27 May, 2024

Research

Research

4DHydro will resolve the lack of synergies between EO and LSM/HM communities and actively contribute to addressing two of Dooges’s fundamental problems in hydrology:

(1) the problem of scale

(2) the problem of parameterization of LSM/HMs at a global scale.

4DHydro aims to foster the collaboration between LSM/HM modelers and EO scientists communities by embracing their expertise to enable credible estimates of water cycle components via hyper-resolution Earth observations and land-surface modelling. In fact, the use of EO products has been shown to have a large positive impact on the performance of LSM/HMs. After 4DHydro , in the group of participants, we should not have hydrologists using satellite data simply as end users, without giving feedback to remote sensing scientists; and on another side, the EO scientists should take care of the suggestions and criticism of hydrologists.

The 4DHydro consortium will conduct an intercomparison of datasets collected in the Community Reference Dataset through a Round Robin methodology to address Task 4's technical requirements. This involves comparing Earth Observation (EO) datasets related to hydrological variables against reference data and each other over selected Regions of Interest (RoIs) using standardized validation methods. The objective is to identify suitable candidates for EO data assimilation in Task 5, as well as to provide an intercomparable uncertainty estimate for assimilation in models. To facilitate the Round Robin, the consortium will develop a validation and intercomparison methodology at high resolution, including quality indexes and uncertainty characterization. While guidelines for validation exist, specific ones for high-resolution datasets are lacking, posing a challenge in the EO community. The comparison will consider datasets' skill in describing processes at approximately 1 km resolution, focusing on representativeness and drivers of (bio)physical processes. The evaluation of the datasets will be conducted based on two main criteria:
1. Performance relative to a determined reference will be assessed using established difference- and covariance-based metrics. These metrics will examine both the temporal and spatial agreement of the datasets. Temporal agreement reflects their ability to capture individual events, while spatial agreement indicates their capacity to represent local fluxes and drivers.

2. The physical representativeness skill of the datasets will be evaluated by considering ancillary data and understanding of the underlying drivers. Additionally, spatial variability will be analyzed to determine if the high level of detail in the datasets provides valuable information or if it merely introduces noise.

• Science Case 1:

What is the benefit of assimilating high resolution EO products on forecasting of extremes in the Rhine river basin?

The Rhine river is one of the largest river basins in Europe. In the last couple of years, both wet and dry extremes affected this river basin, with impacts on agriculture, industry, shipping and its inhabitants. The catchment is reasonably well monitored with regard to rainfall and discharge. Nevertheless, satellite estimates of SWE, SM and other quantities may have added value for improving operational forecasts of both wet and dry extremes. In this science case we will assess the benefit of assimilating high-resolution EO products to adjust high-resolution hydrological models in near real-time to mimic the actual state of the catchment just before forecasting. The effect on forecast quality will be assessed. The results will be discussed with relevant stakeholders in the river basin (i.e. Dutch Ministry of Infrastructure and Water, BfG Bundesanstalt für Gewässerkunde (Germany) and FOEN (Federal Office for the Environment) in Switzerland.

 
• Science Case 2:

What is the benefit of high resolution EO products to monitor and assess water resources and hydropower potential in the Tugela river basin?

The Tugela basin is the largest river in KwaZulu-Natal province, South Africa and is one of the country’s most important rivers. The river is essential for water resources, sanitation and hydropower production and contains several big reservoirs. This science case will help to answer the question: What is the bene- fit of high-resolution EO products to monitor and assessing water resources and hydropower potential? We will contact local water managers (e.g., Department of Water and Sanitation) to act as stakeholders. South Africa has an open data policy (see https://www.dws.gov.za/Hydrology/).

 

• Science Case 3:

Towards improving water management and irrigation in highly anthropized basin: the case of the Po River Basin.

The Po River Basin is characterized by considerable variability of the landscape and of climate and, more importantly, by a strong human impact on the water cycle. Currently, we have only large-scale and rough estimates of the spatial and temporal distribution of the water use throughout the basin mainly due to missing detailed observations of the different components of the water cycle, i.e., rainfall, snow, soil moisture, groundwater, river discharge and evaporation. Using the high-resolution model simula- tions and through their integration with high-resolution EO, and in-situ observations (stemming from WP1 and WP4), the purpose of this science case is to answer the question: What is the spatial-temporal distribution of water uses in the Po Basin? The final aim of the investigation is to set up a system for mon- itoring the basin’s natural and human-modified water cycle to optimize water resource management at the spatial resolution (<1 km , for selected models) needed for decision-making.

 

• Science Case 4:

To what extent can we close the water balance based on the optimal combination of high resolution satellite data, high resolution hydrological models and in situ measurements?

This science case is to demonstrate how much advancement has been made and to what extent we can close the water balance based on available high-resolution EO and hydrological model data and in-situ data. Here we will also use coarser products like GRACE to validate total water storage changes. This assessment will be carried out for all regions of interest, including the whole of Europe (models WP5).

Skip to content