Application of an improved global-scale groundwater model for water table estimation across New Zealand

Many studies underline the importance of groundwater assessment at the larger, i.e. global, scale. The groundwater models used for these assessments are dedicated to the global scale and therefore not often applied for studies in smaller areas, e.g. catchments, because of their simplifying assumptions. In New Zealand, advanced numerical groundwater flow models have been applied in several catchments. However, that application is piecemeal: only for a limited amount of aquifers and through a variety of groundwater model suites, formats, and developers. Additionally, there are large areas where groundwater models and data are sparse. Hence, an inter-catchment, inter-regional, or nationwide overview of important groundwater information, such as the water table, does not exist. The investment needed to adequately cover New Zealand with high-resolution groundwater models in a consistent approach would be significant and is therefore not considered possible at this stage. This study proposes a solution that obtains a nationwide overview of groundwater that bridges the gap between the (too-)expensive advanced local models and the (too-)simple global-scale models. We apply an existing, global-scale, groundwater flow model and improve it by feeding in national input data of New Zealand terrain, geology, and recharge, and by slight adjustment of model parametrisation and model testing. The resulting nationwide maps of hydraulic head and water table depths show that the model points out the main alluvial aquifers with fine spatial detail (200 m grid resolution). The national input data and finer spatial detail result in better and more realistic variations of water table depth than the original, global-scale, model outputs. In two regional case studies in New Zealand, the hydraulic head shows excellent correlation with the available groundwater level data. Sensitivity and other analyses of our nationwide water tables show that the model is mostly driven by recharge, model resolution, and elevation (gravity), and impeded by the geology (permeability). The use of this first dedicated New Zealand-wide model can aid in provision of water table estimates in data-sparse regions. The national model can also be used to solve inconsistency of models in areas of trans-boundary aquifers, i.e. aquifers that cover more than one region in New Zealand. Comparison of the models, i.e. the national application (National Water Table model: NWT) with the global model (Equilibrium Water Table model: EWT), shows that most improvement is achieved by feeding in better and higher-resolution input data. The NWT model still has a bias towards shallow water tables (but less than the EWT model because of the finer model resolution), which could only be solved by feeding in a very high resolution terrain model that incorporates drainage features. Although this is a model shortcoming, it can also be viewed as a valuable indicator of the pre-human water table, i.e. before 90 % of wetlands were drained for agriculture since European settlement in New Zealand. Calibration to ground-observed water level improves model results but can of course only work where there are such data available. Future research should therefore focus on both model improvements and more data-driven, improved estimation of hydraulic conductivity, recharge, and the digital elevation model. We further surmise that the findings of this study, i.e. successful application of a global-scale model at smaller scales, will lead to subsequent improvement of the global-scale model equationsThis research has been part of a PhD study of the lead author at the University of Waikato, New Zealand, supervised by Moira Steyn-Ross. It has been performed as part of the Smart Aquifer Characterisation (SAC) Programme, funded by the Ministry of Business, Innovation and Employment, New Zealand. This project has received co-funding from the European Union's Seventh Framework Programme for Research and Technological Development under grant agreement no. 603608, eartH2Observe. We furthermore would like to thank the reviewers for their valuable comments, Waikato Regional Council and Environment Canterbury for their ground-observed data used in the results section, and Jeremy White (GNS Science) for his advice on the sensitivity analysesS

TOWARD GLOBAL DROUGHT EARLY WARNING CAPABILITY: Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

The need for a global drought early warning framework. Drought has had a significant impact on civilization throughout history in terms of reductions in agricultural productivity, potable water supply, and economic activity, and in extreme cases this has led to famine. Every continent has semiarid areas, which are especially vulnerable to drought. The Intergovernmental Panel on Climate Change has noted that average annual river runoff and water availability are projected to decrease by 10%–13% over some dry and semiarid regions in mid and low latitudes, increasing the frequency, intensity, and duration of drought, along with its associated impacts. The sheer magnitude of the problem demands efforts to reduce vulnerability to drought by moving away from the reactive, crisis management approach of the past toward a more proactive, risk management approach that is centered on reducing vulnerability to drought as much as possible while providing early warning of evolving drought conditions and possible impacts. Many countries, unfortunately, do not have adequate resources to provide early warning, but require outside support to provide the necessary early warning information for risk management. Furthermore, in an interconnected world, the need for information on a global scale is crucial for understanding the prospect of declines in agricultural productivity and associated impacts on food prices, food security, and potential for civil conflict

Toward Global Drought Early Warning Capability - Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

Drought has had a significant impact on civilization throughout history in terms of reductions in agricultural productivity, potable water supply, and economic activity, and in extreme cases this has led to famine. Every continent has semiarid areas, which are especially vulnerable to drought. The Intergovernmental Panel on Climate Change has noted that average annual river runoff and water availability are projected to decrease by 10 percent-13 percent over some dry and semiarid regions in mid and low latitudes, increasing the frequency, intensity, and duration of drought, along with its associated impacts. The sheer magnitude of the problem demands efforts to reduce vulnerability to drought by moving away from the reactive, crisis management approach of the past toward a more proactive, risk management approach that is centered on reducing vulnerability to drought as much as possible while providing early warning of evolving drought conditions and possible impacts. Many countries, unfortunately, do not have adequate resources to provide early warning, but require outside support to provide the necessary early warning information for risk management. Furthermore, in an interconnected world, the need for information on a global scale is crucial for understanding the prospect of declines in agricultural productivity and associated impacts on food prices, food security, and potential for civil conflict. This paper highlights the recent progress made toward a Global Drought Early Warning Monitoring Framework (GDEWF), an underlying partnership and framework, along with its Global Drought Early Warning System (GDEWS), which is its interoperable information system, and the organizations that have begun working together to make it a reality. The GDEWF aims to improve existing regional and national drought monitoring and forecasting capabilities by adding a global component, facilitating continental monitoring and forecasting (where lacking), and improving these tools at various scales, thereby increasing the capacity of national and regional institutions that lack drought early warning systems or complementing existing ones. A further goal is to improve coordination of information delivery for drought-related activities and relief efforts across the world. This is especially relevant for regions and nations with low capacity for drought early warning. To do this requires a global partnership that leverages the resources necessary and develops capabilities at the global level, such as global drought forecasting combined with early warning tools, global real-time monitoring, and harmonized methods to identify critical areas vulnerable to drought. Although the path to a fully functional GDEWS is challenging, multiple partners and organizations within the drought, forecasting, agricultural, and water-cycle communities are committed to working toward its success

Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand

A nationwide model of groundwater recharge for New Zealand (NGRM), as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014) provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation) in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m 3 /s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future collaborative research associated with the NGRM model include: improvement of rainfall-runoff models, establishment of snowmelt and river recharge modules, further improvement of estimates of rainfall and AET, and satellite-derived AET in irrigated areas. Importantly, the quantification of uncertainty, which should be associated with all future models, should give further impetus to field measurements of rainfall recharge for the purpose of model calibration

Explanation of InSAR Phase Disturbances by Seasonal Characteristics of Soil and Vegetation

Seasonal phase disturbances in satellite Interferometric Synthetic Aperture Radar (InSAR) measurements have been reported in other studies to suggest sub-centimetre land surface terrain motion. These have been interpreted in various ways because they correlate with multiple other (sub-)seasonal signatures of, e.g., clay swelling/shrinkage and groundwater level. Recent microwave radar studies mention the occurrence of phase disturbances in different soil types and soil moisture. This study further explored this topic by modeling phase disturbances caused by both soil and vegetation surface characteristics and aimed to interpret what their possible effects on InSAR-interpreted terrain motion is. Our models, based on fundamental microwave reflection and transmission theory, found phase disturbances caused by seasonal variation of soil and vegetation that have the same magnitude as interpreted seasonal land movement in earlier InSAR studies. We showed that small, temporal differences in soil moisture and vegetation can lead to relatively large phase disturbances in InSAR measurements. These disturbances are a result of waves having to comply with boundary conditions at the interface between media with different dielectric properties. The findings of this study explain the seasonal variations found in other InSAR studies and will therefore bring new insights and alternative explanations to help improve interpretation of InSAR-derived seasonal terrain motion

Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand

A nationwide model of groundwater recharge for New Zealand (NGRM), as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014) provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation) in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m 3 /s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future collaborative research associated with the NGRM model include: improvement of rainfall-runoff models, establishment of snowmelt and river recharge modules, further improvement of estimates of rainfall and AET, and satellite-derived AET in irrigated areas. Importantly, the quantification of uncertainty, which should be associated with all future models, should give further impetus to field measurements of rainfall recharge for the purpose of model calibration

Automated data processing of a large-scale airborne time-domain electromagnetic survey by a deep learning expert system

<p>The new generation of airborne electromagnetic (AEM) surveys yield large data sets of thousands of line kilometres. Parts of these data are often contaminated by noise from various sources, e.g. fences, power lines, which corrupts the data to a degree that it can no longer be used. The problem intensifies in urban areas where the risk of data corruption is highest due to dense infrastructure. The inversion of corrupted data risks interpreting spurious subsurface features and flawed geological interpretations. Therefore, in many cases, the corrupted data is identified and culled prior to inversion. This process of culling corrupted data is generally a manual task requiring specialists to examine the data in detail, which is an extremely complex and time-consuming process. Recently, we proposed a deep learning expert system to automate the complex AEM data processing workflows. The proposed method uses a deep convolutional auto-encoder to identify corrupted data, and was trained such that it generalises to diverse geological conditions and various survey areas. In this study, we investigate the generalisation capabilities of our deep learning method on a large AEM survey area in Northland, New Zealand. Our approach takes ~ 600s to process 3984 line kilometres of data and displays strong spatial correlation for the data identified as corrupted. The inversion results show very few potential anomalies in the model space which are being inspected by a manual operator. In general, the proposed approach is generalisable and displays high-quality data processing within short amounts of time, which requires minimal further quality inspection.</p><p>Open-Access Online Publication: October 30, 2023</p&gt

Group on Earth Observations (GEO) global drought early warning information service

Presents a collection of slides covering the following topics: Global Drought Observing Subsystem; Drought Early Warning Information Service; Global Drought Monitor Portal; Global Drought Observing Subsystem; geographic information system; regional drought monitors; continental drought monitors; drought management