The mathematical formulation of the Muskingum method, like that of many numerical schemes used for river routing, requires that all upstream river reaches be updated prior to updating the flow rate of any given reach. Due to this topological constraint, such numerical schemes have traditionally been solved in an upstream-to-downstream manner which imposes inherent limitations on the speedup that can be achieved in a parallel computing environment because each computing core has to wait for completion of all cores addressing upstream subbasins prior to starting its own subbasin. The research presented in this paper quantifies the exact influence among river reaches during the update step of the Muskingum method and shows that the influence decreases with increasing distance between two reaches until it becomes too small to be accounted for by floating-point arithmetic. A formal definition of the minimal distance from which the relative influence becomes numerically inexistent—the radius of influence—is presented. Based on this distance, expressed as a number of river reaches, a new estimate of the maximum theoretical speedup that can be achieved by the Muskingum method or by similar numerical schemes is presented and implies large potential gains in computing time when domains are much larger than the radius of influence. An application to the approximately 180,000 river reaches of the Upper Mississippi River Basin at a 15 min time step over 2004 shows a radius of influence on the order of 150 river reaches. The speedup obtained for this application is much higher than previously thought possible but also much lower than could be attained, suggesting that further investigations are necessary.

© 2015. American Geophysical Union. This study presents a new algorithm for parallel computation of river flow that builds on recent work demonstrating the relative independence of distant river reaches in the update step of the Muskingum method. The algorithm is designed to achieve enhanced fixed-size parallel speedup and uses a mathematical approximation applied at the boundaries of large subbasins. In order to use such an algorithm, a balanced domain decomposition method that differs from the traditional classifications of river reaches and subbasins and based on network topology is developed. An application of the algorithm and domain decomposition method to the Mississippi River Basin results in an eightfold decrease in computing time with 16 computing cores which is unprecedented for Muskingum-type algorithms applied in classic parallel-computing paradigms having a one-to-one relationship between cores and subbasins. An estimated 300 km between upstream and downstream reaches of subbasins guarantees the applicability of the algorithm in our study and motivates further investigation of domain decomposition methods.

Because of the large water storage capacity of soil relative to the atmosphere, changes in soil moisture storage can significantly affect the regional atmospheric budgets of water and energy on monthly, seasonal and longer time scales. Therefore proper modeling of soil liquid water processes is essential to a correct representation of the climate system. This study focuses on the class of summary models of liquid water fluxes in the vadose, or unsaturated zone of the soil, which are applicable to global or regional climate modeling studies. Fourteen such models are represented in this intercomparison study. Observational data from the HAPEX experiment provide validation. Because only limited observational data were available to constrain these models during their development and validation, the models have evolved very diverse treatments of the relevant processes: the basic Darcian (soil internal) and Hortonian (surface liquid flow) processes, as well as the boundary conditions of baseflow drainage and lateral interflow. The annual total local runoff is systematically underestimated by all but one of the participant models. This is one of the few significant biases between the consensus of participant models and the observations. The modeled runoff, averaged over the 14 models, differs from the budget estimate from observations by about 40%. During the period of runoff generation (late winter and early spring) the average model fails to deplete the soil water store as rapidly as is observed, a result consistent with the underprediction of runoff. One cannot rule out insufficient characterization of the field site soils as a primary cause of these discrepancies. Results suggest that model sources of the discrepancy are about equally likely to be related to the prediction of bare soil evaporation (discussed elsewhere in this issue) as they are to the parameterization of runoff and drainage processes.

We use Monte Carlo analysis to show that explicit representation of an aquifer within a land-surface model (LSM) decreases the dependence of model performance on accurate selection of subsurface hydrologic parameters. Within the National Center for Atmospheric Research Community Land Model (CLM) we evaluate three parameterizations of vertical water flow: (1) a shallow soil profile that is characteristic of standard LSMs; (2) an extended soil profile that allows for greater variation in terrestrial water storage; and (3) a lumped, unconfined aquifer model coupled to the shallow soil profile. North American Land Data Assimilation System meteorological forcing data (1997–2005) drive the models as a single column representing Illinois, USA. The three versions of CLM are each run 22,500 times using a random sample of the parameter space for soil texture and key hydrologic parameters. Other parameters remain constant. Observation-based monthly changes in state-averaged terrestrial water storage (dTWS) are used to evaluate the model simulations. After single-criteria parameter exploration, the schemes are equivalently adept at simulating dTWS. However, explicit representation of groundwater considerably decreases the sensitivity of modeled dTWS to errant parameter choices. We show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a congruent set.

NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.

The water-balance components of 16 Soil-Vegetation Atmospheric Transfer (SVAT) schemes were evaluated by comparing predicted and observed streamflow, predicted evapotranspiration and evapotranspiration inferred from an atmospheric moisture budget analysis, and soil moisture storage changes for a seven-year period (1980-1986) using data from the Red-Arkansas River basins of the Southern Great Plains of the USA. The evaluations support the following suggestions: (a) The mean annual runoff of all models follows, at least generally, the strong climatic East-West gradient of precipitation, although most models predict too much runoff in the dry part of the basin. (b) The mean monthly storage change tends to be underestimated, even though all models capture reasonably well the seasonality of the evapotranspiration. (c) The wide range of conceptualizations used for generation of surface and subsurface runoff strongly affect runoff generation on seasonal, and shorter, time scales. Model responses to summer precipitation ranged from almost no summer runoff (one model) to the (more common) situation of persistent overprediction of summer runoff, especially in the driest part of the basin. (d) All models tended to underpredict evapotranspiration in summer and overpredict in winter. (e) Model-derived mean seasonal cycles of changes in soil moisture storage are qualitatively similar to those inferred from observations, but most models do not predict the decrease in April soil moisture storage and the increase in October that is inferred from observations.

The energy components of sixteen Soil-Vegetation Atmospheric Transfer (SVAT) schemes were analyzed and intercompared using 10 years of surface meteorological and radiative forcing data from the Red-Arkansas River basin in the Southern Great Plains of the United States. Comparisons of simulated surface energy fluxes among models showed that the net radiation and surface temperature generally had the best agreement among the schemes. On an average (annual and monthly) basis, the estimated latent heat fluxes agreed (to within approximate estimation errors) with the latent heat fluxes derived from a radiosonde-based atmospheric budget method for slightly more than half of the schemes. The sensible heat fluxes had larger differences among the schemes than did the latent heat fluxes, and the model-simulated ground heat fluxes had large variations among the schemes. The spatial patterns of the model-computed net radiation and surface temperature were generally similar among the schemes, and appear reasonable and consistent with observations of related variables, such as surface air temperature. The spatial mean patterns of latent and sensible heat fluxes were less similar than for net radiation, and the spatial patterns of the ground heat flux vary greatly among the 16 schemes. Generally, there is less similarity among the models in the temporal (interannual) variability of surface fluxes and temperature than there is in the mean fields, even for schemes with similar mean fields.

Sixteen land-surface schemes participating in the project for the Intercomparison of Land-surface Schemes (PILPS) Phase 2(c) were run using 10 years (1979-1988) of forcing data for the Red-Arkansas River basins in the Southern Great Plains region of the United States. Forcing data (precipitation, incoming radiation and surface meteorology) and land-surface characteristics (soil and vegetation parameters) were provided to each of the participating schemes. Two groups of runs are presented. (1) Calibration-validation runs, using data from six small catchments distributed across the modeling domain. These runs were designed to test the ability of the schemes to transfer information about model parameters to other catchments and to the computational grid boxes. (2) Base-runs, using data for 1979-1988, designed to evaluate the ability of the schemes to reproduce measured energy and water fluxes over multiple seasonal cycles across a climatically diverse, continental-scale basin. All schemes completed the base-runs but five schemes chose not to calibrate. Observational data (from 1980-1986) including daily river flows and monthly basin total evaporation estimated through an atmospheric budget analysis, were used to evaluate model performance. In general, the results are consistent with earlier PILPS experiments in terms of differences among models in predicted water and energy fluxes. The mean annual net radiation varied between 80 and 105 W m-2 (excluding one model). The mean annual Bowen ratio varied from 0.52 to 1.73 (also excluding one model) as compared to the data-estimated value of 0.92. The run-off ratios varied from a low of 0.02 to a high of 0.41, as compared to an observed value of 0.15. In general, those schemes that did not calibrate performed worse, not only on the validation catchments, but also at the scale of the entire modeling domain. This suggests that further PILPS experiments on the value of calibration need to be carried out.

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton LS detector at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. With 6 years of data, the neutrino mass ordering can be determined at 3-4 sigma and three oscillation parameters can be measured to a precision of 0.6% or better by detecting reactor antineutrinos. With 10 years of data, DSNB could be observed at 3-sigma; a lower limit of the proton lifetime of 8.34e33 years (90% C.L.) can be set by searching for p->nu_bar K^+; detection of solar neutrinos would shed new light on the solar metallicity problem and examine the vacuum-matter transition region. A core-collapse supernova at 10 kpc would lead to ~5000 IBD and ~2000 (300) all-flavor neutrino-proton (electron) scattering events. Geo-neutrinos can be detected with a rate of ~400 events/year. We also summarize the final design of the JUNO detector and the key R&D achievements. All 20-inch PMTs have been tested. The average photon detection efficiency is 28.9% for the 15,000 MCP PMTs and 28.1% for the 5,000 dynode PMTs, higher than the JUNO requirement of 27%. Together with the >20 m attenuation length of LS, we expect a yield of 1345 p.e. per MeV and an effective energy resolution of 3.02%/\sqrt{E (MeV)}$ in simulations. The underwater electronics is designed to have a loss rate <0.5% in 6 years. With degassing membranes and a micro-bubble system, the radon concentration in the 35-kton water pool could be lowered to <10 mBq/m^3. Acrylic panels of radiopurity <0.5 ppt U/Th are produced. The 20-kton LS will be purified onsite. Singles in the fiducial volume can be controlled to ~10 Hz. The JUNO experiment also features a double calorimeter system with 25,600 3-inch PMTs, a LS testing facility OSIRIS, and a near detector TAO.