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Droughts are often long-lasting phenomena, without a distinct start or end and with impacts cascading across sectors and systems, creating long-term legacies. Nevertheless, our current perceptions and management of droughts and their impacts are often event-based, which can limit the effective assessment of drought risks and reduction of drought impacts. Here, we advocate for changing this perspective and viewing drought as a hydrological–ecological–social continuum. We take a systems theory perspective and focus on how “memory” causes feedback and interactions between parts of the interconnected systems at different timescales. We first discuss the characteristics of the drought continuum with a focus on the hydrological, ecological, and social systems separately, and then we study the system of systems. Our analysis is based on a review of the literature and a study of five cases: Chile, the Colorado River basin in the USA, northeast Brazil, Kenya, and the Rhine River basin in northwest Europe. We find that the memories of past dry and wet periods, carried by both bio-physical (e.g. groundwater, vegetation) and social systems (e.g. people, governance), influence how future drought risk manifests. We identify four archetypes of drought dynamics: impact and recovery, slow resilience building, gradual collapse, and high resilience–big shock. The interactions between the hydrological, ecological, and social systems result in systems shifting between these types, which plays out differently in the five case studies. We call for more research on drought preconditions and recovery in different systems, on dynamics cascading between systems and triggering system changes, and on dynamic vulnerability and maladaptation. Additionally, we advocate for more continuous monitoring of drought hazards and impacts, modelling tools that better incorporate memories and adaptation responses, and management strategies that increase societal and institutional memory. This will help us to better deal with the complex hydrological–ecological–social drought continuum and identify effective pathways to adaptation and mitigation.

Description of thermal regimes in flowing waters is key to understanding physical processes, enhancing predictive abilities, and improving bioassessments. Spatially and temporally sparse data sets, especially in logistically challenging mountain environments, have limited studies on thermal regimes, but inexpensive sensors coupled with crowd-sourced data collection efforts provide efficient means of developing large data sets for robust analyses. Here, thermal regimes are assessed using annual monitoring records compiled from several natural resource agencies in the northwestern United States that spanned a 5-year period (2011–2015) at 226 sites across several contiguous montane river networks. Regimes were summarized with 28 metrics and principal component analysis (PCA) was used to determine those metrics which best explained thermal variation on a reduced set of orthogonal axes. Four principal components (PC) accounted for 93.4 % of the variation in the temperature metrics, with the first PC (49 % of variance) associated with metrics that represented magnitude and variability and the second PC (29 % of variance) associated with metrics representing the length and intensity of the winter season. Another variant of PCA, T-mode analysis, was applied to daily temperature values and revealed two distinct phases of spatial variability – a homogeneous phase during winter when daily temperatures at all sites were  ∘C and a heterogeneous phase throughout the year's remainder when variation among sites was more pronounced. Phase transitions occurred in March and November, and coincided with the abatement and onset of subzero air temperatures across the study area. S-mode PCA was conducted on the same matrix of daily temperature values after transposition and indicated that two PCs accounted for 98 % of the temporal variation among sites. The first S-mode PC was responsible for 96.7 % of that variance and correlated with air temperature variation (r=0.92), whereas the second PC accounted for 1.3 % of residual variance and was correlated with discharge (r=0.84). Thermal regimes in these mountain river networks were relatively simple and responded coherently to external forcing factors, so sparse monitoring arrays and small sets of summary metrics may be adequate for their description. PCA provided a computationally efficient means of extracting key information elements from the temperature data set used here and could be applied broadly to facilitate comparisons among more diverse stream types and develop classification schemes for thermal regimes.

Snow surface temperature is a key control on and result of dynamically coupled energy exchanges at the snow surface. The snow surface temperature is the result of the balance between external forcing (incoming radiation) and energy exchanges above the surface that depend on surface temperature (outgoing longwave radiation and turbulent fluxes) and the transport of energy into the snow by conduction and meltwater influx. Because of the strong insulating properties of snow, thermal gradients in snow packs are large and nonlinear, a fact that has led many to advocate multiple layer snowmelt models over single layer models. In an effort to keep snowmelt modeling simple and parsimonious, the Utah Energy Balance (UEB) snowmelt model used only one layer but allowed the snow surface temperature to be different from the snow average temperature by using an equilibrium gradient parameterization based on the surface energy balance. Although this procedure was considered an improvement over the ordinary single layer snowmelt models, it still resulted in discrepancies between modeled and measured snowpack energy contents. In this paper we evaluate the equilibrium gradient approach, the force-restore approach, and a modified force-restore approach when they are integrated as part of a complete energy and mass balance snowmelt model. The force-restore and modified force-restore approaches have not been incorporated into the UEB in early versions, even though Luce and Tartoton have done work in calculating the energy components using these approaches. In addition, we evaluate a scheme for representing the penetration of a refreezing front in cold periods following melt. We introduce a method to adjust effective conductivity to account for the presence of ground near to a shallow snow surface. These parameterizations were tested against data from the Central Sierra Snow Laboratory, CA, Utah State University experimental farm, UT, and subnivean snow laboratory at Niwot Ridge, CO. These tests compare modeled and measured snow surface temperature, snow energy content, snow water equivalent, and snowmelt outflow. We found that with these refinements the model is able to better represent the snowpack energy balance and internal energy content while still retaining a parsimonious one layer format.

The snow surface temperature is an important quantity in the snow energy balance, since it modulates the exchange of energy between the surface and the atmosphere as well as the conduction of energy into the snowpack. It is therefore important to correctly model snow surface temperatures in energy balance snowmelt models. This paper focuses on the relationship between snow surface temperature and conductive energy fluxes that drive the energy balance of a snowpack. Time series of snow temperature at the surface and through the snowpack were measured to examine energy conduction in a snowpack. Based on these measurements we calculated the snowpack energy content and conductive energy flux at the snow surface. We then used these estimates of conductive energy flux to evaluate formulae for the calculation of the conductive flux at the snow surface based on surface temperature time series. We use a method based on Fourier frequency analysis to estimate snow thermal properties. Among the formulae evaluated, we found that a modified force-restore formula, based on the superimposition of the force-restore equation capturing diurnal fluctuations on a gradually changing temperature gradient, had the best agreement with observations of heat conduction. This formula is suggested for the parameterization of snow surface temperature in a full snowpack energy balance model.