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.     

From micro scale to boundary value problem: using a micromechanically based model

Acta Geotechnica - A 3D multi-scale approach is presented to investigate the mechanical behavior of a macroscopic specimen consisting of a granular assembly, as a boundary value problem. The core...     

Image-aided platform orientation determination with a GNSS/low-cost IMU system using robust-adaptive Kalman filter

The availability of miniaturized sensors with enhanced capabilities, new methods for image processing, and easy access to small and low-weight airborne platforms for data acquisition, including unmanned vehicles, opens new possibilities for geodetic navigation applications and developing new developments in sensor fusion. In this context, the development of efficient methods, based on low-cost sensors, to extract precise georeferenced information from digital cameras is of utmost interest. We present a method to improve the performance of the integration of GNSS/low-cost IMU by exploiting the orientation changes retrieved from digital images. In this work, a robust-adaptive Kalman filter is also introduced to further improve the performance of the method deployed. The adaptive factor and the robust factor accomplished are determined by innovation information and the threshold value of orientation changes between consecutive images. Results from airborne tests used to assess the performance of the method are presented. The results show that using a non-metric camera, the Euler angle estimation accuracy of the GNSS/low-cost IMU integration can be improved to be close to 0.5 degree and an additional improvement, which can reach 59%, can be achieved after using the robust-adaptive Kalman filter.     

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.     

Developing the ability to model acid-rock interactions and mineral dissolution during the RMA stimulation test performed at the Soultz-sous-Forêts EGS site,France

The Soultz Enhanced Geothermal System (EGS) reservoir's response to chemical stimulation is assessed by numerical simulation of coupled thermo-hydraulic-chemical processes. To assess chemical interactions between host rocks and a mixture of HCl and HF as well as its potential effects on the Soultz EGS reservoir, new modelling efforts using the FRACHEM code have been initiated. This article presents the model calibration and results. Simulations consider realistic conditions with available data sets from the EGS system at Soultz. Results indicate that the predicted amount of fracture sealing minerals dissolved by injection of a mixture of acids Regular Mud Acid (RMA) was consistent with the estimated amount from the test performed on GPK4 well at Soultz EGS site. Consequently reservoir porosity and permeability can be enhanced especially near the injection well by acidizing treatment.     

Effects of sinuosity factor on hydrodynamic parameters estimation in karst systems: a dye tracer experiment from the Beyyayla sinkhole (Eskişehir,Turkey)

The sinuosity factor (SF) is a critical value in karst systems in terms of estimating their hydrodynamic parameters including groundwater velocity, coefficient of dispersion, etc., through dye tracer experiments. SF has been used in a number of different dye tracer experiments in karstic systems to estimate a representative flow path. While knowing SF is crucially important in the estimation of hydrodynamic parameters, its calculation is associated with significant uncertainty due to the complexity of subsurface karstic features. And yet, only a few studies have discussed its uncertainties, which might lead some errors in estimation of hydrodynamic parameters from dye tracer experiment. In this study, dye tracer experiments were conducted in two consecutive years (2003 and 2004) representing low and high flow conditions in the Beyyayla sinkhole (Eski?ehir, Turkey) where the flow path is well known. Uranine was used in experiments as a tracer and QTRACER computer program was used to determine the hydrodynamic properties of the Beyyayla karst system as well as to gain insights into the effects of SF from dye tracer experiments on estimated parameters. The results showed that the breakthrough curve follows a unimodal and a bimodal distribution in low and high flow conditions, respectively. These different distributions stem from the water transport mechanisms, where velocities were calculated as 58.2 and 93.6 m h^?1 during low and high flow conditions observed in a spring emerging from the south side of the studied system. The results also show that the coefficient of dispersion, Reynolds number, and Peclet number increased and longitudinal dispersivity decreased with the higher flow rate. Furthermore, the estimated parameters did not vary with either the flow conditions or the tracer transit time, but they have shown some variations with SF. When SF was increased by 50 %, a change in these parameters was obtained in the range of 50–125 %.