Search

The uncertainty of hydrological forecasts is strongly related to the uncertainty of the rainfall field due to the nonlinear relationship between the spatio-temporal pattern of rainfall and runoff. Rain gauges are typically considered to provide reference data to rebuild precipitation fields. However, due to the density and the distribution variability of the rain gauge network, the rebuilding of the precipitation field can be affected by severe errors which compromise the hydrological simulation output. On the other hand, retrievals obtained from remote sensing observations provide spatially resolved precipitation fields, improving their representativeness. In this regard, the comparison between simulated and observed river flow discharge is crucial for assessing the effectiveness of merged precipitation data in enhancing the model's performance and its ability to realistically simulate hydrological processes. This paper aims to investigate the hydrological impact of using the merged rainfall fields from the Italian rain gauge network and the NASA Global Precipitation Measurement (GPM) IMERG precipitation product. One aspect is to highlight the benefits of applying the cellular automata algorithm to pre-process input data in order to merge them and reconstruct an improved version of the precipitation field. The cellular automata approach is evaluated in the Tanaro River basin, one of the tributaries of the Po River in Italy. As this site is characterized by the coexistence of a variety of natural morphologies, from mountain to alluvial environments, as well as the presence of significant civil and industrial settlements, it makes it a suitable case study to apply the proposed approach. The latter has been applied over three different flood events that occurred from November to December 2014. The results confirm that the use of merged gauge–satellite data using the cellular automata algorithm improves the performance of the hydrological simulation, as also confirmed by the statistical analysis performed for 17 selected quality scores. [ABSTRACT FROM AUTHOR]

The uncertainty of hydrological forecasts is strongly related to the uncertainty of the rainfall field due to the nonlinear relationship between the spatio-temporal pattern of rainfall and runoff. Rain gauges are typically considered as reference data to rebuild precipitation fields. However, due to the density and the distribution variability of the raingauge network, the rebuilding of the precipitation field can be affected by severe errors which compromise the hydrological simulation output. On the other hand, retrievals obtained from remote sensing observations provide spatially resolved precipitation fields improving their representativeness. In this regard, this paper aims to investigate the impact of using the merged rainfall fields from the rain gauge Italian rainfall network and the NASA Global Precipitation Measurement (GPM) IMERG precipitation product on the hydrological simulation performance. In particular, one aspect is to highlight the benefits of applying the Cellular Automata algorithm to pre-process input data in order to merge them and reconstruct an improved version of the precipitation field. The cellular automata approach is evaluated in the Tanaro River Basin, one of the tributaries of the Po River in Italy. As this site is characterized by the coexistence of a variety of natural morphologies, from mountain to alluvial environments, as well as the presence of significant civil and industrial settlements, it makes it a suitable case study to apply the proposed approach. The latter has been applied over three different flood events occurred from November to December 2014. The results confirm that the use of merged gauge-satellite data using the Cellular Automata algorithm improves the performance of the hydrological simulation, as also confirmed by the statistical analysis performed for seventeen selected quality scores. [ABSTRACT FROM AUTHOR]

Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices. [ABSTRACT FROM AUTHOR]

Black carbon (BC), brown carbon (BrC) and soil dust are the most radiation absorbing aerosols (RAA). When RAA are deposited on the snowpack, they lower the snow albedo, increasing the absorption of the solar radiation. The climatic impact associated to snow darkening induced by RAA is highly uncertain. In this work, a 5-years simulation with GEOS-Chem global chemistry and transport model was performed, in order to calculate the present-day radiative forcing (RF) of RAA in snow. RF was estimated taking simultaneously into account the presence of BC, BrC, and mineral soil dust in snow. Modelled BC and black carbon equivalent (BCE) mixing ratios in snow and the fraction of light absorption due to non-BC compounds (fnon-BC) were compared with worldwide observations. We showed as BC, BCE and fnon-BC, obtained from deposition and precipitation fluxes, reproduce the regional variability and order of magnitude of the observations. Global mean all sky total RAA, BC, BrC and dust snow RF are 0.068, 0.033, 0.0066, and 0.012 W/m² respectively. At global scale, non-BC compounds account for 40 % of RAA snow RF, while anthropogenic RAAs contribute to the forcing for 56 %. With regard to non-BC compounds, the largest impact of BrC has been found during summer in the Arctic (+0.13 W/m²). In the middle latitudes of Asia, dust forcing in spring accounts for the 50 % (+0.24 W/m²) of the total RAAs RF. Uncertainties in absorbing optical properties, RAA mixing ratio in snow, snow grain dimension, and snow cover fraction result in an overall uncertainty of -50% / +61 %, -57 % / +183 %, -63 % / +112 %, and -49 % / +77 % in BC, BrC, dust and total RAAs snow RF, respectively. [ABSTRACT FROM AUTHOR]

According to the last Italian "Report on Hydrogeological Instability", almost 8% of the national territory is affected by landslide processes, being one third classified as rapid processes triggered by heavy or persistent precipitation, as well as earthquakes. Nowadays, landslide occurrences rate in Italy is estimated to be in the order of hundreds events per year (e.g. 172 events in 2017). As commonly recognized by the scientific community, heavy and sudden precipitation events are increasing in the last decades, due to global warning: therefore we can expect increasing rates and increasing population exposure to this kind of hydrogeological risk, consequently. For this reason, many landslide forecasting techniques have been developed at regional scale, especially for shallow landslides and debris flows that are generally activated by severe precipitation events. The forecast is based on rainfall quantification, which represents the most easily detectable physical quantity. Then, forecasting landslide models are usually based on rain empirical thresholds, that needs to be locally defined and usually revised for each element at risk, according to historical data that are not always available.In this work, we propose a new approach, based on the Landslide Activation Index (LAI), inspired to the already tested CAI index, used for flood and flash flood forecasting through hydrological modeling. Following the same concept and starting from the rainfall spatialization techniques used in the CHyM hydrological model, the LAI index considers the drained rainfall in each point of the rebuilt drainage network during the runoff time and can be used to predict landslide risk over wide areas, by using a unique threshold over identified slopes, susceptible to collapse. [ABSTRACT FROM AUTHOR]