Estimating surface energy fluxes: a key component for estimating potential evaporation

A model has been developed that can predict the solar and infrared downwelling radiation fluxes using ground based measurements of the air temperature, relative humidity and the cloud cover. The algorithm has been validated using several years of ground-based data for 15 sites across the globe (13 sites from the Baseline Surface Radiation Network (BSRN), as well as data for two sites in Crete). These stations cover a wide range of climatic conditions, including those of arctic, desert, sub-tropical, Mediterranean, as well as elevated sites. The RMS residual for the monthly mean short wave (SW) solar flux (approximately 0.2 to 3 μm) is typically 12 Wm-2 (mean observed daily SW flux across all stations is 305 Wm-2), while the thermal IR flux (roughly 4-50 μm) derived using the algorithms gives RMS residuals of approximately 8 Wm-2 (mean observed daily IR flux across all stations is 180 Wm-2). Daily observed and modelled fluxes, as well as residuals are shown for 8 of the stations in Figure 1. As well as the radiation fluxes, the model also estimates the atmospheric water vapour content, which has been tested using available radiosonde data for 8 of the stations. In comparison with the observed mean water vapour content, the values derived by the algorithms have typical values for bias of 0.01 g cm-2 and RMS residual of 0.15 g cm-2 (mean across all stations is 1.65 g cm-2), accounting for 80 % of the observed variation. Since the model uses readily available meteorological data, the net radiation flux at the surface can readily be calculated (given the surface albedo), providing an estimate of a dominant term in estimating potential evaporation and evapotranspiration

Identification of spatial and temporal patterns of Australian daily rainfall under a changing climate

Rainfall is a highly variable component of the climate system. There are substantial spatial and temporal variations in the frequency and spatial distribution of rainfall events. Little attention has been paid to the slow but ongoing variations of the spatial patterns of daily rainfall, especially over broad spatial scales. A better understanding of these variations and their long term trends would reduce uncertainty in environmental and natural resource models and improve assessment of ongoing climate change. This study examined the spatial cross-correlations of daily rainfall at 2322 high quality long run rainfall stations across Australia from 1910 to 2011, and assessed their spatial and temporal patterns. It was found that: 1) There has been a long term continuous strengthening in the spatial cross-correlation of daily rainfall across Australia over this period. Most of this strengthening has occurred since the 1970s; 2) The strengthening is more consistent in winter and spring but has occurred in all four seasons; 3) Southeastern Australia has had the most stable strengthening pattern over a broader spatial scale; 4) The strengthening suggests an increase in the broad scale coherence of daily rainfall, such as found in frontal rainfall; 5) These findings are consistent with recent reported changes in synoptic scale climatic driving processes, especially the increasing frequency of frontal systems and the decreasing frequency of storm events in the Australian region. An increase in the broad scale coherence of rainfall is likely to improve the accuracy of daily rainfall interpolation and influence dependent hydrological modelling. Interactions of data quality with the derived correlation patterns are also discussed

A review of surrogate models and their application to groundwater modeling

The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context

Geospatial data pre-processing on watershed datasets: A GIS approach

Spatial data mining helps to identify interesting patterns from the spatial data sets. However, geo spatial data requires substantial data pre-processing before data can be interrogated further using data mining techniques. Multi-dimensional spatial data has been used to explain the spatial analysis and SOLAP for pre-processing data. This paper examines some of the methods for pre-processing of the data using Arc GIS 10.2 and Spatial Analyst with a case study dataset of a watershed

A comparison of global sensitivity techniques andsampling method

Inspired by Tarantola et al. (2012), we extend their analysis to include the Latin hypercube and Random sampling methods. In their paper, they compared Sobol’ quasi-Monte Carlo and Latin supercube sampling methods by using a V-function and variance-based sensitivity analysis. In our case we compare the convergence rate and average error between Sobol’, Latin hypercube, and Random sampling methods from the Chaospy library, keeping everything else the same as in their paper. We added the Random sampling method to test if the other two sampling methods are indeed superior. The results from our code confirm the results of their paper, where Sobol’ has better performance than Latin hypercube sampling in most cases, whilst they both have higher efficiency than is achieved with Random sampling. In addition we compared the explicit forms of ‘Jansen 1999’ total effects estimator used in Tarantola et al. (2012) with the ‘Sobol’ 2007’ estimator, again keeping sample sizes and the test function the same. Results confirm that the ‘Jansen 1999’ estimator is more efficient than ‘Sobol’ 2007’. The presentation will also include the Morris sampling method and other test functions to further test efficiency among all the sampling methods on different cases

The effects of climate change on ecologically-relevant flow regime and water quality attributes

The management of freshwater ecosystems is usually targeted through the regulation of water quantity (limiting diversions and providing environmental flows) and regulation of water quality (setting limits or targets for constituent concentrations). Clima

An approach to assess and manage nutrient loads in two coastal catchments of the Eurobodalla region, NSW, Australia

This paper describes a research programme to estimate nitrogen (N), phosphorus (P) and sediment event-based loads in the Moruya and Tuross River catchments of the New South Wales south coast. The research programme is designed within the context of an integrated catchment modelling framework (CatchMODS), to assess relative contributions from diffuse sources of nutrient and sediment export loads, and provide information for catchment management. In particular, the relative potential risk to water quality from dairying in the Eurobodalla region is being evaluated using a farm-scale nutrient budget approach. Predominant land uses in the Moruya and Tuross River catchments are conservation and production forests, national parks, cattle grazing, and dairy production. There is little information on the quality of water entering the catchment estuaries, particularly during storm events when the majority of sediment and nutrients is transported to estuaries. The use of catchment models is commonly required to assist catchment managers to investigate water quality impacts at a catchment scale due to cost restrictions and data availability. To assess nutrient and sediment loads and enable management to achieve sustainable practices, the CatchMODS model is linked with a field-based data collection programme including water quality sampling to estimate suspended sediment, and total and dissolved nutrient loads on an event basis. Potential sources of nutrients in the catchments are likely to include diffuse forest and agricultural inputs and gully erosion. Diffuse source pastoral agriculture has been linked to decreases in water quality and recreational use of surface waters. To assess the potential impact of dairying, an evaluation of nutrient inputs and outputs, including leaching/runoff losses using a nutrient budget approach for a typical dairy farm in the Eurobodalla region, was undertaken. The Overseer nutrient budget model was used. Farms were divided into relatively homogeneous management areas, namely irrigated-block, non-irrigated and effluent-application areas for use in the model. The model produces nutrient budget inputs and outputs for a range of nutrients for the farm as a whole and for each individual management block. Initial results in this paper indicate N fertiliser usage on the dairy farms in this region is relatively low. Results indicate whole-farm long-term dairy farm leaching losses were 11 kg N/ha/year, which are considered low relative to other published studies. The overall whole-farm long-term leaching/runoff losses for P were estimated at 1.4 kg P/ha/year. Predicted whole-farm N concentration in drainage water at current average fertiliser usage is 3 ppm. This concentration is less than the guideline maximum for drinking water, although environmental acceptability depends on the sensitivity of receiving waters. In contrast, the simulated drainage N concentrations are greater than the guideline for lowland rivers in southeast Australia. Further evaluation of soil information, nutrient management and subsequent implications for water quality in the catchments as a whole is being investigated