Drought assessment has been outpaced by climate change: empirical arguments for a paradigm shift.

Despite the acceleration of climate change, erroneous assumptions of climate stationarity are still inculcated in the management of water resources in the United States (US). The US system for drought detection, which triggers billions of dollars in emergency resources, adheres to this assumption with preference towards 60-year (or longer) record lengths for drought characterization. Using observed data from 1,934 Global Historical Climate Network (GHCN) sites across the US, we show that conclusions based on long climate records can substantially bias assessment of drought severity. Bias emerges by assuming that conditions from the early and mid 20th century are as likely to occur in today's climate. Numerical simulations reveal that drought assessment error is relatively low with limited climatology lengths (~30 year) and that error increases with longer record lengths where climate is changing rapidly. We assert that non-stationarity in climate must be accounted for in contemporary assessments to more accurately portray present drought risk.

Vulnerable Waters are Essential to Watershed Resilience.

Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor.

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Differential use of winter precipitation by upper and lower elevation Douglas fir in the Northern Rockies.

In temperate regions such as the American west, forest trees often exhibit growth sensitivity to climatic conditions of a particular season. For example, annual tree ring growth increments may correlate well with winter precipitation, but not with summer rainfall, suggesting that trees rely more on winter snow than summer rain. Because both the timing and character of seasonal western climate patterns are expected to change considerably over coming decades, variation in the importance of different seasonal moisture sources for trees can be expected to influence how different forest trees respond to climate change as a whole, with shifts in seasonality potentially benefitting some trees while challenging others. In this study, we inferred patterns of tree water use in Douglas fir trees from the Northern Rockies for 2 years using stable water isotopes, while simultaneously quantifying and tracking precipitation inputs to soil moisture across a vertical soil profile. We then coupled water source use with daily measurements of radial growth to demonstrate that soil moisture from winter precipitation accounted for 87.5% and 84% of tree growth at low and high elevations, respectively. We found that prevailing soil moisture conditions drive variation in the depth at which trees access soil water, which in turn determines which seasonal precipitation inputs are available to support tree growth and function. In general, trees at lower elevations relied more on winter precipitation sourced from deep soils while trees at higher elevations made better use of summer rains sourced from near-surface soil layers. As both the timing of seasons and phase of precipitation (rain vs. snow) are likely to change considerably across much of the west, such patterns in tree water use are likely to play a role in determining the evolution of forest composition and structure in a warming climate.

Hydrometeorology organizes intra-annual patterns of tree growth across time, space and species in a montane watershed.

Tree radial growth is often systematically limited by water availability, as is evident in tree ring records. However, the physiological nature of observed tree growth limitation is often uncertain outside of the laboratory. To further explore the physiology of water limitation, we observed intra-annual growth rates of four conifer species using point dendrometers and microcores, and coupled these data to observations of water potential, soil moisture, and vapor pressure deficit over 2 yr in the Northern Rocky Mountains, USA. The onset of growth limitation in four species was well explained by a critical balance between soil moisture supply and atmospheric demand representing relatively mesic conditions, despite the timing of this threshold response varying by up to 2 months across topographic and elevation gradients, growing locations, and study years. Our findings suggest that critical water deficits impeding tissue growth occurred at relatively high water potential values, often occurring when hydrometeorological conditions were relatively wet during the growing season (e.g. in early spring in some cases). This suggests that species-specific differences in water use strategies may not necessarily affect tree growth, and that tissue growth may be more directly linked to environmental moisture conditions than might otherwise be expected.