Mission
Hydroterra+, hereafter referred to as “H+”, is a proposed mission aimed at monitoring rapid processes (ranging from hours to days) of the water cycle, with a particular focus on Europe, the Mediterranean basin, and certain parts of Africa.
These areas are highly vulnerable to climate change, and H+ specifically targets a significant gap in observations related to the water cycle – processes occurring on timescales of hours to days at a regional level (current and planned low Earth orbit missions do not adequately address this requirement).
H+ represents an evolution of the Hydroterra concept initially proposed for Earth Explorer 10, benefiting from the insights gained during its Phase 0 study and subsequent field experiments to enhance scientific preparedness.
The design of H+ revolves around its science objectives (SO), specifically selected to target crucial uncertainties in water cycle science that require improved temporal sampling:
- SO1: intense storms (Mesoscale Convective Systems, frontal systems, and thunderstorms) which are responsible for the most damaging weather events and for the largest part of annual rainfall in the Mediterranean region as well as in the Tropics;
- SO2: the diurnal land surface water budget (soil moisture, vegetation, irrigation) – key processes which are poorly observed and understood currently;
- SO3: the cryosphere water budget – snow accumulation and melting, since this has significant daily variation, is poorly observed currently, and controls water downstream of mountain areas.
This area of research requires the quantification of water at the land surface and in the atmospheric layer. Frequent radar imaging is well-suited for such measurements and contributes to a fourth objective related to hazards posed by the solid Earth:
- SO4: ground motion on timescales of hours to days related to earthquakes, volcanoes and landslides.
Along these lines, the main technical objectives for the Phase 0 study are:
- Develop significant testable science objectives which are robustly related to geophysical observables and corresponding Level 1 data products, for each of the candidate science areas (hydro-meteorology, etc.);
- Ensure that the mission concept has sufficient technical maturity to present a convincing implementation case and with imaging performance to satisfy the science objectives.
This is requiring for:
- Formulating convincing cases in each scientific discipline that clearly define and justify specific, testable scientific objectives;
- Establishing the measurement requirements associated with these scientific objectives, framed in terms of relevant geophysical variables and their association with Level 1 measurements;
- Assessing the viability of the mission concept’s imaging performance to ensure it meets the quality criteria essential for providing Level 1 observations that align with the scientific objectives
SO1
science questions
SO2
science questions
SO3
science questions
SO4
science questions
SO1 science questions
Recent studies in hydrometeorology have concentrated on extreme weather phenomena, including severe storms and flooding, as these events exert significant societal effects and present considerable forecasting difficulties. The Mediterranean region poses challenges in this regard, owing to its intricate morphological features and varied topography, as well as the frequent occurrence of extreme rainfall events. The presence of significant quantities of water vapour in the atmospheric column, quantified as the Integrated Water Vapour (IWV), and rapid ascent of moist air are a necessary condition for heavy rainfall to occur (Doswell III et al., 1996; Flaounas et al., 2019; Lebeaupin Brossier et al., 2008), especially in case of mesoscale convective systems (MCS) which are among the weather phenomena responsible for the most intense rainfall in the Mediterranean area. Another identified mechanism that can initiate MCS events is the variability of Surface Soil Moisture (SSM) across distances of 10 kilometers or more. Investigations in the Sahel (Taylor et al., 2011) reveal a robust relationship between surface moisture disparities and the development of MCS, with subsequent research indicating that such moisture differences may also enhance the intensity of MCS (Klein and Taylor, 2020). The main drawback of high-resolution Numerical Weather Prediction (NWP) modeling is the limited availability of appropriate high-resolution observations for assimilation. Studies have demonstrated that integrating spaceborne observations of IWV, SSM, and GNSS data can lead to improved forecasts of intense rainfall events. Within the Mediterranean area and in the Sahel area, there is a distinct necessity for precise anticipation of hydro-meteorological extremes. This calls for the implementation of a flash flood forecasting (FFF) workflow that incorporates both meteorological and hydrological models, specifically for the smaller Mediterranean basins (Siccardi et al., 2005), and comprehensive high spatial and temporal resolution observations including IWV and SSM. None of the observations currently available can provide a high-resolution spatio-temporal synoptic picture of the water vapor, potentially fundamental to improve the predictability of severe hydro-meteorological events, including MCS. The incorporation of ZTD maps derived from Earth Observation data into NWP models, with a focus on high spatial resolution and rapid revisit times, is projected to advance the prediction capabilities of extreme weather events.
Therefore, some of the science questions related to the use of IWV and (soil moisture) data in the meteorological part of the are:
- Which is the optimal IWV and SSM spatial/temporal resolution for the reduction of uncertainties in the meteorological part of FFF?
- Which atmospheric model structure best exploits the information provided by high resolution IWV and SM observations?
- Is there a period of the year where observations provide more information for FFF?
- Which are the best approaches to assimilate IWV and SSM into meteorological models?
SO2 science questions
Soil moisture is a key variable in the terrestrial hydrological cycle. It controls the partitioning of incoming radiation into latent, sensible, and ground heat fluxes and represents the interface between the atmosphere and the land surface/subsurface components of the water cycle. Soil moisture governs the generation of surface runoff and infiltration, thus strongly influencing both flood generation and long-term water availability for irrigation through groundwater resources. This SO2 focuses on the potential benefits of Hydroterra+ for the spatio-temporal quantification of soil moisture, particularly for improving flash flood forecasting and assessing water availability for irrigation. Two main regions will be considered: Central Europe and the Mediterranean, and Sub-Saharan West Africa. Both are climate- and water-sensitive regions, and both provide supporting ground observations.
Regarding the hydrological component of flash flood forecasting, current results suggest that we are still learning how best to exploit high-resolution observations of hydrological variables such as surface soil moisture (Brocca et al., 2017; 2024). Studies based on real data have produced contrasting outcomes, ranging from significant improvements to performance deterioration (e.g., Brocca et al., 2012; Cenci et al., 2017; Alvarez-Garreton, 2015; Brocca et al., 2024). These differences may arise from uncertainties in satellite products or from scale mismatches between observations and the spatial scales represented by hydrological and geo-hydrological models used for floods and landslides prediction.
To improve water availability assessments and obtain regional estimates, we will apply high-resolution fully coupled atmosphere–hydrology model systems. These systems allow investigation of complex soil–atmosphere interactions and feedbacks between soil moisture dynamics and precipitation. Regional climate simulations using coupled land–atmosphere models can consistently resolve the process chain from precipitation formation in the atmosphere to runoff production in streams (Arnault et al., 2016). Such models enable detailed analysis of feedback processes linking soil moisture distribution with convective cloud initiation (Arnault et al., 2021). However, the realism of these simulations must be validated against multi-variable observational datasets with comparable spatial and temporal resolution. Surface soil moisture (SSM) and Integrated Water Vapour (IWV) observations represent promising datasets for improving coupled model calibration (Patil et al., 2021; Wagner et al., 2022) and enhancing predictive skill for flood forecasting and water availability. Remote sensing products such as those envisioned by Hydroterra+ can bridge the resolution gap between field-scale measurements and kilometer-scale model outputs (Wolf et al., 2016; Kiese et al., 2018; Bliefernicht et al., 2018; Fersch et al., 2020).
Our science questions related to the use of Hydroterra+ satellite data in the hydrological part of the modelling chains for flash flood and landslide forecasting, and for supporting water management are:
- Which is the optimal SSM spatial/temporal resolution for the reduction of uncertainties in floods and landslides forecasting? And for estimating irrigation water use?
- Which hydrological model structure best exploits the information provided by high resolution SSM observations?
Is there a period of the year where observations provide more information for floods and landslides forecasting? And for irrigation water use estimation? - How far can soil moisture products reproduce field measurements of observatories and particularly Cosmic Ray Neuron Sondes (CRNS) derived aggregated values?
- How can soil moisture products be assimilated into Earth System models?
- How to improve the representation of physical processes in Earth System models to better reproduce soil moisture products?
- Can groundwater recharge be better estimated in Earth System Models by assimilation of SSM observations?
Our studies will focus on two target regions: Central Europe and the Mediterranean for flood and landslide analysis, and West Africa for water availability and soil moisture–precipitation feedback investigations.
SO3 science questions
Seasonal snow represents a major freshwater reservoir in mid and high latitudes and is essential for ecosystems, agriculture, and human societies. Snow formation, metamorphism, and melting play a key role in runoff generation and soil moisture replenishment. Despite its importance for climate, ecosystems, and water resources, comprehensive observations of key physical snowpack parameters are still lacking. These parameters are needed for monitoring, modelling, and predicting snowpack evolution, its contribution to runoff, and the boundary conditions controlling surface–atmosphere exchanges of energy and mass. A major limitation of current snow monitoring systems is the lack of continuous, spatially detailed observations of snow mass (snow water equivalent, SWE) and of melt and refreezing conditions, which are key variables for physically based snow models.
Many snow process models have been developed to simulate snow cover evolution and snowmelt runoff at different spatial and temporal scales (Magnusson et al., 2015). Current models used in the Alpine region are spatially distributed energy- and mass-balance models with multilayer snowpack representation, driven by meteorological nowcasting data or outputs of regional atmospheric forecast models (e.g. Vionnet et al., 2012; Olefs et al., 2020; Mott et al., 2023). Recent developments increasingly focus on data assimilation using remote sensing products. However, progress is limited by the lack of suitable satellite observations of key snow parameters, particularly coherent time series of SWE and snow melt–freeze conditions.
Passive microwave observations have provided space-based SWE measurements since the 1980s, offering long historical records. However, their spatial resolution is insufficient for mountain regions and complex terrain (Lettenmair, 2015). Synthetic Aperture Radar (SAR) provides higher spatial resolution, and several approaches using SAR backscatter in the Ku- to C-band range have been proposed to estimate SWE. A major limitation is the ambiguity between SWE effects and snow microstructure on the radar signal. Repeat-pass differential SAR interferometry (D-InSAR) at C-band or lower frequencies offers a physically based method for mapping SWE at high spatial resolution by measuring radar phase-delay changes in dry snowpacks (Nagler et al., 2022). A challenge is temporal decorrelation of repeat-pass SAR data due to snowfall altering the backscatter signal. This limitation could be mitigated by geosynchronous SAR systems providing dense D-InSAR time series for monitoring SWE changes (DSWE).
Geosynchronous SAR systems could also provide dense time series of snow melt states, enabling identification of dry, wet, and refreezing phases. Accurate representation of melt–freeze processes is essential for modelling snowpack energy balance, temperature, liquid water content, and water movement within the snowpack (Livneh et al., 2010; Quéno et al., 2020). Melt–refreeze cycles are particularly relevant in deep alpine snowpacks, where thermal inertia affects seasonal evolution. Refreezing processes can form crust layers that modify permeability and water transport, influencing runoff timing (Albert and Perron, 2000; Quéno et al., 2020). Continuous observations, especially from the early melt season, are therefore essential for representing these processes in snow models (Wever et al., 2016). Sub-daily geosynchronous SAR observations could provide a unique dataset for snow model initialization and validation.
SO4 science questions
EO-SAR ~weekly revisit time remains a major limitation for using InSAR to monitor rapidly evolving ground deformation, such as that occurring during and after earthquakes, volcanic crises, or during the buildup to landslides (Biggs and Wright, 2020). The availability of data with a short temporal revisit would allow investigation of several important scientific questions that cannot currently be addressed reliably using other satellite missions or ground-based observations. These include:
Volcanic processes:
- What processes occur as magma rises towards an eruptive event, and what deformation do these processes cause?
- Can we use short-revisit deformation data to reliably predict the onset of eruption or transitions from effusive to explosive activity, as occurred during the 2020/2021 St Vincent eruption (Dualeh et al., 2023)?
- Can we assimilate change mapping data (from amplitude and coherence) into lava flow models to forecast the route taken by lava flows?
Tectonic processes:
- Are there any precursory ground movements in the hours/days/weeks before major eruptions (as has been hypothesised by Bletery and Nocquet (2023) and others?
- Following an earthquake, can we use moderate-resolution deformation and change mapping data to reliably map earthquake damage in near-real time?
What are the frictional properties of faults, and how do these relate to local geology? Can rapid postseismic deformation observations in the hours/days following an earthquake constrain these properties? - How does the unusual viewing geometry (compared to LEOs) help constrain long-term tectonic strains (particularly NS motions, which are only weakly sensed by LEO InSAR)?
Landslides:
- How do the motions of deep-seated landslides accelerate in the buildup to catastrophic failures?
- Can short-revisit ground motion data be used to improve landslide failure forecasting over wide areas?
- Can short-revisit ground motion data be used to identify fast-moving landslides that are invisible to 12-day revisit LEO satellites?
- How do unstable slopes respond to shaking due to earthquakes?
- How do unstable slopes respond to extreme weather?