A catchment-scale model of mountain stream channel morphologies in southeast Australia

The position of mountain streams high in the channel network and their proportional dominance mean that channel modifications and adjustments within these systems will have important implications for downstream processes and linkages. This study develops an analysis framework for examining the catchment-scale distribution of reach morphologies, and the relationship among reach type, catchment lithology and flow competence in southeast Australian mountain streams. The analysis framework is applied to three catchments which have contrasting proportions of the two dominant lithologies of the region, Devonian granites and Ordovician metasediments.The model successfully delineated 68% of reach types, and the resultant spatial maps allowed the effects of stream network position and catchment specific controls on channel morphology to be evaluated. Maximum lengths of the majority of reach morphology types were in second-order streams and the maximum number of morphology types (six) was present in third-order streams, with dramatic reductions in reach type variability as the network expands. The position of catchment lithology within the channel network structure was recognized as more important than the aerial extent of a particular lithology on the distribution and abundance of reach morphologies. The model provides an important tool in the management of channel networks for the protection or restoration of ecological diversity, by identifying river segments and tributaries with high morphological diversity.     

Modelling plumes of overland flow from logging tracks

Most land‐based forestry systems use extensive networks of unsealed tracks to access the timber resource. These tracks are normally drained by constructing cross‐banks, or water bars, across the tracks immediately following logging. Cross‐banks serve three functions in controlling sediment movement within forestry compartments:

• 1. they define the specific catchment area of the snig track (also known as skid trails) so that the overland flow does not develop sufficient energy to cause gullies, and sheet and rill erosion is reduced;
• 2. they induce some sediment deposition as flow velocity reduces at the cross‐bank;
• 3. they redirect overland flow into the adjacent general harvesting area (GHA) so that further sediment deposition may take place.

This paper describes a simple model that predicts the third of these functions in which the rate of runoff from the track is combined with spatial attributes of the track and stream network. Predictions of the extent of the overland flow plumes and the volume of water delivered to streams is probabilistically presented for a range of rainfall‐event scenarios with rainfall intensity, time since logging and compartment layout as model inputs. Generic equations guiding the trade‐off between intercross‐bank length and flow path distance from cross‐bank outlet to the stream network needed for infiltration of track runoff are derived. Copyright © 2002 John Wiley & Sons, Ltd. Copyright © 2002 John Wiley & Sons, Ltd.     

Overprinting porphyry‐type veinlets on the intrusive rocks and phreatomagmatic breccias in the Southwest prospect,southwestern Sto. Tomas II (Philex), Baguio District,Philippines

The Southwest prospect is located at the southwestern periphery of the Sto. Tomas II porphyry copper–gold deposit in the Baguio District, northwestern Luzon, Philippines. The Southwest prospect hosts a copper‐gold mineralization related to a complex of porphyry intrusions, breccia facies, and overlapping porphyry‐type veinlets emplaced within the basement Pugo metavolcanics rocks and conglomerates of the Zigzag Formation. The occurrences of porphyry‐type veinlets and potassic alteration hosted in the complex are thought to be indications of the presence of blind porphyry deposits within the Sto. Tomas II vicinity. The complex is composed of at least four broadly mineralogically similar dioritic intrusive rocks that vary in texture and alteration type and intensity. These intrusions were accompanied with at least five breccia facies that were formed by the explosive brecciation, induced by the magmatic–hydrothermal processes and phreatomagmatic activities during the emplacement of the various intrusions. Hydrothermal alteration assemblages consisting of potassic, chlorite–magnetite, propylitic and sericite–chlorite alteration, and contemporaneous veinlet types were developed on the host rocks. Elevated copper and gold grades correspond to (a) chalcopyrite–bornite assemblage in the potassic alteration in the syn‐mineralization early‐mineralization diorite (EMD) and contemporaneous veinlets and (b) chalcopyrite‐rich mineralization associated with the chalcopyrite–magnetite–chlorite–actinolite±sericite veinlets contemporaneous with the chlorite–magnetite alteration. Erratic remarkable concentrations of gold were also present in the late‐mineralization Late Diorite (LD). High X_Mg of calcic amphiboles (>0.60) in the intrusive rocks indicate that the magmas have been oxidizing since the early stages of crystallization, while a gap in the composition of Al between the rim and the cores of the calcic amphiboles in the EMD and LD indicate decompression at some point during the crystallization of these intrusive rocks. Fluid inclusion microthermometry suggests the trapping of immiscible fluids that formed the potassic alteration, associated ore mineralization, and sheeted quartz veinlets. The corresponding formation conditions of the shallower and deeper quartz veinlets were estimated at pressures of 50 and 30 MPa and temperatures of 554 and 436°C at depths of 1.9 and 1.1 km. Temperature data from the chlorite indicate that the chalcopyrite‐rich mineralization associated with the chlorite–magnetite alteration was formed at a much lower temperature (ca. 290°C) than the potassic alteration. Evidence from the vein offsetting matrix suggests multiple intrusions within the EMD, despite the K‐Ar ages of the potassic alteration in EMD and hornblende in the LD of about the same age at 3.5 ± 0.3 Ma. The K‐Ar age of the potassic alteration was likely to be thermally reset as a result of the overprinting hydrothermal alteration. The constrained K‐Ar ages also indicate earlier formed intrusive rocks in the Southwest prospect, possibly coeval to the earliest “dark diorite” intrusion in the Sto. Tomas II deposit. In addition, the range of δ³⁴S of sulfide minerals from +1.8‰ to +5.1‰ in the Southwest prospect closely overlaps with the rest of the porphyry copper and epithermal deposits in the Sto. Tomas II deposit and its vicinity. This indicates that the sulfides may have formed from a homogeneous source of the porphyry copper deposits and epithermal deposits in the Sto. Tomas II orebody and its vicinity. The evidence presented in this work proves that the porphyry copper‐type veinlets and the adjacent potassic alteration in the Southwest prospect are formed earlier and at a shallower level in contrast with the other porphyry deposits in the Baguio District.     

Discussion of “Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge”

Citation Littlewood, I. G., Croke, B. F. W. & Young, P. C. (2011) Discussion of “Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge” by Y. Wang, B. He & K. Takase (2009, Hydrol. Sci. J. 54(5), 886–898). Hydrol. Sci. J. 56(3), 521–524.     

Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas

As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output, without the requirement of having to specify the actual mathematical form of the relation between the input and output variables, it has the versatility for modeling a wide range of complex non-linear phenomena. In this study, groundwater contamination by nitrate, the ANNs are applied as a new type of model to estimate the nitrate contamination of the Gaza Strip aquifer. A set of six explanatory variables for 139 sampled wells was used and that have a significant influence were identified by using ANN model. The Multilayer Perceptrons (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Linear Networks were used. The best network found to simulate Nitrate was MLP with six input nodes and four hidden nodes. The input variables are: nitrogen load, housing density in 500-m radius area surrounding wells, well depth, screen length, well discharge, and infiltration rate. The best network found had good performance (regression ratio 0.2158, correlation 0.9773, and error 8.4322). Bivariate statistical test also were used and resulting in considerable unexplained variation in nitrate concentration. Based on ANN model, groundwater contamination by nitrate depends not on any single factor but on the combination of them.