Realistic forecasting of groundwater level, based on the eigenstructure of aquifer dynamics

Conference paper presented at the MODSIM03, International Congress on Modelling and Simulation, held July 2003, Jupiters Hotel and Casino, Townsville, Queensland.Short-term management of groundwater resources, especially during droughts, can be assisted by forecasts of groundwater levels. Such forecasts need to account for the natural dynamic behaviour of the aquifer, likely recharge scenarios, and recent but unknown abstractions. These requirements mean that forecasts, at say monthly intervals, need to be updated with current observations on a real-time basis. One established procedure for this kind of problem is to fit autoregressive, moving-average, exogenous-variable (ARMAX) time-series models to the history of groundwater levels in response to estimates of land surface recharge. The ARMAX difference equations are then converted into forecast equations that allow real-time updating to include recent forecast errors as an additional source of information. Some disadvantages of this pure time-series analysis approach are the apparent lack of physical concepts in the model formulation and statistical aspects of model identification and calibration that are related to the inherent structure of ARMAX equations. This paper addresses these issues by describing a method for formulating ARMAX forecast equations from a linear system description based on the eigenvalues and eigenvectors (eigenstructure) of the dynamic behaviour of an aquifer. For the piezometric response of a heterogeneous aquifer to a fixed spatial distribution of land surface recharge, with time-varying magnitude, only a few eigenvalues are significant for describing the dynamics. The resulting model has a simple robust parameter structure, and is easily calibrated and implemented in spreadsheet form. The eigenstructure approach enables transfer of some parameter information from locations with good data records to those with sparse data. This modelling approach is demonstrated with monthly values of land surface recharge, estimated from a daily water balance model, and groundwater level data from an observation well in a 2000 km² alluvial aquifer in Canterbury, New Zealand

Dynamic analysis of groundwater discharge and partial-area contribution to Pukemanga Stream, New Zealand

The proportion and origin of groundwater contribution to streamflow from agricultural catchments is relevant to estimation of the effects of nitrate leached from the soil on the quality of surface waters. This study addresses the partitioning of streamflow contributions from near-surface runoff and from groundwater, each with different contributing land area, on a steep pastoral hillslope in a humid climate. The 3 ha headwater catchment of the perennial Pukemanga Stream, in the North Island of New Zealand, was instrumented for continuous observation of climatic data, streamflow and groundwater level. The dynamics of groundwater levels and groundwater contribution to streamflow were analysed by means of a one-parameter, eigenvalue-eigenfunction description of a 1-D aquifer model. Model results for seven years of daily data predict that 36–44% of the topographical catchment contributes groundwater to the stream. The remaining groundwater generated within the catchment contributes to streamflow outside the catchment. Groundwater was calculated to be 58–83% of observed annual streamflow from the topographical catchment. When the smaller groundwater catchment is taken into account, the groundwater contribution to streamflow is 78–93% on a unit area basis. Concurrent hourly data for streamflow and groundwater levels at two sites indicate the dynamic behaviour of a local groundwater system. Groundwater flow dynamics that support the perennial nature of this headwater stream are consistent with the size of the groundwater body, porosity of the subsurface material, and hydraulic conductivity derived from partitioning of streamflow contributions

The occurrence and origin of salinity in non-coastal groundwater in the Waikato region

Aims The aims of this project are to describe the occurrence, and determine the origin of non-coastal saline groundwater in the Waikato region. High salinity limits the use of the water for supply and agricultural use. Understanding the origin and distribution of non-coastal salinity will assist with development and management of groundwater resources in the Waikato. Method The occurrence of non-coastal groundwater salinity was investigated by examining driller’s records and regional council groundwater quality information. Selected wells were sampled for water quality analyses and temperatures were profiled where possible. Water quality analyses include halogens such as chloride, fluoride, iodide and bromide. Ratios of these ions are useful to differentiate between geothermal and seawater origins of salinity (Hem, 1992). Other ionic ratio approaches for differentiating sources and influences on salinity such as those developed by Alcala and Emilio (2008) and Sanchez-Martos et al., (2002), may also be applied. Potential sources of salinity include seawater, connate water, geothermal and anthropogenic influences. The hydrogeologic settings of saline occurrence were also investigated, to explore the potential to predict further occurrence. Results Numerous occurrences of non-coastal saline groundwater have been observed in the Waikato region. Where possible, wells with relatively high total dissolved solids (TDS) were selected for further investigation. Several groundwater samples are moderately saline and exceed the TDS drinking water aesthetic guideline of 1,000 g m-3 (Ministry of Health, 2008). Selected ion ratios (predominantly halogens) were used to assist in differentiating between influences on salinity such as seawater and geothermal. Bromide to iodide ratios, in particular, infer a greater geothermal influence on salinity, although other ratios are not definitive. The anomalously elevated salinity observed appears natural but nevertheless has constrained localised groundwater resource development for dairy factory, industrial and prison water supply use. Further work may show some relationship with geology or tectonics, which could assist prediction of inland saline groundwater occurrence

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models

Australasian Groundwater Conference: Groundwater in a Changing World

© Copyright is retained by the author/s of each abstract.The Australasian Groundwater Conference (AGC) was held in Brisbane Queensland, 24-27 November 2019. This conference was an epic event filled with informative presentations, entertaining networking events and stunning field trips exploring the sights and sounds that this subtropical dynamic region has to offer. The AGC 2019 featured a stimulating technical program around the theme of “Groundwater in a Changing World” that covered a broad range of applications to resources, infrastructure and environment. The program included stimulating plenary speakers, engaging panel discussions and enticing social events. Over 600 groundwater researchers, industry professionals and policy development specialists from around the region attended this unique event. There were many opportunities on offer for delegates to share their experiences, inform best practice, and identify the steps they can take to bring about lasting improvements to the management of our vital groundwater resources. Our hard working volunteer organisational team wishes to thank sponsors, speakers, delegates, exhibitors and volunteers for making the conference such a huge success

On the propagation of drought : how climate and catchment characteristics influence hydrological drought development and recovery

Drought is a severe natural disaster resulting in high economic loss and huge ecological and societal impacts. In this thesis drought is defined as a period of below-normal water availability in precipitation (meteorological drought), soil moisture (soil moisture drought), or groundwater and discharge (hydrological drought), caused by natural variability in climate. Drought propagation is the change of the drought signal as it moves from anomalous meteorological conditions to a hydrological drought through the terrestrial part of the hydrological cycle. The objective of this PhD research is to investigate the processes underlying drought propagation and their relation with climate and catchment characteristics, both on the catchment scale and on the global scale. The catchment-scale studies are based on five headwater catchments in Europe with contrasting climate and catchment characteristics. In one of these case study areas, anthropogenic influence on the water system was significant, resulting in severe water scarcity. As I only study natural processes in this thesis, there was a need to separate drought (as defined in this thesis) from human-induced water scarcity in this case study area. I proposed an observation-modelling framework that consists of a hydrological model to simulate the ‘naturalised’ situation and an anomaly analysis method to quantify drought and water scarcity events. Both the time series and the anomaly characteristics of the ‘disturbed’ and ‘naturalised’ situation were compared to quantify human and natural influences on the hydrological system. After simulation of hydrometeorological variables of all case study areas with a conceptual hydrological model and drought identification with the variable threshold level method, time series and characteristics of drought events were analysed. I classified the drought events into six hydrological drought types that are the result of the interplay of temperature, precipitation, evapotranspiration and storage in different seasons. The most common hydrological drought type develops as a result of a rainfall deficit. However, in the development of the most severe hydrological drought events temperature and storage-related processes play an important role, for example through a lack of recovery of the drought. As I aimed to investigate drought propagation also on larger scales, I tested an ensemble mean of a number of large-scale models (both land-surface models and global hydrological models) on their ability to reproduce the drought propagation processes found in the case study areas. The large-scale models did simulate general aspects of drought propagation (e.g. fewer and longer drought events in discharge than in precipitation), but the above-mentioned effects of temperature and storage-related processes were only partly reproduced. In the large-scale model ensemble, daily runoff reacted almost immediately to changes in precipitation, resulting in important deficiencies in drought simulation in cold and semi-arid climates and regions with large storage. For the time being, this limits the use of large-scale models for the study of processes underlying drought propagation on a global scale. Consequently, I used a synthetic conceptual hydrological model to study drought propagation on the global scale. I focused on climate control by isolating forcing effects from effects of catchment properties. The drought characteristics (duration and deficit combined) of both soil moisture and subsurface discharge exhibited strongly non-linear patterns in seasonal climates. The non-linear effects in soil moisture drought were caused by the fact that the development of soil moisture droughts in warm seasonal climates is limited by the wilting point. Hydrological droughts in both warm and cold seasonal climates showed a strong increase of deficit with duration due to a lack of recovery in the dry season or snow season, respectively. This effect was strongest in cold seasonal climates, which indicates that for the development and recovery of within-year hydrological drought temperature is an important factor. The overall conclusion of this research is that, although drought is a complex, nonlinear phenomenon with drought characteristics varying with climate type and catchment characteristics, generic patterns can be derived that reflect the different hydrological processes underlying drought propagation. These processes result in different hydrological drought types that are shown to play a role both on the catchment scale and on the global scale. The non-linear effects of snow and storage-related processes on drought are not incorporated sufficiently in the currently-used large-scale models and drought indices. Possible future steps include more focus on catchment control, in particular the representation of storage, and the role of temperature and evapotranspiration. Additionally, the findings of this research can be applied to hydrological drought forecasting, prediction in ungauged basins, and prediction under global change. </p