The INTERROGATION project will unveil the role of climatic and human-controlled drivers for drought risk mitigation and will develop
a prototype of a decision support system to ensure an integrated management of available water resources during the increasingly
frequent drought events affecting the Mediterranean area. The work plan will focus on the Po river basin, an exemplary case study
for its topographic, physiographic and geomorphic characteristics, as well as its economic importance, and will be built over three
Pillars (Figure 1), namely (1) accurate in situ and high resolution reanalysis/ satellite data, (2) hydrological modelling and uncertainty
assessment, (3) retrospective analysis of past multi-year drought events and generation of “what-if” scenarios.
The three Pillars will be intimately connected to each other to conduct the core research of the INTERROGATION project: high
resolution in situ, reanalysis and satellite data will be used as inputs of an advanced hydrological modelling for a synoptic
reconstruction of the multi-year drought events occurred in the period 1990-2020 over the Po river basin. The spatial and temporal
connection between natural and human processes on drought development will be investigated by accounting also for uncertainties.
The knowledge acquired on the study of past drought events will be exploited to generate plausible “what-if” scenarios of climate,
land use and water management. These scenarios will be used to outline operating strategies and develop a decision support system
that will guide stakeholders towards an integrated management of water resources during drought events. For the first time, the
INTERROGATION project will integrate new high resolution remote sensing and reanalysis products with advanced modelling tools to
(i) provide a comprehensive knowledge of the drought impacts across the Po river basin, (ii) identify a set of proactive and timely
actions to prevent, in advance, the negative effects of drought events, and (iii) develop a forecasting tool including a robust
assessment of uncertainty in the predictions.