Permafrost active layer thickness (ALT) is a sensitive indicator of permafrost response to climate change. In recent decades, ALT has increased at sites across the Arctic, concurrent with observed increases in annual minimum streamflow (baseflow). The trends in ALT and baseflow are thought to be linked via: 1) increased soil water storage capacity due to an increased active layer, and 2) enhanced soil water mobility within a more continuous active layer, both of which support higher baseflow in Arctic rivers. One approach to analyzing these changes in ALT and baseflow is to use baseflow recession analysis, which is a classical method in hydrology that relates groundwater storage S to baseflow Q with a power law-like relationship Q = aSb. For the special case of a linear reservoir (b=1.0), the baseflow recession method has been extended to quantify changes in ALT from streamflow measurements alone. We test this approach at sites across the North American Arctic and find that catchments underlain by permafrost behave as nonlinear reservoirs, with scaling exponents b~1.5–3.0, undermining the key assumption of linearity that is commonly applied in this method. Despite this limitation, trends in a provide insight into the relationship between changing ALT and changing Arctic baseflow. Although care should be taken to ensure the theoretical assumptions are met, baseflow recession analysis shows promise as an empirical approach to constrain modeled permafrost change at the river basin scale.

The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.

Streamflow, biogeochemical cycling, and flux transport models rely on digital representations of river networks. At local to regional extents, such representations can be very detailed and account for individual hydrologic features such as dams and river diversions. However, at continental to global extents, these hydrologic features are often far less resolved. This lack of detail can lead to a mismatch between the resolution of hydrologic features relative to the resolution of the network itself. One solution to such mismatches is to impose a “global” standard hydrologic feature resolution. However, this approach may fail to provide critical information that is essential for accurate modeling because it removes hydrologic feature data (such as lakes) that could otherwise be passed to calibration and fitting routines. In this research, we test how variations in river network resolution may introduce such resolution mismatches. Using Viti Levu, Fiji as a case study, we show that even small “coarsening” of network resolution has a significant effect on lake representation and has carry-on effects on overall network transport. Because these effects were less pronounced for networks with larger lakes, this indicates that including lakes even in coarse models may be very informative given the extent to which available chemical and hydrologic data is skewed towards larger lakes.

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity (VIC) hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators within the sub-watersheds. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak streamflow are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. Overall, results indicate a concerning increase in drought risk. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time. Our unsupervised ML approach can be applied to other spatiotemporal data to process and understand vast arrays of data associated with climate impacts analysis of hydrologic change, assisting planners to rapidly assess potential risks associated with extreme events.

Permafrost underlies approximately one fifth of the global land area and affects ground stability, freshwater runoff, soil chemistry, and surface‑atmosphere gas exchange. The depth of thawed ground overlying permafrost (active layer thickness, ALT) has broadly increased across the Arctic in recent decades, coincident with a period of increased streamflow, especially the lowest flows (baseflow). Mechanistic links between ALT and baseflow have recently been explored using linear reservoir theory, but most watersheds behave as nonlinear reservoirs. We derive theoretical nonlinear relationships between long‑term average saturated soil thickness η (proxy for ALT) and long-term average baseflow. The theory is applied to 38 years of daily streamflow data for the Kuparuk River basin on the North Slope of Alaska. Between 1983–2020, the theory predicts that η increased 0.11±0.17 [2σ] cm a-1, or 4.4±6.6 cm total. The rate of change nearly doubled to 0.20±0.24 cm a-1 between 1990–2020, during which time field measurements from CALM (Circumpolar Active Layer Monitoring) sites in the Kuparuk indicate η increased 0.31±0.22 cm a-1. The predicted rate of change more than doubled again between 2002–2020, mirroring a near doubling of observed ALT rate of change. The inferred increase in η is corroborated by GRACE (Gravity Recovery and Climate Experiment) satellite gravimetry, which indicates that terrestrial water storage increased ~0.80±3.40 cm a-1, ~56% higher than the predicted increase in η. Overall, hydrologic change is accelerating in the Kuparuk River basin, and we provide a theoretical framework for estimating changes in active layer water storage from streamflow measurements alone.