ALBERT

Description: Climate change affects the global water cycle and has the potential to alter water availability for food–energy–water production, and for ecosystems services, on regional and local scales. An understanding of these effects is crucial for assessing future water availability, and for the development of sustainable management plans. Here, we investigate the influence of anticipated climate change on water security in the Jaguari Basin, which is the main source of freshwater for 9 million people in the São Paulo metropolitan region (SPMR). First, we calibrate and evaluate a hydrological model using daily observed data, obtaining satisfactory coefficient of determination and Kling–Gupta efficiency values for both periods. To represent possible climate change scenarios up to 2095, we consider two International Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP4.5 and RCP8.5) and use an ensemble of future projections generated by 17 general circulation models (GCMs). These data were used to drive the hydrological model to generate projected scenarios of streamflow. We then used indicators of water scarcity and vulnerability to carry out a quantitative analysis of provision probability. Our results indicate that streamflow can be expected to exhibit increased interannual variability, significant increases in flow rate between January and March, and a 2-month extension of the hydrological dry season (currently June to September) until November. The latter includes a more than a 35 % reduction in streamflow during September through November (with a 〉 50 % reduction in October). Our findings indicate an increased risk of floods and droughts accompanied by an expansion of the basin critical period, and our analysis of the water security indices identifies October and November as the most vulnerable months. Overall, our analysis exposes the fragility of water security in the São Paulo metropolitan region, and provides valuable technical and scientific information that can be used to guide regional plans and strategies to cope with potential future water scarcity.

Description: To achieve water resource sustainability in the water-limited southwestern US, it is critical to understand the potential effects of proposed forest thinning on the hydrology of semi-arid basins, where disturbances to headwater catchments can cause significant changes in the local water balance components and basinwise streamflows. In Arizona, the Four Forest Restoration Initiative (4FRI) is being developed with the goal of restoring 2.4 million acres of ponderosa pine along the Mogollon Rim. Using the physically based, spatially distributed triangulated irregular network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model, we examine the potential impacts of the 4FRI on the hydrology of Tonto Creek, a basin in the Verde–Tonto–Salt (VTS) system, which provides much of the water supply for the Phoenix metropolitan area. Long-term (20-year) simulations indicate that forest removal can trigger significant shifts in the spatiotemporal patterns of various hydrological components, causing increases in net radiation, surface temperature, wind speed, soil evaporation, groundwater recharge and runoff, at the expense of reductions in interception and shading, transpiration, vadose zone moisture and snow water equivalent, with south-facing slopes being more susceptible to enhanced atmospheric losses. The net effect will likely be increases in mean and maximum streamflow, particularly during El Niño events and the winter months, and chiefly for those scenarios in which soil hydraulic conductivity has been significantly reduced due to thinning operations. In this particular climate, forest thinning can lead to net loss of surface water storage by vegetation and snowpack, increasing the vulnerability of ecosystems and populations to larger and more frequent hydrologic extreme conditions on these semi-arid systems.

Description: Surface topography is an important source of information about the functioning and form of a hydrological landscape. Because of its key role in explaining hydrological processes and structures, and also because of its wide availability at good resolution in the form of digital elevation models (DEMs), it is frequently used to inform hydrological analyses. Not surprisingly, several hydrological indices and models have been proposed for linking geomorphic properties of a landscape with its hydrological functioning; a widely used example is the “height above the nearest drainage” (HAND) index. From an energy-centered perspective HAND reflects the gravitational potential energy of a given unit mass of water located on a hillslope, with the reference level set to the elevation of the nearest corresponding river. Given that potential energy differences are the main drivers for runoff generation, HAND distributions provide important proxies to explain runoff generation in catchments. However, as expressed by the second law of thermodynamics, the driver of a flux explains only one aspect of the runoff generation mechanism, with the driving potential of every flux being depleted via entropy production and dissipative energy loss. In fact, such losses dominate when rainfall becomes runoff, and only a tiny portion of the driving potential energy is actually transformed into the kinetic energy of streamflow. In recognition of this, we derive a topographic index called reduced dissipation per unit length index (rDUNE) by reinterpreting and enhancing HAND following a straightforward thermodynamic argumentation. We compare rDUNE with HAND, and with the frequently used topographic wetness index (TWI), and show that rDUNE provides stronger discrimination of catchments into groups that are similar with respect to their dominant runoff processes. Our analysis indicates that accounting for both the driver and resistance aspects of flux generation provides a promising approach for linking the architecture of a system with its functioning and is hence an appropriate basis for developing similarity indices in hydrology.

Description: Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

Description: The increasing diversity and resolution of spatially distributed data on terrestrial systems greatly enhance the potential of hydrological modeling. Optimal and parsimonious use of these data sources requires, however, that we better understand (a) which system characteristics exert primary controls on hydrological dynamics and (b) to what level of detail do those characteristics need to be represented in a model. In this study we develop and test an approach to explore these questions that draws upon information theoretic and thermodynamic reasoning, using spatially distributed topographic information as a straightforward example. Specifically, we subdivide a mesoscale catchment into 105 hillslopes and represent each by a two-dimensional numerical hillslope model. These hillslope models differ exclusively with respect to topography-related parameters derived from a digital elevation model (DEM); the remaining setup and meteorological forcing for each are identical. We analyze the degree of similarity of simulated discharge and storage among the hillslopes as a function of time by examining the Shannon information entropy. We furthermore derive a “compressed” catchment model by clustering the hillslope models into functional groups of similar runoff generation using normalized mutual information (NMI) as a distance measure. Our results reveal that, within our given model environment, only a portion of the entire amount of topographic information stored within a digital elevation model is relevant for the simulation of distributed runoff and storage dynamics. This manifests through a possible compression of the model ensemble from the entire set of 105 hillslopes to only 6 hillslopes, each representing a different functional group, which leads to no substantial loss in model performance. Importantly, we find that the concept of hydrological similarity is not necessarily time invariant. On the contrary, the Shannon entropy as measure for diversity in the simulation ensemble shows a distinct annual pattern, with periods of highly redundant simulations, reflecting coherent and organized dynamics, and periods where hillslopes operate in distinctly different ways. We conclude that the proposed approach provides a powerful framework for understanding and diagnosing how and when process organization and functional similarity of hydrological systems emerge in time. Our approach is neither restricted to the model nor to model targets or the data source we selected in this study. Overall, we propose that the concepts of hydrological systems acting similarly (and thus giving rise to redundancy) or displaying unique functionality (and thus being irreplaceable) are not mutually exclusive. They are in fact of complementary nature, and systems operate by gradually changing to different levels of organization in time.

Description: Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.

Description: Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models, and a key modeler decision is the selection of the performance metric to be optimized. It has been common to used squared error performance metrics, or normalized variants such as Nash–Sutcliffe Efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimation of high flows. However, we find that NSE-based model calibrations actually result in poor reproduction of high flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models, the Variable Infiltration Capacity model (VIC) and the mesoscale Hydrologic Model (mHM) and evaluate their ability to simulate high flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta Efficiency (KGE) and variants, and (3) Annual Peak Flow Bias (APFB), where the latter is an application-specific hydrologic signature metric that focuses on annual peak flows. As expected, the application specific APFB metric produces the best annual peak flow estimates; however, performance on other high flow related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20% worse, primarily due to the tendency of NSE to result in underestimation of observed flow variability. Meanwhile, the use of KGE results in annual peak flow estimates that are better than from NSE, with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Overall this work highlights the need for a fuller understanding of performance metric behavior and design in relation to the desired goals of model calibration.

Description: The increasing diversity and resolution of spatially distributed data on terrestrial systems greatly enhances the potential of hydrological modeling. Optimal and parsimonious use of these data sources implies, however, that we better understand (a) which system characteristics exert primary controls on hydrological dynamics and (b) to what level of detail do those characteristics need to be represented in a model. In this study we develop and test an approach to explore these questions that draws upon information theoretic and thermodynamic reasoning, using spatially distributed topographic information as a straightforward example. Specifically, we subdivide a meso-scale catchment into 105 hillslopes and represent each by a two dimensional numerical hillslope model. These hillslope models differ exclusively with respect to topography related parameters derived from a digital elevation model; the remaining setup and meteorological forcing for each are identical. We analyze the degree of similarity of simulated discharge and storage among the hillslopes as a function of time by examining the Shannon information entropy. We furthermore derive a compressed catchment model by clustering the hillslope models into functional groups of similar runoff generation using normalized mutual information as a distance measure. Our results reveal that, within our given model environment, only a portion of the entire amount of topographic information stored within a digital elevation model is relevant for the simulation of distributed runoff and storage dynamics. This manifests through a possible compression of the model ensemble from the entire set of 105 hillslopes to only 6 hillslopes, each representing a different functional group, which leads to no substantial loss in model performance. Importantly, we find that the concept of hydrological similarity is not necessarily time-invariant. On the contrary, the Shannon entropy as measure for diversity in the simulation ensemble shows a distinct annual pattern, with periods of highly redundant simulations, reflecting coherent and organized dynamics, and periods where hillslopes operate in distinctly different ways. We conclude that the proposed approach provides a powerful framework for understanding and diagnosing how and when process organization and functional similarity of hydrological systems emerges in time. Our approach is neither restricted to the model, nor to model targets or the data source we selected in this study. Overall, we propose that the concepts of hydrological systems acting similarly (and thus giving rise to redundancy) or displaying unique functionality (and thus being irreplaceable) are not mutually exclusive. They are in fact of complementary nature, and systems operate by gradually changing to different levels of organization in time.

Description: Surface topography is an important source of information about the functioning and form of a hydrological landscape. Because of its key role in explaining hydrological processes and structures, and also because of its wide availability at good resolution in the form of digital elevation models (DEM), it is frequently used to inform hydrological analyses. Not surprisingly, several hydrological indices and models have been proposed to link geomorphic properties of a landscape with its hydrological functioning; a widely used example is the Height Above the Nearest Drainage (HAND) index. From an energy-centered perspective HAND reflects the gravitational potential energy of a given unit mass of water located on a hillslope, with the reference level set to the elevation of the nearest corresponding river. Given that potential energy differences are the main drivers for runoff generation, HAND distributions provide important proxies to explain runoff generation in catchments. However, as expressed by the second law of thermodynamics, the driver of a flux explains only one aspect of the runoff generation mechanism, with the driving potential of every flux being depleted via entropy production and dissipative energy loss. In fact, such losses dominate runoff generation in a catchment, and only a tiny portion of the driving potential energy is actually transformed into the kinetic energy of streamflow. In recognition of this, we derive a new topographic index named dissipation per unit length (DUNE) by re-interpreting and enhancing the HAND index. We compare DUNE with HAND, and with the topographic wetness index (TWI), and show that DUNE provides stronger discrimination of catchments into groups that are similar with respect to runoff generation. Our analysis indicates that accounting for both the driver and resistance aspects of flux generation provides a promising approach to linking the architecture of a system with its functioning.

Description: Climate change affects the global water cycle and has the potential to alter water availability for food-energy-water production, and for ecosystems services, on regional and local scales. An understanding these effects is crucial to assessing future water availability, and for the development of sustainable management plans. Here, we investigate the influence of anticipated climate change on water security in the Jaguari Basin, which is the main source of freshwater for 9 million people in the Sao Paulo Metropolitan Region (SPMR). First, we calibrate and evaluate a hydrological model using daily observed data, obtaining satisfactory Coefficient of Determination and Kling-Gupta efficiency values for both periods. To represent possible climate change scenarios up to 2095, we consider two International Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and use an ensemble of future projections generated by 17 General Circulation Models (GCMs). These data were used to drive the hydrological model to generate projected scenarios of streamflow. Then we used indicators of water scarcity and vulnerability to carry out a quantitative analysis of provision probability. Our results indicate that streamflow can be expected to exhibit increased interannual variability, significant increases in flow rate between January and March, and an extension of the dry season (currently June to September) until November. The latter includes more than 35 % reduction in streamflow during September through November (with 〉 50 % reduction in October). Our findings indicate an increased risk of floods and droughts accompanied by an expansion of the basin critical period, and our analysis of the Water Security Indices identifies October and November as the most vulnerable months. Overall, our analysis exposes the fragility of water security in the Sao Paulo metropolitan region, and provides valuable technical and scientific information that can be used to guide regional plans and strategies to cope with potential future water scarcity.