Description: General circulation models (GCMs) provide reliable simulations of global and continental scale atmospheric variables, yet have limited skill in simulating variables important for water resource management at regional to catchment scales. GCM simulations suffer from a range of uncertainties leading to transient (changing over time) and systemic (consistent over time) biases in the output when compared to observed records. An important GCM bias in managing water resources infrastructure is the under-representation of interannual variability, or persistence, a characteristic central to the simulation of floods and droughts. This study presents a performance metric, the aggregated persistence score (APS), which is used to assess the reliability of GCMs in simulating precipitation persistence. The APS identifies regions where GCMs poorly represent the amount of interannual variability seen in the observed precipitation. This study calculated the APS at monthly aggregations for GCM precipitation as well as GCM precipitation that was bias corrected to better represent low-frequency variability. It was found that there were (1) large spatial variations in the skill of GCMs to capture observed rainfall persistence, (2) widespread under-simulation of rainfall persistence characteristics in GCMs, and (3) substantial improvement in rainfall persistence after applying bias correction.

Description: Accurate quantification of evaporation from small water storages is essential for water management and planning, particularly in water-scarce regions. In order to ascertain suitable methods for direct measurement of evaporation from small water bodies, this study presents a comparison of eddy covariance and scintillometry measurements from a reservoir in southeast Queensland, Australia. The work presented expands on a short study presented by McJannet et al. (2011) to include comparisons of eddy covariance measurements and scintillometer derived predictions of surface energy fluxes under a wide range of seasonal weather conditions. In this study analysis was undertaken to ascertain whether important theoretical assumptions required for both techniques are valid in the complex environment of a small reservoir. Statistical comparison, energy balance closure and the relationship between evaporation measurements and key environmental controls were used to compare the results of the two techniques. Reasonable agreement was shown between the sensible heat flux measurements from eddy covariance and scintillometry, while scintillometer derived estimates of latent heat flux were approximately 21 % greater than eddy covariance measurements. We suggest possible reasons for this difference and provide recommendations for further research for improving measurements of surface energy fluxes over small water bodies using EC and scintillometry.

Description: We explore the possibility of using remotely-sensed soil moisture data and in-situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically-based and fully-coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc-second (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200 000 km 2 ), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc-minute resolution (50 km at the equator). From the evaluation of these runs, we then introduce a step-wise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The step-wise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large extent coupled groundwater-land surface models in other basins or even globally.

Description: A new Lagrangian particle-based method is presented to simulate reactive transport in natural porous media. This technique is based on Modified Moving Particle Semi-implicit (MMPS) and takes as input high-resolution voxel images of natural porous media. The flow field in the medium is computed by solving the incompressible Navier-Stokes equations. Moreover, a multi-component ion transport model is coupled with a homogeneous and heterogeneous reactions module to handle pore-space alteration (i.e., pore-wall dissolution). The model is first successfully validated against the experimental data available in the literature. Subsequently, x-ray micro-tomographic images of two naturally-occurring porous media are used to investigate the impact of reaction kinetics and porespace topology on pore-space alteration induced by brine acidification in subsurface conditions. We observed that at the normal rates of reactions no significant change in porosity and permeability takes place in the short term. Whereas, higher reaction rates caused major changes in the macroscopic properties (e.g., porosity and permeability) of the rocks. We also show that these changes are strongly affected by the rocks’ pore-scale topologies.

Description: Hyporheic fluxes influence ecological processes across a continuum of timescales. However, few studies have been able to characterize hyporheic fluxes and residence time distributions (RTDs) over timescales of days to years, during which evapotranspiration (ET) and seasonal flood pulses create unsteady forcing. Here we present a data-driven, particle-tracking piston model that characterizes hyporheic fluxes and RTDs based on measured vertical head differences. We used the model to test the relative influence of ET and seasonal flood pulses in the Everglades (FL, USA), in a manner applicable to other low-energy floodplains or broad, shallow streams. We found that over the multi-year timescale, flood pulses that drive relatively deep (~1 m) flow paths had the dominant influence on hyporheic fluxes and residence times but that ET effects were discernible at shorter timescales (weeks to months) as a break in RTDs. Cumulative RTDs on either side of the break were generally well represented by lognormal functions, except for when ET was strong and none of the standard distributions applied to the shorter timescale. ET increased hyporheic fluxes by 1-2 orders of magnitude at the monthly timescale and decreased six-year mean residence times by 53-87%. Long, slow flow paths driven by flood pulses increased six-year hyporheic fluxes by another 1-2 orders of magnitude, to a level comparable to that induced over the short term by shear flow in streams. Results suggest that models of intermediate-timescale processes should include at least two storage zones with different RTDs, and that supporting field data collection occur over 3-4 years.

Description: [1] Statistical hydraulic models predict the frequency distributions of point hydraulic variables, relative to their reach-averaged values, in a stream reach based on its average characteristics (e.g., discharge, depth, width, average particle size). The models initially developed in Europe have not been tested for steeper streams (〉 4%) with coarse grain size. We recorded water velocities and depths in 44 reaches of steep streams in tropical islands and the Alps during 69 surveys. We fitted the observed distributions of velocities and depths using a mixture of two distributions, one with low variance and the other with a high variance. Then, we predicted the mixing parameter on the basis of the reach-averaged characteristics. We compared the observed and predicted frequencies for five classes of velocities, including a class of negative velocities, and four classes of water depths. The predictions of class frequencies have a bias of ≤ 5%. Our statistical model of velocity distribution predicts the frequencies of velocity classes with an explained variance between 33-72% for four classes of velocity and null for a class of intermediate velocity. The statistical model of depth distributions was less efficient with an explained variance between 25-38% for three classes of depth and null for large depths. The average Froude number, the total height of large drops relative to the reach length and the average slope are the main explanatory variables of velocity and depth distributions.

Description: This paper describes a probabilistic reservoir inflow forecasting system that explicitly attempts to sample from major sources of uncertainty in the modelling chain. Uncertainty in hydrologic forecasts arises due to errors in the hydrologic models themselves, their parameterizations, and in the initial and boundary conditions (e.g., meteorological observations or forecasts) used to drive the forecasts. The Member-to-Member (M2M) ensemble presented herein uses individual members of a numerical weather model ensemble to drive two different distributed hydrologic models, each of which is calibrated using three different objective functions. An ensemble of deterministic hydrologic states is generated by spinning up the daily simulated state using each model and parameterization. To produce probabilistic forecasts, uncertainty models are used to fit probability distribution functions (PDF) to the bias-corrected ensemble. The parameters of the distribution are estimated based on statistical properties of the ensemble and past verifying observations. The uncertainty model is able to produce reliable probability forecasts by matching the shape of the PDF to the shape of the empirical distribution of forecast errors. This shape is found to vary seasonally in the case-study watershed. We present an “intelligent” adaptation to a Probability Integral Transform (PIT)-based probability calibration scheme that relabels raw cumulative probabilities into calibrated cumulative probabilities based on recent past forecast performance. As expected, the intelligent scheme, which only applies calibration corrections when probability forecasts are deemed sufficiently unreliable, improves reliability without the inflation of ignorance exhibited in certain cases by the original PIT-based scheme.

Description: In an effort to develop methods based on integrating the subsurface to the atmospheric boundary layer to estimate evaporation, we developed a model based on the coupling of Navier-Stokes free flow and Darcy flow in porous medium. The model was tested using experimental data to study the effect of wind speed on evaporation. The model consists of the coupled equations of mass conservation for two-phase flow in porous medium with single-phase flow in the free flow domain under non-isothermal, non-equilibrium phase change conditions. In this model, the evaporation rate and soil surface temperature and relative humidity at the interface come directly from the integrated model output. To experimentally validate numerical results, we developed a unique test system consisting of a wind tunnel interfaced with a soil tank instrumented with a network of sensors to measure soil-water variables. Results demonstrated that, by using this coupling approach, it is possible to predict the different stages of the drying process with good accuracy. Increasing the wind speed increases the first stage evaporation rate and decreases the transition time between two evaporative stages (soil water flow to vapor diffusion controlled) at low velocity values; then, at high wind speeds the evaporation rate becomes less dependent on the wind speed. On the contrary, the impact of wind speed on second stage evaporation (diffusion-dominant stage) is not significant. We found that the thermal and solute dispersion in free flow systems has a significant influence on drying processes from porous media and should be taken into account.