As observed in the last years, flood and drought events are getting more likely to happen due to ... more As observed in the last years, flood and drought events are getting more likely to happen due to climate change and can cause significant environmental, social and economic damages.For this reason, already in 2021, the Po River District Authority (AdbPo) undertook the implementation of the GEOframe modelling system on the whole territory of the district in accordance with the GCU-M (Gruppo di Coordinamento Unificato-Magre) to update the existing numerical modelling for water resource management and with the objective of producing a better quantification and forecast of the spatial and temporal water availability across the entire river basin and, finally, to improve the planning activity of theAuthority.The GEOframe system was developed by a scientific international community, led by the University of Trento, and is a semi-distributed conceptual model, with high modularity and flexibility, completely open-source.The implementation of GEOframe in the Po River District has begun in the Valle d’Aosta Region, the most upstream part of the district.After an initial part of meteorological data collection, validation, spatial interpolation, and geomorphological analysis, a first running of the model to assess all the components of the hydrological balance (evapotranspiration, snow accumulation, water storage and discharge) was carried out.Consequently, the calibration phase started, consisting of the research of the values of the characteristic parameters of the model which fit the discharge evolution recorded in the hydrometers of the region in the best possible way, comparing the modelled discharge trend with the measured one.The calibration, based on KGE method, has been executed in 10 hydrometers in Valle d’Aosta across a 4 years period. The results were encouraging, with an objective function of 0.76 at the closure point of the region.The same process is now in progress in Piemonte, one of the biggest regions of Italy, which contains more than 100 hydrometers. The resulting objective functions are in general rather high and will be presented in this work.At the same time, thanks to the geomorphological analysis, most part of Po River District (up to Pontelagoscuro (FE)), which totally occupies more than 42,000 km2, has been divided into subbasins, the hydrological reference units where the simulation process takes place, and this dataset will be open-source and shared with the scientific community.On the other hand, the interpolation and spatialization of the meteorological data will be carried out according to the 1 km2 European Environmental Agency reference grid.In conclusion, in this initial stage of implementation of the model and calibration of its parameters, it was possible to assess the capacity of GEOframe to simulate not only the water discharge but also the other components of the water cycle, namely the evapotranspiration, the water storage and the snow accumulation. Furtheremore, implementing GEOframe in a mountainous area underlines the importance and the influence that snow and glaciers, especially in a higher temperature scenario due to climate change, can have on water availability and, therefore, a better modelling component of these elements will be implemented in the future developments of GEOframe.
<p>Snow water equivalent is a key variable in hydrology. An accurate SWE estimation... more <p>Snow water equivalent is a key variable in hydrology. An accurate SWE estimation is crucial for runoff prediction, especially for catchments with strong nival regimes. Direct observations are unfortunately rare and are available only at a point scale. Accurate spatialized estimates of SWE are thus difficult to be obtained. Physically based models often suffer from the inaccuracies of input data and uncertainty of model parametrization. In this sense, the integration of traditional techniques with remote sensing observation is valuable. Although current satellite missions do not provide direct SWE observation, they allow us to extract important proxy information that is crucial for SWE reconstruction. In this sense, we propose to exploit optical and radar sensors to retrieve accurate information on the persistence of snow on the ground. In fact, the longer the persistence, the deeper the snowpack. To achieve enough spatial and temporal detail, we merged multi-scale information from MODIS, Sentinel-2, and Landsat missions. The key idea is to exploit the snow pattern persistence that we can observe with good spatial detail from Landsat and Sentinel-2 missions to reconstruct the scene when a low-resolution image (MODIS) is acquired. Furthermore, information on the duration of the melting phase can also be retrieved by exploiting the synthetic aperture radar (SAR) mounted on board of Sentinel-1. Hence, we can estimate the number of days of melting. In-situ data, when available, are also exploited in the reconstruction. In detail, air temperature is used to estimate the potential melting and the snow depth increases to determine the number of days in accumulation. The reconstruction approach is then simple: by knowing the days in melting, the total amount of melted SWE is determined. Assuming that the melted SWE is equal to the accumulated SWE, we can redistribute SWE throughout the season using a simple approach as the degree day. The final output is a daily time-series with a spatial resolution of few dozens of m. One of the major advantage of the proposed approach, compared to more traditional SWE estimation techniques, is that it does not depend from precipitation observation, often highly uncertain in high-elevation catchments. When evaluated against a reference product (i.e., Airborne Snow Observatory), the method shows a bias of -22 mm and an RMSE of 212 mm for a catchment of 970 km2 in Sierra Nevada (CA). In this work, we investigate the relationship between the melted SWE and the measured riverine discharge for a number of catchments in South Tyrol (Italy). The results may be of great interest, especially for poorly monitored basins with highly variable snow accumulation that are exploited for hydroelectric energy production. In detail, we propose a long-term analysis on SWE time-series to understand if there are evident trends that might improve hydroelectric power management.  </p>
Hack\u27s law is reviewed, emphasizing its implications for the elongation of river basins as wel... more Hack\u27s law is reviewed, emphasizing its implications for the elongation of river basins as well as its connections with their fractal characteristics. The relation between Hack\u27s law and the internal structure of river basins is investigated experimentally through digital elevation models. It is found that Hack\u27s exponent, elongation, and some relevant fractal characters are closely related. The self-affine character of basin boundaries is shown to be connected to the power law decay of the probability of total contributing areas at any link and to Hack\u27s law. An explanation for Hack\u27s law is derived from scaling arguments. From the results we suggest that a statistical framework referring to the scaling invariance of the entire basin structure should be used in the interpretation of Hack\u27s law
The paper presents the integration of the GEOtop model into the Object Modeling Sys- tem version ... more The paper presents the integration of the GEOtop model into the Object Modeling Sys- tem version 3.0 (OMS3) and its application. GEOtop is a physically based spatially distributed rainfall-runoff model, performing water and energy budgets. The OMS3 integration widened the application range of GEOtop as presented in the paper. By running GEOtop as an OMS3 model component it can interact with the GIS uDig-JGrass to utilize other geo-processing, visualization, and modeling components. Furthermore, OMS3 components for automatic calibration, sensitivity analysis, or meteorological interpolation can now interact with GEOtop. Finally, a case study of the model application is presented. Results in terms of soil water content and suction are compared with measured data. Model performance is evaluated by computing traditional goodness of fit indi- ces such as Nash Sutcliffe and percent bias.
The EPA Storm Water Management Model (SWMM) is a robust software, widely used in urban catchments... more The EPA Storm Water Management Model (SWMM) is a robust software, widely used in urban catchments. However, it lacks two important aspects: a module for Storm Water Drainage System (SWDS) design and a flexible model structure. JSWMM, a new SWMM-based Java software, is developed to overcome these constraints. The SWMM data structure is refactored following the object oriented paradigm, while the computational core is split and redesigned as OMS3-compliant components. This approach allows for easily modifying and extending available modules by adding new functionalities, e.g. infiltration as part of runoff computation, different equation to evaluate evapotranspiration, etc. Input and output of JSWMM are maintained fully compatible with those of SWMM with which it remains, therefore, interoperable. The SWDS design module is based on the Geomorphological Instantaneous Unit Hydrograph theory by Rodríguez-Iturbe et al. (1979) and by Rigon et al. (2016). It automates the process of pipe di...
In hydrological modeling snowmelt is computed along two different approaches: the physically base... more In hydrological modeling snowmelt is computed along two different approaches: the physically based one simulates the snowpack evolution in terms of accumulation and ablation by means of solution of the energy balance equation; the second, simpler approach, uses instead the meteorological variables as indices of physical processes. The simplified models are limited to forecasting only the snow water equivalent (SWE, the mass of liquid water in the snowpack) and not other variables.
Hydrology and Earth System Sciences Discussions, 2016
The theory of travel time and residence time distributions is reworked from the point of view of ... more The theory of travel time and residence time distributions is reworked from the point of view of the hydrological storages and fluxes involved. The forward and backward travel time distri- bution functions are defined in terms of conditional probabilities. We explain Niemi's formula and show how it can be interpreted as an expression of the Bayes theorem. Some connections between this theory and population theory are identified by introducing an expression which connects life expectancy with travel times. The theory can be applied to conservative solutes, including a method of estimating the storage selection functions. An example, based on the Nash hydrograph, illustrates some key aspects of the theory.
As observed in the last years, flood and drought events are getting more likely to happen due to ... more As observed in the last years, flood and drought events are getting more likely to happen due to climate change and can cause significant environmental, social and economic damages.For this reason, already in 2021, the Po River District Authority (AdbPo) undertook the implementation of the GEOframe modelling system on the whole territory of the district in accordance with the GCU-M (Gruppo di Coordinamento Unificato-Magre) to update the existing numerical modelling for water resource management and with the objective of producing a better quantification and forecast of the spatial and temporal water availability across the entire river basin and, finally, to improve the planning activity of theAuthority.The GEOframe system was developed by a scientific international community, led by the University of Trento, and is a semi-distributed conceptual model, with high modularity and flexibility, completely open-source.The implementation of GEOframe in the Po River District has begun in the Valle d’Aosta Region, the most upstream part of the district.After an initial part of meteorological data collection, validation, spatial interpolation, and geomorphological analysis, a first running of the model to assess all the components of the hydrological balance (evapotranspiration, snow accumulation, water storage and discharge) was carried out.Consequently, the calibration phase started, consisting of the research of the values of the characteristic parameters of the model which fit the discharge evolution recorded in the hydrometers of the region in the best possible way, comparing the modelled discharge trend with the measured one.The calibration, based on KGE method, has been executed in 10 hydrometers in Valle d’Aosta across a 4 years period. The results were encouraging, with an objective function of 0.76 at the closure point of the region.The same process is now in progress in Piemonte, one of the biggest regions of Italy, which contains more than 100 hydrometers. The resulting objective functions are in general rather high and will be presented in this work.At the same time, thanks to the geomorphological analysis, most part of Po River District (up to Pontelagoscuro (FE)), which totally occupies more than 42,000 km2, has been divided into subbasins, the hydrological reference units where the simulation process takes place, and this dataset will be open-source and shared with the scientific community.On the other hand, the interpolation and spatialization of the meteorological data will be carried out according to the 1 km2 European Environmental Agency reference grid.In conclusion, in this initial stage of implementation of the model and calibration of its parameters, it was possible to assess the capacity of GEOframe to simulate not only the water discharge but also the other components of the water cycle, namely the evapotranspiration, the water storage and the snow accumulation. Furtheremore, implementing GEOframe in a mountainous area underlines the importance and the influence that snow and glaciers, especially in a higher temperature scenario due to climate change, can have on water availability and, therefore, a better modelling component of these elements will be implemented in the future developments of GEOframe.
<p>Snow water equivalent is a key variable in hydrology. An accurate SWE estimation... more <p>Snow water equivalent is a key variable in hydrology. An accurate SWE estimation is crucial for runoff prediction, especially for catchments with strong nival regimes. Direct observations are unfortunately rare and are available only at a point scale. Accurate spatialized estimates of SWE are thus difficult to be obtained. Physically based models often suffer from the inaccuracies of input data and uncertainty of model parametrization. In this sense, the integration of traditional techniques with remote sensing observation is valuable. Although current satellite missions do not provide direct SWE observation, they allow us to extract important proxy information that is crucial for SWE reconstruction. In this sense, we propose to exploit optical and radar sensors to retrieve accurate information on the persistence of snow on the ground. In fact, the longer the persistence, the deeper the snowpack. To achieve enough spatial and temporal detail, we merged multi-scale information from MODIS, Sentinel-2, and Landsat missions. The key idea is to exploit the snow pattern persistence that we can observe with good spatial detail from Landsat and Sentinel-2 missions to reconstruct the scene when a low-resolution image (MODIS) is acquired. Furthermore, information on the duration of the melting phase can also be retrieved by exploiting the synthetic aperture radar (SAR) mounted on board of Sentinel-1. Hence, we can estimate the number of days of melting. In-situ data, when available, are also exploited in the reconstruction. In detail, air temperature is used to estimate the potential melting and the snow depth increases to determine the number of days in accumulation. The reconstruction approach is then simple: by knowing the days in melting, the total amount of melted SWE is determined. Assuming that the melted SWE is equal to the accumulated SWE, we can redistribute SWE throughout the season using a simple approach as the degree day. The final output is a daily time-series with a spatial resolution of few dozens of m. One of the major advantage of the proposed approach, compared to more traditional SWE estimation techniques, is that it does not depend from precipitation observation, often highly uncertain in high-elevation catchments. When evaluated against a reference product (i.e., Airborne Snow Observatory), the method shows a bias of -22 mm and an RMSE of 212 mm for a catchment of 970 km2 in Sierra Nevada (CA). In this work, we investigate the relationship between the melted SWE and the measured riverine discharge for a number of catchments in South Tyrol (Italy). The results may be of great interest, especially for poorly monitored basins with highly variable snow accumulation that are exploited for hydroelectric energy production. In detail, we propose a long-term analysis on SWE time-series to understand if there are evident trends that might improve hydroelectric power management.  </p>
Hack\u27s law is reviewed, emphasizing its implications for the elongation of river basins as wel... more Hack\u27s law is reviewed, emphasizing its implications for the elongation of river basins as well as its connections with their fractal characteristics. The relation between Hack\u27s law and the internal structure of river basins is investigated experimentally through digital elevation models. It is found that Hack\u27s exponent, elongation, and some relevant fractal characters are closely related. The self-affine character of basin boundaries is shown to be connected to the power law decay of the probability of total contributing areas at any link and to Hack\u27s law. An explanation for Hack\u27s law is derived from scaling arguments. From the results we suggest that a statistical framework referring to the scaling invariance of the entire basin structure should be used in the interpretation of Hack\u27s law
The paper presents the integration of the GEOtop model into the Object Modeling Sys- tem version ... more The paper presents the integration of the GEOtop model into the Object Modeling Sys- tem version 3.0 (OMS3) and its application. GEOtop is a physically based spatially distributed rainfall-runoff model, performing water and energy budgets. The OMS3 integration widened the application range of GEOtop as presented in the paper. By running GEOtop as an OMS3 model component it can interact with the GIS uDig-JGrass to utilize other geo-processing, visualization, and modeling components. Furthermore, OMS3 components for automatic calibration, sensitivity analysis, or meteorological interpolation can now interact with GEOtop. Finally, a case study of the model application is presented. Results in terms of soil water content and suction are compared with measured data. Model performance is evaluated by computing traditional goodness of fit indi- ces such as Nash Sutcliffe and percent bias.
The EPA Storm Water Management Model (SWMM) is a robust software, widely used in urban catchments... more The EPA Storm Water Management Model (SWMM) is a robust software, widely used in urban catchments. However, it lacks two important aspects: a module for Storm Water Drainage System (SWDS) design and a flexible model structure. JSWMM, a new SWMM-based Java software, is developed to overcome these constraints. The SWMM data structure is refactored following the object oriented paradigm, while the computational core is split and redesigned as OMS3-compliant components. This approach allows for easily modifying and extending available modules by adding new functionalities, e.g. infiltration as part of runoff computation, different equation to evaluate evapotranspiration, etc. Input and output of JSWMM are maintained fully compatible with those of SWMM with which it remains, therefore, interoperable. The SWDS design module is based on the Geomorphological Instantaneous Unit Hydrograph theory by Rodríguez-Iturbe et al. (1979) and by Rigon et al. (2016). It automates the process of pipe di...
In hydrological modeling snowmelt is computed along two different approaches: the physically base... more In hydrological modeling snowmelt is computed along two different approaches: the physically based one simulates the snowpack evolution in terms of accumulation and ablation by means of solution of the energy balance equation; the second, simpler approach, uses instead the meteorological variables as indices of physical processes. The simplified models are limited to forecasting only the snow water equivalent (SWE, the mass of liquid water in the snowpack) and not other variables.
Hydrology and Earth System Sciences Discussions, 2016
The theory of travel time and residence time distributions is reworked from the point of view of ... more The theory of travel time and residence time distributions is reworked from the point of view of the hydrological storages and fluxes involved. The forward and backward travel time distri- bution functions are defined in terms of conditional probabilities. We explain Niemi's formula and show how it can be interpreted as an expression of the Bayes theorem. Some connections between this theory and population theory are identified by introducing an expression which connects life expectancy with travel times. The theory can be applied to conservative solutes, including a method of estimating the storage selection functions. An example, based on the Nash hydrograph, illustrates some key aspects of the theory.
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Papers by Riccardo Rigon