Present and Future High-Resolution Climate Forcings over Semiarid Catchments: Case of the Tensift (Morocco)

In semiarid areas, the climate is characterized by strong spatiotemporal variability while the meteorological ground network is often very sparse. In this context, the spatial distribution of meteorological variables is thus a real issue for watershed hydrology, agronomy and the study of surface–atmosphere retroaction in these regions. The aim of this study is twofold: (1) to evaluate and to adapt a reanalysis system “Système d’Analyse Fournissant des Renseignements Adaptés à la Nivologie” (SAFRAN) to map the meteorological variables on the Tensift catchment (Morocco) between 2004 and 2014; (2) to project temperature and precipitation for the 2041–2060 horizon at high-resolution based on the Euro-CORDEX database at 12 km resolution (using two Representative Concentration Pathway -RCPs- scenarios and four Regional Climate Models), on the SAFRAN reanalysis and on a network of meteorological stations. SAFRAN was assessed: (1) based on leave-one-out for a station located in the plain and another in the mountains; (2) by comparison to another re-analysis system named the Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet); (3) by comparison to in situ measurements of snowfall at one station and to the daily Snow Cover Area derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) product at the catchment scale. The evaluation of the SAFRAN reanalysis showed that an irregular grid up to 1 km resolution is better for reproducing meteorological variables than the regular version of SAFRAN at 8 km, especially in mountains. The projection of the SAFRAN forcing is conducted in three steps corresponding to the three subsections below: (1) disaggregation of the Euro-CORDEX climate scenarios using the Q–Q approach based on stations data; (2) computation of the spatialized delta-change between historical and future Euro-CORDEX runs after Q–Q correction; (3) futurization of SAFRAN using the spatialized delta change values. The mountainous area is expected to face a higher increase in air temperature than the plains, reaching +2.5 °C for RCP8.5 and +1.71 °C for RCP4.5 over 2041–2060. This warming will be accompanied by a marked decrease in precipitation (−16% for RCP8.5). These present and future spatialized data sets should be useful for impact studies, in particular those focusing on water resources.

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

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.