Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

Precessional forcing of tropical vegetation carbon storage

Since the Mid Pleistocene Revolution, which occurred about one million years ago, global temperatures have fluctuated with a quasi‐periodicity of ca. 100 ka. The pattern of past change in the extent of woodlands, and therefore by inference vegetation carbon storage, has been demonstrated to have a strong positive link with this global temperature change at high and mid latitudes. However, understanding of climate systems and ecosystem function indicates that the pattern of woodland change at low latitudes may follow a fundamentally different pattern. We present output from the intermediate complexity model GENIE‐1, comprising a single transient simulation over the last 800 ka and a 174‐member ensemble of 130 ka transient simulations over the last glacial cycle. These simulations suggest that while vegetation carbon storage in mid–high northern latitudes robustly follows the characteristic ca. 100 ka cycle, this signal is not a robust feature of tropical vegetation, which is subject to stronger direct forcing by the precessional (21 ka) orbital cycle (albeit with a highly uncertain response). We conclude that the correlation of palaeoenvironmental records from low latitudes with global temperature change must be done with caution. Copyright © 2011 John Wiley & Sons, Ltd.     

Fingerprinting the 8.2 ka event climate response in a coupled climate model

Using results from coupled climate model simulations of the 8.2 ka climate event that produced a cold period over Greenland in agreement with the reconstructed cooling from ice cores, we investigate the typical pattern of climate anomalies (fingerprint) to provide a framework for the interpretation of global proxy data for the 8.2 ka climate event. For this purpose we developed an analysis method that isolates the forced temperature response and provides information on spatial variations in magnitude, timing and duration that characterise the detectable climate event in proxy archives. Our analysis shows that delays in the temperature response to the freshwater forcing are present, mostly in the order of decades (30 a over central Greenland). The North Atlantic Ocean initially cools in response to the freshwater perturbation, followed in certain parts by a warm response. This delay, occurring more than 200 a after the freshwater pulse, hints at an overshoot in the recovery from the freshwater perturbation. The South Atlantic and the Southern Ocean show a warm response reflecting the bipolar seesaw effect. The duration of the simulated event varies for different areas, and the highest probability of recording the event in proxy archives is in the North Atlantic Ocean area north of 40° N. Our results may facilitate the interpretation of proxy archives recording the 8.2 ka event, as they show that timing and duration cannot be assumed to correspond with the timing and duration of the event as recorded in Greenland ice cores. Copyright © 2011 John Wiley & Sons, Ltd.     

A smoothed particle hydrodynamics modelling of soil–water mixing and resulting changes in average strength

Soil–water interaction is a pivotal process in many underwater geohazards such as underwater landslides where soil sediments gradually evolve into turbidity currents after interactions with ambient water. Due to the large deformations, multiphase interactions and phase changes this involves, investigations from numerical modelling of the transition process have been limited so far. This study explores a simple numerical replication of such soil–water mixing with respect to changes in average strength using smoothed particle hydrodynamics (SPH). A uniform viscoplastic model is used for both the solid-like and fluid-like SPH particles. The proposed numerical solution scheme is verified by single-phase dam break tests and multiphase simple shear tests. SPH combinations of solid-like and fluid-like particles can replicate the clay–water mixture as long as the liquidity index of the solid-like particles is larger than unity. The proposed numerical scheme is shown to capture key features of an underwater landslide such as hydroplaning, water entrainment and wave generation and thus shows promise as a tool to simulate the whole process of subaquatic geohazards involving solid–fluid transition during mass transport.     

Analytical prediction of aggregates' effects on the ITZ volume fraction and Young's modulus of concrete

In order to evaluate analytically the ITZ volume fraction (f_ITZ) in concrete, a three phase model is proposed for the random concrete microstructure using the Voronoï tessellation. Within this model, the ITZ local thickness is a statistical variable depending on the local paste thickness available between each couple of neighbouring aggregates. The f_ITZ is found to not exceed 7% for typical concretes. Then, the concrete Young's modulus is predicted analytically using a four‐phase generalized self consistent model but in which the proposed f_ITZ is considered. It is found that the concrete Young's modulus increases when increasing aggregates volume fraction, aggregates maximum size and the proportion of coarse aggregates and when decreasing the ITZ thickness and Young's modulus. Finally, the validity of the proposed model is discussed based on a comparison between its predictions and three sets of experimental results related to normal and high strength concretes taken from literature. Copyright © 2016 John Wiley & Sons, Ltd.     

Experimental determination of REE partition coefficients between zircon,garnet and melt: a key to understanding high‐T crustal processes

The partitioning of rare earth elements (REE) between zircon, garnet and silicate melt was determined using synthetic compositions designed to represent partial melts formed in the lower crust during anatexis. The experiments, performed using internally heated gas pressure vessels at 7 kbar and 900–1000 °C, represent equilibrium partitioning of the middle to heavy REE between zircon and garnet during high‐grade metamorphism in the mid to lower crust. The D_REE (zircon/garnet) values show a clear partitioning signature close to unity from Gd to Lu. Because the light REE have low concentrations in both minerals, values are calculated from strain modelling of the middle to heavy REE experimental data; these results show that zircon is favoured over garnet by up to two orders of magnitude. The resulting general concave‐up shape to the partitioning pattern across the REE reflects the preferential incorporation of middle REE into garnet, with D_Gd (zircon/garnet) ranging from 0.7 to 1.1, D_Ho (zircon/garnet) from 0.4 to 0.7 and D_Lu (zircon/garnet) from 0.6 to 1.3. There is no significant temperature dependence in the zircon–garnet REE partitioning at 7 kbar and 900–1000 °C, suggesting that these values can be applied to the interpretation of zircon–garnet equilibrium and timing relationships in the ultrahigh‐T metamorphism of low‐Ca pelitic and aluminous granulites.     

Severe soil erosion during a 3‐day exceptional rainfall event: combining modelling and field data for a fallow cereal field

Exceptional rainfall events cause significant losses of soil, although few studies have addressed the validation of model predictions at field scale during severe erosive episodes. In this study, we evaluate the predictive ability of the enhanced Soil Erosion and Redistribution Tool (SERT‐2014) model for mapping and quantifying soil erosion during the exceptional rainfall event (~235 mm) that affected the Central Spanish Pyrenees in October 2012. The capacity of the simulation model is evaluated in a fallow cereal field (1.9 ha) at a high spatial scale (1 × 1 m). Validation was performed with field‐quantified rates of soil loss in the rills and ephemeral gullies and also with a detailed map of soil redistribution. The SERT‐2014 model was run for the six rainfall sub‐events that made up the exceptional event, simulating the different hydrological responses of soils with maximum runoff depths ranging between 40 and 1017 mm. Predicted average and maximum soil erosion was 11 and 117 Mg ha^?1 event^?1, respectively. Total soil loss and sediment yield to the La Reina gully amounted to 16.3 and 9.0 Mg event^?1. These rates are in agreement with field estimations of soil loss of 20.0 Mg event^?1. Most soil loss (86%) occurred during the first sub‐event. Although soil accumulation was overestimated in the first sub‐event because of the large amount of detached soil, the enhanced SERT‐2014 model successfully predicted the different spatial patterns and values of soil redistribution for each sub‐event. Further research should focus on stream transport capacity. Copyright © 2014 John Wiley & Sons, Ltd.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.