Hydrological Model Structure Research Articles

Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration

Abstract. Global-scale assessments of freshwater fluxes and storages by hydrological models under historic climate conditions are subject to a variety of uncertainties. Using the global hydrological model WaterGAP (Water – Global Assessment and Prognosis) 2.2, we investigated the sensitivity of simulated freshwater fluxes and water storage variations to five major sources of uncertainty: climate forcing, land cover input, model structure/refinements, consideration of human water use and calibration (or no calibration) against observed mean river discharge. In a modeling experiment, five variants of the standard version of WaterGAP 2.2 were generated that differed from the standard version only regarding the investigated source of uncertainty. The basin-specific calibration approach for WaterGAP was found to have the largest effect on grid cell fluxes as well as on global AET (actual evapotranspiration) and discharge into oceans for the period 1971–2000. Regarding grid cell fluxes, climate forcing ranks second before land cover input. Global water storage trends are most sensitive to model refinements (mainly modeling of groundwater depletion) and consideration of human water use. The best fit to observed time series of monthly river discharge or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by daily reanalysis-based WFD/WFDEI (combination of Watch Forcing Data based on ERA40 and Watch Forcing Data based on ERA-Interim) climate data. Discharge computed by a calibrated model version using monthly CRU TS (Climate Research Unit time-series) 3.2 and GPCC (Global Precipitation Climatology Center) v6 climate input reduced the fit to observed discharge for most stations. Taking into account uncertainties of climate and land cover data, global 1971–2000 discharge into oceans and inland sinks ranges between 40 000 and 42 000 km3 yr−1. Global actual evapotranspiration, with 70 000 km3 yr−1, is rather unaffected by climate and land cover uncertainties. Human water use reduced river discharge by 1000 km3 yr−1, such that global renewable water resources are estimated to range between 41 000 and 43 000 km3 yr−1. The climate data sets WFD (available until 2001) and WFDEI (starting in 1979) were found to be inconsistent with respect to shortwave radiation data, resulting in strongly different actual evapotranspiration. Global assessments of freshwater fluxes and storages would therefore benefit from the development of a global data set of consistent daily climate forcing from 1900 to present.

Intercomparison of hydrological model structures and calibration approaches in climate scenario impact projections

Summary The objective of this paper is to investigate the effects of hydrological model structure and calibration on climate change impact results in hydrology. The uncertainty in the hydrological impact results is assessed by the relative change in runoff volumes and peak and low flow extremes from historical and future climate conditions. The effect of the hydrological model structure is examined through the use of five hydrological models with different spatial resolutions and process descriptions. These were applied to a medium sized catchment in Belgium. The models vary from the lumped conceptual NAM, PDM and VHM models over the intermediate detailed and distributed WetSpa model to the fully distributed MIKE SHE model. The latter model accounts for the 3D groundwater processes and interacts bi-directionally with a full hydrodynamic MIKE 11 river model. After careful and manual calibration of these models, accounting for the accuracy of the peak and low flow extremes and runoff subflows, and the changes in these extremes for changing rainfall conditions, the five models respond in a similar way to the climate scenarios over Belgium. Future projections on peak flows are highly uncertain with expected increases as well as decreases depending on the climate scenario. The projections on future low flows are more uniform; low flows decrease (up to 60%) for all models and for all climate scenarios. However, the uncertainties in the impact projections are high, mainly in the dry season. With respect to the model structural uncertainty, the PDM model simulates significantly higher runoff peak flows under future wet scenarios, which is explained by its specific model structure. For the low flow extremes, the MIKE SHE model projects significantly lower low flows in dry scenario conditions in comparison to the other models, probably due to its large difference in process descriptions for the groundwater component, the groundwater–river interactions. The effect of the model calibration was tested by comparing the manual calibration approach with automatic calibrations of the VHM model based on different objective functions. The calibration approach did not significantly alter the model results for peak flow, but the low flow projections were again highly influenced. Model choice as well as calibration strategy hence have a critical impact on low flows, more than on peak flows. These results highlight the high uncertainty in low flow modelling, especially in a climate change context.

Systematic assessment of the uncertainty in integrated surface water‐groundwater modeling based on the probabilistic collocation method

AbstractSystematic uncertainty analysis (UA) has rarely been conducted for integrated modeling of surface water‐groundwater (SW‐GW) systems, which is subject to significant uncertainty, especially at a large basin scale. The main objective of this study was to explore an innovative framework in which a systematic UA can be effectively and efficiently performed for integrated SW‐GW models of large river basins and to illuminate how process understanding, model calibration, data collection, and management can benefit from such a systematic UA. The framework is based on the computationally efficient Probabilistic Collocation Method (PCM) linked with a complex simulation model. The applicability and advantages of the framework were evaluated and validated through an integrated SW‐GW model for the Zhangye Basin in the middle Heihe River Basin, northwest China. The framework for systematic UA allows for a holistic assessment of the modeling uncertainty, yielding valuable insights into the hydrological processes, model structure, data deficit, and potential effectiveness of management. The study shows that, under the complex SW‐GW interactions, the modeling uncertainty has great spatial and temporal variabilities and is highly output‐dependent. Overall, this study confirms that a systematic UA should play a critical role in integrated SW‐GW modeling of large river basins, and the PCM‐based approach is a promising option to fulfill this role.

Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures

Abstract. Diagnosing the impacts of climate change on water resources is a difficult task pertaining to the uncertainties arising from the different modelling steps. Lumped hydrological model structures contribute to this uncertainty as well as the natural climate variability, illustrated by several members from the same Global Circulation Model. In this paper, the hydroclimatic modelling chain consists of twenty-four potential evapotranspiration formulations, twenty lumped conceptual hydrological models, and seven snowmelt modules. These structures are applied on a natural Canadian sub-catchment to address related uncertainties and compare them to the natural internal variability of simulated climate system as depicted by five climatic members. Uncertainty in simulated streamflow under current and projected climates is assessed. They rely on interannual hydrographs and hydrological indicators analysis. Results show that natural climate variability is the major source of uncertainty, followed by potential evapotranspiration formulations and hydrological models. The selected snowmelt modules, however, do not contribute much to the uncertainty. The analysis also illustrates that the streamflow simulation over the current climate period is already conditioned by the tools' selection. This uncertainty is propagated to reference simulations and future projections, amplified by climatic members. These findings demonstrate the importance of opting for several climatic members to encompass the important uncertainty related to the climate natural variability, but also of selecting multiple modelling tools to provide a trustworthy diagnosis of the impacts of climate change on water resources.

A strategy for diagnosing and interpreting hydrological model nonstationarity

AbstractThis paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aiming to improve hydrological models and their predictive ability under changing hydroclimatic conditions. The strategy consists of four elements: (i) detecting potential systematic errors in the calibration data; (ii) hypothesizing a set of “nonstationary” parameterizations of existing hydrological model structures, where one or more parameters vary in time as functions of selected covariates; (iii) trialing alternative stationary model structures to assess whether parameter nonstationarity can be reduced by modifying the model structure; and (iv) selecting one or more models for prediction. The Scott Creek catchment in South Australia and the lumped hydrological model GR4J are used to illustrate the strategy. Streamflow predictions improve significantly when the GR4J parameter describing the maximum capacity of the production store is allowed to vary in time as a combined function of: (i) an annual sinusoid; (ii) the previous 365 day rainfall and potential evapotranspiration; and (iii) a linear trend. This improvement provides strong evidence of model nonstationarity. Based on a range of hydrologically oriented diagnostics such as flow‐duration curves, the GR4J model structure was modified by introducing an additional calibration parameter that controls recession behavior and by making actual evapotranspiration dependent only on catchment storage. Model comparison using an information‐theoretic measure (the Akaike Information Criterion) and several hydrologically oriented diagnostics shows that the GR4J modifications clearly improve predictive performance in Scott Creek catchment. Based on a comparison of 22 versions of GR4J with different representations of nonstationarity and other modifications, the model selection approach applied in the exploratory period (used for parameter estimation) correctly identifies models that perform well in a much drier independent confirmatory period.

Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models

Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce the input uncertainty first by combining climate models and then use the multimodel climate forecasts with multiple watershed models (MM-P), (2) ingest the individual climate forecasts (without multimodel combination) with various watershed models and then combine the streamflow predictions that arise from all possible combinations of climate and watershed models (MM-Q), (3) combine the streamflow forecasts obtained from multiple watershed models based on strategy (1) to develop a single streamflow prediction that reduces uncertainty in both climate forecasts and watershed models (MM-PQ). For this purpose, we consider synthetic schemes that generate streamflow and climate forecasts, for comparing the performance of three strategies with the true streamflow generated by a given hydrologic model. Results from the synthetic study show that reducing input uncertainty first (MM-P) by combining climate forecasts results in reduced error in predicting the true streamflow compared to the error of multimodel streamflow forecasts obtained by combining streamflow forecasts from all-possible combination of individual climate model with various hydrologic models (MM-Q). Since the true hydrologic model structure is unknown, it is desirable to consider MM-PQ as an alternate choice that reduces both input uncertainty and hydrologic model uncertainty. Application on two watersheds in NC also indicates that reducing the input uncertainty first is critical before reducing the hydrologic model uncertainty.

Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the upper Medway catchment of southeast England using the UK NIMROD radar rainfall estimates, using three hydrological models based upon three very different structures (e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF). We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

Modelling the impact of stormwater source control infiltration techniques on catchment baseflow

AbstractStormwater management increasingly recognises the need to emulate, to the maximum extent possible, the flow regime of receiving waters in their pre‐development state. Hydrological models play a central role in assessing the catchment‐scale impacts of alternative stormwater management strategies. However, because of the complexity of physical processes involved in urban hydrology, particularly subsurface flows, the predictive performance of such models is often low. We investigated how the structure of hydrological models influenced the prediction of urbanisation and stormwater management impacts on baseflow. We calibrated three conceptual models of the same reference catchment and compared the modelled flow regime from different stormwater management scenarios, using each of the three model structures. Scenarios were assessed using six metrics, characterising the whole streamflow regime and in particular baseflow. Although the three models of the reference catchment represented the observed hydrograph well, the most complex structure developed using a thorough diagnostic of the catchment behaviour better captured the change in hydrological regime during dry years. Predictions of baseflow changes due to urbanisation varied significantly according to the model structure. Similarly, the models showed distinct responses to the stormwater management scenarios applied, especially for scenarios involving infiltration of stormwater at source. Our results confirm the importance of predicting the consequences of land use changes with conceptual models that are consistent with the hydrological behaviour of the study catchment. Future work should help to quantify the uncertainties due to model structure and thus provide practical guidance to the use of catchment models for assessing stormwater management strategies. Copyright © 2013 John Wiley & Sons, Ltd.

On hydrological model complexity, its geometrical interpretations and prediction uncertainty

Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to minimizing an upper bound on prediction error, defined here as the mathematical expectation of empirical risk. In this paper, we derive an upper bound that is free from assumptions on data and underlying process distribution as well as on independence of model predictions over time. We demonstrate that hydrological model complexity, as defined in the presented theoretical framework, plays an important role in determining the upper bound. The model complexity also acts as a stabilizer to a hydrological model selection problem if it is deemed ill-posed. We provide an algorithm for computing complexity of any arbitrary hydrological model. We also demonstrate that hydrological model complexity has a geometric interpretation as the size of model output space. The presented theory is applied to quantify complexities of two hydrological model structures: SAC-SMA and SIXPAR. It detects that SAC-SMA is indeed more complex than SIXPAR. We also develop an algorithm to estimate the upper bound on prediction error, which is applied on five different rainfall-runoff model structures that vary in complexity. We show that a model selection problem is stabilized by regularizing it with model complexity. Complexity regularized model selection yields models that are robust in predicting future but yet unseen data.

Evaluating the ability of a hydrologic model to replicate hydro‐ecologically relevant indicators

AbstractIt is a common practice to employ hydrologic models for assessing alterations to streamflow as a result of anthropogenically driven changes, such as riverine, land use, and climate change. However, the ability of the models to replicate different components of the hydrograph simultaneously is not clear. Hence, this study evaluates the ability of a standard hydrologic model set‐up: Variable Infiltration Capacity (VIC) hydrologic model for two headwater sub‐basins in the Fraser River (Salmon and Willow), British Columbia, Canada, with climate inputs derived from observations and statistically downscaled global climate models (GCMs); to simulate six general water resource indicators (WRIs) and 32 ecologically relevant indicators of hydrologic alterations (IHA). The results show a generally good skill of the observation‐driven VIC model in replicating most of the WRIs and IHAs. Although the WRIs, including annual volume, centre of timing, and seasonal flows, and the IHAs, including maximum and minimum flows, were reasonably well replicated, statistically significant differences in some of the monthly flows, number and duration of flow pulses, rise and fall rates, and reversals were noted. In the case of GCM‐driven results, additional monthly, maximum, and minimum flow indicators produced statistically significant differences. A number of issues with the model input/output data, hydrologic model parametrization and structure as well as downscaling methods were identified, which lead to such discrepancies. Therefore, there is a need to exercise caution in the use of model‐simulated indicators. Overall, the WRIs and IHAs can be useful tools for evaluating changes in an altered hydrologic system, provided the skill and limitations of the model in replicating these indicators are understood. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2013 John Wiley & Sons Ltd.

Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications

Quantile hydrologic model selection and structure deficiency assessment is applied in three case studies. The performance of quantile model selection problem is rigorously evaluated using a model structure on the French Broad river basin data set. The case study shows that quantile model selection encompasses model selection strategies based on summary statistics and that it is equivalent to maximum likelihood estimation under certain likelihood functions. It also shows that quantile model predictions are fairly robust. The second case study is of a parsimonious hydrological model for dry land areas in Western India. The case study shows that an intuitive improvement in the model structure leads to reductions in asymmetric loss function values for all considered quantiles. The asymmetric loss function is a quantile specific metric that is minimized to obtain a quantile specific prediction model. The case study provides evidence that a quantile-wise reduction in the asymmetric loss function is a robust indicator of model structure improvement. Finally a case study of modeling daily streamflow for the Guadalupe River basin is presented. A model structure that is least deficient for the study area is identified from nine different model structures based on quantile structural deficiency assessment. The nine model structures differ in interception, routing, overland flow and base flow conceptualizations. The three case studies suggest that quantile model selection and deficiency assessment provides a robust mechanism to compare deficiencies of different model structures and helps to identify better model structures. In addition to its novelty, quantile hydrologic model selection is a frequentist approach that seeks to complement existing Bayesian approaches to hydrological model uncertainty.

Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory

A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies structural deficiency. This leads to a formal definition of model structure deficiency (or rigidity). Model structure deficiency introduces a bias in the prediction of an observed quantile, which is often not equal across quantiles. Structure deficiency is therefore diagnosed when any two quantile predictions for a given model structure cross since unequal bias across quantiles result in quantile predictions crossing. The analysis further suggests that the optimal value of quantile specific loss functions order different model structures by its structure deficiencies over a range of quantiles. In addition to such novelties, quantile hydrologic model selection is a frequentist approach that seeks to complement existing Bayesian approaches to hydrological model uncertainty.

Spatial variability of hydrological processes and model structure diagnostics in a 50 km2catchment

In this paper, we develop diagnostic methods to assess spatial variability in hydrological processes, particularly those relevant to catchment modelling. We target a range of catchment responses, including runoff volume, runoff timing, storage–discharge relationships, and threshold responses to rainfall and soil moisture. The diagnostics allow us to map the scales and patterns of process variability, to test whether climate or physical catchment characteristics can be used to predict patterns in processes, and to explore the implications for appropriate spatial variability in hydrological model structures or parameters. We apply the diagnostic tests to the mid-sized (50 km2) Mahurangi catchment in Northland, New Zealand, combining data from 28 flow gauges, 13 rain gauges, and 18 soil moisture measurement sites to build a comprehensive description of spatial variation in catchment response. The results show a complex picture: different diagnostics reveal different patterns of hydrological processes, and large variations in processes occur, even over the short length scales involved (~10 km). Catchment and climate characteristics almost all show the same pattern, i.e. that subcatchments in the far North and South of the Mahurangi are similar to each other (higher elevation, steep, forested), but contrast with central subcatchments (lower elevation, shallower slopes, pasture). Surprisingly, this pattern is not reflected in the patterns of diagnostic indices, demonstrating the difficulty of defining realistic a priori estimates of spatial variability in processes. We discuss how process variations correspond to the design of components of a simple lumped conceptual model. In the Mahurangi catchment, we find that spatial variations in multiple aspects of the hydrological response imply a need for spatial variation in both model structures and parameters. Copyright © 2013 John Wiley & Sons, Ltd.

Testing the realism of model structures to identify karst system processes using water quality and quantity signatures

[1] Many hydrological systems exhibit complex subsurface flow and storage behavior. Runoff observations often only provide insufficient information for unique process identification. Quantitative modeling of water and solute fluxes presents a potentially more powerful avenue to explore whether hypotheses about system functioning can be rejected or conditionally accepted. In this study we developed and tested four hydrological model structures, based on different hypotheses about subsurface flow and storage behavior, to identify the functioning of a large Mediterranean karst system. Using eight different system signatures, i.e., indicators of particular hydrodynamic and hydrochemical characteristics of the karst system, we applied a novel model evaluation strategy to identify the best conceptual model representation of the karst system within our set of possible system representations. Our approach to test model realism consists of three stages: (1) evaluation of model performance with respect to system signatures using automatic calibration, (2) evaluation of parameter identifiability using Sobol's sensitivity analysis, and (3) evaluation of model plausibility by combining the results of stages (1) and (2). These evaluation stages eliminated three out of four model structures and lead to a unique hypothesis about the functioning of the studied karst system. We used the estimated parameter values to further quantify subsurface processes. The chosen model is able to simultaneously provide high performances for eight system signatures with realistic parameter values. Our approach demonstrates the benefits of interpreting different tracers in a hydrologically meaningful way during model evaluation and identification.

Uncertainty in high and low flows due to model structure and parameter errors

This paper aims to investigate the uncertainty in simulated extreme low and high flows originating from hydrological model structure and parameters. To this end, three different rainfall-runoff models, namely GR4J, HBV and Xinanjiang, are applied to two subbasins of Qiantang River basin, eastern China. The Generalised Likelihood Uncertainty Estimation approach is used for estimating the uncertainty of the three models due to parameter values, henceforth referred as parameter uncertainty. Uncertainty in simulated extreme flows is evaluated by means of the annual maximum discharge and mean annual 7-day minimum discharge. The results show that although the models have good performance for the daily flows, the uncertainty in the extreme flows could not be neglected. The uncertainty originating from parameters is larger than uncertainty due to model structure. The parameter uncertainty of the extreme flows increases with the observed discharge. The parameter uncertainty in both the extreme high flows and the extreme low flows is the largest for the HBV model and the smallest for the Xinanjiang model. It is noted that the extreme low flows are mostly underestimated by all models with optimum parameter sets for both subbasins. The largest underestimation is from Xinanjiang model. Therefore it is not reliable enough to use only one set of the parameters to make the prediction and carrying out the uncertainty study in the extreme discharge simulation could give an overall picture for the planners.

A framework to assess the realism of model structures using hydrological signatures

Abstract. The use of flexible hydrological model structures for hypothesis testing requires an objective and diagnostic method to identify whether a rainfall-runoff model structure is suitable for a certain catchment. To determine if a model structure is realistic, i.e. if it captures the relevant runoff processes, both performance and consistency are important. We define performance as the ability of a model structure to mimic a specific part of the hydrological behaviour in a specific catchment. This can be assessed based on evaluation criteria, such as the goodness of fit of specific hydrological signatures obtained from hydrological data. Consistency is defined as the ability of a model structure to adequately reproduce several hydrological signatures simultaneously while using the same set of parameter values. In this paper we describe and demonstrate a new evaluation Framework for Assessing the Realism of Model structures (FARM). The evaluation framework tests for both performance and consistency using a principal component analysis on a range of evaluation criteria, all emphasizing different hydrological behaviour. The utility of this evaluation framework is demonstrated in a case study of two small headwater catchments (Maimai, New Zealand, and Wollefsbach, Luxembourg). Eight different hydrological signatures and eleven model structures have been used for this study. The results suggest that some model structures may reveal the same degree of performance for selected evaluation criteria while showing differences in consistency. The results also show that some model structures have a higher performance and consistency than others. The principal component analysis in combination with several hydrological signatures is shown to be useful to visualise the performance and consistency of a model structure for the study catchments. With this framework performance and consistency are evaluated to identify which model structure suits a catchment better compared to other model structures. Until now the framework has only been based on a qualitative analysis and not yet on a quantitative analysis.

Correcting the radar rainfall forcing of a hydrological model with data assimilation: application to flood forecasting in the Lez catchment in Southern France

Abstract. The present study explores the application of a data assimilation (DA) procedure to correct the radar rainfall inputs of an event-based, distributed, parsimonious hydrological model. An extended Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. This work focuses primarily on the uncertainty in the rainfall data and considers this as the principal source of error in the simulated discharges, neglecting simplifications in the hydrological model structure and poor knowledge of catchment physics. The study site is the 114 km2 Lez catchment near Montpellier, France. This catchment is subject to heavy orographic rainfall and characterised by a karstic geology, leading to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because the radar rainfall input to the model depends on geographical features and cloud structures, it is particularly uncertain and results in significant errors in the simulated discharges. This study seeks to demonstrate that a simple DA algorithm is capable of rendering radar rainfall suitable for hydrological forecasting. To test this hypothesis, the DA analysis was applied to estimate a constant hyetograph correction to each of 19 flood events. The analysis was carried in two different modes: by assimilating observations at all available time steps, referred to here as reanalysis mode, and by using only observations up to 3 h before the flood peak to mimic an operational environment, referred to as pseudo-forecast mode. In reanalysis mode, the resulting correction of the radar rainfall data was then compared to the mean field bias (MFB), a corrective coefficient determined using rain gauge measurements. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash-Sutcliffe efficiency criteria compared to the MFB correction. In pseudo-forecast mode, the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge, but uncertainty from the model parameterisation diminishes data assimilation efficiency. While the DA algorithm used is this study is effective in correcting uncertain radar rainfall, model uncertainty remains an important challenge for flood forecasting within the Lez catchment.

Partitioning Evapotranspiration Using an Eddy Covariance‐Based Technique: Improved Assessment of Soil Moisture and Land–Atmosphere Exchange Dynamics

The capability to partition evapotranspiration (ET) measurements into two components, namely direct evaporation (E) from soil, leaf, and litter surfaces and transpiration (T) via plants is critical toward better defining hydrological processes along the soil-plant-atmosphere continuum. Such information has practical applications ranging from water resource management to climate modeling. A recently developed partitioning technique was employed using high frequency measurements of water vapor and carbon dioxide collected by an eddy covariance flux system, which was deployed over a corn (Zea mays L.) field near Beltsville, MD for a full growing season. The partitioning technique is based on flux-variance similarity theory and has the benefit of relying on routinely collected eddy covariance measurements, with no additional instrumentation required. Results showed an increase in the T/ET ratio from ∼5% during the early portion of the growing season to ∼70–80% by the time the corn crop reached maturity. This was consistent with observed dynamics of the soil moisture profile, which indicated that water was removed from deeper soil layers as the growing season progressed. The partitioned estimates of T are shown to be useful for the appropriate calculation of canopy conductance, a key variable in land surface models. Finally, the transient impact of rainfall events were examined, in which the suppression of T and enhancement of E were observed for up to several days following rainfall. Insight gained through the partitioning of ET fluxes has the potential to significantly improve the structure and parameterization of land surface hydrological models.

Do time‐variable tracers aid the evaluation of hydrological model structure? A multimodel approach

In this paper we explore the use of time‐variable tracer data as a complementary tool for model structure evaluation. We augment the modular rainfall‐runoff modeling framework FUSE (Framework for Understanding Structural Errors) with the ability to track the age distribution of water in all model stores and fluxes. We therefore gain the novel ability to compare tracer/water age signatures measured in a catchment with those predicted using hydrological models built from components based on four existing popular models. Key modeling decisions available in FUSE are evaluated against streamflow tracer dynamics using weekly observations of tracer concentration which reflect the tracer transit time distribution (TTD). Model structure choice is shown to have a significant effect on simulated water age characteristics, even when simulated flow series are very similar. We show that for a Scottish case study catchment, careful selection of model structure enables good predictions of both streamflow and tracer dynamics. We then use FUSE as a hypothesis testing tool to understand how different model characterization of TTDs and mean transit times affect multicriteria model performance. We demonstrate the importance of time variation in TTDs in simulating water movement along fast flow pathways, and investigate sensitivity of the models to assumptions about our ability to sample fast, near‐surface flow.

Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081–2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.