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Montane snowpack in the Sierra Nevada provides critical water resources for ecological functions and downstream communities. Forest removal allows us to manage the snowpack in montane forests and mitigate the effect of climate on water resources. Little is known about the mid- to long-term effects that changing snowpack following forest disturbance has on tree re-growth, and how tree re-growth might in turn affect snowpack accumulation and melt. We use a 1-m resolution process-based snow model (SnowPALM) coupled with a stand-scale ecohydrological model (RHESSys) that resolves water, energy and carbon cycling to represent tree growth, and to quantify how trees and snowpack co-evolve following two disturbance scenarios (thinning and clearcutting) over a period of 40 years in a small 100 m x 234 m mid-elevation forested area in the Sierra Nevada, California. We first calculate the impact of forest disturbance on the snowpack assuming no tree regrowth and then we compare it with scenarios that include the feedback of trees regrowth on the snowpack. Without tree regrowth, snow accumulation and melt volume increase on average by roughly 5 % and 13 % following thinning and clearcutting, respectively. With tree regrowth, a regrowth rate of 0.75 and 1.15 m/decade are found for thinning and clearcutting, respectively, along with a decrease of melt volumes of 2.5 to 0.9 mm/decade, respectively. About 50 % of the snowmelt volume gains from forest thinning are lost after 40 years of regrowth, whereas only about 7 % is lost from clearcutting after the same period, which are largely explained by changes to canopy interception and sublimation. This proof-of-concept study is expected to shed light into the coevolution of montane forests and snowpack response to forest disturbance. 

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. 

• 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. 

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.