An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observations, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions.Since 2021, NASA’s Advanced Information Systems Technology (AIST) program has invested in two ESDT efforts to tackle the impacts of our changing climate. The establishment of ESDT for flood and air quality enabled our teams to formalize the software framework. The open-source framework is called the Integrated Digital Earth Analysis System (IDEAS). By working with the Apache Science Data Analytics Platform (SDAP) community, IDEAS is now a subproject of SDAP. The paper presents the ongoing development of IDEAS and its current applications.

This work explores a new concept of operations for observation of Earth events by a satellite sensor web that is able to reason about its capabilities and plan observations based on detected or requested events on Earth’s surface. An intelligent agile satellite sensor web is shown to produce more than twice the number of observations of a nadir-looking sensor web.

Monitoring of short-lived and highly-dynamic processes and events such as floods or forest fires has gained an increasing interest in Earth Observation, particularly as global climate change is affecting these processes. The observation of such dynamic events is often limited by the response time of human operation of Earth-Observing satellites or UAVs.To address this bottleneck, this paper presents a Modified Asynchronous Consensus Constraint-Based Bundle Algorithm (MACCBBA) for observation task allocation in Sensor Web mission concepts for Earth Observation. This algorithm allows for the decentralized allocation of observation tasks amongst a network of Satellites and UAVs based on recently measured data processed on board, or on messages received from other sensors or from the ground. This algorithm is also capable of reaching a feasible plan in a dynamic communications network such as the ones present in some Sensor Web mission concepts and allows for complex temporal constraints and dependencies between tasks to model the value of near-simultaneous co-observations by complementary or synergistic sensors.

The challenges in operational flood forecasting lie in producing reliable forecasts given constrained computational resources and within processing times that are compatible with near-real-time forecasting. Flood hydrodynamic models exploit observed data from gauge networks, e.g. water surface elevation (WSE) and/or discharge that describe the forcing time-series at the upstream and lateral boundary conditions of the model. A chained hydrologic-hydraulic model is thus interesting to allow extended lead time forecasts and overcome the limits of forecast when using only observed gauge measurements. This research work focuses on comprehensively reducing the uncertainties in the model parameters, hydraulic state and especially the forcing data in order to improve the overall flood reanalysis and forecast performance. It aims at assimilating two main complementary EO data sources, namely in-situ WSE and SAR-derived flood extent observations.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA's Advanced Information Systems Technology (AIST)'s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)'s FloodDAM project and the Centre National d'Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

GNSS-R is a technique that has demonstrated sensitivity to inland water bodies. Observations from CYGNSS can be used to map inland water bodies and extracting information from CYGNSS observations is the subject of many ongoing investigations. While the information in CYGNSS observations is useful, we are exploring methods to leverage the strengths of CYGNSS together with the strengths of other observations. This work is driven by the development of a Bayesian approach for combining synergistic observations together with those from CYGNSS. To support this approach, we developed methods for representing information from CYGNSS observations probabilistically. In this paper, we develop a logistic regression model to estimate surface water probability from CYGNSS observations. Understanding how to use CYGNSS to estimate surface water is the necessary first step in the development of a data fusion approach to surface water mapping. Although this work focuses on utilizing the GNSS-R data from CYGNSS, the data fusion approach we develop will serve as the preparatory framework for utilization of all GNSS-R constellations in hydrological data fusion in the future.

River flows are an essential component of the water cycle and are directly accessible for human consumption and activities. River water flux (i.e., river discharge) not only can be measured locally atin situgauges but also can be estimated at larger scales with the river routing models. However, the number ofin situgauges is declining worldwide while emerging river-related products from satellites are becoming more available. Especially, discharge products based on satellite altimetry water elevations are emerging. These altimetry missions provide different spatial and temporal coverages and may not provide the same amount of information. In this study, discharge products from two satellite altimetry missions (ENVISAT and JASON-2) were assimilated into the large-scale hydrologic model Intéractions Sol-Biosphére-Atmosphére-CNRM’s Total Runoff and Integrating Pathways (ISBA-CTRIP) using an ensemble Kalman filter, to correct the simulated discharge. This work investigates whether it is better to assimilate products with a dense spatial coverage but a lower temporal sampling (ENVISAT) or the opposite (JASON-2). Three experiments have been performed: the first two assimilated each product separately, and the last one assimilated the combined product. The open-loop normalized root-mean-square error evaluated againstin situdischarge (RMSEn) is 69%. RMSEn is decreased for all experiments. Specifically, it is slightly lower when assimilating ENVISAT-based discharge product (51%) than JASON-2 product (53%) as the ENVISAT-based product spatial coverage is denser. The best results are obtained when both products are assimilated (RMSEn=49%). These results are very encouraging and could be improved when the future Surface Water and Ocean Topography (SWOT) wide swath altimetry mission discharge product will be available.