This paper describes a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia. Data have been archived from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN) since its inception in September 2001. The Murrumbidgee Catchment represents a range of conditions typical of much of temperate Australia, with climate ranging from semiarid to humid and land use including dry land and irrigated agriculture, remnant native vegetation, and urban areas. There are a total of 38 soil moisture‐monitoring sites across the Murrumbidgee Catchment, with a concentration of sites in three subareas. The data set is composed of 0–5 (or 0–8), 0–30, 30–60, and 60–90 cm average soil moisture, soil temperature, precipitation, and other land surface model forcing at all sites, together with other ancillary data. These data are available on the World Wide Web at http://www.oznet.org.au.
Abstract. The statistical behaviour and distribution of highresolution (6 min) rainfall intensity within the wet part of rainy days (total rainfall depth >10 mm) is investigated for 42 stations across Australia. This paper compares nine theoretical distribution functions (TDFs) in representing these data. Two goodness-of-fit statistics are reported: the Root Mean Square Error (RMSE) between the fitted and observed within-day distribution; and the coefficient of efficiency for the fit to the highest rainfall intensities (average intensity of the 5 highest intensity intervals) across all days at a site. The three-parameter Generalised Pareto distribution was clearly the best performer. Good results were also obtained from Exponential, Gamma, and two-parameter Generalized Pareto distributions, each of which are two parameter functions, which may be advantageous when predicting parameter values. Results of different fitting methods are compared for different estimation techniques. The behaviour of the statistical properties of the within-day intensity distributions was also investigated and trends with latitude, Köppen climate zone (strongly related to latitude) and daily rainfall amount were identified. The latitudinal trends are likely related to a changing mix of rainfall generation mechanisms across the Australian continent.
The statistical behaviour and distribution of high-resolution (6 min) rainfall intensity within the wet part of rainy days (total rainfall depth >10 mm) is investigated for 42 stations across Australia. This paper compares nine theoretical distribution functions (TDFs) in representing these data. Two goodness-of-fit statistics are reported: the Root Mean Square Error (RMSE) between the fitted and observed within-day distribution; and the efficiency of prediction of the highest rainfall intensities (average intensity of the 5 highest intensity intervals). The three-parameter Generalised Pareto distribution was clearly the best performer. Good results were also obtained from Exponential, Gamma, and two-parameter Generalized Pareto distributions, each of which are two parameter functions, which may be advantageous when predicting parameter values. Results of different fitting methods are compared for different estimation techniques. The behaviour of the statistical properties of the within-day intensity distributions was also investigated and trends with latitude, Köppen climate zone (strongly related to latitude) and daily rainfall amount were identified. The latitudinal trends are likely related to a changing mix of rainfall generation mechanisms across the Australian continent
Riparian zones are considered to be a good way of reducing water flow and sediment losses to streams, but is planting trees further away from the stream bank just as effective? Here we have used a combination of analytical models and numerical models to estimate the likely effects of the positioning of trees in a catchment on the hydrologic response. An analytical model of a planar slope was used extended in a piecewise manner to determine the effect of varying roughness of a section of the slope on runoff depth, velocity and quantity. This was compared to a numerical solution of the full flow equation on a slope. Results show that the analytical solution predicts a larger runoff depth than the numerical solution, which is to be expected as it ignores some of the terms in the full solution. The numerical model shows the same abrupt transient in head (height of water on soil surface) at a change in roughness assumed in the analytical model.A uniform planar slope of length of 100 m was split into 4 equal quarters and the effect of slope, runoff rate and roughness on the discharge rate at each quarter and at the bottom of the slope was investigated with the analytical model. This showed that the discharge rate would change in quarter with different roughness but relax back to the original discharge rate in the next quarter of the slope, when the changed occurred in the upper 3 quarters of the slope. Only when the roughness change occurred in the last quarter of the slope was the discharge rate affected at the bottom of the slope. Slope angle was found to have the least effect on changing discharge rate at the bottom of the slope. The numerical solution though, could not produce a stable solution when the length of the slope length, runoff rate, roughness and slope angle were large, while the analytical solution was able to produce results in all cases considered.Neither the analytical or numerical solutions of flow down the sloping surface included the effect of prior soil conditions on the amount of runoff generated. In order to investigate soil and climate effects on runoff the problem was also solved using the THALES catchment model. Results with the catchment model THALES generally supported the analytical model but also allow the climate and soils (infiltration and evapotranspiration) when the vegetation was changed to be assessed. Three contrasting sites were chosen; Melbourne, Brisbane and Perth, along with three soil materials (clay(C), clay loam (CL) and sandy loam (SL)). The soil materials were used to created soil profiles with four 0.3 m layers (total depth 1.2 m); soil#1 SL for all four layers; soil#2 SL for top layer and CL for lower 3 layers; and soil#3 C for all 4 layers. Two slopes; A1-10° and A2-30° were used, and combined with three soils and 3 sites resulted in 18 scenarios. The results showed that the planting of trees at different positions of the slope had an effect for sandy loam soils and moderate slopes in a winter dominated rainfall climate like Melbourne. However, for a summer dominated r...
A number of groundwater hydrograph time series models have been proposed over recent years but, to our knowledge, there has been no systematic review of their performance and thus no means of selecting a model to suit the prevailing conditions. This paper presents an evaluation of the new groundwater hydrograph time series models presented in Peterson and Western (2011) against existing models on 620 bore hydrographs distributed throughout Victoria. Bores that monitor water level under natural conditions and having at least 20 years of data were used. The aim of this study is to rigorously demonstrate the strength of the Peterson and Western (2011) models (hence referred to as soil moisture store-transfer function noise model, or SMS-TFN) and ascertain which forms of the various soil moisture components within the model perform best and under what conditions. To assess the relative performance, the widely used HARTT model (Ferdowsian et al. 2001, Ferdowsian et al. 2002) and the standard transfer function noise model (von Asmuth et al., 2002) were also investigated. This investigation into the groundwater head time series modelling was assessed by evaluating the performance of eleven model variants of three classes of models (SMS-TFN, TFN, HARTT) using the Coefficient of Efficiency (CoE) and the Akaike Information Criterion (AIC) as the performance measures for the calibration and evaluation periods. The results showed that the SMS-TFN model (Peterson and Western, 2011), significantly improves the predictive model performance compared to the performance of the traditional TFN model. The SMS-TFN model with ground water recharge as the forcing component shows better model calibration and predictive performances than models with infiltration as the forcing component. These model variants produced the best median calibration period CoE of 0.655 (where 1.0 is a perfect fit) and the best evaluation period unbiased CoE of 0.270 (see Figure 1). The predictive performance of the HARTT model was shown to be highly variable and inconsistent across the bore hydrographs tested. If a sustainable linear time trend exits in the bore hydrograph, the model produced good results as indicated by performance measures during both calibration and validation periods. However, in the absence of such trends, the model performed poorly. This illustrates the potential risk in assuming a non-climatic time trend which may or may not exist in the bore hydrographs. More importantly, the SMS-TFN model with ground water recharge as the forcing component was shown to be the most robust model which can explain most of the bore hydrographs from climate data alone.
Mapping of groundwater level observations often makes very little use of auxiliary data and is often undertaken simply by manual interpolation or ordinary kriging of the heads. Recently, a number of geostatistical methods have emerged that significantly improve estimates by incorporating the land surface elevation and groundwater flow or drawdown equations. However, at the regional scale heads are influenced by numerous other factors that cannot be considered by these methods. Such factors include the land cover type, aquifer basement elevation and upper limits to the heads (such as the land surface). Furthermore, all existing methods fail to include observation uncertainty; produce poor measures of prediction uncertainty; and assume the random field to be multi-Gaussian; that is, the spatial correlation in heads are independent of the head magnitude. To overcome these limitations and to make better use of the observation data, this paper presents a novel indicator geostatistical simulation method for mapping unconfined heads. The simulation method produces many equally probable maps and by post-processing produces quantitative uncertainty maps. Other post-processing could produce new products such as the probability of a stream having a gaining or losing hydraulic gradient and, if multiple time points are mapped, probabilistic changes in storage. To demonstrate the methodology, this paper presents an application for the Broken catchment, Victoria. The method combines a multi-variate version of kriging with external drift (KED) and a modified Markov-Bayes indicator simulation algorithm to facilitate inclusion of physical constrains to groundwater head and soft data such as landuse. The KED facilitates inclusion of continuous variables that are linearly correlated with head and is used to produce a surface that results from these variables alone. As the difference between this surface and the observations were found to be spatially correlated, and approximately first and second order stationary, the head estimate was able to be refined using indicator kriging (IK) simulations. While IK was essential for inclusion of the land class data and the groundwater head constraints, it also allowed the spatial correlation to vary with the magnitude of the heads. In effect this means it relaxes an assumption required for multi-Gaussian methods such as sequential Gaussian simulations. The entire methodology was implemented within the R statistics package using the Gstat library and modified GSLib algorithms. The source code will be made publicly available with a forthcoming journal paper. The study area comprised of both the Broken River and Broken Creek catchments with a 20 kilometre buffer to minimise boundary artefacts. Groundwater observations comprised of data from The Department of Primary Industries, Victoria; The Department of Sustainability and Environment, Victoria; and Department of Water and Energy, NSW. Importantly, the land class was found to be statistically important in estimating heads and the spatial correla...
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