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&thinsp;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&thinsp;% 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 equations.</p

On the assessment of the moisture transport by the Great Plains low-level jet

Low-level jets (LLJs) can be defined as wind corridors of anomalously high wind speed values located within the first kilometre of the troposphere. These structures are one of the major meteorological systems in the meridional transport of moisture on a global scale. In this work, we focus on the southerly Great Plains low-level jet, which plays an important role in the moisture transport balance over the central United States. The Gulf of Mexico is the main moisture source for the Great Plains low-level jet (GPLLJ), which has been identified as a key factor for rainfall modulation over the eastern and central US. The relationship between moisture transport from the Gulf of Mexico to the Great Plains and precipitation has been well documented in previous studies. Nevertheless, a large uncertainty still remains in the quantification of the moisture amount actually carried by the GPLLJ. The main goal of this work is to address this question. For this purpose, a relatively new tool, the regional atmospheric Weather Research and Forecasting Model with 3-D water vapour tracers (WRF-WVT; Insua-Costa and Miguez-Macho, 2018) is used together with the Lagrangian model FLEXPART to estimate the load of precipitable water advected within the GPLLJ. Both models were fed with data from ERA Interim. From a climatology of jet intensity over a 37-year period, which follows a Gaussian distribution, we select five cases for study, representing the mean and 1 and 2 standard deviations above and below it. Results show that the jet is responsible for roughly 70&thinsp;%–80&thinsp;% of the moisture transport occurring in the southern Great Plains when a jet event occurs. Furthermore, moisture transport by the GPLLJ extends to the north-east US, accounting for 50&thinsp;% of the total in areas near the Great Lakes. Vertical distributions show the maximum of moisture advected by the GPLLJ at surface levels and maximum values of moisture flux about 500&thinsp;m above, in coincidence with the wind speed profile.</p

On the Long-Term Hydroclimatic Sustainability of Perennial Bioenergy Crop Expansion over the United States

Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only over suitable abandoned and degraded farmlands to avoid competition with existing food cropping systems. Results show that near-surface cooling (locally, up to 5°C) is greatest during the growing season over portions of the central United States. For some regions, principal impacts are restricted to a reduction in near-surface temperature (e.g., eastern portions of the United States), whereas for other regions deployment leads to soil moisture reduction in excess of 0.15–0.2 m3 m−3 during the simulated 10-yr period (e.g., western Great Plains). This reduction (~25%–30% of available soil moisture) manifests as a progressively decreasing trend over time. The large-scale focus of this research demonstrates the long-term hydroclimatic sustainability of large-scale deployment of perennial bioenergy crops across the continental United States, revealing potential hot spots of suitable deployment and regions to avoid

The concurrence of atmospheric rivers and explosive cyclogenesis in the North Atlantic and North Pacific basins

Abstract. The explosive cyclogenesis of extratropical cyclones and the occurrence of atmospheric rivers are characteristic features of a baroclinic atmosphere, and are both closely related to extreme hydrometeorological events in the mid-latitudes, particularly on coastal areas on the western side of the continents. The potential role of atmospheric rivers in the explosive cyclone deepening has been previously analysed for selected case studies, but a general assessment from the climatological perspective is still missing. Using ERA-Interim reanalysis data for 1979–2011, we analyse the concurrence of atmospheric rivers and explosive cyclogenesis over the North Atlantic and North Pacific basins for the extended winter months (ONDJFM). Atmospheric rivers are identified for almost 80 % of explosive deepening cyclones. For non-explosive cyclones, atmospheric rivers are found only in roughly 40 % of the cases. The analysis of the time evolution of the high values of water vapour flux associated with the atmospheric river during the cyclone development phase leads us to hypothesize that the identified relationship is the fingerprint of a mechanism that raises the odds of an explosive cyclogenesis occurrence and not merely a statistical relationship. These new insights on the relationship between explosive cyclones and atmospheric rivers may be helpful to a better understanding of the associated high-impact weather events

Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea

In this paper, the prediction skills of five ensemble methods for temperature and precipitation are discussed by considering 20 yr of simulation results (from 1989 to 2008) for four regional climate models (RCMs) driven by NCEP-Department of Energy and ECMWF Interim Re-Analysis (ERA-Interim) boundary conditions. The simulation domain is the Coordinated Regional Downscaling Experiment (CORDEX) for East Asia. and the number of grid points is 197 x 233 with a 50-km horizontal resolution. Three new performance-based ensemble averaging (PEA) methods are developed in this study using 1) bias, root-mean-square errors (RMSEs) and absolute correlation (PEA_BRC). RMSE and absolute correlation (PEA RAC), and RMSE and original correlation (PEA_ROC). The other two ensemble methods are equal-weighted averaging (EWA) and multivariate linear regression (Mul_Reg). To derive the weighting coefficients and cross validate the prediction skills of the five ensemble methods. the authors considered 15-yr and 5-yr data, respectively, from the 20-yr simulation data. Among the five ensemble methods, the Mul_Reg (EWA) method shows the best (worst) skill during the training period. The PEA_RAC and PEA_ROC methods show skills that are similar to those of Mul_Reg during the training period. However, the skills and stabilities of Mul_Reg were drastically reduced when this method was applied to the prediction period. But, the skills and stabilities of PEA_RAC were only slightly reduced in this case. As a result. PEA RAC shows the best skill, irrespective of the seasons and variables, during the prediction period. This result confirms that the new ensemble method developed in this study. PEA_RAC. can be used for the prediction of regional climate.open7

Regional climate modeling for Asia

The regional climate model (RCM) with higher resolution and sophisticated physical processes can reproduce and project fine-scale climate information, which cannot be captured by the global climate model (GCM). Therefore, we developed the Seoul National University Regional Climate Model (SNURCM) in the 1990s to simulate the intrinsic and detailed climate prevailing in Asia. In this study, we reviewed the developmental processes of the SNURCM and its application researches. In the simulation of regional climate over Asia, systematic errors can be generated because of natural characteristics such as complex land-surface conditions and topography, warm ocean conditions, and strong seasonal monsoon circulation and convection. Numerous methods and techniques have been applied to reduce these errors and improve the SNURCM. For long-term simulations without climate drift, the spectral nudging technique as well as the traditional relaxation method was employed for the boundary conditions. To represent reasonable interactions between earth systems, a simple ocean model and an advanced land-surface model were implemented into the SNURCM. Physical schemes for precipitation and vertical diffusion developed for short-term numerical weather prediction models were optimized or improved for long-term simulation. The SNURCM has been applied to future climate projection, reproduction of extreme climate, and seasonal forecasting. Furthermore, the model has served as a part of the multi-model comparison program and an ensemble of international research programs

A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

A new moisture tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with a 1-month long simulation over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50 and 60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socioeconomic impacts