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When nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The Lower Mississippi river basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate-nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region. [ABSTRACT FROM AUTHOR]

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex watershed processes and their responses to a changing climate. However, calibrating these simulators to observed data remains a significant challenge due to several persistent issues including: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified model representations of complex natural hydrologic processes. Here we demonstrate how Simulation-Based Inference (SBI) can help address both these challenges for parameter estimation. SBI uses a learned mapping between parameter space and observed data to estimate parameters for generation of calibrated model simulations. To demonstrate the potential of SBI in hydrologic modelling, we conduct a set of synthetic experiments to infer two common physical parameters, Manning's coefficient and hydraulic conductivity, using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep learning (DL) components to the SBI approach, including an 'emulator' as a surrogate for the process-based simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that where uncertainty about model structure is significant, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework to cases where model structure(s) may not be adequate are introduced using a performance-weighting approach. [ABSTRACT FROM AUTHOR]

Following the recent development trend in the struggle for cleaning the earth’s environment, solar is the one of most promising area that can partially be used as a replaceable energy from non-renewable fuel sources. As such, it plays a significant role in protecting the environment from global warming. As solar power does not emit harmful gases into the atmosphere, its production, distribution, setup, and operation are vital should the production remain constant. Even solar energy waste emissions are small; when compared to current energy sources, the amount of harmful gases is negligible. This paper presented an integrated approach for site of solar plants by using data envelopment analysis (DEA) and Fuzzy Analytical Network Process (FANP). Furthermore, these integrated methodologies, incorporated with the most relevant parameters of requirements for solar plants, are introduced. First, the paper considers an integrated hierarchical DEA and FANP model for the optimal geographical location of solar plants in Mekong Delta Region, Vietnam. Using the proposed model for implementation would allow the renewable energy policy makers to select and control the optimal location for allocating and constructing a solar energy power plant in Vietnam. This is the preferred strategy for location optimization problems associated with solar plant units in Vietnam and around the world.

High-resolution, spatially-distributed process-based models are a well-established tool to explore complex watershed processes and how they may evolve under a changing climate. While these models are powerful, calibrating them can be difficult because they are costly to run and have many unknown parameters. To solve this problem, we need a state-of-the-art, data- driven approach to model calibration that can scale to the high-compute, high-dimensional hydrologic simulators that drive innovation in our field today. Simulation- Based Inference (SBI) uses deep learning methods to learn a probability distribution of simulation parameters by comparing simulator outputs to observed data. The inferred parameters can then be used to run calibrated model simulations. This approach has pushed boundaries in simulator-intensive research from cosmology, particle physics, and neuroscience, but is less familiar to hydrology. The goal of this paper is to introduce SBI to the field of watershed modeling by benchmarking and exploring its performance in a set of synthetic experiments. We use SBI to infer two common physical parameters of hydrologic process-based models, Manning’s Coefficient and Hydraulic Conductivity, in a snowmelt-dominated catchment in Colorado, USA. We employ a process-based simulator (ParFlow), streamflow observations, and several deep learning components to confront two recalcitrant issues related to calibrating watershed models: 1) the high cost of running enough simulations to do a calibration; 2) finding ‘correct’ parameters when our understanding of the system is uncertain or incomplete. In a series of experiments, we demonstrate the power of SBI to conduct rapid and precise parameter inference for model calibration. The workflow we present is general-purpose, and we discuss how this can be adapted to other hydrology-related problems. [ABSTRACT FROM AUTHOR]