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At least 343 records · Page 19

Transparent window film with embedded nano-shades for thermoregulation

Every year, more than 20% of the energy consumption in the United States and more than 10% of the global consumption is used towards HVAC (Heating, Ventilating, Air-Conditioning) systems in buildings. Although continued efforts in advancing renewable energy, efficient appliances, and smart building systems are desired, the most critical factors that cause extensive indoor energy consumption are thermal leakage and thermal waste generation. We demonstrate a smart window film that allows natural lighting with a clear view while blocking oblique incident sunlight to make the building interior cooler without using electricity or generating heat. Building upon the concept of, often overlooked, traditional window shades and privacy window films, we emphasize and elevate the application and impact of using magnetically arranged nanoscale material for energy conservation purpose in buildings. By embedding vertically aligned Ni flakes (VANF) in a polymer matrix film, nanoscale mirror array serve as shades to block the high-angled incident sunlight, blocking approximately 73% of total solar irradiance and bringing down the indoor temperature by 1.92 °C during the daytime in a model house while providing nearly 90% transparency. Global application was simulated by applying the respective solar angle and intensity at selected latitudes, indicating their potential. This passively energy-conserving smart film is a low cost, maintenance free, and simple product for users and easy-to-manufacture product for the industry.

36 MATERIALS SCIENCE↗

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).

54 ENVIRONMENTAL SCIENCES↗

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗

Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information

Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This work investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.

42 ENGINEERING↗

Decoupling of particles and dissolved iron downstream of Greenlandic glacier outflows

Glaciers can be a significant and locally dominant source of iron (Fe), a biologically essential micronutrient, in high latitude coastal seas. The vast majority of this glacial Fe delivery is associated with particles, yet the speciation of the solid-phase Fe and specifically the relationships that govern exchange between particulate and dissolved Fe phases in these environments are poorly described. Here, in this work, we performed measurements of in situ dissolved Fe (dFe) along meltwater and particle plumes in three transects around Disko Bay and Ameralik Fjord (West Greenland). Measurements of dFe were combined with Fe K-edge X-ray absorption spectroscopy analysis of ~40 suspended sediment samples obtained from the same transects and from select depth profiles down to 300 m. We observed relatively constant dFe levels (4 to 10 nM for nearly all dFe measurements) across fjords with widely varying particulate Fe(II) contents (from 20 to 90% Fe(II)), indicating that dFe concentrations had little dependence on the oxidation state of Fe in the suspended sediment. Particulate Fe data were grouped by underlying bedrock geology, with suspended sediment consisting of 80-90% biotite-like Fe(II) in fjords with Precambrian shield geology and poorly-ordered Fe(III) particles (<20-30% Fe(II)) in one fjord with suspended sediments derived from tertiary basalts. Our characterization data indicated no significant change in the average Fe oxidation state and bonding environment of particles along the fjord transects, implying that Fe(II) in biotite-like coordination is not a readily labile Fe form on this spatial scale. Finally, our results suggest that dFe in these glacially-modified coastal waters is buffered at a relatively constant low nM concentration due to factors other than particle Fe mineralogy and that glacier-derived Fe phases are relatively inert on this spatial scale.

54 ENVIRONMENTAL SCIENCES↗

Cave-based chronology of valley incision in the glaciated Northern Carpathians

While glaciers leave a mark on mountain landscapes, the importance of glacial erosion in mountains at a global scale remains insufficiently constrained despite over a century of research aimed at quantifying the rates and magnitudes of such erosion. Measuring rates of landscape change across the Quaternary in repeatedly glaciated mountain landscapes requires the ability to accurately partition the relative role of glacial versus fluvial processes in forming relief; however, the modification of landforms by surface erosion hinders quantification of process rates over the last few million years. This study presents 32 paired 26 Al and 10 Be cosmogenic nuclide burial ages from allochthonous, quartz-bearing sediment deposited within a paleophreatic karst system in the northern Carpathians. These data quantify Pliocene-to-present timing of valley incision and karst system reactivation, as well as erosion rates over time. Post-burial, muon production-correction yields 24 fully resolved ages, six minimum ages, and two unresolvable ages. Burial ages are interpreted to document the tempo of valley incision via phreatic passage formation and reveal that ∼80% of current valley relief (∼700 m) had developed by approximately 1.6 Ma. Paleoerosion rates derived from this dataset suggest little change in the processes generating sediment washed into the karst networks since the Pliocene. These results suggest that river incision dominated relief creation in Carpathian valleys that bear the imprint of glaciation. Our findings suggest the importance of investigating other mid-latitude mountainous regions worldwide to determine if they have similar erosion and topographic histories.

Carpathians↗

Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better?

Here, developing reliable predictions of soil attributes is necessary to understand northern cold-region climate-soil feedback. Calibration models using near-infrared (NIR) and mid-infrared (MIR) spectroscopy were developed to predict eight commonly measured soil properties for 119 soil samples representing a range of vegetation types, parent materials, and soil types spanning >23° of latitude from southeast Alaska to the Canadian high Arctic. In order to obtain a more accurate prediction, this study compared the performance of linear and non-linear calibration techniques, including lasso regression (Lasso), support vector machine (SVM), random forest (RF) and classic partial least squares (PLS) to predict different soil properties of these soils. Comparing the four models, we noticed that their performance was quite similar for MIR overall, while NIR achieved better results with a PLS model for our dataset. PLS coupled with MIR showed a better performance for soil parameters, such as total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC) and clay (R-squared of 0.9, 0.81, 0.80, and 0.84) when compared with NIR (R-squared of 0.85, 0.72, 0.81 and 0.68). However, using either MIR or NIR spectroscopy, PLS predictions for bulk density (BD) and sand content were not accurate. The variable importance analysis based on the PLS model successfully estimated the relative contribution of wavelengths influencing soil property predictions most. Overall, TOC, TN, CEC and clay mineral predictions are closely related to the occurrence of specific spectral bands in the MIR region. For example, wavelengths at 2978 and 1761 cm -1 for TOC and TN, as well as at 3064 cm -1 for CEC, were selected as the most influential predictor variables. We demonstrated that MIR spectroscopy is a powerful tool for more extensive monitoring in soils of the northern cold climate region; however, NIR could be utilized for rapid estimates when the highest accuracy is not essential.

54 ENVIRONMENTAL SCIENCES↗

Soil temperature mitigation due to vegetation biophysical feedbacks

Understanding the interactions between temperature regimes and vegetation cover is one of the key scientific topics in global environmental change and land surface process research. Recently, the prominent global greening trend has inspired considerable interest in examining climate feedbacks to vegetation changes. However, the impacts of recent widespread vegetation cover changes on soil temperature have been less documented and have received insufficient attention. Here, a high-resolution regional climate model was used to examine the potential impact of vegetation cover changes over the past 37 years, obtained as satellite observational data, on regional air and soil temperatures across the Heilong-Amur River Basin. Our sensitivity experiments indicated that the 1.85% increase in the regional fractional vegetation cover (FVC) from 1982 to 2018 cooled the air temperature by 0.045°C and the soil temperature by 0.19°C and that this cooling effect would continue in the near future, from 2016-2018 to 2051-2053. We found that soil temperatures were more sensitive to vegetation cover changes than air temperatures, particularly in mid-high latitude forest ecosystems; this finding helps to explain the nonsignificant decrease in soil temperatures in forest ecosystems over the past 40 years detected in the ERA5-Land reanalysis. Further, our results also suggest that vegetation restoration in forest ecosystems could mitigate the effects of climate change on permafrost environments by increasing the number of soil freezing days.

54 ENVIRONMENTAL SCIENCES↗

Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State

Spring phenology plays an essential role in climate change, terrestrial ecosystem, and public health. Field-based monitoring and understanding of changes in spring phenology for long periods and in large regions are challenging due to the limited in-site observations. Space-based remotely sensed observations offer great potentials for monitoring decadal spring phenology changes from regional to global scales. However, the coarse-scale remotely sensed observations are insufficient to capture fine-scale spring phenology dynamics, especially in urban areas, and this makes it challenging for understanding the combined effects of climate change and urbanization on spring phenology. We derived the start of phenology season (SOS) in New York State using 30 m Landsat observations from 1990 to 2015 to understand the impact of the environment and urbanization on SOS. The results show that SOS for different years reveals heterogeneous spatial distribution. Most regions of New York State have been experiencing significant spring phenology changes in form of earlier onset of vegetation greening, ranging from 0.2 to 0.6 day/year during 1990 to 2015, and this trend varies slightly with latitudes and urbanization levels. Further, spatial correlation analysis shows that the increase in temperature and urbanization could both promote the advancement of SOS. However, the effect of urbanization (partial correlation coefficient (R) ranges from −0.289 to −0.542) on SOS is greater than the effect of temperature (R ranges from 0.006 to −0.192). The study generates a high spatio-temporal resolution spring phenology dataset for ecological, environmental and public health studies, especially in urban areas, and reveals the importance of better accounting for the urbanization effects when quantifying the SOS dynamics in phenology models.

Landsat↗

Solving the auroral-arc-generator question by using an electron beam to unambiguously connect critical magnetospheric measurements to auroral images

Here, an active mapping mission is described that unambiguously connects measurements in the Earth's magnetosphere to visible aurora in the atmosphere. The core of the mission is an electron-beam source operated on a spacecraft in the equatorial magnetosphere, with the electron beam traveling along the Earth's magnetic-field lines to the atmosphere, depositing its energy to create an optical beam-spot in the atmosphere at the footpoint of the spacecraft's magnetic-field line. This optical spot can be imaged by ground-based cameras, putting the location of the spacecraft's magnetic footpoint into the context of the optical aurora. Scientific instruments carried on the spacecraft make critical measurements of the properties of the magnetosphere at the locations where the magnetosphere powers the aurora, allowing the determination of the plasma-physics mechanisms by which the magnetosphere drives the aurora, in particular answering the outstanding question of how the magnetosphere drives low-latitude auroral arcs. Long-standing questions in magnetosphere-ionosphere coupling that have not been answered because we could not unambiguously connect locations in the magnetosphere with their image in the ionosphere will finally be addressed. In this paper the properties of a “standard” growth-phase auroral arc are collected, theories of the magnetospheric generation of auroral arcs are reviewed, and critical magnetospheric measurements to discern the mechanisms that drive auroral arcs are determined. Further, the plasma physics of the experiment is investigated, including spacecraft-charging mitigation, beam stability, beam scattering, and electron orbit theory. Tradeoffs (keV versus MeV) concerning the energy of the electron beam are enumerated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Diurnal variation of the fair-weather atmospheric electric field in Binchuan, China

With the purpose of understanding the variation of fair-weather atmospheric electric field, the vertical component of atmospheric electric field (recorded as Ez) has been measured in the town Binchuan (25°50'N, 100°34'E, 1438.7 m asl) of Dali city, Yunnan, China from June 14, 2017 to December 1, 2020. Automatic measured visual range is used as a proxy of aerosol concentration. The mean diurnal variation curve of the fair-weather Ez in Binchuan, having two peaks occurred at around 9LT and 20LT, shows little correlation with the well-known Carnegie Curve and other two high-latitude sites' Curves. While strong negative correlation between fair-weather Ez and visual range is found, indicating the variation of fair-weather is dominated by aerosol concentration. The main source of aerosol concentration variation probably sunrise and sunset effect. We support our findings by comparing Ez, visual range with sunrise and sunset times. The peaks (troughs) of Ez (visual range) are proved to have weak correlations with anthropogenic pollution through comparing the Ez, visual range under workdays and holidays.

54 ENVIRONMENTAL SCIENCES↗

Background Concentrations of Argon-39 in Shallow Soil Gas

While radioisotopes of noble gases are known to be indicators of underground nuclear explosions (UNE), McIntyre et al. (2017) was the first to report the presence of 39Ar in shallow soil gas in association with a decades old UNE. While this finding hinted at the potential application of 39Ar to be used as an indicator of an UNE, doing so would also require an understanding of the natural concentrations of 39Ar present in soil gas. Without knowing the expected range and variability of naturally occurring concentrations of 39Ar, it is difficult to determine what measured concentrations would be indicative of an elevated concentration. This paper presents results from 16 soil gas samples and three atmospheric air samples collected from various locations across the western United States. Shallow soil gas samples were collected into self-contained underwater breathing apparatus (SCUBA) tanks using a custom-built soil gas sampling system and then processed and analyzed for 39Ar. The measured concentrations of 39Ar varied from atmospheric air concentrations to about 3.5 times atmospheric air concentrations. The variation in concentration was primarily attributed to the latitude the sample was collected at, which was consistent with previous work (Johnson et al. 2015). The results presented here represent the first measurements of natural background 39Ar concentrations in shallow soil gas. This data will be necessary if 39Ar is to be used as an indicator of UNE.

Fritz, Brad G.↗

Tracking snowmelt during hydrological surface processes using a distributed hydrological model in a mesoscale basin on the Tibetan Plateau

We report that mountain snowpack is an important water resource for the high altitude and latitude regions where the terrain is complex. However, the snowmelt pathway and its actual contribution to streamflow and soil moisture are rarely reported and remain unclear in such regions. To fill in this knowledge gap, we incorporate a snowmelt pathway tracking algorithm to a high-resolution physics-based distributed-hydrology-soil-vegetation model (DHSVM), to track snowmelt movement and quantify snowmelt contributions in the surface hydrologic processes. A simple reservoir operation scheme is also incorporated in the model. The modified model is applied to a dammed meso-scale watershed in the northeast Tibetan Plateau, China to study the snow and reservoir effects. The results show that annual snow contribution to soil moisture (SC-SM) and snow contribution to streamflow (SC-S) significantly decrease over 1965-2019. At a monthly scale, SC-SM has the largest amplitude at the top soil layer and its peak in the deeper layer lags behind the upper layer, and mean monthly SC-S at all stations show bimodal distributions corresponding to snowfall season. Reservoir regulation has subtle impacts (≤2.0%) on SC-S. If the current climate change rate continues, monthly and annual streamflow at the outlet will decrease primarily due to snowpack reduction. To mitigate climate change impacts, better water resource management is needed in this watershed.

54 ENVIRONMENTAL SCIENCES↗

Soil frost controls streamflow generation processes in headwater catchments

The relationship between snowmelt and spring streamflow is changing under warming temperatures and diminishing snowpack. At the same time, the hydrologic connectivity across catchment landscape elements, such as snowpack and surface wetlands, can play a critical role in controlling the routing of snowmelt to streams. The role of hydrologic connectivity is important in headwater regions of the continental northern latitudes, where catchments have low topographic relief and seasonally frozen ground. Nevertheless, the effects of soil frost on the sequence, timing, and magnitudes of hydrologic events that drive the movement of water from a snowpack to a stream are not fully understood. Therefore, we examine two questions: First, what is the flowpath that snow melt and precipitation from spring rain events takes to generate spring streamflow, and second, what hydrologic, climatic, or landscape variables exert the most control on the magnitude of streamflow? In this work, we use long-term hydrological records from the two reference basins at the Marcell Experimental Forest in northern Minnesota to analyze the cascading effects across precipitation, snow, water table elevation, soil frost, and streamflow in peatland-dominated headwater catchments. We identify a sequence of fill-and-spill effects across the landscape that control the timing of spring streamflow generation. Then, we use stepwise regression to show that soil frost is a key supporting predictor for both the magnitude of streamflow in the spring as it adds significantly to the predictive power of precipitation and water table elevation. Our results highlight the importance of recognizing the role of soil frost, when present, on the partitioning of snowmelt between overland runoff and water table recharge during the critical snowmelt period, as well as the later partitioning between evapotranspiration and subsurface flows.

54 ENVIRONMENTAL SCIENCES↗

Quantifying the long-term changes of terrestrial water storage and their driving factors

Global warming is expected to cause changes in terrestrial water storage (TWS) across the land surface, with widespread impacts on ecosystems and society. Although extensive research has been performed to analyze TWS changes and possible drivers during the post-2000 period, longer-term evolution of TWS and associated environmental forcings remain relatively unexplored. In this study, we evaluated the performance of the Energy Exascale Earth System model (E3SM) land model ELM version 1 (ELM v1) in simulating global TWS, and used factorial simulations of ELMv1 to quantify global TWS changes and their drivers during 1948–2012. We found that ELM’s agreed best with existing satellites and reconstruction datasets in temperate regions unaffected by irrigation. Biome- and climate zone-averaged TWS mainly increased at rates between 0 and 10 mm/year over 1948–2012, but the second half of that period saw smaller positive trends than the first half or even negative trends. Climate change explained >80 % of the TWS trends across most biomes and climate zones, followed by land use and land cover change. The physiological and phenological effects of CO2 primarily induced noticeable TWS trends in the more humid biomes and climate zones across different latitudes. In contrast, nitrogen deposition and aerosol deposition generally had smaller and negative impacts across the biomes and climate regions. Among the meteorological drivers analyzed, the long-term average imbalance between precipitation (P), evapotranspiration (E), and runoff (Q) contributed >50 % of the TWS trends in most biomes and climate zones, with nonlinearity being induced by spatially heterogenous changes in E/P and Q/P ratios. The accumulated detrended anomalies in P, E, and Q also often contributed substantially, while the trends difference between P, E, and Q contributed little. Together, these findings unveiled an intensification of the global TWS and its diverse patterns of climate change and different non-withdrawal human-induced alterations, contributing to a more comprehensive understanding and projection of the global water cycle.

54 ENVIRONMENTAL SCIENCES↗

Glacial fluctuations in tropical Africa during the last glacial termination and implications for tropical climate following the Last Glacial Maximum

The tropics are a dynamic component of the global climate system and exert a profound influence on modern climate variability. Yet the role of the tropics in past climate changes, for example during the last glacial termination, is uncertain. This uncertainty is due in part to the relative paucity of terrestrial temperature records from the tropics, which hinders efforts to understand the mechanisms that influenced deglacial warming and abrupt climate events. Tropical glaciers are sensitive to changes in climate, and mapping and dating past tropical glacial fluctuations provides a valuable record of terrestrial climate changes in the low latitudes. We report a chronology of past glacial extents in the Rwenzori Mountains of Uganda (0.3°N, 29.9°E) based on 51 beryllium-10 ( 10 Be) surface-exposure ages from three separate glacial catchments. Results indicate that Rwenzori glaciers retreated considerably during Heinrich Stadial 1. The rate of net glacial recession slowed between ~15 and 11 ka, although glaciers continued to retreat during this period. Rwenzori glaciers then retreated more rapidly during the early Holocene. The Rwenzori glacial chronology is broadly similar to glacial chronologies reconstructed elsewhere in East Africa and in tropical South America. We suggest that this similarity may reflect coherent, tropics-wide temperature fluctuations during the last deglaciation. We report that the Rwenzori glacial chronology provides crucial information on the global footprint of deglacial warming and abrupt climate events and, thus, the potential mechanisms that influenced tropical climate during the last glacial termination.

58 GEOSCIENCES↗

Wave climate and energy resources in the Mariana Islands from a 42-year high-resolution hindcast

We report a high-resolution wave hindcast from 1979 to 2020 was developed for Guam and the Commonwealth of the Northern Mariana Islands, which are influenced by mesoscale and synoptic weather systems from the western Pacific Rim to the Central Pacific. The spectral wave modeling utilized structured global and unstructured regional grids with improved resolution in nearshore waters, where wave resources development is most feasible. The hindcast demonstrates its capability in capturing multi-modal seas through reproduction of bulk and partitioned wave parameters derived from altimetry and buoy measurements. The local wave climate is influenced by the year-round trade winds and tropical cyclones as well as seasonal monsoons, frontal systems, and mid-latitude high- and low-pressure systems. The strong intra-annual variation of the significant wave height and power flux reflects transitions between local and distant weather systems, which account for the diverse wave conditions comprised of short-period wind waves, intermediate-period seas, and long-period swells around the islands. Among the Pacific island regions, spectral partitioning is particularly important for describing the dominant wave components by period and direction with pertinent information conducive to energy resources characterization and coastal zone management in the Mariana Islands.

16 TIDAL AND WAVE POWER↗

The impact of multi-sensor land data assimilation on river discharge estimation

River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. In this study, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R 2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. Finally, by assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to predict the terrestrial hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗