smart water systems in developing countries requires to rethink not only the
technologies themselves but also the business models around them.
In times of IoT and ‘social sensing’, also the observation of hydrological
contexts may take benefit from low cost and ‘pervasive’ sensing systems, opening
the possibility to novel approaches for capturing relevant information. In this frame,
one of the main issues to solve is the availability of heterogeneous and intermittent
observations. The chapter by Mazzoleni et al. [7] describes novel methods for
optimally assimilating such observations into hydrological models, focusing on
the particular application of flood prediction. The aim of this chapter is to explore
numerical approaches for integrating crowdsourced observations from static social
sensors within hydrological and hydrodynamic modelling framework to improve
flood prediction. The distinctive characteristic of such heterogeneous observations
is their varying lifespan and their spatial distribution, which make more complex
the implementation of standard model updating techniques. This chapter applies
different innovative assimilation techniques within two case studies, where synthetic flow observations are generated to represent the different intermittency and
accuracy scenarios of the crowdsourced observations. It was found that
crowdsourced observations can significantly improve flood prediction if integrated
into hydrological and hydraulic models. Moreover, a network of low-cost static
social sensors can actually complement traditional networks of static physical
sensors, for the purpose of improving flood forecasting accuracy.
Precipitation is a key hydrological process in the water cycle, whose observation
is increasingly required for modern water and environmental management. Conventional precipitation measurements by rain gauges cannot provide sufficient
spatial and temporal coverage for many hydrological applications, such as urban
drainage system modelling. Weather radar is a remote sensing instrument that has
been increasingly used to estimate precipitation for a variety of hydrological and
meteorological applications, including real-time flood forecasting, severe weather
monitoring and warning, and short-term precipitation forecasting. Weather radar
provides unique observations of precipitating systems at fine spatial and temporal
resolutions. The potential benefit of using radar rainfall in hydrology is huge, but
practical hydrological applications of weather radar have been limited by the
inherent uncertainties and errors in radar rainfall estimates. Uncertainties in radar
rainfall estimates can lead to large errors in their applications, so radar rainfall
measurements must be corrected before the data are used quantitatively. Nanding
and Rico-Ramirez [8] have introduced the latest advances in the measurement and
forecasting of precipitation with weather radar. The common uncertainty sources
include radar hardware calibration, echoes due to non-meteorological origin, attenuation, variations in the vertical profile of reflectivity, and variations of raindrop
size distribution. The techniques for adjusting radar rainfall with rain gauge measurements are described. Precipitation forecasting (called ‘nowcasting’) using
weather radar is valuable in its applications in real-time flood forecasting.
Accurate soil moisture information is critically important for hydrological
applications such as water resources management and hydrological modelling.
This is because soil moisture is an important element in the ecosystem and
Preface
xiii
technologies themselves but also the business models around them.
In times of IoT and ‘social sensing’, also the observation of hydrological
contexts may take benefit from low cost and ‘pervasive’ sensing systems, opening
the possibility to novel approaches for capturing relevant information. In this frame,
one of the main issues to solve is the availability of heterogeneous and intermittent
observations. The chapter by Mazzoleni et al. [7] describes novel methods for
optimally assimilating such observations into hydrological models, focusing on
the particular application of flood prediction. The aim of this chapter is to explore
numerical approaches for integrating crowdsourced observations from static social
sensors within hydrological and hydrodynamic modelling framework to improve
flood prediction. The distinctive characteristic of such heterogeneous observations
is their varying lifespan and their spatial distribution, which make more complex
the implementation of standard model updating techniques. This chapter applies
different innovative assimilation techniques within two case studies, where synthetic flow observations are generated to represent the different intermittency and
accuracy scenarios of the crowdsourced observations. It was found that
crowdsourced observations can significantly improve flood prediction if integrated
into hydrological and hydraulic models. Moreover, a network of low-cost static
social sensors can actually complement traditional networks of static physical
sensors, for the purpose of improving flood forecasting accuracy.
Precipitation is a key hydrological process in the water cycle, whose observation
is increasingly required for modern water and environmental management. Conventional precipitation measurements by rain gauges cannot provide sufficient
spatial and temporal coverage for many hydrological applications, such as urban
drainage system modelling. Weather radar is a remote sensing instrument that has
been increasingly used to estimate precipitation for a variety of hydrological and
meteorological applications, including real-time flood forecasting, severe weather
monitoring and warning, and short-term precipitation forecasting. Weather radar
provides unique observations of precipitating systems at fine spatial and temporal
resolutions. The potential benefit of using radar rainfall in hydrology is huge, but
practical hydrological applications of weather radar have been limited by the
inherent uncertainties and errors in radar rainfall estimates. Uncertainties in radar
rainfall estimates can lead to large errors in their applications, so radar rainfall
measurements must be corrected before the data are used quantitatively. Nanding
and Rico-Ramirez [8] have introduced the latest advances in the measurement and
forecasting of precipitation with weather radar. The common uncertainty sources
include radar hardware calibration, echoes due to non-meteorological origin, attenuation, variations in the vertical profile of reflectivity, and variations of raindrop
size distribution. The techniques for adjusting radar rainfall with rain gauge measurements are described. Precipitation forecasting (called ‘nowcasting’) using
weather radar is valuable in its applications in real-time flood forecasting.
Accurate soil moisture information is critically important for hydrological
applications such as water resources management and hydrological modelling.
This is because soil moisture is an important element in the ecosystem and
Preface
xiii