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Dams disrupt the natural flow of water and sediment along rivers. Reservoirs trap a significant amount of sediment, which substantially alters downstream hydrology, channel morphology, and sediment transport capacity. The longitudinal recovery of suspended sediment concentration (SSC) along rivers is potentially a new metric for estimating downstream responses to dams over space, rather than time, but is rarely quantified due to the lack of spatial SSC data. Satellites can estimate SSC along rivers where no field data exist and provide a high enough spatial resolution for assessing downstream recovery at the scale of tens to hundreds of kilometers. Here, we use a recently published database of spatially explicit SSC observations derived from Landsat to quantify if a river recovers or not, the SSC recovery percentage, and SSC recovery length downstream of large dams across the Contiguous United States (CONUS). Rivers recover SSC downstream of most dams (71%). The chance of a river recovering and the length of river required to recover SSC is associated primarily with the size of the reservoir (mean storage, km3) and the size of the river (mean discharge, m3/s). Rivers were more likely to recover SSC downstream of run‐of‐river, navigation dams compared to large storage or hydropower dams. Our results suggest that rivers typically recover suspended sediment downstream of dams, influenced by factors like dam storage, purpose, and river channel characteristics. Satellite‐derived suspended sediment concentration (SSC) shows rivers recovery to pre‐reservoir SSC downstream of 71% of dams in the USThe length of river required to longitudinally recover SSC downstream of dams is most strongly associated with reservoir and river sizeMultidecadal satellite SSC observations provide a new metric for assessing the long‐term mean downstream response to dams Satellite‐derived suspended sediment concentration (SSC) shows rivers recovery to pre‐reservoir SSC downstream of 71% of dams in the US The length of river required to longitudinally recover SSC downstream of dams is most strongly associated with reservoir and river size Multidecadal satellite SSC observations provide a new metric for assessing the long‐term mean downstream response to dams

The Mackenzie River Delta in Canada is a mediator of hydrological transport between the expansive Mackenzie River watershed and the Beaufort Sea. Within the delta, lakes frequently act as water and sediment traps, limiting or delaying the movement of material to the coastal ocean. The degree to which this filtering takes place depends on the ease with which sediment‐laden water is transported from distributary channels into deltaic lakes, referred to as functional lake‐to‐channel connectivity, which varies both spatially and temporally. Tracking of connectivity has previously been limited to either small regions of the delta or has focused on a snapshot of connectivity at a single instance in time. Here we describe an algorithm that uses Landsat imagery to track summertime functional lake‐to‐channel connectivity of 10,362 lakes between 1984 and 2022 on an image‐by‐image basis. We calculate a total average connected lake area of 1400.7 km2during the 2 weeks after peak discharge, 763.6 km2higher than previous estimates, suggesting a larger influence of connected lakes on water movement through the delta than previously estimated. We also identify water level thresholds that lead to the initiation of high sediment river water movement into 5,989 lakes (908 lakes with uncertainty ≤±0.5 m), and identify an additional 2899 lakes whose connectivity does not vary at all. As the Arctic hydrological cycle responds to climate change, this work lays a foundation for tracking the movement of water, and the matter it carries, from the Mackenzie River watershed to the Beaufort Sea. The Mackenzie River Delta in Arctic Canada lies at the intersection between the Northwest Territories and the Beaufort Sea. The delta contains over 45,000 mostly small lakes, which are either isolated from nearby river channels or connected to channels via movement of floodwater over land or via feeder channels of their own. This connectivity influences the amount of sediment that is able to make it from the Mackenzie River to the coastal ocean and additionally controls plant and plankton productivity within lakes. In this research, we use satellite imagery to expand our understanding of connectivity in the delta from 141 lakes to 10,362 lakes. We find that between 1984 and 2022, connectivity is highly variable through both space and time. On average, connected lakes can store 7% of riverine floodwater, higher than any other studied Arctic delta. Additionally, we calculate how high river water needs to reach in order to flow into 908 lakes, and identify 2,899 lakes whose connectivity does not vary at all. As the Arctic hydrological cycle responds to climate change, this work lays a foundation for tracking the movement of water, and the materials carried within, from the Mackenzie River watershed to the Beaufort Sea. We track functional lake‐to‐channel connectivity in 10,362 lakes in the Mackenzie Delta between 1984 and 2022 using remote sensingConnected lakes have the potential to attenuate, on average, 7% of floodwater in the Mackenzie Delta, higher than any other measured deltaWe identify water levels required to connect 908 lakes and find 2,899 lakes whose connectivity is stable regardless of water level We track functional lake‐to‐channel connectivity in 10,362 lakes in the Mackenzie Delta between 1984 and 2022 using remote sensing Connected lakes have the potential to attenuate, on average, 7% of floodwater in the Mackenzie Delta, higher than any other measured delta We identify water levels required to connect 908 lakes and find 2,899 lakes whose connectivity is stable regardless of water level

Descriptions of river network topology do not include lakes/reservoirs that are connected to rivers. We describe the properties and scaling patterns of river network topology across the contiguous United States: how lake/reservoir abundance, median lake/reservoir size, and median lake/reservoir spacing change with river size. Typically, lake/reservoir abundance decreases, median lake/reservoir size increases, but median lake/reservoir spacing is uniform across river size. There is a characteristic lake/reservoir size of 0.01–0.05 km2and a characteristic lake/reservoir spacing of 1–5 km that shifts to 27–61 km in larger rivers. Climate explains more of the variance in river network topology than both glacial history and constructed reservoirs. Our results provide conceptual models for building river network topologies to assess how lake/reservoir abundance, size, and spacing effect the transport, storage, and cycling of water, materials, and organisms across networks. Rivers and lakes/reservoirs serve different hydrologic, ecologic, and economic roles and are often studied as separate systems. Yet there are many lakes/reservoirs connected to rivers and this connectivity has profound impacts on sediment storage and transport, biogeochemical cycles, and aquatic habitat in both rivers and lakes/reservoirs. Defining the topology of river networks is critical for understanding these processes across entire networks of connected rivers and lakes/reservoirs. We defined river network topology by scaling the abundance, size, and spacing of lakes/reservoirs with river size and identified broadscale controls of network topology. Lakes/reservoirs connected to rivers decrease in abundance, increase in size, but are uniform in spacing as river size increasesRiver network topology with lakes/reservoirs can be classified into four types across the contiguous United StatesClimate is a major control of river network topology with lakes/reservoirs

Continuous depth to groundwater (DTG) data collection is challenging in remote regions. Community participation offers a way to both increase data collection and involves the local community in scientific projects. Local knowledge, which is often descriptive, can be difficult to include in quantitative analysis; however, it can increase scientists' ability to formulate hypotheses or identify relevant environmental processes. We show how Community Science Research can add useful descriptive information for a study based in rural Colombia. To estimate the spatiotemporal distribution of DTG, the community collected water level measurements during a wet (La Niña) year and an average year. We built one spatial and two spatiotemporal models (with and without probabilistic data) using Bayesian Maximum Entropy. Due to the inclusion of local knowledge, the spatiotemporal model with probabilistic data reduced its mean square error by a factor of 15 compared to the spatial model. Using this model, we found that 13% of the study area has a high probability of very shallow DTG (<0.1 m) during an average year, whereas during La Niña, this area increases to 56%. The difference in shallow DTG between the average and wet year implies that after reaching a precipitation threshold, the study region may lose its flow regulation capacity, contributing to flooding during extreme precipitation events. Our approach presents a method to incorporate local knowledge in data‐driven models by combining qualitative and quantitative information. Groundwater is a key source of water supply in many regions, supporting crop yields and maintaining water levels in rivers and wetlands. In unconfined aquifers, groundwater may reach the surface during wet periods, contributing to overland flow and intensifying erosion. Identifying groundwater level changes helps to establish water and land management activities. However, continuous depth to groundwater (DTG) data collection, essential for identifying groundwater level changes, is challenging in remote rural areas. We show how Community Science Research, an approach involving active community participation, added crucial information to a statistical model to represent shallow aquifer's groundwater levels in Colombia. The community collected DTG during an extreme wet year and an average year in a middle‐low‐elevation watershed. We created DTG maps using three statistical models. DTG is better represented by the model that combines descriptive observations with DTG measurements. We also created a map with the probability that the groundwater is near the surface and showed that the area was much larger in the wet year than during the average year. This difference implies that after the watershed receives a lot of precipitation, its flow regulation capacity decreases, which is threatened by land‐use activities. We built one spatial and two spatiotemporal models using Bayesian Maximum Entropy to estimate Depth to Groundwater (DTG) in a rural regionThe addition of probabilistic data, based on local knowledge collected through Community Science Research, improved the space‐time modelThe watershed's portion more likely to have shallow DTG increased from 13% in an average year to 56% in a La Niña year We built one spatial and two spatiotemporal models using Bayesian Maximum Entropy to estimate Depth to Groundwater (DTG) in a rural region The addition of probabilistic data, based on local knowledge collected through Community Science Research, improved the space‐time model The watershed's portion more likely to have shallow DTG increased from 13% in an average year to 56% in a La Niña year

In situ river discharge estimation is a critical component of studying rivers. A dominant method for establishing discharge monitoring in situ is a temporary gauge, which uses a rating curve to relate stage to discharge. However, this approach is constrained by cost and the time to develop the stage‐discharge rating curve, as rating curves rely on numerous flow measurements at high and low stages. Here, we offer a novel alternative approach to traditional temporary gauges: estimating Discharge via Arrays of Pressure Transducers (DAPT). DAPT uses a Bayesian discharge algorithm developed for the upcoming Surface Water Ocean Topography satellite (SWOT) to estimate in situ discharge from automated water surface elevation measurements. We conducted sensitivity tests over 4,954 model runs on five gauged rivers and conclude that the DAPT method can robustly reproduce discharge with an average Nash‐Sutcliffe Efficiency (NSE) of 0.79 and Kling‐Gupta Efficiency of 0.78. Further, we find that the DAPT method estimates discharge similarly to an idealized temporary gauge created from the same input data (NSE differences of less than 0.1), and that results improve significantly with accurate priors. Finally, we test the DAPT method in nine poorly gauged rivers in a realistic and complex field setting in the Peace‐Athabasca Delta, and show that the DAPT method largely outperforms a temporary gauge in this time and budget constrained setting. We therefore recommend DAPT as an effective tool for in situ discharge estimation in cases where there is not enough time or resources to develop a temporary gauge. We adapt a discharge algorithm designed for an upcoming satellite mission for in situ discharge estimationThe method relies on an array of pressure transducers installed along a mass conserved channelEstimating river discharge using multiple pressure transducers is a viable alternative to installing a temporary gauge We adapt a discharge algorithm designed for an upcoming satellite mission for in situ discharge estimation The method relies on an array of pressure transducers installed along a mass conserved channel Estimating river discharge using multiple pressure transducers is a viable alternative to installing a temporary gauge