{"id":477363,"date":"2026-10-07T10:49:41","date_gmt":"2026-10-07T17:49:41","guid":{"rendered":"https:\/\/climatescience.press\/?p=477363"},"modified":"2026-10-07T10:49:44","modified_gmt":"2026-10-07T17:49:44","slug":"nasa-satellite-exposes-serious-flaws-in-global-river-models","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=477363","title":{"rendered":"NASA Satellite Exposes Serious Flaws in Global River Models"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"485\" data-attachment-id=\"477364\" data-permalink=\"https:\/\/climatescience.press\/?attachment_id=477364\" data-orig-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?fit=1168%2C784&amp;ssl=1\" data-orig-size=\"1168,784\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?fit=723%2C485&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?resize=723%2C485&#038;ssl=1\" alt=\"Illustration of a NASA satellite in orbit displaying global river models, highlighting areas of high uncertainty and accuracy. The map shows regions with serious data discrepancies over 40%, and indicates confidence levels in river modeling, noting high uncertainty in dam impact and flaws in arid zone models.\" class=\"wp-image-477364\" srcset=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?resize=1024%2C687&amp;ssl=1 1024w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?resize=300%2C201&amp;ssl=1 300w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?resize=768%2C516&amp;ssl=1 768w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?resize=640%2C430&amp;ssl=1 640w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?w=1168&amp;ssl=1 1168w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><figcaption class=\"wp-element-caption\">AI generated by Grok<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Newly available satellite data from NASA\u2019s Surface Water and Ocean Topography (SWOT) mission reveal significant flaws in many global river models, particularly for dammed rivers, arid- region rivers, and Arctic rivers.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A study led by hydrologist Colin Gleason of the University of Massachusetts Amherst, published in <em>Geophysical Research Letters<\/em> (title: \u201cSWOT, Empiricism, and River Modeling\u201d), compared <strong>state- of- the- art machine- learning river models <\/strong>against SWOT observations. After quality controls, the team analyzed 68,347 river reaches representing about 38% of global discharge.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Key findings include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Models struggle most with <strong>dammed rivers, rivers in arid climates<\/strong> (especially populated ones such as parts of Australia, Central Asia, the southwestern U.S., and Mexico), <strong>multi- channel rivers<\/strong>, and <strong>many Arctic rivers.<\/strong> Parts of Siberia and China are particularly difficult.<a href=\"https:\/\/www.eurekalert.org\/news-releases\/1146566\" target=\"_blank\" rel=\"noopener\">\u2060<\/a><\/li>\n\n\n\n<li>Less than 10% of reaches show<strong> \u201cserious error\u201d<\/strong>, but these are often among the most important for water resources, hydropower, irrigation planning, and climate- change projections.<a href=\"https:\/\/www.eurekalert.org\/news-releases\/1146566\" target=\"_blank\" rel=\"noopener\">\u2060<\/a><\/li>\n\n\n\n<li>Models perform better on wider rivers and on <strong>\u201cnormal\u201d<\/strong> single- thread rivers without dams, glacial influence, or estuaries. However, only about 11% of Earth\u2019s rivers fit that idealized description,<strong> \u201ca weird river is the norm\u201d<\/strong> <strong>.<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Gleason notes that errors in these models directly undermine climate predictions and irrigation forecasts. Dammed rivers pose special challenges because models must account for human operations (e.g., pumped- storage hydropower that can change depth by more than a meter per day, as on the Connecticut River). Arid- region difficulties stem largely from delayed, non- obvious groundwater effects. Arctic modeling suffers from sparse training data for machine- learning systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The study frames SWOT as an <em>\u201cearly microscope\u201d <\/em>for rivers:<\/strong> it encourages relying more on direct satellite measurements rather than models alone, while also identifying where models need structural improvements to incorporate realistic hydraulics and human influences.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The press release (dated 6 October 2026, by Julia Westbrook of UMass Amherst) and related coverage in outlets such as <em>Science<\/em> highlight how SWOT\u2019s simultaneous measurements of water level and width expose the true complexity of rivers shaped by dams, braiding, freezing, and human water use.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"848\" data-attachment-id=\"477370\" data-permalink=\"https:\/\/climatescience.press\/?attachment_id=477370\" data-orig-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?fit=2128%2C2497&amp;ssl=1\" data-orig-size=\"2128,2497\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?fit=723%2C848&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=723%2C848&#038;ssl=1\" alt=\"World map displaying SES (Social-Ecological System) scores with two panels. The top panel shows SES ranges from -1 to 1 with gradient colors indicating various thresholds. The bottom panel indicates skill correlation with colors representing anticorrelation, below skill threshold, and skillful areas.\" class=\"wp-image-477370\" srcset=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=873%2C1024&amp;ssl=1 873w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=256%2C300&amp;ssl=1 256w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=768%2C901&amp;ssl=1 768w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=1309%2C1536&amp;ssl=1 1309w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=1745%2C2048&amp;ssl=1 1745w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?resize=640%2C751&amp;ssl=1 640w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-43.png?w=1446&amp;ssl=1 1446w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><figcaption class=\"wp-element-caption\">Global distribution of SWOT-derived SES. Credit: <em>Geophysical Research Letters<\/em> (2026). DOI: 10.1029\/2026gl124323<\/figcaption><\/figure>\n\n\n\n<p class=\"has-large-font-size wp-block-paragraph\"><strong>SWOT, Empiricism, and River Modeling<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Answers to questions about freshwater sustainability are answered by modeling rivers, as it is impossible to measure the millions of rivers on the planet in the field. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We use a recently launched satellite (SWOT) designed to measure rivers in a new way to assess an ensemble of global river models. We use SWOT to see which rivers are modeled in an opposite way to what the satellite observes: for example, when SWOT sees the river get deeper than it observed it last week, but the models say the river flow decreased from last week (or vice versa). <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We see that models struggle in arid and populated regions, and do relatively well in areas with less human development. However, the less densely populated Arctic is also a challenge. This research reveals the basic spatial pattern of global river modeling errors for the first time, setting the stage for a new, improved generation of models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The study uses an inductive approach grounded in SWOT\u2019s simultaneous measurements of river water-surface height and extent (via radar interferometry) to evaluate global river models. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These models include traditional physics- based hydrologic and land- surface models coupled with routing schemes, machine -learning (e.g., LSTM) approaches that estimate discharge, and hybrids. Models were assessed only if they produce daily discharge (ideally also depth, width, and velocity) mapped to vector river channels in the SWORD database.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Main methodological approach<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>High- quality SWOT height observations are compared with modeled discharge on the same day via the SWOT- ensemble- Spearman (SES) correlation (Spearman rank correlation, chosen because the height\u2013 discharge relationship is strong, monotonic, and nonlinear).<\/li>\n\n\n\n<li>An ensemble of machine- learning models is preferred; traditional physics- based ensembles fill gaps where ML output is unavailable.<\/li>\n\n\n\n<li>Models were run in a largely<strong> \u201cnaturalized\u201d<\/strong> mode (human influences appear only indirectly via training gauges) so that satellite data could independently reveal human impacts rather than assuming them a priori.<\/li>\n\n\n\n<li>SWOT height skill is strong (68th- percentile error of ~16 cm in detecting height changes in the validation set).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Broader implications<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Current global river models are limited by simplified channel geometry, incomplete representation of dams and water withdrawals, uncertain snowpack timing, and sparse data on human operations. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">SWOT provides <strong>\u201cdetail at scale\u201d<\/strong>, direct observations of real river complexity (dam operations, multi-channel partitioning, abrupt stage changes, infrastructure constraints, etc.) that most models cannot currently reproduce. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The authors argue for a revived empiricism:<\/strong> use SWOT measurements first and trust the observations, while also adapting model structures so they can assimilate realistic hydraulic states rather than relying on oversimplified assumptions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The paper positions SWOT as a tool that both diagnoses existing model weaknesses on a reach- by- reach basis and supplies the primary data needed to build the next generation of more realistic global river models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">(The full article is open access on the Wiley Online Library page you linked. Supporting Information is also available there.)<br><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Journal information:<\/strong> <a href=\"https:\/\/phys.org\/journals\/geophysical-research-letters\/\">Geophysical Research Letters<\/a> <em>Volume 53, Issue 16, e2026GL124323<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>DOI:<\/strong> <a href=\"https:\/\/dx.doi.org\/10.1029\/2026gl124323\" target=\"_blank\" rel=\"noopener\">10.1029\/2026gl124323<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Provided:<\/strong> <a href=\"https:\/\/phys.org\/partners\/university-of-massachusetts-amherst\/\">University of Massachusetts Amherst<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>First published:<\/strong> 24 August 2026<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Authors:<\/strong> <a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Gleason\/Colin+J.\">Colin J. Gleason<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Bates\/Paul+D.\">Paul D. Bates<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Durand\/Michael+T.\">Michael T. Durand<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Feng\/Dongmei\">Dongmei Feng<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Getirana\/Augusto\">Augusto Getirana<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Lin\/Peirong\">Peirong Lin<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Oubanas\/Hind\">Hind Oubanas<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Pavelsky\/Tamlin+M.\">Tamlin M. Pavelsky<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Shen\/Chaopeng\">Chaopeng Shen<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Smith\/Laurence+C.\">Laurence C. Smith<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Wang\/Jida\">Jida Wang<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Yamazaki\/Dai\">Dai Yamazaki<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Yang\/Yuan\">Yuan Yang<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/An\/Heejin\">Heejin An<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Bennitt\/Fiona+B.\">Fiona B. Bennitt<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Chuter\/Stephen+J.\">Stephen J. Chuter<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Fang\/Chenqi\">Chenqi Fang<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Flores\/Jonathan+A.\">Jonathan A. Flores<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Friedmann\/Elisa\">Elisa Friedmann<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Jarugula\/Sreelekha\">Sreelekha Jarugula<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Ji\/Haoyu\">Haoyu Ji<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Langhorst\/Theodore\">Theodore Langhorst<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Long\/Di\">Di Long<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Vu\/Dung+T.\">Dung T. Vu<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Yin\/Ziyun\">Ziyun Yin<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Allen\/George+H.\">George H. Allen<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Andreadis\/Konstantinos+M.\">Konstantinos M. Andreadis<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Biancamaria\/Sylvain\">Sylvain Biancamaria<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Brown\/Casey+M.\">Casey M. Brown<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/David\/C%C3%A9dric+H.\">C\u00e9dric H. David<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Pan\/Ming\">Ming Pan<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Neal\/Jeffrey+C.\">Jeffrey C. Neal<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Coss\/Stephen+P.\">Stephen P. Coss<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Harlan\/Merritt+E.\">Merritt E. Harlan<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Saemian\/Peyman\">Peyman Saemian<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Simmons\/Travis+T.\">Travis T. Simmons<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Tebaldi\/Nikki\">Nikki Tebaldi<\/a>,\u00a0<a href=\"https:\/\/agupubs.onlinelibrary.wiley.com\/authored-by\/Tourian\/M.+J.\">M. J. Tourian<\/a><br><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Abstract<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing \u223c38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi-channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Newly available satellite data from NASA\u2019s Surface Water and Ocean Topography (SWOT) mission reveal significant flaws in many global river models, particularly for dammed rivers, arid- region rivers, and Arctic rivers.<\/p>\n<p>The study frames SWOT as an \u201cearly microscope\u201d for rivers: it encourages relying more on direct satellite measurements rather than models alone, while also identifying where models need structural improvements to incorporate realistic hydraulics and human influences.<\/p>\n","protected":false},"author":121246920,"featured_media":477364,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","advanced_seo_description":"Explore how NASA's SWOT satellite reveals critical flaws in global river models, highlighting the challenges of dammed and arid-region rivers.","jetpack_seo_html_title":"NASA's SWOT Reveals Flaws in Global River Modeling Accuracy","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691846119,691846118,691846117,691846116,691846120,691846111,691846112,691846113,691846114],"class_list":["post-477363","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-arctic-rivers","tag-arid-region-rivers","tag-dammed-rivers","tag-global-river-models","tag-multi-channel-rivers","tag-state-of-the-art-machine-learning-river-models","tag-surface-water-and-ocean-topography-swot","tag-sword-database","tag-swot-ensemble-spearman-ses","fallback-thumbnail"],"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-20bp","jetpack-related-posts":[{"id":286535,"url":"https:\/\/climatescience.press\/?p=286535","url_meta":{"origin":477363,"position":0},"title":"Colorado River Flow Data Disproves \u201cClimate Change Warming\u201d Computer Model Flow Reduction Claims","author":"uwe.roland.gross","date":"11\/04\/2023","format":false,"excerpt":"The next question is, does climate change even predict future reductions of precipitation over the Colorado River watershed? The following plot shows an average of 183 climate model simulations of average yearly precipitation in an area approximating the Colorado River watershed. The models suggest a slight increase in total precipitation\u2026","rel":"","context":"In \"climate change warming\u201d\"","block_context":{"text":"climate change warming\u201d","link":"https:\/\/climatescience.press\/?tag=climate-change-warming"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/11\/0horseshoe-bend-iconic_s.jpg?fit=1200%2C1200&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/11\/0horseshoe-bend-iconic_s.jpg?fit=1200%2C1200&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/11\/0horseshoe-bend-iconic_s.jpg?fit=1200%2C1200&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/11\/0horseshoe-bend-iconic_s.jpg?fit=1200%2C1200&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/11\/0horseshoe-bend-iconic_s.jpg?fit=1200%2C1200&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":463445,"url":"https:\/\/climatescience.press\/?p=463445","url_meta":{"origin":477363,"position":1},"title":"Satellite Data Show Tropical Forests React Differently to Climate Across Continents","author":"uwe.roland.gross","date":"08\/16\/2026","format":false,"excerpt":"Satellite data from NASA\u2019s GEDI mission show that tropical forests on different continents respond differently to climate factors such as temperature and aridity, with effects further modified by soils, topography, and other local conditions.","rel":"","context":"In \"aboveground biomass (AGB)\"","block_context":{"text":"aboveground biomass (AGB)","link":"https:\/\/climatescience.press\/?tag=aboveground-biomass-agb"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Satellite-Data-Show-Tropical-Forests-React-Differently-to-Climate-Across-Continents.jpg?fit=1168%2C784&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Satellite-Data-Show-Tropical-Forests-React-Differently-to-Climate-Across-Continents.jpg?fit=1168%2C784&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Satellite-Data-Show-Tropical-Forests-React-Differently-to-Climate-Across-Continents.jpg?fit=1168%2C784&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Satellite-Data-Show-Tropical-Forests-React-Differently-to-Climate-Across-Continents.jpg?fit=1168%2C784&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Satellite-Data-Show-Tropical-Forests-React-Differently-to-Climate-Across-Continents.jpg?fit=1168%2C784&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":463261,"url":"https:\/\/climatescience.press\/?p=463261","url_meta":{"origin":477363,"position":2},"title":"Record Low Lake Mead: \u201cMillennium Drought\u201d or Inevitable Result of Demand Exceeding Supply?","author":"uwe.roland.gross","date":"08\/16\/2026","format":false,"excerpt":"The Colorado River Compact (1922) and subsequent Law of the River allocated more water than the long-term average supply supports. Early-20th-century flows were unusually high; allocations (roughly 15 million acre-feet split between Upper and Lower Basins, plus obligations to Mexico) assumed higher yields that have not materialized on average.","rel":"","context":"In \"agriculture\"","block_context":{"text":"agriculture","link":"https:\/\/climatescience.press\/?tag=agriculture"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Record-Low-Lake-Mead-Millennium-Drought-or-Inevitable-Result-of-Demand-Exceeding-Supply.jpg?fit=1168%2C784&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Record-Low-Lake-Mead-Millennium-Drought-or-Inevitable-Result-of-Demand-Exceeding-Supply.jpg?fit=1168%2C784&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Record-Low-Lake-Mead-Millennium-Drought-or-Inevitable-Result-of-Demand-Exceeding-Supply.jpg?fit=1168%2C784&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Record-Low-Lake-Mead-Millennium-Drought-or-Inevitable-Result-of-Demand-Exceeding-Supply.jpg?fit=1168%2C784&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/08\/0-Record-Low-Lake-Mead-Millennium-Drought-or-Inevitable-Result-of-Demand-Exceeding-Supply.jpg?fit=1168%2C784&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":476933,"url":"https:\/\/climatescience.press\/?p=476933","url_meta":{"origin":477363,"position":3},"title":"Scientists Map Sky Rivers Over South America \u2014 And They Work Just Like Rivers on the Ground","author":"uwe.roland.gross","date":"10\/05\/2026","format":false,"excerpt":"\u201cFlying rivers\u201d (also called aerial rivers) above South America form organized drainage networks that structurally resemble terrestrial river systems, according to a 2026 study led by a National Taiwan University (NTU) interdisciplinary team and published in Nature Communications. Not the short- lived atmospheric rivers you hear about in weather reports.\u2026","rel":"","context":"In \"Aerial river outfall\"","block_context":{"text":"Aerial river outfall","link":"https:\/\/climatescience.press\/?tag=aerial-river-outfall"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Meta-Scientists-Map-Sky-Rivers-Over-South-America-%E2%80%94-And-They-Work-Just-Like-Rivers-on-the-Ground.jpg?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Meta-Scientists-Map-Sky-Rivers-Over-South-America-%E2%80%94-And-They-Work-Just-Like-Rivers-on-the-Ground.jpg?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Meta-Scientists-Map-Sky-Rivers-Over-South-America-%E2%80%94-And-They-Work-Just-Like-Rivers-on-the-Ground.jpg?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Meta-Scientists-Map-Sky-Rivers-Over-South-America-%E2%80%94-And-They-Work-Just-Like-Rivers-on-the-Ground.jpg?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Meta-Scientists-Map-Sky-Rivers-Over-South-America-%E2%80%94-And-They-Work-Just-Like-Rivers-on-the-Ground.jpg?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":403848,"url":"https:\/\/climatescience.press\/?p=403848","url_meta":{"origin":477363,"position":4},"title":"WMO: 2024 Was Dry and Hot With Lots of Rain","author":"uwe.roland.gross","date":"09\/21\/2025","format":false,"excerpt":"Apparently it\u2019s not the broken meteorological models, climate climate is making the world more unpredictable.","rel":"","context":"In \"climate model predictions\"","block_context":{"text":"climate model 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3x"},"classes":[]},{"id":262531,"url":"https:\/\/climatescience.press\/?p=262531","url_meta":{"origin":477363,"position":5},"title":"New Study: Land loss due to human-altered sediment budget in the Mississippi River Delta","author":"uwe.roland.gross","date":"06\/17\/2023","format":false,"excerpt":"Research from scientists at Louisiana State University and Indiana University reveals new information about the role humans have played in large-scale land loss in the Mississippi River Delta\u2014crucial information in determining solutions to the crisis.","rel":"","context":"In \"Land loss\"","block_context":{"text":"Land loss","link":"https:\/\/climatescience.press\/?tag=land-loss"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0293A0745.webp?fit=1200%2C900&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0293A0745.webp?fit=1200%2C900&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0293A0745.webp?fit=1200%2C900&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0293A0745.webp?fit=1200%2C900&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0293A0745.webp?fit=1200%2C900&ssl=1&resize=1050%2C600 3x"},"classes":[]}],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-42.png?fit=1168%2C784&ssl=1","_links":{"self":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/477363","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/users\/121246920"}],"replies":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=477363"}],"version-history":[{"count":20,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/477363\/revisions"}],"predecessor-version":[{"id":477385,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/477363\/revisions\/477385"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/media\/477364"}],"wp:attachment":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=477363"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=477363"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=477363"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}