Academic literature on the topic 'Water balance (Hydrology) Australia Mathematical models'

In the perspective of Darwinian hydrology, Budyko hypotheses can be the foundation of approaches for developing models. Numerous Budyko-type models meeting established boundary conditions (water and energy limits) have been developed based on the Budyko hypothesis on the long-term-average annual mass and energy balance. Some of these models are grounded on empirical bases, while others have been formulated on sophisticated mathematical developments. We analyze the basic hypotheses underlying some Budyko-type models; we first describe some published models and then examine their underlying hypotheses in a hydrologically intuitive space (precipitation versus runoff). The analyses show that the models studied are a consequence of assuming that two parallel straight lines (of unit slope) of different intercepts are indeed equal (proportionality hypothesis). This hypothesis gives rise to different Budyko-type models that, although mathematically correct and meeting the limits (partially) related to the Budyko hypotheses, do not yield any information about what happens between those limits. To overcome the extreme energy limit, an expolinear model is introduced.

Abstract. This study explores the dominant processes that may be responsible for the observed streamflow response in Seventeen Mile Creek, a tropical catchment located in a monsoonal climate in Northern Territory, Australia. The hydrology of this vast region of Australia is little understood due to the low level of information and gauging that is available. Any insights that can be gained from the few well gauged catchments that exist can be valuable for predictions and water resource assessments in other poorly gauged or ungauged catchments in the region. To this end, the available rainfall and runoff data from Seventeen Mile Creek catchment are analyzed through the systematic and progressive development and testing of rainfall-runoff models of increasing complexity, by following the "downward" or "top-down" approach. At the end a multiple bucket model (4 buckets in parallel) is developed. Modelling results suggest that the catchment's soils and the landscape in general have a high storage capacity, generating a significant fraction of delayed runoff, whereas saturation excess overland flow occurs only after heavy rainfall events. The sensitivity analyses carried out with the model with regard to soil depth and temporal rainfall variability reveal that total runoff from the catchment is more sensitive to rainfall variations than to soil depth variations, whereas the partitioning into individual components of runoff appears to be more influenced by soil depth variations. The catchment exhibits considerable inter-annual variability in runoff volumes and the greatest determinant of this variability turns out to be the seasonality of the climate, the timing of the wet season, and temporal patterns of the rainfall. The water balance is also affected by the underlying geology, nature of the soils and the landforms, and the type, density and dynamics of vegetation, although, information pertaining to these is lacking.

Abstract. This study explores the dominant processes that may be responsible for the observed streamflow response in Seventeen Mile Creek, a tropical catchment located in a monsoonal climate in Northern Territory, Australia. The hydrology of this vast region of Australia is poorly understood due to the low level of information and gauging that are available. Any insights that can be gained from the few well gauged catchments that do exist can be valuable for predictions and water resource assessments in other poorly gauged or ungauged catchments in the region. To this end, the available rainfall and runoff data from Seventeen Mile Creek catchment are analyzed through the systematic and progressive development and testing of rainfall-runoff models of increasing complexity, by following the "downward" or "top-down" approach. This procedure resulted in a multiple bucket model (4 buckets in parallel). Modelling results suggest that the catchment's soils and the landscape in general have a high storage capacity, generating a significant fraction of delayed runoff, whereas saturation excess overland flow occurs only after heavy rainfall events. The sensitivity analyses carried out with the model with regard to soil depth and temporal rainfall variability revealed that total runoff from the catchment is more sensitive to rainfall variations than to soil depth variations, whereas the partitioning into individual components of runoff appears to be more influenced by soil depth variations. The catchment exhibits considerable inter-annual variability in runoff volumes and the greatest determinant of this variability turns out to be the seasonality of the climate, the timing of the wet season, and temporal patterns of the rainfall. The water balance is also affected by the underlying geology, nature of the soils and the landforms, and the type, density and dynamics of vegetation, although information pertaining to these is lacking.

Abstract. In Earth system modelling, a description of the energy budget of the vegetated surface layer is fundamental as it determines the meteorological conditions in the planetary boundary layer and as such contributes to the atmospheric conditions and its circulation. The energy budget in most Earth system models has been based on a big-leaf approach, with averaging schemes that represent in-canopy processes. Furthermore, to be stable, that is to say, over large time steps and without large iterations, a surface layer model should be capable of implicit coupling to the atmospheric model. Surface models with large time steps, however, have difficulties in reproducing consistently the energy balance in field observations. Here we outline a newly developed numerical model for energy budget simulation, as a component of the land surface model ORCHIDEE-CAN (Organising Carbon and Hydrology In Dynamic Ecosystems – CANopy). This new model implements techniques from single-site canopy models in a practical way. It includes representation of in-canopy transport, a multi-layer long-wave radiation budget, height-specific calculation of aerodynamic and stomatal conductance, and interaction with the bare-soil flux within the canopy space. Significantly, it avoids iterations over the height of the canopy and so maintains implicit coupling to the atmospheric model LMDz (Laboratoire de Météorologie Dynamique Zoomed model). As a first test, the model is evaluated against data from both an intensive measurement campaign and longer-term eddy-covariance measurements for the intensively studied Eucalyptus stand at Tumbarumba, Australia. The model performs well in replicating both diurnal and annual cycles of energy and water fluxes, as well as the vertical gradients of temperature and of sensible heat fluxes.

For around a decade, deep learning – the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers – modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources, identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of “Gartner Hype Curve”, which in the general details describes a life cycle of modern technologies.

Based on the determination of the zero-flux plane, a water balance procedure for large-scale experimental databases was automated to estimate the soil water balance based on soil water content distribution with depth through time. The automated procedure was verified using data from the BOREAS project obtained in three Intensive Field Campaigns during the spring and summer of 1994. The data used correspond to four tower sites measuring atmospheric fluxes above the forest canopy from the Northern and Southern Study Areas and are designated according to the predominant vegetation in the area as Old Jack Pine and Young Jack Pine. The total hydraulic head through time at these sites is determined to identify the position of the zero-flux plane, which separates that part of the soil profile in which water flow is upward from the region in which the water flow is downward. In conjunction with precipitation and soil water content data, the procedure allows estimation of the actual soil water balance, the water used from the region above the zero-flux plane being evapotranspiration, and the change in soil water content below the mean zero-flux plane being drainage. Prior to this study, no published attempt had been made to automate a water balance procedure for large-scale experimental databases based on the position of the zero-flux plane and soil water content distribution through time.
Graduation date: 1997

Monthly water balance models are important tools for hydrological impact assessment of climate change. Traditionally monthly models adopt a conceptual, lumped-parameter approach. Based on an extensive survey and review of existing monthly water balance models, six models with different conceptualization and structure, i.e., Thomthwaite-Mather, Belgium, Xinanjiang, Guo, WatBal and Schaake, were compared through calibration and validation using observed data of hydrology and climate of 1960-1988 in the Dongjiang basin. The model comparison offered insights for the development of a monthly distributed model which integrates the spatial variations of basin terrain and rainfall into runoff simulation. An innovative feature of the new model is that the spatial distribution of soil moisture capacity which is described as a parabolic curve in Xinanjiang model is represented by a cumulative frequency curve of index of relative difficulty of runoff generation based on the concept of topographic index in TOPMODEL. The calibration and validation results show that the developed model with only three parameters is suitable for monthly runoff simulation in the Dongjiang basin.
The developed model was applied to evaluate the changes in water availability in the Dongjiang basin under hypothetical climate change scenarios and those derived from projections of three General Circulation Models (GCM), i.e., CGCM1, CSIRO and ECHAM4. Sensitivity analyses based on hypothetical scenarios suggest that climatic change has greater effects on runoff than on soil moisture and greater effects on water availability in dry months than in wet months. The effects of precipitation changes on the amount of runoff and soil moisture can be characterized by a magnification factor whereas temperature increases alone produce negligible effects. Hydrological simulation with inputs of three GCM-generated scenarios indicates that annual and rain-season runoff will increase by 0.3°io to 13.9% and 7.6% to 12.0%, respectively, by the 2050s. Dry-season runoff will change between -23.2% and +26.4%. Average annual and dry-season soil moisture will decrease by 1.3% to 6.9% and 1.0% to 8.1%, respectively. Soil moisture will demonstrate little change in rain-season. Increase in annual runoff and reduction in annual soil moisture will be apparent over the whole basin, but there is relatively little consistency among the three GCM-generated scenarios as to the magnitudes of spatial change in runoff and soil moisture. Although these results are not definitive statements as to what will happen to runoff and soil moisture in the Dongjiang basin, they rather have significant implications for the study of response strategies of water supply and flood control to climate change.
Jiang Tao.
"July 2005."
Advisers: Chen Yongqin; Lam Kin-che.
Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0149.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (p. 174-190).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
School code: 1307.