River flooding can have severe societal, economic, and environmental consequences. However, limited understanding of the regional differences in flood‐generating mechanisms results in poorly ...understood historical flood trends and uncertain predictions of future flood conditions. Through systematic data analyses of 420 catchments we expose the primary drivers of flooding across the contiguous United States. This is achieved by exploring which flood‐generating processes control the seasonality and magnitude of maximum annual flows. The regional patterns of seasonality and interannual variabilities of maximum annual flows are, in general, poorly explained by rainfall characteristics alone. For most catchments soil moisture dependent precipitation excess, snowmelt, and rain‐on‐snow events are found to be much better predictors of the flooding responses. The continental‐scale classification of dominant flood‐generating processes we generate here emphasizes the disparity in timing and variability between extreme rainfall and flooding and can assist predictions of flooding and flood risk within the continental U.S.
Key Points
Regional differences in mechanisms that control the U.S. flood timing and magnitude are exposed
Disparity in timing and variability between floods and rainfall emphasizes the importance of hydrological processes
Classification of dominant flood‐generating mechanisms provides guidance to flood studies
Precipitation (P) and potential evaporation (Ep) are commonly studied drivers of changing freshwater availability, as aridity (Ep/P) explains ∼90% of the spatial differences in mean runoff across the ...globe. However, it is unclear if changes in aridity over time are also the most important cause for temporal changes in mean runoff and how this degree of importance varies regionally. We show that previous global assessments that address these questions do not properly account for changes due to precipitation, and thereby strongly underestimate the effects of precipitation on runoff. To resolve this shortcoming, we provide an improved Budyko‐based global assessment of the relative and absolute sensitivity of precipitation, potential evaporation, and other factors to changes in mean‐annual runoff. The absolute elasticity of runoff to potential evaporation changes is always lower than the elasticity to precipitation changes. The global pattern indicates that for 83% of the land grid cells runoff is most sensitive to precipitation changes, while other factors dominate for the remaining 17%. This dominant role of precipitation contradicts previous global assessments, which considered the impacts of aridity changes as a ratio. We highlight that dryland regions generally display high absolute sensitivities of runoff to changes in precipitation, however within dryland regions the relative sensitivity of runoff to changes in other factors (e.g., changing climatic variability, CO2‐vegetation feedbacks, and anthropogenic modifications to the landscape) is often far higher. Nonetheless, at the global scale, surface water resources are most sensitive to temporal changes in precipitation.
Key Points
Budyko‐based global assessment for the sensitivity of runoff to changes in precipitation, potential evaporation, and other factors
At a global scale, surface water resources are most sensitive to changes in precipitation, but regional exceptions exist
In dry lands, sensitivities of runoff to precipitation and potential evaporation changes are lower than the sensitivity to all other factors
Classification is essential in the study of natural systems, yet hydrology has no formal way to structure the climatic forcing that underlies hydrologic response. Various climate classification ...systems can be borrowed from other disciplines but these are based on different organizing principles than a hydrological classification might need. This work presents a hydrologically informed way to quantify global climates, explicitly addressing the shortcomings in earlier climate classifications. In this work, causal factors (climate) and hydrologic response (streamflow) are separated, meaning that our classification scheme is based only on climatic information and can be evaluated with independent streamflow data. Using gridded global climate data, we calculate three dimensionless indices per grid cell, describing annual aridity, aridity seasonality, and precipitation‐as‐snow. We use these indices to create several climate groups and define the membership degree of 1,103 catchments to each of the climate groups, based on each catchment's climate. Streamflow patterns within each group tend to be similar, and tend to be different between groups. Visual comparison of flow regimes and Wilcoxon two‐sample statistical tests on 16 streamflow signatures show that this index‐based approach is more effective than the often‐used Köppen‐Geiger classification for grouping hydrologically similar catchments. Climate forcing exerts a strong control on typical hydrologic response and we show that at the global scale both change gradually in space. We argue that hydrologists should consider the hydroclimate as a continuous spectrum defined by the three climate indices, on which all catchments are positioned and show examples of this in a regionalization context.
Key Points
Dimensionless numbers that describe a location's aridity, seasonality of aridity, and snowfall can define the global hydroclimate
Seasonal streamflow regimes and values of hydrologic statistics are similar in locations with similar values for the dimensionless numbers
This approach to hydrologic climate classification is more informative than Köppen‐Geiger classes, especially in snow‐dominated areas
This work advances a unified approach to process‐based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) of hydrologic processes and ...scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. In this paper, we introduce the general approach used in SUMMA, detailing the spatial organization and model simplifications, and how different representations of multiple physical processes can be combined within a single modeling framework. We discuss how SUMMA can be used to systematically pursue the method of multiple working hypotheses in hydrology. In particular, we discuss how SUMMA can help tackle major hydrologic modeling challenges, including defining the appropriate complexity of a model, selecting among competing flux parameterizations, representing spatial variability across a hierarchy of scales, identifying potential improvements in computational efficiency and numerical accuracy as part of the numerical solver, and improving understanding of the various sources of model uncertainty.
Key Points:
Modeling template formulated using a general set of conservation equations
Evaluation focuses on flux parameterizations and spatial variability/connectivity
Systematic approach helps improve model fidelity and uncertainty characterization
Unanswered questions on the Budyko framework Berghuijs, Wouter R.; Gnann, Sebastian J.; Woods, Ross A.
Hydrological processes,
December 2020, Volume:
34, Issue:
26
Journal Article
This paper evaluates the use of field data on the spatial variability of snow water equivalent (SWE) to guide the design of distributed snow models. An extensive reanalysis of results from previous ...field studies in different snow environments around the world is presented, followed by an analysis of field data on spatial variability of snow collected in the headwaters of the Jollie River basin, a rugged mountain catchment in the Southern Alps of New Zealand. In addition, area‐averaged simulations of SWE based on different types of spatial discretization are evaluated. Spatial variability of SWE is shaped by a range of different processes that occur across a hierarchy of spatial scales. Spatial variability at the watershed‐scale is shaped by variability in near‐surface meteorological fields (e.g., elevation gradients in temperature) and, provided suitable meteorological data is available, can be explicitly resolved by spatial interpolation/extrapolation. On the other hand, spatial variability of SWE at the hillslope‐scale is governed by processes such as drifting, sloughing of snow off steep slopes, trapping of snow by shrubs, and the nonuniform unloading of snow by the forest canopy, which are more difficult to resolve explicitly. Subgrid probability distributions are often capable of representing the aggregate‐impact of unresolved processes at the hillslope‐scale, though they may not adequately capture the effects of elevation gradients. While the best modeling strategy is case‐specific, the analysis in this paper provides guidance on both the suitability of several common snow modeling approaches and on the choice of parameter values in subgrid probability distributions.
Key Points
Spatial variability is shaped by a mix of processes across a hierarchy of scales
Probability distributions effectively represent variability at unresolved scales
Explicitly resolving all processes requires an extremely fine horizontal grid
Terrestrial water storage is the primary source of river flow. We introduce storage sensitivity of streamflow (ϵS), which for a given flow rate indicates the relative change in streamflow per change ...in catchment water storage. ϵS can be directly derived from streamflow observations. Analysis of 725 catchments in Europe reveals that ϵS is high in, e.g., parts of Spain, England, Germany, and Denmark, whereas flow regimes in parts of the Alps are more resilient (that is, less sensitive) to storage changes. A regional comparison of ϵS with observations indicates that ϵS is significantly correlated with variability of low (R2 = 0.41), median (R2 = 0.27), and high flow conditions (R2 = 0.35). Streamflow sensitivity provides new guidance for a changing hydrosphere where groundwater ion and climatic changes are altering water storage and flow regimes.
Key Points
Storage sensitivity of streamflow expresses the change in streamflow per change in water storage
Differences in streamflow sensitivity to storage changes are quantified for 725 European catchments
Storage sensitivity of streamflow is positively correlated with flow variability in our study region
Hillslope threshold response to storm rainfall is poorly understood. Basic questions regarding the type, location, and flow dynamics of lateral, subsurface flow remain unanswered, even at our most ...intensively studied field sites. Here we apply a forensic approach where we combined irrigation and excavation experiments at the well studied Maimai hillslope to determine the typology and morphology of the primary lateral subsurface flowpaths, and the control of bedrock permeability and topography on these flowpaths. The experiments showed that downslope flow is concentrated at the soil bedrock interface, with flowpath locations controlled by small features in the bedrock topography. Lateral subsurface flow is characterized by high velocities, several orders of magnitude greater than predicted by Darcy’s Law using measured hydraulic conductivities at the site. We found the bedrock to be moderately permeable, and showed that vertical percolation of water into the bedrock is a potentially large component of the hillslope water balance. Our results suggest that it is the properties of the bedrock (topography and permeability) that control subsurface flow at Maimai, and the soil profile plays a less significant role than previously thought. A companion paper incorporates these findings into a conceptual model of hydrological processes at the site to explore the generalities of whole-hillslope threshold response to storm rainfall.
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