Resilient planning optimization through spatially explicit, Bi-directional sociohydrological modeling.

Stormwater runoff is one critical urban issue that exemplifies the complexity in coupling human and natural systems. Innumerable studies have described and assessed the hydrological responses that result from land-use changes through a 'post land use change' hydrological analysis. Complex systems theory, however, suggests that the urban and ecological systems operate as an intertwined whole. This means that typical one-directional analysis can miss critical components of a bi-directional sociohydrological process. In addition, there is a difference in physical scales between hydrological analysis and policymaking that is often left unresolved. Typical hydrological models are limited to a watershed and are not easily applied to policymaking that is generally demarcated by a political boundary. These types of models also lack the spatial explicitness needed for physical design responses. To address these issues, we develop an integrated, finely scaled, spatially explicit sociohydrological modeling system. The coupled land use/stormwater model projects and assesses bi-directional sociohydrological impacts to changing land uses. We apply and test the system in McHenry County, Illinois, by modeling three scenarios to the year 2045. The results show that residential and commercial developments exhibit different responses to hydrological variables, resulting in varying patterns of land use locational choices. We also find that there is a conflict between developmental preferences that prefer to be located near water (housing) and those that prefer to be located away from runoff-prone water areas (commercial land uses). Our bi-directional modeling system simulates cell-to-cell interactions to produce quantifiable and practically useful outputs. The output for McHenry County, Illinois, includes specific, locational information on how to optimize developmental regulations in response to the contradictory developmental preferences and, more importantly, how to live with runoff in the context of resilience. This research supports the need for cell-based forward-looking modeling to better understand complex urban systems and strategically establish a resilient built environment.

A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP.

Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.

Linking terrestrial biogeochemical processes and water ages to catchment water quality: A new Damköhler analysis based on coupled modeling of isotope tracers and nitrate dynamics.

Catchment-scale nitrate dynamics involve complex coupling of hydrological transport and biogeochemical transformations, imposing challenges for source control of diffuse pollution. The Damköhler number (Da) offers a dimensionless dual-lens concept that integrates the timescales of exposure and processing, but quantifying both timescales in heterogeneous catchments remains methodologically challenging. Here, we propose a novel spatio-temporal framework for catchment-scale quantification of Da based on the ecohydrological modeling platform EcH2O-iso that coupled isotope-aided water age tracking and nitrate modeling. We examined Da variability of soil denitrification in the heterogeneous Selke catchment (456 km2, central Germany). Results showed that warm-season soil denitrification was of catchment-wide significance (Da >1), while its high spatial variations were co-determined by varying exposure times and removal efficiencies (e.g., channel-connected lowland areas are hotspots). Moreover, Da seasonally shifted from processing-dominance to transport-dominance during the wet-spring season (from >1 to <1), implying important linkages between summer terrestrial denitrification and subsequent winter river water quality. Under the prolonged 2018-2019 droughts, denitrification removal generally reduced, resulting in further accumulation in agricultural soils. Moreover, the space-time responses of Da variability indicated important implications for catchment water quality. The older water in lowland areas exhibited extra risks of groundwater contamination, whilst agricultural areas in the hydrologically responsive uplands became sensitive hotspots for export and river water pollution. Importantly, the lowland pixels intersecting river channels exhibited high removal efficiencies, as well as high resilience to the disturbances (wet-spring Da shifted to >1 under drought conditions). The proposed catchment-wide Da framework is implied by mechanistic modeling, which is transferable across various environmental conditions. This could shed light on understanding of catchment N processes, and thus providing site-specific implications of non-point source pollution controls.

Coupled model for microbial growth and phase mass transfer in pressurized batch reactors in the context of underground hydrogen storage.

A rising interest in a strong hydrogen economy as a part of the future net-zero economy results in an increasing necessity to store hydrogen as a raw material or an energy carrier. Experience and studies show that storing hydrogen in deep underground sites could enable microbial conversion of hydrogen. To predict and examine the loss of hydrogen, laboratory studies, and analysis are essential. A growth model is required to interpret batch or chemostat experiments. With this model, the parameters of microbial growth, and the conversion of hydrogen can be specified. This study presents experiments with methanogens and a hydrogen/carbon dioxide gas mixture performed in batch reactors. Further, the microbial growth was modeled by a double Monod model with hydrogen and carbon dioxide as the limiting substrates. As the amount of carbon dioxide dissolved in the water phase can not be neglected, both phases were considered in the proposed model. The mass-transfer rate between the gas and water phase was implemented by a linear relation including the concentrations in both phases and the mass-transfer coefficient. With the resulting coupled model, it was possible to match the pressure behavior in the reactor and conclude the microbial growth kinetics. Two types of methanogenic species were tested to validate the model. The mass transfer coefficient proves to impact the growth behavior in porous media. The mathematical model and experimental data are necessary to determine the growth rate and yield coefficient.

Model behavior regarding in- and below-cloud 137Cs wet scavenging following the Fukushima accident using 1-km-resolution meteorological field data.

Wet deposition remains an important source of uncertainty in modeling of the atmospheric transport of 137Cs following the Fukushima Daiichi Nuclear Power Plant accident. Its behavior is often difficult to investigate owing to the limited resolution of meteorological field data and inconsistent model implementations. This study investigated the detailed behavior of 25 combinations of in- and below-cloud wet scavenging models using high-resolution (1 km × 1 km) meteorological input. These combinations were all implemented in the Weather Research and Forecasting-Chemistry model, thereby enabling consistent evaluation. The 1-km-resolution simulations were compared with simulations obtained previously using 3-km-resolution meteorological field data. Results revealed that rainfall of <1 mm/h is critical for simulation accuracy. The 1-km results revealed better representation of rainfall than that revealed by the 3-km results, but with spatiotemporal variability in accuracy. Owing to their sensitivity to rainfall, single-parameter wet deposition models showed improvements in performance in the 1-km simulations relative to that in the 3-km simulations. The multiparameter models showed more robust performance in terms of both simulations, and the Roselle-Mircea model presented the best performance among the 25 models considered. Wind transport showed substantial influence on the removal of atmospheric 137Cs, and it was nonnegligible even during periods in which wet deposition was dominant. The 1-km-resolution simulations effectively reproduced local-scale 137Cs concentrations but with deviations in timing, mainly because of biased wind direction. These findings indicate the necessity for a refined wind and dispersion model for local-scale simulation of 137Cs concentration.

Coupled modeling of in- and below-cloud wet deposition for atmospheric 137Cs transport following the Fukushima Daiichi accident using WRF-Chem: A self-consistent evaluation of 25 scheme combinations.

Wet deposition, including both in- and below-cloud scavenging, is critical for the atmospheric transport modeling of 137Cs following the Fukushima Daiichi Nuclear power plant (FDNPP) accident. Although intensively investigated, wet deposition simulation is still subject to uncertainties of meteorological inputs and wet scavenging modeling, leading to biased 137Cs transport prediction. To reduce the dual uncertainties, in- and below-cloud wet scavenging schemes of 137Cs were simultaneously integrated into Weather Research and Forecasting-Chemistry (WRF-Chem), yielding online coupled modeling of meteorology and the two wet scavenging processes. The integration was performed using 25 combinations of different in- and below-cloud schemes, covering most schemes in the literature. Two microphysics schemes were also tested to better reproduce the precipitation. The 25 models and the ensemble mean of 9 representative models were systematically compared with the below-cloud-only WRF-Chem model, using the cumulative deposition and atmospheric concentrations of 137Cs measurements. The results reveal that, with the Morrison's double moment cloud microphysics scheme, the developed models could better reproduce the rainfall and substantially improve the cumulative deposition simulation. The in-cloud scheme is influential to the model behaviors and those schemes considering cloud parameters also improve the atmospheric concentration simulations, whereas the others solely dependent on the rain intensity are sensitive to meteorology. The ensemble mean achieves satisfactory performance except one plume event, but still outperforms most models.

Integrating fast and slow processes is essential for simulating human-freshwater interactions.

Integrated modeling is a critical tool to evaluate the behavior of coupled human-freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human-freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human-freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.

Performance analysis of rigorous coupled-wave analysis and its integration in a coupled modeling approach for optical simulation of complete heterojunction silicon solar cells.

A variety of light management structures have been introduced in solar cells to improve light harvesting and further boost their conversion efficiency. Reliable and accurate simulation tools are required to design and optimize the individual structures and complete devices. In the first part of this paper, we analyze the performance of rigorous coupled-wave analysis (RCWA) for accurate three-dimensional optical simulation of solar cells, in particular heterojunction silicon (HJ Si) solar cells. The structure of HJ Si solar cells consists of thin and thick layers, and additionally, micro- and nano-textures are also introduced to further exploit the potential of light trapping. The RCWA was tested on the front substructure of the solar cell, including the texture, thin passivation and contact layers. Inverted pyramidal textures of different sizes were included in the simulations. The simulations rapidly converge as long as the textures are small (in the (sub)micrometer range), while for larger microscale textures (feature sizes of a few micrometers), this is not the case. Small textures were optimized to decrease the reflectance, and consequently, increase the absorption in the active layers of the solar cell. Decreasing the flat parts of the texture was shown to improve performance. For simulations of structures with microtextures, and for simulations of complete HJ Si cells, we propose a coupled modeling approach (CMA), where the RCWA is coupled with raytracing and the transfer matrix method. By means of CMA and nanotexture optimization, we show the possible benefits of nanotextures at the front interface of HJ Si solar cells, demonstrating a 13.4% improvement in the short-circuit current density with respect to the flat cell and 1.4% with respect to the cell with double-sided random micropyramids. We additionally demonstrate the ability to simulate a combination of nano- and microtextures at a single interface, although the considered structure did not show an improvement over the pyramidal textures.

Physical-biological coupling induced aggregation mechanism for the formation of high biomass red tides in low nutrient waters.

Port Shelter is a semi-enclosed bay in northeast Hong Kong where high biomass red tides are observed to occur frequently in narrow bands along the local bathymetric isobars. Previous study showed that nutrients in the Bay are not high enough to support high biomass red tides. The hypothesis is that physical aggregation and vertical migration of dinoflagellates appear to be the driving mechanism to promote the formation of red tides in this area. To test this hypothesis, we used a high-resolution estuarine circulation model to simulate the near-shore water dynamics based on in situ measured temperature/salinity profiles, winds and tidal constitutes taken from a well-validated regional tidal model. The model results demonstrated that water convergence occurs in a narrow band along the west shore of Port Shelter under a combined effect of stratified tidal current and easterly or northeasterly wind. Using particles as dinoflagellate cells and giving diel vertical migration, the model results showed that the particles aggregate along the convergent zone. By tracking particles in the model predicted current field, we estimated that the physical-biological coupled processes induced aggregation of the particles could cause 20-45 times enhanced cell density in the convergent zone. This indicated that a high cell density red tide under these processes could be initialized without very high nutrients concentrations. This may explain why Port Shelter, a nutrient-poor Bay, is the hot spot for high biomass red tides in Hong Kong in the past 25 years. Our study explains why red tide occurrences are episodic events and shows the importance of taking the physical-biological aggregation mechanism into consideration in the projection of red tides for coastal management.