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    Assessing water availability in the Oroua River Catchment : a thesis presented in partial fulfillment of the requirements for a Master Degree in Applied Science (Agricultural Engineering), Massey University
    (Massey University, 2002) Reyes, Mona Liza Fortunado de los
    The study estimated the 1993-1998 natural flows as well as their corresponding reliabilities along Kiwitea Stream and Oroua River upstream of the old Kawa Wool station. These estimates could present a baseline condition for assessing the hydrologic capability of the catchment for the existing rights and the amount of streamflow still available for additional rights. The study demonstrated that water availability modeling could be a useful tool in water resource management and planning for the Oroua catchment. The "usual" or high river flow allocation management for the Oroua River wherein a right may abstract water up to its permitted rates could be modeled in WRAP. The results of the simulation based on full abstraction of permitted rates suggested that on a monthly basis, there was enough flow physically available to meet all consented abstraction rights including the minimum flow requirement at Almadale and Spur Road stations throughout the 1993- 1998 simulation period. The study had identified an apparent shortcoming of the WRAP model in simulating the MWRC's water allocation schemes at times of low river flow wherein water rights are either restricted or curtailed whenever the flow reached the set monthly flow threshold and the minimum flow level. The WRAP program was lacking of a mechanism or algorithm that will allow a water diversion target to vary depending on a gauged flow at other locations. The study demonstrated that the criteria stipulated in the Oroua Catchment Water Allocation Regional Plan for rostering abstraction at times of low river flow could be accounted in WRAP water availability modeling using a weighted ranked priority scheme. The results of simulation apportioning the combined maximum abstraction rates for irrigation purposes, based on prior use and natural upstream-to-downstream location among irrigation rights, indicated a minimal increase in the utilization of available water of the Oroua River. Thus, with increased water use as a management objective, such options would not be an attractive alternative. To facilitate relevant hydrologic and institutional water availability and reliability assessment of the Oroua River, it is recommended that a modification be made in the WRAP program to include mechanism or algorithms that will allow automatic change of diversion target as a function of gauged flow. Also, a shorter computational interval, such as weekly or daily, would yield more relevant results for real-time water management for the Oroua River. For future simulation or modeling studies for the Oroua River, there is a need to have an actual streamflow measurement or gauging station downstream of the river for validation purposes. There is also a need to have data on actual abstractions and discharges to the Oroua River and its tributaries.
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    Application of Markov chain model in streamflow forecasting : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Geography at Massey University
    (Massey University, 1996) Mpelasoka, Freddie Simon
    This thesis presents an approach to streamflow forecasting based on a Markov chain model to estimate conditioned probabilities that a one time-step ahead streamflow forecast will be within a certain streamflow range. In this application a set of "states of flow" defined over streamflow ranges (intervals) forms a finite state space of a Markov chain. Flood forecasting is made by focusing on a preselected state of flow as a flood state. A multi-objective (two criteria) function for the quantification of the model performance is introduced. Specifically designed for a flood forecasting and warning system the two criteria are the probability of issuing a false alarm and the probability of failing to forecast a flood event. The goal is to minimize both criteria simultaneously together with a preference of accepting more false alarms than misses. The model has two options of making a forecast: (1) a Threshold Forecast (ThF) approach in which a forecast is based on the probability of making a one-step transition from any state into the flood state; (2) the Most Probable Event (MPE) forecast approach selects the state of flow where the next streamflow is most likely to occur. Forecasts being probabilistic, there are several options for deciding on when it is appropriate to issue a flood warning in the probabilistic framework. A search for the appropriate probability p0 is made on interval [0,1] through evaluation of the objective function at each p0, using data sets from three North Island catchments ( Akitio River, Makakahi River and Kiwitea Stream). The model applying the option of threshold forecasts performed generally well depending on the relative costs assigned to false alarms and misses. The model performed better on the Akitio River which has strongly fluctuating streamflows than on the Makakahi River and Kiwitea Stream which have relatively modest variations in flows. When the Model applied the option of the most probable event forecasts did not perform well as the probabilities of false alarms were found to be too high for the model to be accepted. The outcome of this study suggests a simple short-term flood forecasting procedure especially for rivers with strongly fluctuating flows.