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• First-generation Earth system digital twins, such as the Digital Twin Earth (DTE) for hydrology, create important opportunities for “learning by doing” that will ensure DTEs evolve to provide credible, reliable, and useful information. • Recent DTEs for hydrology demonstrate the complexity of the cyberinfrastructure needed to support the integration of a diversity of high-resolution datasets—often through machine learning techniques— while also providing initial insights into how critical errors in these approaches might be identi!ed. • To remain useful, DTEs will need to be able to continuously evolve—this will require innovations in visualization, cross-disciplinary collaboration, and complementary tools that draw from advances in relevant research communities. 

Abstract Atmospheric nitrogen (N) deposition and climate change are transforming the way N moves through dryland watersheds. For example, N deposition is increasing N export to streams, which may be exacerbated by changes in the magnitude, timing, and intensity of precipitation (i.e., the precipitation regime). While deposition can control the amount of N entering a watershed, the precipitation regime influences rates of internal cycling; when and where soil N, plant roots, and microbes are hydrologically coupled via diffusion; how quickly plants and microbes assimilate N; and rates of denitrification, runoff, and leaching. We used the ecohydrological model RHESSys to investigate (a) how N dynamics differ between N‐limited and N‐saturated conditions in a dryland watershed, and (b) how total precipitation and its intra‐annual intermittency (i.e., the time between storms in a year), interannual intermittency (i.e., the duration of dry months across multiple years), and interannual variability (i.e., variance in the amount of precipitation among years) modify N dynamics and export. Streamflow nitrate (NO₃⁻) export was more sensitive to increasing rainfall intermittency (both intra‐annual and interannual) and variability in N‐limited than in N‐saturated model scenarios, particularly when total precipitation was lower—the opposite was true for denitrification which is more sensitive in N‐saturated than N‐limited scenarios. N export and denitrification increased or decreased more with increasing interannual intermittency than with other changes in precipitation amount. This suggests that under future climate change, prolonged droughts that are followed by more intense storms may pose a major threat to water quality in dryland watersheds. 

Exponentially growing publication rates are increasingly problematic for interdisciplinary fields like Critical Zone (CZ) science. How does one “keep up” across different, but related fields with unique hypotheses, field techniques, and models? By surveying CZ academics in the Western US, a region with substantial CZ research, we document the challenge. While conventional knowledge synthesis products-particularly review papers clearly support knowledge transfer, they are static and limited in scope. More informal paths for knowledge transfer, including social networking at conferences and academic mentorship, are useful but are unstructured and problematic for young scientists or others who may not have access to these resources. While new machine-learning tools, including ChatGPT, offer new ways forward for knowledge synthesis, we argue that they do not necessarily solve the problem of information overload in CZ Science. Instead, we argue that what we need is a community driven, machine aided knowledge tool that evolves and connects, but preserves the richness of detail found in peer-reviewed papers. The platform would be designed by CZ scientists, machine-aided and built on the strengths of people-driven synthesis. By involving the scientist in the design of this tool, it will better reflect the practice of CZ science-including hypothesis generation, testing across different time and space scales and in different time periods and locations, and, importantly, the use and evaluation of multiple, often sophisticated methods including fieldwork, remote sensing, and modeling. We seek a platform design that increases the findability and accessibility of current working knowledge while communicating the CZ science practice. 

Abstract Water temperatures in mountain streams are likely to rise under future climate change, with negative impacts on ecosystems and water quality. However, it is difficult to predict which streams are most vulnerable due to sparse historical records of mountain stream temperatures as well as complex interactions between snowpack, groundwater, streamflow and water temperature. Minimum flow volumes are a potentially useful proxy for stream temperature, since daily streamflow records are much more common. We confirmed that there is a strong inverse relationship between annual low flows and peak water temperature using observed data from unimpaired streams throughout the montane regions of the United States' west coast. We then used linear models to explore the relationships between snowpack, potential evapotranspiration and other climate‐related variables with annual low flow volumes and peak water temperatures. We also incorporated previous years' flow volumes into these models to account for groundwater carryover from year to year. We found that annual peak snowpack water storage is a strong predictor of summer low flows in the more arid watersheds studied. This relationship is mediated by atmospheric water demand and carryover subsurface water storage from previous years, such that multi‐year droughts with high evapotranspiration lead to especially low flow volumes. We conclude that watershed management to help retain snow and increase baseflows may help counteract some of the streamflow temperature rises expected from a warming climate, especially in arid watersheds. 

Modern forest management generally relies on thinning treatments to reduce fuels and mitigate the threat of catastrophic wildfire. They have also been proposed as a tool to augment downstream flows by reducing evapotranspiration. Warming climates are causing many forests to transition from snow-dominated to rain-dominated precipitation regimes—in which water stores are depleted earlier in the summer. However, there are relatively few studies of these systems that directly measure the hydrologic impacts of such treatments during and following snow-free winters. This work compares the below-canopy meteorological and subsurface hydrologic differences between two thinning prescriptions and an unaltered Control during periods of extreme drought and near-record precipitation (with little snow). The field site was within a coniferous forest in the rain-snow transition zone of the southern Cascades, near the Sierra Nevada Range of California. Both thinning-prescriptions had a modest and predictable impact on below-canopy meteorology, which included their causing lower nighttime minimum temperatures in the critical summer months and higher wind speeds. Relative to the Control, both treatments affected soil moisture storage by delaying its annual decline and increasing its minimum value by the end of the season. The onset of soil moisture depletion was strongly tied to the magnitude of winter precipitation. In dry years, it began much earlier within the dense Control stand than in the treated ones, and, without snow, soil moisture was not replenished in the late spring. During high precipitation years, the storage capacity was topped off for all three stands, which resulted in similar timing of moisture decline across them, later in the season. The two thinning prescriptions increased stores through the height of summer (in wet and drought years). Finally, the basal area increment (BAI) of the remaining trees rose in both, suggesting they used the excess moisture to support rapid growth. 

Abstract Climate change and nitrogen (N) pollution are altering biogeochemical and ecohydrological processes in dryland watersheds, increasing N export, and threatening water quality. While simulation models are useful for projecting how N export will change in the future, most models ignore biogeochemical “hotspots” that develop in drylands as moist microsites in the soil become hydrologically disconnected from plant roots when soils dry out. These hotspots enable N to accumulate over dry periods and rapidly flush to streams when soils wet up. To better project future N export, we developed a framework for representing hotspots using the ecohydrological model RHESSys. We then conducted a series of virtual experiments to understand how uncertainties in model structure and parameters influence N export to streams. Modeled N export was sensitive to three major factors (a) the abundance of hotspots in a watershed: N export increased linearly and then reached an asymptote with increasing hotspot abundance; this occurred because carbon and N inputs eventually became limiting as hotspots displaced vegetation cover, (b) the soil moisture threshold required for subsurface flow from hotspots to reestablish: peak streamflow N export increased and then decreased with an increasing threshold due to tradeoffs between N accumulation and export that occur with increasingly disconnected hotspots, and (c) the rate at which water diffused out of hotspots as soils dried down: N export was generally higher when the rate was slow because more N could accumulate in hotspots over dry periods, and then be flushed more rapidly to streams at the onset of rain. In a case study, we found that when hotspots were modeled explicitly, peak streamflow nitrate export increased by 29%, enabling us to better capture the timing and magnitude of N losses observed in the field. N export further increased in response to interannual precipitation variability, particularly when multiple dry years were followed by a wet year. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality. 

Abstract. Mountain pine beetle (MPB) outbreaks in the western United States result inwidespread tree mortality, transforming forest structure within watersheds.While there is evidence that these changes can alter the timing and quantity of streamflow, there is substantial variation in both the magnitude and direction of hydrologic responses, and the climatic and environmental mechanisms driving this variation are not well understood. Herein, we coupled an eco-hydrologic model (RHESSys) with a beetle effects model and applied it to a semiarid watershed, Trail Creek, in the Bigwood River basin in central Idaho, USA, to examine how varying degrees of beetle-caused tree mortality influence water yield. Simulation results show that water yield during the first 15 years after beetle outbreak is controlled by interactions between interannual climate variability, the extent of vegetation mortality, and long-term aridity. During wet years, water yield after a beetle outbreak increased with greater tree mortality; this was driven by mortality-caused decreases in evapotranspiration. During dry years, water yield decreased at low-to-medium mortality but increased at high mortality. The mortality threshold for the direction of change was location specific. The change in water yield also varied spatially along aridity gradients during dry years. In wetter areas of the Trail Creek basin, post-outbreak water yield decreased at low mortality (driven by an increase in ground evaporation) and increased when vegetation mortality was greater than 40 % (driven by a decrease in canopy evaporation and transpiration). In contrast, in more water-limited areas, water yield typically decreased after beetle outbreaks, regardless of mortality level (although the driving mechanisms varied). Our findings highlight the complexity and variability of hydrologic responses and suggest that long-term (i.e., multi-decadal mean) aridity can be a useful indicator for the direction of water yield changes after a disturbance.