Abstract. Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products, we use Soil Moisture Active Passive (SMAP) and Sentinel-1 data together with a unique data set of ground-based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a high-latitude study area within a highly heterogeneous landscape and diverse land use in Denmark, we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ CRNS observations. Sentinel-1 backscatter in both VV and VH polarizations shows a strong correlation with CRNS soil moisture at higher spatial resolutions (20–400 m) and exhibits distinct and meaningful signals at different land cover types. Satisfactory statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. Accounting for different land use in the downscaling algorithm additionally improved the spatial distribution. However, the downscaling algorithm investigated here does not fully account for the vegetation dependency at sub-kilometre resolution. The study suggests that future research focussing on further modifying the downscaling algorithm could improve representative soil moisture patterns at a fine scale since backscatter signals are clearly informative. Highlights. Backscatter produces informative signals even at high resolutions. At the 100 m scale, the Sentinel-1 VV and VH polarizations are soil moisture dependent. The downscaling algorithm is improved by introducing land-cover-dependent clusters. The downscaled satellite and CRNS soil moisture agree best at the agricultural site.

The paper analyzes the national DK-model hydrological information and prediction (HIP) system and HIP portal viewed as a ‘digital twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can make room for plug-in submodels with real-time boundary conditions made available from an HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges were addressed as part of the Joint Governmental Digitalization Strategy 2016–2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terabytes of hydrological model data to the portal, with robust calibration methods and hybrid machine learning (ML) being key parts of the deliverables. This paper discusses the challenges and potentials of further developing the HIP digital twin with ‘plug-in digital twins’ for local river basins, including feedback to the national level.

Abstract. Even though irrigation is the largest direct anthropogenic interference with the terrestrial water cycle, limited knowledge on the amount of water applied for irrigation exist. Quantification of irrigation via evapotranspiration (ET) or soil moisture residuals between remote sensing models and hydrological models, with the latter acting as baselines of natural conditions without the influence of irrigation, have successfully been applied in various regions. Here, we implement an novel ensemble methodology to estimate the precision of ET-based net irrigation quantification by combining different ET and precipitation products in the Indus and Ganges basins. A multi-model calibration of 15 models independently calibrated to simulate natural rainfed ET was conducted prior to the irrigation quantification. Based on the ensemble average, the 2003–2013 net irrigation amounts to 246 mm/year (78 km3/year) and 115 mm/year (76 km3/year) in Indus and Ganges basin, respectively. Net irrigation in Indus basin is evenly split between dry and wet period, whereas 73 % of net irrigation occurs during the dry period in Ganges basin. We found that although annual ET from remote sensing models varied by 91.5 mm/year, net irrigation precision was within 25 mm/season during the dry period, which emphasizes the robustness the applied multi-model calibration approach. Net irrigation variance was found to decrease as ET uncertainty decreased, which related to the climatic conditions, i.e. high uncertainty under arid conditions. A variance decomposition analysis showed that ET uncertainty accounted for 81 % of the overall net irrigation variance and that the influence of precipitation uncertainty was seasonally dependent, i.e. with an increase during the monsoon season. The results underline the robustness of the framework to support large scale sustainable water resource management of irrigated land.

The paper analyses the national DK-model Hydrological Information and Prediction (HIP) system and HIP portal viewed as a ‘Digital Twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can give room for plug-in sub-models with real-time boundary conditions made available from a HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges have been addressed as part of the Joint Governmental Digitalization Strategy 2016-2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terra-byte of hydrological model data to the portal with robust calibration methods and hybrid Machine Learning (ML) being key parts of the deliverables. The paper discusses the challenges and potentials of further developing the HIP Digital Twin with ‘plug-in Digital Twins’ for local river basins including feedback to the national level.