The session presents and discusses recent advances in the remote sensing of hydrological cycle components as well as the application of remote sensing in hydrological modeling, often enhanced through the use of in-situ data collection, to better understand gaps, opportunities, and challenges in the study and simulation of hydrological processes in the 21st century. In particular, we encourage submissions focused on:
* Advanced remote sensing of water cycle components such as evapotranspiration, infiltration, and water abstraction for agricultural use
* Hydrological extremes such as floods and droughts
* Improving monitoring in poorly gauged and ungauged basins
* Developing novel methods of gathering and combining in-situ benchmark data with remote sensing approaches
*Optimal integration of remote sensing and hydrological modelling and the performance of using remotely sensed data in driving hydrological models, multi-variable calibration and spatial evaluation
* Tools tailored for end-users out of the remote sensing community
* Integrating remote sensing into operational systems (flood alerts, drought risks, …)
*The added-value of spatially downscaling remotely sensed data to improve hydrological modelling
This session is co-organised by the International Commission of Remote Sensing (ICRS) of the International Association of Hydrological Sciences (IAHS) in the context of the HELPING scientific decade (Hydrology Engaging Local People IN one Global world).
Orals: Wed, 6 May, 14:00–15:45 | Room 2.44
In many countries and regions of Southeast Asia, meteorological observation networks remain sparse, and high-accuracy precipitation data with rapid latency which are crucial for disaster mitigation are still not operationally available. Existing satellite precipitation products include those from the Global Precipitation Measurement Mission (GPM), such as NASA IMERG, and the JAXA GSMaP products. However, the near-real-time versions IMERG Early Run, GSMaP NOW, and GSMaP NRT exhibit data latencies of approximately 4 h, 1 h, and 4 h, respectively. Moreover, their estimation accuracies differ, and the most rapid product, GSMaP NOW, still shows limitations even in the qualitative detection of heavy rainfall.
Therefore, the objective of this study is to develop a near-real-time satellite precipitation product by integrating high-frequency infrared imagery from geostationary meteorological satellites with the GSMaP series. The datasets used include infrared imagery from the geostationary meteorological satellites Himawari‑8/9, microwave-based precipitation estimates from the GSMaP series, and elevation data derived from MERIT DEM. Precipitation estimation is performed using a deep-learning approach, in which infrared imagery, microwave precipitation data, and elevation data are used as input variables, and the output is a rainfall distribution with a spatial resolution of 2 km. The model is trained using meteorological radar data over Japan and subsequently applied to Southeast Asia.
The estimated precipitation product has a spatial resolution of 2 km, a temporal resolution of 10 min, and a data latency of 1 h. The results demonstrate that the proposed product successfully reproduces the heavy rainfall event that occurred in southern Thailand in late November 2025 and outperforms the existing GSMaP products.
How to cite: Fujimoto, K. and Tebakari, T.: Development of a Deep Learning–Based Satellite Precipitation Product for Hydrological Applications and Evaluation of Its Reproducibility for Extreme Rainfall, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16074, https://doi.org/10.5194/egusphere-egu26-16074, 2026.
Accurate estimation of evapotranspiration (ET) is fundamental for optimizing irrigation management in semi-arid regions, where water scarcity imposes severe constraints on agricultural productivity. The FAO-56 dual crop coefficient methodology provides a standardized framework for ET estimation; however, its fixed parameterization often fails to represent the spatial and temporal variability characteristic of heterogeneous cropping systems. To address these limitations, this study applies the Satellite Monitoring of Irrigation (SAMIR) model, a spatially distributed approach derived from the FAO-56 formulation that dynamically estimates basal crop coefficients (Kcb) from NDVI and explicitly accounts for vertical soil water redistribution. A data-fusion scheme combining Landsat and MODIS imagery was employed to produce daily NDVI maps at 30 m resolution, enabling high-resolution monitoring across an entire agricultural plain. Model performance was assessed by comparing ET estimates from a calibrated SAMIR configuration, the standard FAO-56 formulation, and three global satellite-based products (PML v2, WaPOR, and SSEBop) against in situ flux measurements at three contrasting sites within the Tensift Basin, Morocco: a drip-irrigated olive orchard (R3), a heterogeneous semi-arid landscape monitored by a large-aperture scintillometer (TAH-LAS), and a dense drip-irrigated wheat field (CHI-EC1). The calibrated SAMIR model consistently outperformed all other approaches, achieving monthly R² values of 0.50, 0.28, and 0.58 with corresponding RMSE of 0.85, 0.85, and 1.03 mm d⁻¹ at R3, TAH-LAS, and CHI-EC1, respectively. While the uncalibrated FAO-56 and PML v2 products exhibited moderate accuracy under certain conditions, WaPOR and SSEBop showed larger errors and lower correlations, including negative R² values and substantial PBIAS in sparse canopy environments. These findings demonstrate that spatially explicit, NDVI-driven modeling incorporating soil water dynamics and local calibration substantially improves ET estimation in semi-arid agricultural systems relative to both traditional FAO-56 approaches and existing global ET datasets.
How to cite: Manyari, Y., Simonneaux, V., Kharrou, M. H., Auclair, J., Khabba, S., and Er-raki, S.: Evaluating an NDVI-Driven, Locally Calibrated FAO-56 Evapotranspiration Model Against Global ET Products and In-Situ Measurements in Semi-Arid Agriculture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2279, https://doi.org/10.5194/egusphere-egu26-2279, 2026.
Abstract
Reliable knowledge of soil moisture at daily, field-scale (∼30 m) resolution is essential for flood forecasting, agricultural water management, and other applications that depend on accurate characterization of near-surface hydrologic conditions. This work presents a thermal-inertia-based framework that integrates multi-sensor remote sensing, in situ observations, and physically based modeling to estimate soil moisture at high spatial and temporal resolution while maintaining physical interpretability. Hourly land surface temperatures (LST) are obtained at 30 m resolution by downscaling geostationary GOES observations using Landsat-derived spatial covariates, enabling construction of spatially-detailed diurnal surface temperature time series. Hourly surface energy-balance components are retrieved from ERA5, providing net radiative forcing information, from which ground heat flux is calculated as a residual. ERA5 fluxes represent spatially aggregated thermal forcing across widely heterogeneous landscapes. Local (30 m scale) subsurface heat dynamics vary greatly due to differences in soil properties, vegetation, and moisture state. In situ observations from the International Soil Moisture Network (ISMN) are used to model how local subsurface heat dynamics depart from those implied by coarse-scale energy forcing. This step employs a support vector regression (SVR) modeling strategy that is well suited for multiple, non-linear, and non-independent predictors. The SVR model derives a physically interpretable quantity representing heat dynamics within the soil profile as a function of surface thermal response (LST magnitude, diurnal amplitude, and phase lag) and geographic context. Soil moisture is subsequently estimated using an SVR approach. In this step, soil moisture is inferred from observed LST amplitude and LST lag relative to radiative forcing, together with the modeled representation of subsurface heat dynamics. To address periods when acceptable thermal remote sensing is unavailable, the framework is coupled with a physically based water-balance model. This model propagates soil moisture states through intervals with limited thermal data. The resulting hybrid framework enables daily, field-scale soil moisture estimation that is physically grounded and well suited for hydrologic forecasting, agricultural decision support, and environmental monitoring.
How to cite: Zollweg, J.: Remote sensing of soil moisture at high spatiotemporal resolution using thermal inertia , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15108, https://doi.org/10.5194/egusphere-egu26-15108, 2026.
Environmental flow (e-flow) assessment requires robust characterization of both naturalized (baseline) and regulated (current) flow regimes at ecologically relevant spatial scales. The Lerma–Santiago River Basin (Mexico) is one of the most socio-economically important and hydrologically monitored basins in the country, yet translating point-based discharge observations into consistent, basin-wide flow information for environmental flow assessments remains challenging due to flow regulation, reservoirs, diversions, and spatial heterogeneity in climate forcing. Here we present a high-resolution hydrological modelling framework designed to generate spatially explicit discharge time series for environmental flow assessment and monitoring across the full basin.
The modelling system couples the VegET agro-hydrologic model for runoff generation with the vector-based mizuRoute routing model to simulate discharge along a MERIT-derived high-density river network and associated hydrologic response units (HRUs), enabling flow estimation across both major rivers and smaller tributaries. To quantify uncertainty linked to precipitation forcing, VegET is driven by multiple rainfall datasets (CHIRPS v2, CHIRPS v3, ERA5, and MSWEP), producing an ensemble of runoff and discharge simulations. Two flow regimes are generated: (i) a naturalized regime using VegET–mizuRoute to represent baseline hydrology without reservoir regulation, and (ii) a regulated regime integrating mizuLake, which explicitly accounts for lakes and reservoirs within routing to reproduce anthropogenically altered flow dynamics. Given the strong monitoring network in the basin, simulated discharge is additionally bias-corrected using observed streamflow, improving the realism of monthly hydrographs and low-flow/high-flow characteristics.
The resulting discharge products provide a consistent, reach-scale representation of flow variability and uncertainty, delivering the core hydrological inputs required to define, compare, and track e-flow targets across the Lerma–Santiago Basin under both naturalized and managed conditions.
How to cite: Velpuri, N. M., Akpoti, K., Owusu, A., Bakuri, R., Yeboah, F., Koduah, R., Palacios-Sanchez, L., Mar Pecero, M., Galindo, S., and Avila Cedillo, R.: Modeling naturalized vs regulated flow regimes for E-Flows: Bias-corrected VegET–mizuRoute–mizuLake modelling in the Lerma–Santiago Basin, Mexico, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15713, https://doi.org/10.5194/egusphere-egu26-15713, 2026.
The Satluj-Ghaggar river floodplains comprise a heterogeneous landscape, encompassing urban, agricultural, and industrial regions, which extend across the Punjab, Chandigarh, Haryana, and Himachal Pradesh states of the northern Indo-Gangetic Plains (IGP). Previous studies have reported groundwater quality deterioration with harmful heavy metals such as Mn, Fe, Ni, Zn, As, Tl, U, and Se exceeding WHO/BIS thresholds and associated health impacts, including gastrointestinal and skin issues (Kumar et al., 2024), along with groundwater over-extraction-induced land subsidence in parts of this study region, such as western Lundiana, where reported subsidence rates range from -2 to -21 mm/yr (Shankar et al., 2024). However, integrated assessments linking groundwater stress to both human health risks and infrastructure damage remain limited.
This study provides a comprehensive evaluation of groundwater-related health hazards and land subsidence-driven infrastructure exposure, utilizing hydro-geochemical observations and satellite-based analysis. Using hydro-chemical analysis, we computed non-carcinogenic health indices (HI) for pre- and post-monsoon seasons based on concentrations of 19 heavy metals measured in groundwater (n = 69) and surface water (n = 11). The results indicate that areas exposed to moderate (HI = 1-4) to severe (HI > 4) health risks increased during the post-monsoon season, with children exhibiting higher vulnerability than adults. By integrating population data into the exposure analysis, we observe that rapidly urbanizing areas exhibit high exposure to health risks.
For assessing land deformation and infrastructure vulnerability resulting from groundwater over-drafting, we use multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time-series analysis over the period 2016-2023, utilizing Sentinel-1 data from the European Space Agency (ESA). We observed an average vertical land motion rate of approximately -6.1 mm/yr across the floodplain. Such deformation trends correlate with long-term average groundwater-level declines of approximately -0.3 m/yr observed in nearly 150 monitoring wells of Central Groundwater Board (CGWB) across the region.
The ongoing subsidence poses increasing risks to built-up areas, mainly near the Chandigarh, Mohali, Kharar, Rajpura, and Deabassi regions, where surface cracks and structural damage have been reported. Using Land Use Land Cover (LULC) map and subsidence results, it was calculated from ArcGIS that approximately 28% of built-up land is exposed to severe (< -20 mm/yr) subsidence-induced potential infrastructure damage. We are further analysing seasonal variations in both health and infrastructure risks to identify periods of higher vulnerability.
How to cite: Girap, A., Chawla, S., Bhagat, S., Kumar, M., and Ojha, C.: Assessing Human Health and Infrastructure Vulnerability to Groundwater Stress in the Northern Indo-Gangetic Plains, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20238, https://doi.org/10.5194/egusphere-egu26-20238, 2026.
Runoff and snow simulations can be enhanced using multi-spectral snow cover mapping. The main focus of this research is to evaluate the potential of a new snow cover fraction (SCF) product to improve hydrological simulations. The SCF product derived from combined Sentinel-3 SLSTR and OLCI observations based on a multispectral unmixing method at a spatial resolution of 0.00200° × 0.00200°. This study first assesses the accuracy of a new snow cover fraction (SCF) product against in-situ snow depth measurements at climate stations. It then investigates the impact of assimilating this product on runoff and snow simulation using a conceptual semi-distributed hydrological model. For this purpose, the hydrologic model is calibrated with and without snow cover product using multi-objective calibration and single-objective calibration schemes, respectively. Based on the multi-objective calibration, both runoff and snow are optimized, whereas the single-objective calibration approach focuses on runoff alone. We evaluated the proposed framework across 188 catchments in Austria. The results showed a strong agreement between SCF and snow depth measurements, with a median overall accuracy of about 95%. The analysis of the results demonstrated that the added value of incorporating snow products into model calibration is more pronounced for snow simulation than for runoff estimation. Hence, runoff and snow simulations improved in 39% and 84% of catchments during the validation period, respectively. The findings reveal that our approach enhances the model’s efficiency to more effectively capture snow cover dynamics, which supports more consistent water balance simulations and provides a stronger basis for modeling of snow-induced high-flow conditions.
How to cite: Tanhapour, M., Parajka, J., Schwaizer, G., Vreugdenhil, M., Kohnová, S., Hlavčová, K., Výleta, R., Szolgay, J., and Okhravi, S.: The value of Sentinel-3 snow cover fraction data in improving hydrological simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4893, https://doi.org/10.5194/egusphere-egu26-4893, 2026.
The agricultural sector is the biggest and least efficient water user, accounting for around 70% of total water use in the Mediterranean region, which is already strongly impacted by climate change with prolonged drought periods, imposing limitation to irrigation water availability. The objective of this study was to develop a procedure for the monitoring irrigation water use and evapotranspiration across different agricultural districts in the Po Valley at 30 m of spatial resolution from 2022 to 2024 and over the entire agricultural area of the basin at 250 m from 2015 to 2024.
The analysis is based on the FEST-EWB model, that computes continuously in time both soil moisture (SM) and evapotranspiration based on the coupling of the energy and water balances. A specific model procedure has been implemented to consider the presence of flooded paddies. The model has been calibrated and validated over non-irrigated areas, against land surface temperature (LST) from downscaled MODIS data at 250m and LANDSAT data at 100 m; Sentinel 1 soil moisture data and local eddy covariance evapotranspiration measurements.
The model has been run using as input the past meteorological forcings (ECMWF ERA5-Land or ground network) and vegetation data from Sentinel2 at 30 m and MODIS at 250 m. Groundwater dynamic was considered from the available groundwater wells from the regional networks.
The actual irrigation volumes have been estimated through the calibrated model implementing three different irrigation strategy: the FAO approach based on SM crop stress thresholds (Allen et al., 1998), the separate and jointly assimilation of satellite LST and SM data to update the modeled fluxes and estimate the irrigation volumes. The different irrigation efficiencies have been considered when modelling the irrigation volumes.
Overall, the results suggested that the yearly total irrigation volumes modeled with the FAO approach are generally underestimated in respect with the observed water allocations. Higher agreement was found when satellite LST or SM are assimilated, but with differences across the years.
How to cite: Corbari, C., Paciolla, N., Angeloni Valente, C., Belluati, C., Berendsen, S., and Sheffield, J.: Multi-scale monitoring of irrigation volumes and evapotranspiration by assimilating satellite data into an energy-water balance model , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12508, https://doi.org/10.5194/egusphere-egu26-12508, 2026.
Much of the change that our planet will experience in the coming decades is driven by climate. In this ever‑warming world, water is of central importance, and Hydroterra+ is an ESA mission concept designed to fill key observation gaps and improve our understanding of the water cycle and water management.
Hydroterra+ focuses primarily on the Mediterranean basin, identified as a climate‑change hotspot due to its transitional position between subtropical and mid‑latitude zones, and sub‑Saharan Africa, one of the regions most affected by increasing climate shocks. The mission also foresees the possibility of monitoring specific areas on an hourly basis, for example in emergency situations or for dedicated scientific studies.
The mission is based on a geostationary Synthetic Aperture Radar (SAR) operating in interferometric mode. Its near‑continuous observation capability, short revisit time, and very wide coverage make Hydroterra+ uniquely suited to monitor water‑related processes on timescales of hours at regional scale. Existing and planned low‑Earth‑orbit missions are poorly matched to this need.
Hydroterra+ will provide a suite of sub‑daily products, including:
- Integrated Water Vapour (IWV) to improve understanding and forecasting of extreme weather events;
- Surface Soil Moisture (SSM) to advance knowledge of soil dynamics and related processes, including agricultural practices;
- Snow Water Equivalent (SWE) and Snow Melt Phases (SMP) to improve understanding of mid‑latitude cryosphere processes;
- Surface Displacements (DQ) and Change Maps (CM) to enhance knowledge of geophysical hazards.
Hydroterra+ is an Earth Explorer 12 candidate mission and is currently progressing through Phase‑0 studies. This paper will present the mission concept and the first results of its feasibility analysis, including outcomes from several Observing System Simulation Experiments (OSSEs) conducted across the scientific mission domains.
How to cite: Rossi, C., Monti Guarnieri, A., and Parodi, A.: Hydroterra+: an EO mission concept to reveal sub-daily water processes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19309, https://doi.org/10.5194/egusphere-egu26-19309, 2026.
Ephemeral sand rivers (ESRs) constitute a widespread but largely overlooked hydrological feature across the West African Sahel. These wide alluvial channels, dry for most of the year, store substantial volumes of subsurface water following seasonal flow events. Despite their importance for hydrological functioning and climate resilience in semi-arid environments, regional-scale information on their distribution, characteristics, and potential subsurface storage remains scarce. This study develops a multi-platform, remote sensing-based methodological framework (integrating satellite imagery, digital elevation data, machine learning and hydrological analysis) to systematically detect and map ESRs, with application in Burkina Faso, Mali, and Niger,
We first delineate a high-resolution river network using MERIT DEM-derived hydrological products refined with national hydrographic datasets and enhanced remote-sensing river masks. River flow intermittency is predicted through a Random Forest model trained on 1,269 gauging stations across Africa, enabling classification of rivers into perennial, weakly intermittent, highly intermittent, and ephemeral categories. Focusing on the ephemeral class draining large catchments (≥ 1,000 km²), we define a 250-m buffer along selected river reaches to support consistent remote sensing analysis.
Sentinel-2 multi-temporal imagery (2020-2024) is used to characterize land surface conditions and separate sandy riverbeds from surrounding land cover. An initial evaluation of sand-related spectral indices (NDESI, NSI, NDSI) combined with NDVI reveals that the NDESI-NDVI biplot provides the best discrimination of sandy substrates, but with limited detection performance when applied at regional scale (sensitivity 42-72%). We therefore implement a supervised LULC classification using a Random Forest classifier trained on 89,986 labelled samples derived from 313 ground-truth polygons interpreted from Maxar high-resolution imagery. Multi-season compositing proves essential, as spectral signatures of sand, bare soil, and vegetation vary markedly between dry, wet, and transitional periods. The final classification achieves an overall accuracy of 93% and F1-scores ≥ 0.90 for all classes, clearly outperforming spectral thresholding approaches.
To infer zones with potential shallow groundwater storage, we combine classified sandy riverbeds with riparian vegetation patterns and canopy height data (≥ 5 m). This proxy-based assessment identifies 402 km of ESR segments (19% of total ESR length) exhibiting persistent riparian vegetation indicative of shallow water availability. Although detailed hydro-geophysical verification would be required for site-specific development, these segments represent promising targets for nature-based water storage interventions and smallholder-led agricultural initiatives. The spatial integration of ESR mapping with population distribution highlights areas where such opportunities may be particularly relevant, although further socio-hydrological analysis is needed to quantify practical accessibility and use.
This study demonstrates the value of synthesising DEM-based hydrological information, multispectral satellite observations, canopy height products, and machine learning to characterize hydrological processes in data-sparse semi-arid environments. The resulting ESR inventory provides a foundation framework for improved understanding of river intermittency, subsurface storage dynamics, and seasonally accessible alluvial aquifers across the Sahel, and offers a scalable framework for application to other dryland regions worldwide.
How to cite: Yonaba, R., Belemtougri, A., Michailovsky, C., Stigter, T., Mounirou, L. A., and Van der Zaag, P.: A Multi-Source Remote Sensing and Machine Learning Framework for Detecting Ephemeral Sand Rivers across the West African Sahel, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-122, https://doi.org/10.5194/egusphere-egu26-122, 2026.
Conceptual hydrological models play a central role in streamflow estimation, yet their lumped formulations often fail to represent spatial variability in hydrological processes, thereby limiting their performance. With the growing availability of satellite observations, global reanalysis products and high-resolution terrain datasets, grid-based conceptual modelling has become increasingly feasible. However, despite the widespread use of the Génie Rural à 4 Paramètres Journalier (GR4J) model, its grid-to-grid implementations remain limited, even though these frameworks offer clear advantages for capturing spatial heterogeneity and enabling modelling in data-scarce regions. This study presents a grid-based GR4J framework coupled with Muskingum-Cunge routing and driven entirely by remote sensing and reanalysis-based inputs, applied across four Australian catchments representing tropical, semi-arid, temperate, and humid subtropical climates. To implement this framework, the catchments were discretised into 0.1° grids aligned with the spatial resolution of GPM IMERG precipitation and GLEAM potential evapotranspiration, enabling these inputs to be applied at the grid level to generate runoff. Flow routing was done using Muskingum-Cunge method through the channel grids obtained based on flow-direction map derived from a digital elevation model (DEM), enabling sequential upstream-to-downstream runoff transfer across the grid network. Model calibration and validation were carried out using observed daily streamflow at the study catchment outlets for the periods 2005–2018 and 2018-2023 respectively. Model performance was evaluated using the Kling-Gupta Efficiency (KGE) under two configurations: the grid-based framework and the conventional lumped GR4J model. Both achieved calibration KGE values above 0.6; however, the grid-based model consistently showed superior performance during validation. The tropical basin exhibited the greatest improvement, with KGE increasing from 0.09 to 0.51, while the semi-arid and temperate basins showed 21% and 14% gains, respectively. Performance in the humid subtropical basin remained comparable across both configurations. Overall, the grid-based framework shows clear benefits in accounting for spatial variability.
How to cite: Nair, G. B. and Ramsankaran, R.: A Grid-Based Conceptual Hydrological Modelling Framework Using Remotely Sensed Inputs: Preliminary Insights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2131, https://doi.org/10.5194/egusphere-egu26-2131, 2026.
Accurate precipitation monitoring is critical for Small Island Developing States (SIDS) like Saint Lucia, where complex topography and high vulnerability to tropical cyclones necessitate precise data for disaster preparedness and water resource management. While ground-based radar provides high-resolution estimates, it is spatially limited. Conversely, satellite products offer global coverage but often suffer from accuracy issues at island scales. This study presents a comprehensive evaluation and calibration framework comparing NASA’s IMERG and ERA5 reanalysis products downscaled to 2km spatial resolution against Caribbean radar observations with 1km spatial resolution. The objective is to quantify satellite performance during extreme weather events and demonstrate a robust operational workflow for bias correction.
The analysis employs a dual-framework approach. First, we conducted a spatial and temporal validation across nine major hurricane and storm events (2007-2024), including Hurricanes Dean and Tomas. This phase utilized Root Mean Square Error (RMSE) and Structural Similarity Index (SSIM) to assess agreement between radar, IMERG, and ERA5 datasets, alongside a point-based comparison at the Grace Station utilizing rain gauge data. Second, we developed a seven-step operational workflow using continuous 2021 data to implement pixel-wise Quantile Mapping bias correction. This workflow involved parallel processing of raw radar imagery, precise temporal-spatial matching, and the training of reusable correction functions.
Analysis of the storm events reveals that uncorrected satellite and reanalysis products systematically underestimate rainfall intensity, particularly during hazardous convective peaks. Satellite storm-mean values often capture only 10-60% of radar-observed totals. While ERA5 and IMERG exhibit comparable performance, both struggle to resolve fine-scale convective structures, yielding modest SSIM scores (averaging 0.15-0.40) that degrade as storm intensity increases. Point-based analysis at Grace Station highlights distinct "smoothing" effects in gridded products. For example, during Hurricane Tomas, a rain gauge recorded a peak intensity of 1516 mm/h (likely an extreme burst or anomaly) and radar recorded 33 mm/h, whereas satellite and ERA5 estimates were smoothed to 15.33 mm/h and 19.18 mm/h, respectively.
To address the identified underestimation, the Quantile Mapping method applied to the 2021 dataset yielded significant improvements. The correction reduced systematic bias by 87% (from a relative bias of -185% to -23%) and decreased the Mean Absolute Error (MAE) by 43%. Crucially, the correction dramatically improved the spatial structure of the precipitation fields, raising the temporal SSIM by 70% (from 0.490 to 0.834). The methodology successfully extended the dynamic range of satellite estimates to match radar observations, correcting the "capped" maximum values (from ~8 mm to >87 mm) and enabling a more realistic representation of extreme events. This study confirms that while raw satellite and reanalysis products underestimate intense Caribbean precipitation, they can be effectively calibrated using ground-based radar. The proposed workflow establishes a reusable framework for training bias correction functions, allowing meteorologists and hydrologists in Saint Lucia to better model flood risks and enhance climate resilience.
How to cite: shafei, A. and Cioffi, F.: Bridging the Gap Between Radar and Satellite: A Multi-Source Validation and Bias Correction Framework for Precipitation Estimation in Saint Lucia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5151, https://doi.org/10.5194/egusphere-egu26-5151, 2026.
Accurate water resources assessment is the fundament of integrated water resources management. However, conventional assessment methods rely on sparse gauging networks, empirical reduction procedures, and intensive calibration, which can lead to reduction distortion, failure, and large uncertainty in data-limited basins. This study developed a total remote sensing process framework to assessment water resources. It retrieves precipitation, condensate and evapotranspiration by remote sensing data, and constrains the fluxes into a set of dual-closure equations which simultaneously enforces the water-energy balances. Taking Yongding River Basin as the testing region, the results show that runoff, that is the blue water resources, generated start from elevation of 2400 m, accumulating along downslope, and reaches the maximum with 2.049 billion m³ at elevation of 1050 m. Below the elevation, the water resources presented a dissipation toward outlet. The assessment is not only reduced the dependency of ground observation, but also increased the detailedness, including formation, retention, dissipation across complex terrain and consumption. It provides a water resource accounting solution for where situ observations are incomplete or inaccessible.
How to cite: Chen, J. and Wang, Z.: Coupling Water-Energy Balances for Remote Sensing Water Resources Assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8859, https://doi.org/10.5194/egusphere-egu26-8859, 2026.
The coastal waters formed by the Saemangeum Dam are difficult to monitor and predict in terms of water quality because of complex mixing of seawater and freshwater caused by artificial water gate control, high optical variability, and other factors. The Saemangeum Dam area on South Korea's west coast is a representative artificial coastal water system where inflow of watershed water, seawater exchange through water gate operation, and nutrient accumulation interact nonlinearly, frequently leading to eutrophication and algal blooms.
This research developed a forecasting system for chlorophyll-a (Chl-a) and total phosphorus (T-P) levels in the Saemangeum aquatic region by integrating geostationary satellite GOCI data with artificial intelligence methods. We combined GOCI observations from 2011 to 2020 with in situ water quality measurements from 13 sites to compare machine learning and deep learning algorithms for estimating water quality.
To identify effective input variables for the optically complex Saemangeum environment, satellite reflectance was combined with meteorological information, gate-controlled water exchange, and nutrient indicators. Seven input scenarios were designed to evaluate how progressive variable integration influences prediction performance, and representative machine learning and deep learning models were compared.
Results showed that scenarios incorporating nutrient-related variables yielded the most robust predictions for both chlorophyll-a and total phosphorus. While deep learning models captured complex relationships under standard evaluation, spatially independent validation highlighted that machine learning approaches maintained more stable generalization under strong spatial heterogeneity and limited training data. This finding suggests that model suitability depends on data structure and validation context rather than algorithm complexity alone.
Overall, the artificial intelligence-based water quality prediction system presented in this study can effectively monitor fluctuations in chlorophyll a and T-P in embankment reservoir waters, and can be utilized as a practical tool for early warning systems and the development of water quality management policies. It is expected to contribute to strategies for responding to algal blooms and managing large-scale artificial coastal waters.
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00515357).
How to cite: Lee, Y., Kim, D., Kim, S., Han, D., Kim, H., Kim, S., and Yeom, J.-M.: Satellite-Based Estimation of Chlorophyll-a and Total Phosphorus in Saemangeum’s Hydrodynamically Complex Waters Using Machine and Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9162, https://doi.org/10.5194/egusphere-egu26-9162, 2026.
Sentinel-1 SAR enables high-resolution soil moisture estimation using the C-band backscatter coefficient. To account for attenuation and volume scattering effects in densely vegetated areas, soil moisture retrieval methods using the Water Cloud Model (WCM) are widely employed. Traditional WCM utilizes the Normalized Difference Vegetation Index (NDVI) and C-band Radar Vegetation Index (RVI) as vegetation parameters, which have limitations due to the saturation of the NDVI and low penetration of C-band. To overcome these problems, this study introduced SMAP L-band Vegetation Optical Depth (VOD) as a vegetation parameter for WCM and applied it to the complex mountainous terrain of the Korean Peninsula. In the parameter estimation process of the WCM, the quantitative relationship between in-situ observations and soil texture was established, enabling the dynamic spatial extension of model parameters to ungauged regions. The validation results with in-situ soil moisture data showed improved correlation coefficient R and ubRMSE compared to existing WCM methods. It was found to enhance the accuracy of soil moisture estimation by more precisely correcting signal attenuation caused by vegetation in complex terrain. This study demonstrates the validity of high-resolution hydrological parameter estimation in complex terrain through satellite data fusion and is expected to provide essential foundational information for precise drought monitoring and water resource management in the future.
Keywords: Soil Moisture, Sentinel-1, Vegetation Optical Depth, Water Cloud Model, Multi-source fusion, Complex terrain
Acknowledgment
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Climate, Energy and Environment (MCEE)(RS-2023-00230286). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2022-NR070339).
How to cite: Jeong, J., Kim, D., Lee, S., Kim, W., and Choi, M.: Integration of SMAP L-band VOD and Multi-source Satellite Data for Improved Sentinel-1 Soil Moisture Retrieval in Complex Terrains, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16272, https://doi.org/10.5194/egusphere-egu26-16272, 2026.
Annual monsoon inundations are essential ecosystem services in the Cambodian Mekong Delta. These floods support agricultural cycles, fish production, ecosystem regulation and potentially groundwater recharge. However, recent development of hydropower and irrigation infrastructures, land use and climate change have influenced hydrological processes including surface water and groundwater exchanges. Satellite imagery like Sentinel-1 has been widely used in previous studies to map inundation extent due to its temporal availability in all weather conditions. Nevertheless, Sentinel-1 is limited by unwanted interference from vegetation, terrain, and anthropogenic features. Data from the Surface Water and Ocean Topography (SWOT) mission launched in December 2022, equipped with Ka-band Radar Interferometer (KaRIn), provides a complementary perspective by providing two-dimensional water surface elevations and extent from which water volumes can be estimated. To help better characterise surface water flows that may percolate into the deep aquifer, this study presents a methodology for tracking river dynamics and floodplains inundation over the Mekong Delta in Cambodia, using a combination of SWOT and Sentinel-1. The datasets obtained from the two satellites are compared with in-situ water level and discharge data and evaluate their effectiveness for tracking the river dynamics, inundation extent, and flow quantification. This validation is performed over the Tonle Sap River, a tributary of the Mekong River, and provides insights into the functionality of the ecosystem within the study region. This study also explores potential estimation of the possible contribution of the groundwater recharge areas to the groundwater budget.
How to cite: Bala Muhammad, I., Oubanas, H., Orieschnig, C., Malaterre, P.-O., Baudron, P., Massuel, S., Cazals, C., and Lun, S.: Tracking Fluvial Inundations and Groundwater Recharge Areas in the Upper Mekong Delta by Combining Sentinel-1, SWOT, and In-Situ Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12486, https://doi.org/10.5194/egusphere-egu26-12486, 2026.
Precipitation is important for hydrological monitoring and modeling. The accuracy of Mean Areal Precipitation (MAP) estimation relies largely on the the configuration of precipitation network. This study proposes a novel framework for optimizing rain gauge networks by leveraging high-quality reanalysis precipitation data to evaluate MAP estimation. Using the Qingyi River Basin as a case study, we employ the CMA Multi-source Precipitation Analysis System (CMPAS) data to characterize precipitation spatial patterns and establish a benchmark for network optimization. The framework introduces two metrics, i.e., bias of mean areal precipitation (MB) and Kullback-Leibler divergence (KL), to quantify differences between gauge-derived and CMPAS-derived precipitation fields. Evaluation of the current network reveals significant MAP estimation discrepancies in sub-basins with high precipitation variability. Through importance analysis of candidate gauges and hierarchical optimization, we demonstrate that strategic gauge placement guided by precipitation patterns markedly improves MAP estimation accuracy. The optimized network reduces MAP estimation bias by over 5\% in critical sub-basins. This framework offers advantages over traditional methods by enabling preliminary analysis of proposed gauge locations and explicitly incorporating spatial distribution considerations. The methodology proves effective for both network expansion and rationalization while maintaining computational efficiency through its hierarchical optimization strategy
How to cite: Fang, Y.-H., Qian, R., and Cao, Y.: Optimizing Precipitation Gauge Networks for Hydrological Modeling Using High-Quality Reanalysis Data: A Spatial Pattern-Based Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4334, https://doi.org/10.5194/egusphere-egu26-4334, 2026.
The Himalayan River systems sustain the livelihoods of millions of downstream inhabitants by providing water for diverse needs. However, the Himalayas are among the most adversely impacted ecosystems in the world due to global warming and climate change. Many Himalayan basins have reported an increase in extreme precipitation events and floods. As concerns grow about the changing hydrological regime and future water availability, accurately estimating water yield and streamflow under climate change scenarios becomes essential. Hydrological modelling in the Himalayan basins, however, is challenging due to the lack of in-situ measurements. With limited rain gauge networks in these areas, and the majority of observation stations located in valley bottoms, higher-elevation climates remain underrepresented. In recent times, satellite precipitation products (SPPs) and reanalysis products (RAPs) have emerged as alternatives to ground-based observations, offering precipitation estimates with good spatial and temporal resolution. In this study, several SPPs and Re-Analysis Products (RAPs)—including APHRODITE, CHIRPS, ERA5, ERA5-LAND, IMDAA, GPM-IMERG, and PERSIANN—were evaluated for their prediction accuracy and hydrological applications by comparing them with IMD gridded data in the Upper Beas Basin, located in the western Himalayas. The precipitation products (PPs) were assessed based on their ability to capture daily, seasonal, and annual precipitation, as well as extreme precipitation indices (90th, 95th, and 99th percentile rainfall) using statistical metrics such as correlation coefficient (CC), RMSE, R² and relative bias (RB). Their performance in detecting rainfall events was evaluated using Categorical metrics such as POD, FAR, and CSI. Their statistical performance was ranked as: APHRODITE > ERA5-LAND > PERSIANN > ERA5 > GPM > IMDAA > CHIRPS. Overall statistical performances of APHRODITE, ERA5-LAND, ERA5, GPM and PERSIANN were found to be satisfactory. Further, to assess the hydrological utility of these PPs, the SWAT model was employed to generate basin water yield and water balance components using different products. APHRODITE, PERSIANN and GPM satisfactorily reproduced streamflow well (NSE = 0.88, 0.65, & 0.61, RSR = 0.34, 0.59, & 0.62; and PBIAS = -2.18%, -10.86%, & -18.98% respectively). PERSIANN and APHRODITE generated the water yield with an error of 1.68% and 3.91%. Only through hydrological validation, it was revealed that ERA5-LAND, despite ranking second in the statistical evaluation, exhibited poor hydrological performance (PBIAS = -39.14%), while GPM proved to be capable of reproducing streamflow although it performed poorly in statistical evaluation. Hydrological validation not only revealed such discrepancies but also provided insights into water balance components, water yield, and flow extremes such as high flows and low flows. Therefore, this study recommends hydrological validation in addition to statistical evaluation for selecting reliable precipitation datasets for hydrological modelling in complex mountainous regions.
Keywords: Beas basin, Precipitation products, categorical metrics, Hydrological evaluation, streamflow, water yield
How to cite: Somisetty, A., Singh, V., and Sharma, A.: Hydrological Validation of Satellite-based and Reanalysis Precipitation Datasets in a Himalayan River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-689, https://doi.org/10.5194/egusphere-egu26-689, 2026.
With the challenges of cliamte change and increasing food demand, North China has produced more than 65% of cereal productions of China, and is of great importance in food security. However, North China is facing with significant ecological degradation subsequent to water withdrawal for irrigation. As a result, lots of rivers run drying-up and groundwater table declined fast in the past several decades. There is a observed positive precipitation trend in past 2 decades over North China, and most rivers' flow has been measured increasing, accompanied with vegeration revigorous, i.e. increase in vegetatoin cover. Under this background of climate and land cover change, we find groundwater continued to subject overpumping and has played a key role to support the gain of food production and vegetation restoration during this period. In this study, we will present the results in detail to use combined method of ground-based and GRACE observations to screening up groundwater drought risk under the increasing precipitation or wetting background. Using some machine learning algoriths, we extropolated the GRACE observed Terrestiral Water Storage (TWSA) and Groundwater Storage (GWSA) back to 1960s, and proposed a groundwater drought index (GDI) to investigate the groundwater drough characteristics under the effects of climate change and human exploitation. And we found that in major basins across North China, such as Tarim River Basin, Yellow River Basin, Hai River Basin, and Songhua River Basin, groundwater experienced more frequent and severe drought in recent 20 years, than it was in the past 4 decades before 2000. This is mainly caused by agricultural withdrawal and vegetation restoration. And in future, groundwater would be likely encounter with more severe drought threats.
How to cite: Shen, Y., Liu, M., and Guo, Y.: Monitoring groundwater drought risks in major agricultural regions of China: A combined perspective of GRACE- and groundbased obervations and modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2166, https://doi.org/10.5194/egusphere-egu26-2166, 2026.
The mountainous “water towers” of Northern China are crucial for regional water security but face threats from climate change and ecological restoration projects. Understanding their hydrological responses requires tools that capture both intense climatic events and long-term land cover changes. This study presents an integrated assessment using multi-source remote sensing data and hydrological modeling across two critical regions: the Taihang Mountains and the broader Beijing-Tianjin-Hebei (BTH) mountainous area.
We employed the InVEST model to simulate water yield (WY). For the Taihang Mountains (1990–2020), we introduced a detrending analysis framework coupled with the Optimal Parameters-based Geographical Detector (OPGD) to attribute drivers. Results show a significant WY decline (-0.66 mm/yr), primarily (86.46%) driven by climate change. Crucially, OPGD analysis revealed that in areas of sharp decline, precipitation intensity (Q=0.369) was a more dominant factor than total precipitation (Q=0.305), highlighting the key role of changing precipitation patterns.
To assess the long-term impact of large-scale vegetation restoration, we extended the analysis to the BTH mountains over four decades (1980–2020). Multi-period scenario analysis showed that while land use/cover change (LUCC) exerted a short-term negative effect on WY during initial afforestation (2000–2020), it shifted to a positive contribution over the full 40-year period, especially in the Bashang region (+37.80%). This indicates that ecological restoration, despite initial water consumption, can enhance water retention and yield benefits over decadal scales.
Our integrated approach demonstrates that combining process-attribution tools (OPGD) with long-term scenario analysis provides a holistic view of mountain hydrology. The findings underscore that sustainable water management must simultaneously address increasing precipitation extremes and harness the long-term hydrological benefits of ecological restoration.
How to cite: yang, H. and Cao, J.: Integrated Assessment of Water Yield in Northern China's Mountain Using Remote Sensing and Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3210, https://doi.org/10.5194/egusphere-egu26-3210, 2026.
Climate warming is reshaping cryospheric processes, hydroclimatic extremes, and ecosystem stability across the arid regions of Central Asia. This study presents an integrated assessment of climate change impacts on glacier dynamics, snow regimes, hydroclimatic extremes, and associated water–ecological risks.
Results indicate a pronounced decline in snowfall amount and duration since the late 20th century, accompanied by a widespread transition in snow drought regimes from precipitation-limited to temperature-driven conditions. Projections based on glacier evolution models suggest that glacier runoff in much of the Tianshan region is approaching, or has already passed, peak water. Peak glacier runoff in the Western Tianshan is projected to occur between the late 2020s and mid-century, depending on emission pathways, while the Eastern Tianshan likely entered a post-peak phase in the early 2020s. Meanwhile, the accelerated expansion of glacial lakes has raised the risk of glacial lake outburst floods (GLOFs), which are currently 3–4 times higher in the western subregion compared to other areas.
Concurrently, hydroclimatic extremes are intensifying. Heatwaves have become more frequent, longer-lasting, and more severe since the 1980s, particularly in the drylands of Central Asia, where declining soil moisture amplifies surface warming. Compound drought–heatwave events are projected to increase markedly under high-emission scenarios, with prolonged durations exceeding several weeks in some regions. Snow droughts are expected to occur more frequently, with warm snow droughts emerging as the dominant type and accounting for approximately two-thirds of future snow drought events by mid-century. These shifts signal a fundamental reorganization of drought dynamics, with cascading effects on hydrological, agricultural, and ecological systems.
Overall, this research highlights the escalating water-ecological risks in arid Central Asia driven by accelerated cryospheric change and intensifying heat extremes, and shifting drought regimes. The findings emphasize the importance of adaptive water management and climate resilience strategies to support sustainable development in this highly vulnerable region.
How to cite: Chen, Y., Li, Z., and Wang, C.: Water–Ecological Risks in Arid Central Asia Under Climate Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3314, https://doi.org/10.5194/egusphere-egu26-3314, 2026.
Ecological and water conservation projects, such as Returning Agricultural Land to Forest (RAF) and Returning Agricultural Land to River (RAR), have significantly altered land surface conditions and hydrological processes in many river basins. However, quantifying their spatial and temporal impacts remains challenging due to the complexity of land-use/cover change (LUCC) and the need for high-resolution data. This study focuses on the Qin River Basin, a major tributary in the middle reaches of the Yellow River, where RAF and RAR projects have been extensively implemented. We integrate multi-source remote sensing data, including the China Land Cover Dataset (CLCD) and SRTM DEM, with the Soil and Water Assessment Tool (SWAT) to simulate hydrological responses from 2010 to 2018. The model performed robustly (NSE: 0.70–0.72, R²: 0.71–0.79) and revealed that RAF reduced total runoff by 3.00%, with spatially heterogeneous effects: surface runoff increased in northern subbasins, lateral flow decreased in central regions, and groundwater flow rose dramatically (2366.67%). RAR scenarios showed that converting agricultural land to water bodies enhanced runoff components, with greater efficacy on slopes <15° compared to <6°. The study demonstrates the critical role of remote sensing in capturing LUCC dynamics and highlights the importance of spatially explicit planning for sustainable water resource management in semi-humid basins under intensive human intervention. Our approach provides a scalable framework for integrating remote sensing into hydrological modeling to assess and optimize ecological restoration strategies in data-scarce or heterogeneous regions.
How to cite: Cao, J. and yang, H.: Quantifying Hydrological Impacts of Ecological Restoration Projects in a Yellow River Tributary Using Remote Sensing and SWAT Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3640, https://doi.org/10.5194/egusphere-egu26-3640, 2026.
The Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) data set provides a long-term, consistent suite of global ocean-atmosphere climate variables over ice-free oceans derived from satellite passive microwave observations. Designed to support climate monitoring, air-sea interaction studies, and model evaluation, HOAPS offers more than three decades of key parameters such as vertically integrated water vapor, evaporation, near surface specific humidity, near surface wind speed, freshwater flux, latent heat flux, and, recently implemented, liquid water path.
A defining feature of HOAPS is its inter-sensor calibration and physically consistent algorithms, ensuring that all products are produced using a uniform retrieval framework, minimizing artificial trends and discontinuities.
In this presentation, we will focus on the upcoming HOAPS release (HOAPS v5.0), outline recent methodological updates such as newly implemented data sources, updates of the retrieval and radiative transfer model including a new bias correction scheme, improved uncertainty propagation, and more. Additionally, we will demonstrate the usefulness of the dataset through selected examples that highlight variability in the global water cycle. We will also discuss the implementation of a continuous extension of the dataset. HOAPS sustains to serve as a robust satellite-based reference for climate studies of air-sea fluxes and ocean-atmosphere coupling and more.
How to cite: Niedorf, A., Sikorski, T., Fennig, K., Konrad, H., Schröder, M., Bärlin, J., Hollmann, R., Bennartz, R., and Fell, F.: HOAPS: A Satellite based Climate Data Record of Ocean-Atmosphere Interaction Parameters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6780, https://doi.org/10.5194/egusphere-egu26-6780, 2026.
Water vapour is the single most important natural greenhouse gas in the atmosphere, thereby constraining the Earth’s energy balance, directly and indirectly through the water vapour feedback mechanism. In addition, water vapour is a key component of the water cycle. There is consequently the need to consolidate our knowledge of natural variability and past changes in water vapour and to establish climate data records (CDRs) of both total column and vertically resolved water vapour for use in climate research. This is the objective of the ESA Water Vapour Climate Change Initiative (WV_cci).
Within WV_cci a global total column water vapour (TCWV) data record was generated by combining microwave-based TCWV observations over the ice-free ocean with near-infrared imager-based TCWV over land, coastal ocean and sea-ice. The data record relies on microwave imager observations, partly based on a fundamental climate data record from EUMETSAT CM SAF and on near-infrared observations from MERIS, MODIS and OLCI. The microwave and near-infrared data streams are processed independently and combined in a postprocessing, retaining the individual TCWV values and their uncertainties. The precursor version of the data record is freely available to the public via 10.5676/EUM_SAF_CM/COMBI/V001. A new version, which relies on more sensors and features improved stability and uncertainty characterisation, will be released in the coming months. In addition, a high-resolution regional product was generated covering three regions in the sub-tropics and tropics with a spatial resolution of 0.01°.
This presentation will briefly introduce WV_cci and new developments and improvements related to the data record generation. The TCWV over land is reliably possible in clear-sky conditions only. Thus, after aggregation and comparison to all-sky data a clear-sky bias is present. Results from the clear-sky bias analysis will be shown as well. A focus will be on results from intercomparisons and validation, including results from uncertainty validation through comparisons against radiosonde and GNSS observations.
How to cite: Konrad, H., Bärlin, J., Niedorf, A., Danne, O., Schröder, M., Preusker, R., Trent, T., Fischer, J., Brockmann, C., Hegglin, M., and Hollmann, R.: A combined global total column water vapour data record from microwave and near-infrared imager observations: new developments and results from validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6474, https://doi.org/10.5194/egusphere-egu26-6474, 2026.
