The past and future of global river ice.

More than one-third of Earth's landmass is drained by rivers that seasonally freeze over. Ice transforms the hydrologic1,2, ecologic3,4, climatic5 and socio-economic6-8 functions of river corridors. Although river ice extent has been shown to be declining in many regions of the world1, the seasonality, historical change and predicted future changes in river ice extent and duration have not yet been quantified globally. Previous studies of river ice, which suggested that declines in extent and duration could be attributed to warming temperatures9,10, were based on data from sparse locations. Furthermore, existing projections of future ice extent are based solely on the location of the 0-°C isotherm11. Here, using satellite observations, we show that the global extent of river ice is declining, and we project a mean decrease in seasonal ice duration of 6.10 ± 0.08 days per 1-°C increase in global mean surface air temperature. We tracked the extent of river ice using over 400,000 clear-sky Landsat images spanning 1984-2018 and observed a mean decline of 2.5 percentage points globally in the past three decades. To project future changes in river ice extent, we developed an observationally calibrated and validated model, based on temperature and season, which reduced the mean bias by 87 per cent compared with the 0-degree-Celsius isotherm approach. We applied this model to future climate projections for 2080-2100: compared with 2009-2029, the average river ice duration declines by 16.7 days under Representative Concentration Pathway (RCP) 8.5, whereas under RCP 4.5 it declines on average by 7.3 days. Our results show that, globally, river ice is measurably declining and will continue to decline linearly with projected increases in surface air temperature towards the end of this century.

The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers.

SignificanceStream/river carbon dioxide (CO2) emission has significant spatial and seasonal variations critical for understanding its macroecosystem controls and plumbing of the terrestrial carbon budget. We relied on direct fluvial CO2 partial pressure measurements and seasonally varying gas transfer velocity and river network surface area estimates to resolve reach-level seasonal variations of the flux at the global scale. The percentage of terrestrial primary production (GPP) shunted into rivers that ultimately contributes to CO2 evasion increases with discharge across regions, due to a stronger response in fluvial CO2 evasion to discharge than GPP. This highlights the importance of hydrology, in particular water throughput, in terrestrial-fluvial carbon transfers and the need to account for this effect in plumbing the terrestrial carbon budget.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4 ) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4  yr-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

How Have Global River Widths Changed Over Time?

Changes in a river's width reflect natural and anthropogenic impacts on local and upstream/downstream hydraulic and hydrologic processes. Temporal variation of river width also impacts biogeochemical exchange and reflects geomorphologic evolution. However, while global maps of mean river width and dynamic water surface extent exist, there is currently no standardized global assessment of river widths that documents changes over time. Therefore, we made repeated width measurements from Landsat images for all rivers wider than 90 m collected from 1984 to 2020 (named Global LOng-term river Width, GLOW), which consists of ∼1.2 billion cross-sectional river width measurements, with an average of 3,000 width measurements per 10-km reach. With GLOW, we investigated the temporal variations of global river width, quantified by the interquartile range (IQR) and temporal trend. We found that 85% of global rivers have a width IQR <150 m. We also found that 37% of global river segments show significant temporal trends in width over the past 37 years, and this number is higher (46%) for human-regulated rivers. Further, we leveraged machine learning to identify the most important factors explaining river width variations and revealed that these driving factors are significantly different between free-flowing and non-free-flowing rivers. Specifically, the most important factor driving temporal variations in river width is the climate for free-flowing rivers, and is soil condition for human-impacted rivers. Finally, we anticipate that this study and the public release of GLOW will improve the understanding of river dynamics and catalyze additional interdisciplinary studies.

Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches.

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.

Diminishing storage returns of reservoir construction.

Surface water reservoirs are increasingly being relied upon to meet rising demands in the context of growing population and changing climate. However, the amount of water available in reservoirs (and the corresponding trends) have not been well quantified at the global scale. Here we use satellite observations to estimate the storage variations of 7245 global reservoirs from 1999 to 2018. Total global reservoir storage has increased at a rate of 27.82 ± 0.08 km3/yr, which is mainly attributed to the construction of new dams. However, the normalized reservoir storage (NS)-the ratio of the actual storage to the storage capacity-has declined by 0.82 ± 0.01%. The decline of NS values is especially pronounced in the global south, while the global north mainly exhibits an NS increase. With predicted decreasing runoff and increasing water demand, these observed diminishing storage returns of reservoir construction will likely persist into the future.

Satellites reveal hotspots of global river extent change.

Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference.

Greenhouse gas emissions from African lakes are no longer a blind spot.

Natural lakes are thought to be globally important sources of greenhouse gases (CO2, CH4, and N2O) to the atmosphere although nearly no data have been previously reported from Africa. We collected CO2, CH4, and N2O data in 24 African lakes that accounted for 49% of total lacustrine surface area of the African continent and covered a wide range of morphology and productivity. The surface water concentrations of dissolved CO2 were much lower than values attributed in current literature to tropical lakes and lower than in boreal systems because of a higher productivity. In contrast, surface water-dissolved CH4 concentrations were generally higher than in boreal systems. The lowest CO2 and the highest CH4 concentrations were observed in the more shallow and productive lakes. Emissions of CO2 may likely have been substantially overestimated by a factor between 9 and 18 in African lakes and between 6 and 26 in pan-tropical lakes.

Assessing placement bias of the global river gauge network.

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.

Constructing Reservoir Area-Volume-Elevation Curve from TanDEM-X DEM Data.

Area-volume-elevation (AVE) curves are critical for reservoir operation rules. However, such curves are not publicly available for most global reservoirs. Here, we present a framework to derive reservoir AVE curves from TanDEM-X data, using Lake Mead (~600 km2) as an example. First, the maximum water extent from 1984 to 2018-provided by the global surface water (GSW) dataset-was used as a mask to obtain the TanDEM-X data. Then, the TanDEM-X water indication mask (WAM) was applied to the extracted TanDEM-X data to obtain the visible bathymetry, which represents the topography between the maximum extent (according to GSW) and the water extent from WAM. Last, the AVE curve was generated by integrating the volume values from the top to bottom layers. TanDEM-X also captures the elevation values of the transitional waters, which are defined as the difference between the highest and lowest water levels. The transitional waters were obtained by thresholding amplitude and coherence images, and their elevations were then added to the visible bathymetry to extend the AVE curves with an elevation range extending from 344-369 m to 341-369 m. Validation results against in situ lidar survey values suggest a high-accuracy of elevation-area (E-A) relationships with R 2 values of >0.99 and NRMSE values from 2.11% to 2.45%, and elevation-volume (E-V) relationships with R 2 values of 1 and NRMSE values from 1.11% to 1.29%. Results also show that TanDEM-X data can capture the interannual variations due to multiple acquisitions, and that the elevation measurements for the lake shore areas are reliable.

A new vector-based global river network dataset accounting for variable drainage density.

Spatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro-Vector) should enable full global modeling of river system processes at fine spatial resolutions.

Global riverine nitrous oxide emissions: The role of small streams and large rivers.

Nitrous oxide, N2O, is the leading cause of stratospheric ozone depletion and one of the most potent greenhouse gases (GHG). Its concentration in the atmosphere has been rapidly increasing since the green revolution in the 1950s and 1960s. Riverine systems have been suggested to be an important source of N2O, although their quantitative contribution has been estimated with poor precision, ranging between 32.2 and 2100 GgN2O - N/yr. Here, we quantify reach scale N2O emissions by integrating a data-driven machine learning model with a physically-based upscaling model. The application of this hybrid modeling approach reveals that small streams (those with widths less than 10 m) are the primary sources of riverine N2O emissions to the atmosphere. They contribute nearly 36 GgN2O - N/yr; almost 50% of the entire N2O emissions from riverine systems (72.8 Gg2O - N/yr), although they account for only 13% of the total riverine surface area worldwide. Large rivers (widths wider than 175 m), such as the main stems of the Amazon River (~ 6 GgN2O - N/yr), the Mississippi River (~ 2 GgN2O - N/yr), the Congo River (~ 1 GgN2O - N/yr) and the Yang Tze River (~ 0.7 GgN2O - N/yr), only contribute 26% of global N2O emissions, which primarily originate from their water column. This study identifies, for the first time, near-global N2O emission and NO3 removal hot spots within watersheds and thus can aid the development of local- to global-scale management and mitigation strategies for riverine systems with respect to N2O emissions. The presented framework can be extended to quantified biogeochemical, besides N2O emissions, processes at the global scale.

Zero or not? Causes and consequences of zero-flow stream gage readings.

Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed-scale processes. When stream gages read zero, this may indicate that the stream has fully dried; however, zero-flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero-flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero-flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human-driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero-flow interpretations. We also highlight additional methodss for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero-flow gage readings and implications for reach- and watershed-scale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero-flows will only attain greater importance in a more variable and changing hydrologic climate.

What's in a Name? Patterns, Trends, and Suggestions for Defining Non-Perennial Rivers and Streams.

Rivers that cease to flow are globally prevalent. Although many epithets have been used for these rivers, a consensus on terminology has not yet been reached. Doing so would facilitate a marked increase in interdisciplinary interest as well as critical need for clear regulations. Here we reviewed literature from Web of Science database searches of 12 epithets to learn (Objective 1-O1) if epithet topics are consistent across Web of Science categories using latent Dirichlet allocation topic modeling. We also analyzed publication rates and topics over time to (O2) assess changes in epithet use. We compiled literature definitions to (O3) identify how epithets have been delineated and, lastly, suggest universal terms and definitions. We found a lack of consensus in epithet use between and among various fields. We also found that epithet usage has changed over time, as research focus has shifted from description to modeling. We conclude that multiple epithets are redundant. We offer specific definitions for three epithets (non-perennial, intermittent, and ephemeral) to guide consensus on epithet use. Limiting the number of epithets used in non-perennial river research can facilitate more effective communication among research fields and provide clear guidelines for writing regulatory documents.

Global extent of rivers and streams.

The turbulent surfaces of rivers and streams are natural hotspots of biogeochemical exchange with the atmosphere. At the global scale, the total river-atmosphere flux of trace gasses such as carbon dioxide depends on the proportion of Earth's surface that is covered by the fluvial network, yet the total surface area of rivers and streams is poorly constrained. We used a global database of planform river hydromorphology and a statistical approach to show that global river and stream surface area at mean annual discharge is 773,000 ± 79,000 square kilometers (0.58 ± 0.06%) of Earth's nonglaciated land surface, an area 44 ± 15% larger than previous spatial estimates. We found that rivers and streams likely play a greater role in controlling land-atmosphere fluxes than is currently represented in global carbon budgets.

Similarity of stream width distributions across headwater systems.

The morphology and abundance of streams control the rates of hydraulic and biogeochemical exchange between streams, groundwater, and the atmosphere. In large river systems, the relationship between river width and abundance is fractal, such that narrow rivers are proportionally more common than wider rivers. However, in headwater systems, where many biogeochemical reactions are most rapid, the relationship between stream width and abundance is unknown. To constrain this uncertainty, we surveyed stream hydromorphology (wetted width and length) in several headwater stream networks across North America and New Zealand. Here, we find a strikingly consistent lognormal statistical distribution of stream width, including a characteristic most abundant stream width of 32 ± 7 cm independent of discharge or physiographic conditions. We propose a hydromorphic model that can be used to more accurately estimate the hydromorphology of streams, with significant impact on the understanding of the hydraulic, ecological, and biogeochemical functions of stream networks.