Karst regions represent 7–12% of the Earth's continental area, and about one quarter of the global population is completely or partially dependent on drinking water from karst aquifers. Climate ...simulations project a strong increase in temperature and a decrease of precipitation in many karst regions in the world over the next decades. Despite this potentially bleak future, few studies specifically quantify the impact of climate change on karst water resources. This review provides an introduction to karst, its evolution, and its particular hydrological processes. We explore different conceptual models of karst systems and how they can be translated into numerical models of varying complexity and therefore varying data requirements and depths of process representation. We discuss limitations of current karst models and show that at the present state, we face a challenge in terms of data availability and information content of the available data. We conclude by providing new research directions to develop and evaluate better prediction models to address the most challenging problems of karst water resources management, including opportunities for data collection and for karst model applications at so far unprecedented scales.
Key Points
We elaborate the importance of karst water resourcesWe provide a detailed overview of karst modeling approachWe present new methods and directions for their improvement
Key Points
Dominant model processes can be counterintuitive as model complexity increases
Hydrologic modeling benefits from diagnosing time‐varying controls
Comparing controls across models shows how ...model formulation affects behavior
Lumped rainfall‐runoff models are widely used for flow prediction, but a long‐recognized need exists for diagnostic tools to determine whether the process‐level behavior of a model aligns with the expectations inherent in its formulation. To this end, we develop a comprehensive exploration of dominant parameters in the Hymod, HBV, and Sacramento Soil Moisture Accounting (SAC‐SMA) model structures. Model controls are isolated using time‐varying Sobol′ sensitivity analysis for twelve MOPEX watersheds in the eastern United States over a 10 year period. Sensitivity indices are visualized along gradients of observed precipitation and streamflow to identify key behavioral differences between the three models and to connect these back to the models' underlying assumptions. Results indicate that the models' dominant parameters strongly depend on time‐varying hydroclimatic conditions. Parameters associated with surface processes such as evapotranspiration and runoff generally dominate under dry conditions, when high evaporative fluxes are required for accurate simulation. Parameters associated with routing processes typically dominate under high‐flow conditions, when performance depends on the timing of flow events. The results highlight significant inter‐model differences in performance controls, even in cases where the models share similar process formulations. The dominant parameters identified can be counterintuitive; even these simple models represent complex, nonlinear systems, and the links between formulation and behavior are difficult to discern a priori as complexity increases. Scrutinizing the links between model formulation and behavior becomes an important diagnostic approach, particularly in applications such as predictions under change where dominant model controls will shift under hydrologic extremes.
Benchmarking the quality of river discharge data and understanding its information content for hydrological analyses is an important task for hydrologic science. There is a wide variety of techniques ...to assess discharge uncertainty. However, few studies have developed generalized approaches to quantify discharge uncertainty. This study presents a generalized framework for estimating discharge uncertainty at many gauging stations with different errors in the stage‐discharge relationship. The methodology utilizes a nonparametric LOWESS regression within a novel framework that accounts for uncertainty in the stage‐discharge measurements, scatter in the stage‐discharge data and multisection rating curves. The framework was applied to 500 gauging stations in England and Wales and we evaluated the magnitude of discharge uncertainty at low, mean and high flow points on the rating curve. The framework was shown to be robust, versatile and able to capture place‐specific uncertainties for a number of different examples. Our study revealed a wide range of discharge uncertainties (10–397% discharge uncertainty interval widths), but the majority of the gauging stations (over 80%) had mean and high flow uncertainty intervals of less than 40%. We identified some regional differences in the stage‐discharge relationships, however the results show that local conditions dominated in determining the magnitude of discharge uncertainty at a gauging station. This highlights the importance of estimating discharge uncertainty for each gauging station prior to using those data in hydrological analyses.
Key Points:
A generalized framework for discharge uncertainty estimation is presented
Allows estimation of place‐specific discharge uncertainties for many catchments
Local conditions dominate in determining discharge uncertainty magnitudes
Large uncertainties in streamflow projections derived from downscaled climate projections of precipitation and temperature can render such simulations of limited value for decision making in the ...context of water resources management. New approaches are being sought to provide decision makers with robust information in the face of such large uncertainties. We present an alternative approach that starts with the stakeholder's definition of vulnerable ranges for relevant hydrologic indicators. Then the modeled system is analyzed to assess under what conditions these thresholds are exceeded. The space of possible climates and land use combinations for a watershed is explored to isolate subspaces that lead to vulnerability, while considering model parameter uncertainty in the analysis. We implement this concept using classification and regression trees (CART) that separate the input space of climate and land use change into those combinations that lead to vulnerability and those that do not. We test our method in a Pennsylvania watershed for nine ecological and water resources related streamflow indicators for which an increase in temperature between 3°C and 6°C and change in precipitation between −17% and 19% is projected. Our approach provides several new insights, for example, we show that even small decreases in precipitation (∼5%) combined with temperature increases greater than 2.5°C can push the mean annual runoff into a slightly vulnerable regime. Using this impact and stakeholder driven strategy, we explore the decision‐relevant space more fully and provide information to the decision maker even if climate change projections are ambiguous.
Key Points
Method provides valuable information to decision maker in large uncertainties
Stakeholders define critical thresholds for hydrologic indicators of interest
We identify land use and climate change combinations that cause vulnerability
•Performance of transferred parameters is related to similarity in catchment properties.•Classification and regression trees are used to explore this relationship in 83 US catchments.•Climate, ...elevation and agricultural land use are dominant controls on parameter transfer success.
Daily streamflow information is critical for solving various hydrologic problems, though observations of continuous streamflow for model calibration are available at only a small fraction of the world’s rivers. One approach to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) catchment to an ungauged (receiver) catchment of interest. Central to this approach is the selection of a hydrologically similar donor. No single metric or set of metrics of hydrologic similarity have been demonstrated to consistently select a suitable donor catchment. We design an experiment to diagnose the dominant controls on successful hydrologic model parameter transfer. We calibrate a lumped rainfall–runoff model to 83 stream gauges across the United States. All locations are USGS reference gauges with minimal human influence. Parameter sets from the calibrated models are then transferred to each of the other catchments and the performance of the transferred parameters is assessed. This transfer experiment is carried out both at the scale of the entire US and then for six geographic regions. We use classification and regression tree (CART) analysis to determine the relationship between catchment similarity and performance of transferred parameters. Similarity is defined using physical/climatic catchment characteristics, as well as streamflow response characteristics (signatures such as baseflow index and runoff ratio). Across the entire US, successful parameter transfer is governed by similarity in elevation and climate, and high similarity in streamflow signatures. Controls vary for different geographic regions though. Geology followed by drainage, topography and climate constitute the dominant similarity metrics in forested eastern mountains and plateaus, whereas agricultural land use relates most strongly with successful parameter transfer in the humid plains.
Large‐scale hydrological models, simulating the terrestrial water cycle on continental and global scales, are fundamental for many studies in earth system sciences. However, due to imperfect ...knowledge of real world systems, the models cannot be expected to capture all aspects of large‐scale hydrology equally well. To gain insights in the strengths and shortcomings of nine large‐scale hydrological models, we assessed their ability to capture the mean annual runoff cycle. Unlike most other studies that rely on discharge observations from continental scale river basins, our study is based on observed runoff from a large number of small, near‐natural catchments in Europe. We evaluated the models' ability to capture the average magnitude, the amplitude, as well as the timing of the mean annual runoff cycle. Our study revealed large uncertainties when modeling runoff from these small catchments. We identified large differences in model performance, however, the ensemble mean (mean of all model simulations) yielded rather robust predictions. Model performance varied systematically with climatic conditions and was best in regions with little influence of snow. In cold regions, many models exhibited low correlations between observed and simulated mean annual cycles, which can be associated with shortcomings in simulating the timing of snow accumulation and melt. Local (grid cell) scale differences between observed and simulated runoff can be large and local biases often exceeded 100%. These local uncertainties are contrasted by a relatively good regional average performance, ultimately reflecting the purpose of the models, i.e., to capture regional hydroclimatology.
Key Points
Large scale hydrologic models are compared to catchment scale observations
Model performance varies systematically with climatic conditions
The ensemble mean is a robust predictor of regional hydroclimatology
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