A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE \u3e 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.t

A comprehensive review on the design and optimization of surface water quality monitoring networks

This is the final version. Available from Elsevier via the DOI in this record. The surface water quality monitoring network (WQMN) is crucial for effective water environment management. How to design an optimal monitoring network is an important scientific and engineering problem that presents a special challenge in the smart city era. This comprehensive review provides a timely and systematic overview and analysis on quantitative design approaches. Bibliometric analysis shows the chronological pattern, journal distribution, authorship, citation and country pattern. Administration types of water bodies and design methods are classified. The flexibility characteristics of four types of direct design methods and optimization objectives are systematically summarized, and conclusions are drawn from experiences with WQMN parameters, station locations, and sampling frequency and water quality indicators. This paper concludes by identifying four main future directions that should be pursued by the research community. This review sheds light on how to better design and construct WQMNs.Key-Area Research and Development Program of Guangdong ProvinceNational Natural Science Foundation of ChinaInnovation Project of Universities in Guangdong Province-Natural Scienc

Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed

Hydrological modelling in the Canadian sub-Arctic is hindered by sparse meteorological and snowpack data. The snow water equivalent (SWE) of the winter snowpack is a key predictor and driver of spring flow, but the use of SWE data in hydrological applications is limited due to high uncertainty. Global re-analysis datasets that provide gridded meteorological and SWE data may be well suited to improve hydrological assessment and snowpack simulation. To investigate representation of hydrological processes and SWE for application in hydropower operations, global re-analysis datasets covering 1979–2014 from the European Union FP7 eartH2Observe project are applied to global and local conceptual hydrological models. The recently developed Multi-Source Weighted-Ensemble Precipitation (MSWEP) and the WATCH Forcing Data applied to ERA-Interim data (WFDEI) are used to simulate snowpack accumulation, spring snowmelt volume and annual streamflow. The GlobSnow-2 SWE product funded by the European Space Agency with daily coverage from 1979 to 2014 is evaluated against in situ SWE measurement over the local watershed. Results demonstrate the successful application of global datasets for streamflow prediction, snowpack accumulation and snowmelt timing in a snowmelt-driven sub-Arctic watershed. The study was unable to demonstrate statistically significant correlations (p &lt; 0.05) among the measured snowpack, global hydrological model and GlobSnow-2 SWE compared to snowmelt runoff volume or peak discharge. The GlobSnow-2 product is found to under-predict late-season snowpacks over the study area and shows a premature decline of SWE prior to the true onset of the snowmelt. Of the datasets tested, the MSWEP precipitation results in annual SWE estimates that are better predictors of snowmelt volume and peak discharge than the WFDEI or GlobSnow-2. This study demonstrates the operational and scientific utility of the global re-analysis datasets in the sub-Arctic, although knowledge gaps remain in global satellite-based datasets for snowpack representation, for example the relationship between passive-microwave-measured SWE to snowmelt runoff volume.</p

Characterization of phenanthrenequinone-doped poly(methyl methacrylate) for holographic memory

The holographic recording characteristics of phenanthrenequinone- (PQ-) doped poly(methyl methacrylate) are investigated. The exposure sensitivity is characterized for single-hologram recording, and the il M/# is measured for samples as thick as 3 mm. Optically induced birefringence is observed in this material. (C) 1998 Optical Society of America

Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin

While the United Nations Principles of Responsible Management Education (PRME) is a very positive development in the horizon of management education over the last decade, there are still many significant challenges for engaging the mind of the manager in ways that will foster the values of PRME and the UN Global Compact. Responsible management education must address three foundational challenges in business education if it is to actualize the aspirations of PRME: 1) it must confront the cognitional myth that knowing is like looking, 2) it must move beyond mere analysis to systems thinking, and 3) it must transition from a values-neutral stance to a values-driven stance. Using Developing Sustainable Strategies, an MBA practicum in the Sustainable Management Concentration at DePaul University’s Kellstadt Graduate School of Business, as a case study, this article identifies the ways in which Pragmatic Inquiry can addresses these challenges. The method of Pragmatic Inquiry prepares students to become responsible managers, to develop sustainable strategies, and to be creators of shared value. Built from the philosophical foundations of American pragmatism and Bernard Lonergan’s critical realism, Pragmatic Inquiry is an effective method and pedagogy for responsible management education. The final publication is available at http://link.springer.co

Holographic recording in a photopolymer by optically induced detachment of chromophores

We demonstrate holographic recording in a new photopolymer system. The recording material is created by copolymerization of an optically inert monomer, methyl methacrylate, and a second monomer that is optically sensitive. On exposure of the recording material to light, a portion of the optically sensitive component detaches from the polymer matrix and causes hologram amplification through diffusion of the free molecules. We measured postrecording grating amplifications as high as 170% by this process. The recorded holograms are persistent at room temperature under continuous illumination at the recording wavelength. (C) 2000 Optical Society of America

Comparison of the recording dynamics of phenanthrenequinone-doped poly(methyl methacrylate) materials

The comparison between the NCTU and Caltech PQ-PMMA material shows that the difference in their behavior lies in the different concentration of residual MMA in the samples. Experimental evidence shows that during recording, PQ molecules attach to MMA but no diffusion takes place at room temperature. However, the excess of monomer during recording enables photoinduced polymerization as a mechanism for hologram formation leading to high diffraction efficiencies without the need of baking. The grating formed by the PQ-MMA groups is unstable and it can be erased within a few hours of baking

Oracle-based optimization applied to climate model calibration

In this paper, we show how oracle-based optimization can be effectively used for the calibration of an intermediate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle- based optimization tool, Proximal-ACCPM. The oracle is a procedure that finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a Fbest fit_ estimate over 10 times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April–June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios