downstream condition. According to hindcasting closed-loop experiment results, TB-MPC outperforms the deterministic counterpart in terms of decreased downstream flood risk according to different independent forecast scenarios. TB-MPC was also tested considering different number of tree branches, forecast horizons, and different inflow conditions. We conclude that using synthetic PSFs in TB-MPC can provide more robust solutions against forecast uncertainty by resolution of uncertainty in trees.

Modeling streamflows is challenging in snow dominated high altitude regions due to limited observations, harsh topographic conditions and complex snow physics. Different uncertainties arise from multiple sources in modeling and forecasting. The uncertainties of the initial conditions are mainly tackled with data assimilation techniques. On the other hand, the uncertainty of the model structure should also be considered since assimilation techniques can only use same model and parameter sets in each implementation. Generally, this uncertainty can be taken into account using multi-modelling methods that can produce ensemble set of parameters. In order to make use of this approach, this study aims the realization of a novel method that generates a probabilistic estimate of initial states using a multi-parametric modelling method with deterministic Variational Data Assimilation, as referred to the multi-parametric variational data assimilation, MP-VarDA.  The study is accomplished for runoff predictions over the mountainous Eastern part of Turkey concerning the importance of snowmelt and the limited availability of observed data. The model pool is generated with Generalized Likelihood Uncertainty Estimation (GLUE) method with a calibrated hydrological model using HBV. The implementation of MP-VarDA assimilates both discharge and satellite snow observations on snow cover. The preliminary results having 3 model instances are promising to set a model pool for MP-VarDA method which can reduce model uncertainty. The model is also tested via hindcast experiments under close-loop mode in order to assimilate discharge and satellite snow data, and model results showed that runoff and snow state predictions are improved compared to conditional assimilation techniques.

Gauges modify wind fields, producing important undercatch in solid precipitation.  For this reason, solid precipitation measurements show significant bias with respect to real values, especially under windy conditions. In this work we propose a methodology that combines geostatistical and hydrological models to perform a preliminary assessment of global undercatch and precipitation patterns (distribution between solid and liquid phase and spatial gradient with elevation) in alpine regions. It is based on the available information about daily natural streamflow and daily climatic data (precipitation and temperature) in the catchment. We want to analyse long time periods in order to take into account the stochastic behaviour of natural streamflow and climatic variables. A preliminary assessment of temperature and precipitation fields is performed by applying various geostatistical approaches assuming some hypothesis about the relationship between climatic fields and altitude. The generated fields are then employed as inputs of conceptual hydrological models, which includes two parameters to correct the solid and liquid precipitation, respectively. We have considered different hydrological approaches (SRM, HBV and a Témez model with a simple degree-day approach). The parameters are calibrated by minimizing the difference between the simulated and historical natural streamflows and/or snow cover area. It allows us to identify the best combination of geostatistical and hydrological models to approximate streamflow, to perform a global preliminary assessment of the undercatch of solid and liquid precipitation and their precipitation patterns by analysing spatial gradients with elevation. The methodology was applied in the Canales Basin, an alpine catchment of the Sierra Nevada (Spain).This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad) and by the GeoE.171.008-TACTIC project from GeoERA organization funded by European Union’s Horizon 2020 research and innovation program