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Inland waters are important sources of the greenhouse gasses (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) to the atmosphere. While a growing number of global estimates of inland water GHG emissions exists, the integration of inland waters into regional GHG budgets is often hampered by the lack of adequate geo-spatial datasets. Moreover, existing estimates diverge substantially, in part due to persisting uncertainties related to the size and distribution of effective inland water surface areas. In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of inland water surface areas and the effects of seasonal ice cover. We then produce estimates of inland water GHG emissions for 10 extensive land regions that are used for the regional land budgets of RECCAP2. According to our synthesis, global inland waters emit 5.6 (3.5-9.1) Pg CO2 yr-1, 101 (83-135) Tg CH4 yr-1 and 326 (254-592) Gg N2O yr-1. South American rivers contribute about one third of global inland water CO2 emissions. North-American and Russian lakes contribute together one third of global inland water CH4 emissions. Finally, North America alone contributes one fourth of global inland water N2O emissions.The global inland water emissions sum up to a global warming potential (GWP) of an equivalent emission of 13.6 (10.0-20.3) and 8.3 (5.8-12.7) Pg CO2 yr-1 at a 20- and 100-year horizon, respectively. At 100-year horizon, the contribution of CO2 dominates the GWP of global inland water GHG emissions, with rivers being the largest emitters. At the 20-year horizon, on the contrary, lakes and rivers are equally important emitters, and the contributions of CH4 to the GWP of inland water GHG emissions even exceed those of CO2. Contributions of N2O to the GWP appear to be less significant at both time horizons. Normalized to the area of the RECCAP-2 land regions, South America and South East Asia show the highest inland water emission rates in terms of GWP, dominated by riverine CO2 emissions.

Floodplain urbanization is accelerating at an alarming rate in recent decades, which has posed grand challenges to flood risk management. Hydraulic engineering infrastructure is often co-developed alongside this acceleration to mitigate flood risks, but to what extent this co-development matches up with the floodplain urbanization rate needs to be better quantified. In this study, we leverage a range of multi-source geospatial datasets including high-resolution floodplain maps, urban impervious area maps, global dam/reservoir datasets, and flood fatality data from the Emergency Events Database (EM-DAT) to assess the dynamics of floodplain urbanization (1985–2015) and its interplay with hydraulic engineering development at the global scale. We will report our assessments at both the basin level and the country level to promote the understanding of the hydroclimatic and socioeconomic contexts of floodplain urbanization. Ultimately, results from this study are expected to inform the prioritized regions for flood risk mitigation.

International audience; The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two-thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage-constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.Plain Language Summary The Surface Water and Ocean Topography (SWOT) satellite mission was launched on 15 December 2022. SWOT is designed to produce estimates of river discharge on many rivers where no in situ discharge measurements are currently available. This paper describes how SWOT discharge estimates will be created, and their expected accuracy. SWOT discharge will be estimated using simple flow laws that combine SWOT measurements of river water elevation above sea level, river width, and river slope, with ancillary data such as river bathymetry. We expect that discharge uncertainty will be less than 30% for two-thirds of global reaches and will be dominated by a systematic bias. Temporal variations in river discharge time series are expected to be estimated within 15% for nearly all reaches, thus capturing the response of river discharge to rainfall and snowmelt events, including in basins that are currently ungaged, and providing a new capability for scientists to better track the flows of freshwater water through the Earth system.

Hydrologic data collected from river gauges inform critical decisions for allocating water resources, conserving ecosystems and predicting the occurrence of droughts and floods. The current global river gauge network is biased towards large, perennial rivers, and strategic adaptations are needed to capture the full scope of rivers on Earth. Knowing where and when rivers flow is paramount to managing freshwater ecosystems. Yet stream gauging stations are distributed sparsely across rivers globally and may not capture the diversity of fluvial network properties and anthropogenic influences. Here we evaluate the placement bias of a global stream gauge dataset on its representation of socioecological, hydrologic, climatic and physiographic diversity of rivers. We find that gauges are located disproportionally in large, perennial rivers draining more human-occupied watersheds. Gauges are sparsely distributed in protected areas and rivers characterized by non-perennial flow regimes, both of which are critical to freshwater conservation and water security concerns. Disparities between the geography of the global gauging network and the broad diversity of streams and rivers weakens our ability to understand critical hydrologic processes and make informed water-management and policy decisions. Our findings underscore the need to address current gauge placement biases by investing in and prioritizing the installation of new gauging stations, embracing alternative water-monitoring strategies, advancing innovation in hydrologic modelling, and increasing accessibility of local and regional gauging data to support human responses to water challenges, both today and in the future. US National Science Foundation [DEB-1754389, 2207232] Published version US National Science Foundation(National Science Foundation (NSF)) Public domain – authored by a U.S. government employee

The combined influence of chemical composition, molecular weight (MW) and molecular weight distribution ( Ð ) on the functions and performances of polymeric materials necessitates simultaneous satisfaction of multidimensional requirements during polymer synthesis. However, the complexity of polymerization reactions often dissuades chemists when precisely accessing diversified polymer targets. Herein, we developed a machine learning (ML)-assisted systematical polymerization planning (SPP) platform for addressing this challenge. With ML model providing integrated navigation of the reaction space, this approach can conduct multivariate analysis to uncover complex interactions between the polymerization result and conditions, prescribing optimal reaction conditions to achieve discretionary polymer targets concerning three dimensions including chemical composition, MW and Ð values. Given the increasing importance of polymerization in advanced material engineering, this ML-assisted SPP platform provides a universal strategy to access tailored polymers with on-demand prediction of polymerization parameters.

Downward shortwave radiation Rsd determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale Rsd estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate Rsd by 8%–10% over Asia for 1984–2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%–10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in Rsd can lead to a 20%–60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.

We have successfully constructed a new type of intercalation membrane material by covalently grafting organic tris(hydroxypropyl)phosphine (THPP) molecules onto hydroxylated multi-walled carbon nanotubes (CNT-OH) as a functional interlayer for the advanced LSBs. The as-assembled interlayer has been demonstrated to be responsible for the fast conversion kinetics of polysulfides, the inhibition of polysulfide shuttle effect, as well as the formation of a stable solid electrolyte interphase(SEI) layer. By means of spectroscopic and electrochemical analysis, we further found THPP plays a key role in accelerating the conversion of polysulfides into low-ordered lithium sulfides and suppressing the loss of polysulfides, thus rendering the as-designed lithium-sulfur battery in this work a high capacity, excellent rate performance and long-term stability. Even at low temperatures, the capacity decay rate was only 0.036 % per cycle for 1700 cycles.

We present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.

Rationale: There is a continued need for investigating the roles of microRNAs and their targets on the pathogenesis of pulmonary arterial hypertension (PAH) vascular remodeling. We recently identified the association of myeloid miR-182-3p and its new target, Myeloid-Associated Differentiation Marker (Myadm), with vascular remodeling. Here, we aimed to determine the role of miR-182-3p/Myadm on PAH vascular remodeling and the underlying molecular mechanism. Methods: The miR-182-3p/Myadm expression profiles were detected in PAH patients and experimental rodent models. Loss-of-function and gain-of-function studies using gene knock-in or gene knock-out and the combinations of the proteomic technology and genome-wide ChIP-Seq were employed to determine the downstream targets of miR-182-3p/Myadm in response to monocrotaline (MCT)-induced PAH. Results: The miR-182-3p/Myadm expression was altered in PAH patients and experimental rodent models. Both miR-182-3p inhibitor and overexpression of Myadm augmented the pathological progression in rats in response to MCT-induced PAH. In contrast, miR-182-3p mimic and Myadm gene knockout attenuated the changes in the hemodynamics and structure of the cardio-pulmonary system in MCT-induced PAH in rats. Myadm mediated the proliferation of pulmonary artery smooth muscle cells (PASMCs) by altering the cell cycle kinase inhibitor (p21/Cip1) expression through the transcription factor Krüppel-like factor 4 (KLF4) translocation into the cytoplasm. Conclusion: Our findings indicate the prognostic and therapeutic significance of miR-182-3p in PAH and provide a new regulatory model of the myeloid-derived miR-182-3p/Myadm/KLF4/p21 axis in PAH vascular remodeling.

China’s largest desert freshwater lake, Hongjian Nur (HN), which is the largest habitat of relict gull (Larus relictus), has rapidly changed in recent years. However, it is difficult to quantitatively monitor the dynamics of the lake and determine the causes of its changes due to the lack of in situ observation. In this study, a remote sensing-based approach was utilized to overcome these limitations. The monthly water areas during 1990–2017 were first extracted from Landsat multispectral images via an improved method based on the floating algae index (FAI). Then, lake surface elevations measured by real-time kinematics (RTK) were used to calculate the variations in the water storage of HN. Finally, the driving factors of the rapidly changed HN in different periods were investigated by correlation analysis. The result indicated that the drivers affecting the water storage of HN in different periods were not the same. Climate change was the main driving factor of lake level fluctuation during the HN relatively stable stage (1990–1998). Drought and the intensification of human activities were the main factors for the rapid shrinkage of the HN during 1999–2010. Human activities, especially coal-related industries and reservoir impoundment, likely was the primary factors driving the decrease in the water storage of HN from 2010 to 2015. After 2015, the policies that decreased the water consumed by human activities formulated by the government and humid climate were the main factor for the expansion of HN.

International audience; The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50–100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.Measurements of river flow are essential for the allocation of water resources, flood and drought forecast and mitigation efforts, and others. Access to local measurements is not ubiquitous and is particularly difficult for rivers flowing in remote locations or across country borders. Measurements taken by satellites such as the upcoming Surface Water and Ocean Topography (SWOT) mission will offer freely available global data and methods to estimate discharge using such data have been in development. We conducted a comprehensive assessment of the accuracy and precision of five SWOT discharge inversion algorithms under three conditions: (a) ideal, that is if the measurements were available once a day and contained no error; (b) with no measurement error but changing how frequently the measurements were taken, and (c) under different levels of measurement error. We found that the methods consistently improved over the initial estimates of discharge and we identified river hydraulic properties that increase the chances of successful parameter estimation. We also found that despite the use of very similar discharge equations, the subtle differences in equations among the methods can be important. Finally, we found that at least two methods can work well with the expected amount of measurement error and frequency.

We evaluate water budget components—namely, soil moisture, runoff, evapotranspiration, and terrestrial water storage (TWS)—simulated by the Noah land surface model with multi-parameterization options (Noah-MP) in China, a large geographic domain challenging for hydrological modeling due to poor observational data and a lack of one single parameterization that can fit for complex hydrological processes. By comparing the model simulations with multi-source reference data, we show that Noah-MP can generally reproduce the overall spatiotemporal patterns of runoff and evapotranspiration over six major river basins, with the annual correlation coefficients generally greater than 0.8 and the Nash-Sutcliffe model efficiency coefficient exceeding 0.5. Among the six basins evaluated, the best model performance is seen over the Huaihe River basin. The temporal trend of the modeled TWS anomalies agrees well with GRACE (Gravity Recovery and Climate Experiment) observations, capturing major flood and drought events in different basins. Experiments with 12 selected physical parameterization options show that the runoff parameterization has a stronger impact on the simulated soil moisture-runoff-evapotranspiration relationships than the soil moisture factor for stomatal resistance schemes, a result consistent with previous studies. Overall, Noah-MP driven by GLDAS forcing simulates the hydrological variables well, except for the Songliao basin in northeastern China, likely because this is a transitional region with extensive freeze-thaw activity, while representations of human activities may also help improve the model performance.

Spatiotemporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully designed modeling effort (Variable Infiltration Capacity land surface model and Routing Application for Parallel computatIon of Discharge river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge‐, reanalysis‐, and satellite‐based data. To constrain runoff simulations, we use a set of machine learning‐derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid‐by‐grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible—approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high‐accuracy global digital elevation model at 3‐arcsec resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35‐year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within ±20% (±50%), and 29% (62%) have a monthly Kling‐Gupta Efficiency ≥0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography satellite mission's discharge product, thus named “Global Reach‐level A priori Discharge Estimates for Surface Water and Ocean Topography”. It can also be used in other hydrologic applications requiring spatially explicit estimates of global river flows., Key Points Thirty‐five‐year global reconstruction of river flows at unprecedented 2.94 million reaches extracted from 90‐m high‐accuracy DEMNovel bias correction (“sparse CDF matching”) is conducted grid‐by‐grid against machine learning‐derived global runoff characteristics mapsEvaluation against >14,000 gauges shows data robustness for hydrologic applications and utility in spaceborne discharge estimation efforts

In this article, an inclusive Christian vision for the development sector is sketched. A clearer understanding of the Christian vision is useful to guide Christian development institutions as they deliberate their priorities in the broader development sector. This vision appropriates the Trinity, a core Christian doctrine that is included in all Christian orthodox traditions. It focuses on the revelation of God in this world and the understanding of the missio Dei. The article clarifies the particular way that development institutions participate in this mission, through social justice. Justice is understood as the pursuing of the wellbeing of the other in light of God’s love. For a fuller understanding of God’s love, the way that each divine person clarifies the understanding of social justice, and therefore the subsequent development sector, is outlined.

Impacts of climate change on floods have been recently suggested to be more consistently seen in flood timing (or flood seasonality) as opposed to flood magnitude and frequency. Changes in flood timing can threaten the finely tuned water resource management systems and, if poorly understood, can alter flood risks in unpredictable ways. Nevertheless, patterns of global flood timing trend remain elusive. Whether climate change has played a significant role in shifting flood timing worldwide also remains unknown.Here we obtained an unprecedented set of discharge records from tens of thousands of global gauges and model-reconstructed naturalized discharge at ~3 million river reaches to delineate flood timing trend across the global river networks from 1980 to 2019. Hydroclimate drivers possibly causing these trends, including maximum precipitation, antecedent soil moisture, and snowmelt timing, are also investigated to disentangle climate change signals on floods. We found that the flood timing has been significantly earlier over the lower Mississippi, the Amur and the Amazon river basins, as well as large parts of the high-latitude Northern Hemisphere. Significant later floods are observed over the Yangtze and the lower Congo river basins, and the southeast Asia. However, ascribing these flood timing shifts to changing climate is not as obvious as previously suggested, implying the need for further research on this topic.

Hydrological extreme events such as droughts and floods have a wide range of impacts on society and sectors such as agriculture and energy production. The impact of these extremes are projected to increase with future climate change and there is an urgent need to develop adaptation measures to reduce and manage the impacts. Long-term analysis of hydrological extremes, using a combination of models and climate data, helps better plan and manage water resources under global change. In this study, we modelled and analyzed hydrological extremes of the Volta river basin at very high-resolution (>10000 river reaches) using the Variable Infiltration Capacity (VIC) hydrological model, the vector-based river network routing model (RAPID), and high-resolution meteorological forcing datasets. The output from the VIC model is evaluated at multiple time scales (daily to annual) and for extreme events (droughts and floods) using observed streamflow data during the period 1979-2013. The model performed very well in areas less affected by dams, with performance increasing from daily to annual time scale. The modelled streamflow data is used to assess changes and variability in droughts (duration days and severity) and floods (annual daily maximum). The results show a decreasing and increasing trend in moderate and severe droughts in northern-eastern and southern parts of the basin, respectively. An increasing trend in floods is observed in the upper part of the basin (Black and White Volta) and the main river of the Lower Volta and we found a strong correlation with changes in precipitation and soil moisture.