Coupled Modeling Framework Research Articles

A coupled modeling framework for investigating flow maldistribution in cross-flow-corrugated plate heat exchangers

Accurate prediction of the impact of flow maldistribution on the thermo-hydraulic behavior of plate heat exchangers is important for thermal design and optimization in a number of practical applications. In this paper, a coupled modeling framework, consisting of a heat transfer model and a hydraulic model, is proposed for quantitatively evaluating the impact of maldistribution on the thermo-hydraulic performance of a cross-flow-corrugated plate heat exchanger. The numerical results of the proposed approach are validated by comparison with experimental data. Using the coupled model, the flow distribution nonuniformity, heat exchanger effectiveness deteriorations, and pressure drop deviations are analyzed. Furthermore, the combined effects of side stream maldistribution are investigated for different inlet Reynolds numbers and inlet temperatures. The results demonstrate that when the inlet gas Reynolds number is in the range of 1100–2700, nonuniform distribution of the gas side will cause air maldistribution, and a 10%–30% effectiveness deterioration rate. It is found that air maldistribution will not result in gas maldistribution when the inlet air Reynolds number is in the range of 6000–18 000, but increasing air nonuniformity. Will lead to increasing deterioration in heat exchanger effectiveness. In the presence of air and gas flow maldistribution, the pressure drops on the gas and air sides increase by 5% and less than 12.4%, respectively. This work provides a computational framework for optimization of cross-flow-corrugated plate heat exchangers to achieve desired thermo-hydraulic performance.

The Relative Importance of Antarctic Sea Ice Loss within the Response to Greenhouse Warming

Abstract The response to Antarctic sea ice loss within a coupled modeling framework is examined in comparison to the response to Arctic sea ice loss and within the context of general greenhouse warming. Sea ice loss responses are found to be linear (particularly in response to Antarctic or global sea ice loss) with respect to the degree of imposed perturbation and additive when perturbations are applied in hemispheres separately and concurrently. Globally, and in the tropical Pacific in particular, Antarctic sea ice loss plays a relatively larger role than Arctic sea ice loss in both the atmosphere and the ocean, within the parameters of our experiments. The pattern of response to Antarctic sea ice loss is also found to more closely resemble that of greenhouse warming, again particularly in the tropics. An extension to multiparameter pattern scaling is developed to include a scaling factor for Antarctic change in addition to those for tropical warming and Arctic sea ice loss. The decomposition is applied to the modeled response to Antarctic sea ice loss to break it down into component partial responses that scale with Antarctic, tropical, and Arctic changes. This reveals the aspects of the response that are directly related to Antarctic change, such as an equatorward intensification of tropical precipitation in the Northern Hemisphere, and those that are modified via the induced changes in the tropics and Arctic, such as Northern Hemisphere temperature change. With this, we hope to gain a deeper understanding of the role of each of these changes for the development of physical mechanisms of the response.

The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2

Abstract. A new weakly coupled land data assimilation (WCLDA) system based on the four-dimensional ensemble variational (4DEnVar) method is developed and applied to the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2). The dimension-reduced projection four-dimensional variational (DRP-4DVar) method is employed to implement 4DVar using the ensemble technique instead of the adjoint technique. With an interest in providing initial conditions for decadal climate predictions, monthly mean anomalies of soil moisture and temperature from the Global Land Data Assimilation System (GLDAS) reanalysis from 1980 to 2016 are assimilated into the land component of E3SMv2 within the coupled modeling framework with a 1-month assimilation window. The coupled assimilation experiment is evaluated using multiple metrics, including the cost function, assimilation efficiency index, correlation, root-mean-square error (RMSE), and bias, and compared with a control simulation without land data assimilation. The WCLDA system yields improved simulation of soil moisture and temperature compared with the control simulation, with improvements found throughout the soil layers and in many regions of the global land. In terms of both soil moisture and temperature, the assimilation experiment outperforms the control simulation with reduced RMSE and higher temporal correlation in many regions, especially in South America, central Africa, Australia, and large parts of Eurasia. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought, highlighting the crucial role of land surface in drought lifecycle. The WCLDA system is intended to be a foundational resource for research to investigate land-derived climate predictability.

The Future of Developed Barrier Systems: 1. Pathways Toward Uninhabitability, Drowning, and Rebound

AbstractMany barrier islands and spits (collectively, “barriers”) throughout the world are highly developed. As low‐lying, sandy coastal landforms, barrier systems are naturally reshaped by processes associated with storms and sea‐level rise (SLR). The resulting landscape changes threaten development, and in response, humans employ defensive measures that physically modify barrier geometry to reduce relatively short‐term risk. These measures include the construction of large dunes, emplacement of beach nourishment, and removal of washover. Simulations conducted using a new coupled modeling framework show that, over decades to centuries, measures to protect roadways and communities alter the physical characteristics of barrier systems in ways that ultimately limit their habitability. We find that the pathway toward uninhabitability (via roadway drowning or community narrowing) and future system states (drowning or rebound) depends largely on dune management—because building dunes blocks overwash delivery to the barrier interior—and on initial conditions (barrier elevation and width). In the model, barriers can become lower and narrower with SLR to the point of drowning. The timing and occurrence of barrier drowning depends on randomness in the timing and intensity of storms and dune recovery processes. We find that under a constant rate of SLR, negative feedbacks involving storms can allow barriers that do not drown to rebound toward steady‐state geometries within decades after management practices cease.

Coastal Compound Flood Simulation through Coupled Multidimensional Modeling Framework

Coastal watersheds are prone to compound flooding caused by heavy rainfall, storm surge, and high tide. Accurate prediction and modeling of coastal compound floods (CCF) require precise physical representation in numerical models with appropriate numerical approximation associated with multi-driver physical processes for simulating the flood dynamics. To address this, an integrated and tightly coupled modeling approach is implemented to capture the nonlinear and complex processes involved in CCF and interconnectivity among Multidimensional Hydraulics (pipe and channel), Hydrodynamics (2D overland flow), and distributed Hydrologic (MH3) models are developed. The coupled modeling framework focuses on the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. This framework effectively incorporates the complex drainage network system of tidal creeks, tidal channels, underground sewer networks, and detention ponds of the Charleston Peninsula of South Carolina (SC). The dynamics of the urbanized floodplain in the peninsula are represented using high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM). The overland flow is simulated using energy balance, momentum balance, and diffusive wave methods. The performance of the CCF model is tested for the 2015 SC historical flood and a high tide flood event. The momentum balance-based CCF model shows 98.35% accuracy in identifying street-level flooding locations. Furthermore, the model shows that incorporating DSM processing in the current study improves the accuracy of urban flood simulation by approximately 15% to 33% compared to LiDAR DEM. It is revealed that the momentum balance method outperforms to simulate the flood dynamics. Consequently, this study contributes to the modeling of CCF by developing an integrated MH3 system that enhances the physical representation of coastal hydrology at a fine scale.

Coupled thermo–geophysical inversion for permafrost monitoring

Abstract. This study explores an alternative way of deriving soil thermal properties from surface geophysical measurements. We combined ground surface temperature time series with time lapse geoelectrical acquisitions measured from the ground surface in a fully coupled inversion scheme to calibrate a heat conduction model. The quantitative link between the thermal and geoelectrical parts of the modeling framework is the temperature-dependent unfrozen water content, which is also the main factor influencing electrical response of the ground. The apparent resistivity data were incorporated into the coupled framework without being inverted separately, thus reducing the uncertainty inevitably associated with inverted resistivity models. We show that geoelectrical time lapse data are useful as alternative calibration data and can provide as good results as borehole temperature measurements. The fully coupled modeling framework using field data achieved performance comparable to calibration on borehole temperature records in terms of model fit within 0.6 ∘C, inversion convergence metrics, as well as the predictive performance of the calibrated model.

Evaluation of a regional crop model implementation for sub-national yield assessments in Kenya

CONTEXTCropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVEThis study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODSThe RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONSThe median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ∼1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCEOne of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

A coupled modeling framework to screen reclaimed water supplement schemes in an urban watershed

Study regionThe Daitou Creek Watershed in Tong’an District, Xiamen City, southeast coastal China Study focusReclaiming and reusing wastewater is a viable strategy to mitigate water shortages and improve water quality in riverine ecosystems. There is an urgent need for modeling approaches to provide data support for the implementation of river management strategies. A coupled modeling framework, integrating Soil and Water Assessment Tool (SWAT), Qual2K, and scenario analysis, was developed in this study to assess the potential of reclaimed water supplement in Daitou Creek Watershed during different seasons. New hydrological insight for the regionThe utilization of proposed framework has resulted in the successful reproduction of riverine streamflow and nutrient dynamics across different seasons. An optimal reclaimed water supplement scheme was identified for remediation of riverine water quality in Daitou Creek. Water quality standards can be attained with the following minimum total volume of reclaimed water supplement upstream and downstream: 3 × 104 m3/d and 7.5 × 104 m3/d in wet season, 2 × 104 m3/d and 10 × 104 m3/d in average season, and 3 × 104 m3/d and 10 × 104 m3/d in dry season, respectively. Total phosphorus represents the dominant limiting factor in achieving water quality restoration, requiring more reclaimed water to decrease concentration compared to ammonia nitrogen. The proposed framework facilitates the identification of optimal reclaimed water supplement schemes, presenting urban watersheds with viable solutions for local riverine water quality degradation.

Investigating the response of land–atmosphere interactions and feedbacks to spatial representation of irrigation in a coupled modeling framework

Abstract. The transport of water, heat, and momentum from the surface to the atmosphere is dependent, in part, on the characteristics of the land surface. Along with the model physics, parameterization schemes, and parameters employed, land datasets determine the spatial variability in land surface states (i.e., soil moisture and temperature) and fluxes. Despite the importance of these datasets, they are often chosen out of convenience or owing to regional limitations, without due assessment of their impacts on model results. Irrigation is an anthropogenic form of land heterogeneity that has been shown to alter the land surface energy balance, ambient weather, and local circulations. As such, irrigation schemes are becoming more prevalent in weather and climate models, with rapid developments in dataset availability and parameterization scheme complexity. Thus, to address pragmatic issues related to modeling irrigation, this study uses a high-resolution, regional coupled modeling system to investigate the impacts of irrigation dataset selection on land–atmosphere (L–A) coupling using a case study from the Great Plains Irrigation Experiment (GRAINEX) field campaign. The simulations are assessed in the context of irrigated vs. nonirrigated regions, subregions across the irrigation gradient, and sub-grid-scale process representation in coarser-scale models. The results show that L–A coupling is sensitive to the choice of irrigation dataset and resolution and that the irrigation impact on surface fluxes and near-surface meteorology can be dominant, conditioned on the details of the irrigation map (e.g., boundaries and heterogeneity), or minimal. A consistent finding across several analyses was that even a low percentage of irrigation fraction (i.e., 4 %–16 %) can have significant local and downstream atmospheric impacts (e.g., lower planetary boundary layer, PBL, height), suggesting that the representation of boundaries and heterogeneous areas within irrigated regions is particularly important for the modeling of irrigation impacts on the atmosphere in this model. When viewing the simulations presented here as a proxy for “ideal” tiling in an Earth-system-model-scale grid box, the results show that some “tiles” will reach critical nonlinear moisture and PBL thresholds that could be important for clouds and convection, implying that heterogeneity resulting from irrigation should be taken into consideration in new sub-grid L–A exchange parameterizations.

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport.

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge.

Impact of Tropical Cyclone Wind Forcing on the Global Climate in a Fully Coupled Climate Model

Abstract Tropical cyclones (TCs) alter upper-ocean temperature and influence ocean heat content via enhanced turbulent mixing. A better understanding of the role of TCs within the climate system requires a fully coupled modeling framework, where TC-induced ocean responses feed back to the atmosphere and subsequently to the climate mean state and variability. Here, we investigate the impacts of TC wind forcing on the global ocean and the associated feedbacks within the climate system using the fully coupled Community Earth System Model version 1.3 (CESM1.3). Using the low-resolution version of CESM1.3 (1° atmosphere and ocean grid spacing) with no intrinsic TCs, we conduct a suite of sensitivity experiments by inserting TC winds extracted from a high-resolution (0.25° atmosphere grid spacing) TC-permitting simulation into the low-resolution model. Results from the low-resolution TC experiment are compared to a low-resolution control simulation to diagnose TCs’ impact. We found that the added TC winds can increase ocean heat content by affecting ocean vertical mixing, air–sea enthalpy fluxes, and cloud amount. The added TCs can influence mean SST, precipitation, ocean subsurface temperature, and ocean mixed layer depth. We found a strengthening of the wind-driven subtropical cells and a weakening of the Atlantic meridional overturning circulation due to the changes of surface buoyancy fluxes. TCs in the model cause anomalous equatorward ocean heat convergence in the deep tropics and an increase of poleward ocean heat transport out of the subtropics. Our modeling results provide new insights into the multiscale interactions between TCs and the coupled climate system.

An integrated modeling approach to predict trophic state changes in a large Brazilian reservoir

Process-based ecological models have been used to study freshwater ecosystems and water quality on a broad scale. However, it is also of pivotal importance to incorporate watershed dynamics and nutrient releases in the downstream freshwaters. Integrated modeling approaches have been used to understand the combined effect of climate warming and land use and land cover (LULC) changes in lake ecosystems. Moreover, most basin-scale water quality models require many datasets and parameters to perform reliable simulations which contributes to reduce studies in poorly monitored basins, most of them located in the Global South. In this study, we developed a coupled hydrological-biogeochemical-ecological modeling framework forced by two regionalized climate models and three LULC change scenarios to forecast trophic state changes in a subtropical multipurpose reservoir for the decade 2050-2060. The projections indicated an average air temperature increase between 2°C and 3°C and a downward trend of the average rainfall and longwave radiation for the 2050s in comparison to the last decade. We found a pattern of 28% increase in total phosphorus (TP) and total chlorophyll-a (TChla) concentrations in the reservoir compared with the historical baseline. The climate warming projections along the 2059 projected LULC and basin's increased economic development scenarios have predicted trophic state index (TSI) shifts between mesotrophic and eutrophic conditions (53.3<TSI<57.7). On the other hand, one of the climate projections along the reduced deforestation scenario indicated a trend towards oligotrophication between 2054 and 2056, however higher phosphorus availability (60µg.l−1<TP<100µg.l−1) and phytoplankton biomass (50µg.l−1<TChla<97µg.l−1) would be expected for the entire decade compared to recent years. The proposed coupled modeling framework demonstrated the potential of open-source tools in water quality management studies, especially for poorly monitored basins, based on climate change trends and human pressure.

Evaluating Trade-Off and Synergies of Ecosystem Services Values of a Representative Resources-Based Urban Ecosystem: A Coupled Modeling Framework Applied to Panzhihua City, China

Following significant urban expansion, the ecological problems of resource-based cities are gradually exposed. It is of great significance to study the ecosystem services of resource-based cities to achieve their sustainable development goals and to alleviate the conflicts between environmental protection and the utilization of the surrounding resources. However, in the current research on resource-based cities, few scholars have combined multiple minerals and multiple ecosystem services to explore the impact of mineral resources on the ecosystem. In this study, based on the historical data spanning from 2002 to 2018, we used the CA–Markov model to project the land use of Panzhihua City to 2030. Based on future land use projection, we quantified four ecosystem services (ESs) variables, including water yield, carbon storage, habitat quality, and soil conservation, using the InVEST model from the perspective of land use evolution in Panzhihua City. In addition, we explored the trade-offs and synergies of different ecosystem services and the correlations between different mineral species and ecosystem services using Spearman’s correlation coefficient. Results showed the following: (1) During 2002–2018, water yield service, habitat quality service, and carbon storage service of Panzhihua City decreased year by year, and soil conservation service showed significant fluctuations; most of the low ESs areas were distributed in the central region of Panzhihua. On the contrary, most high ESs areas were located in the forest region. (2) The trade-offs and synergistic relationships among different ecosystem services showed significant spatial variations. There were synergistic relationships among ESs and weak trade-offs between water yield services, soil conservation, and habitat quality services. There was also significant spatial variability in the trade-offs and synergies among ecosystem services, with water production services showing “east trade-offs and west synergies” with soil conservation and habitat quality services, and most of the rest showing trade-offs in urban areas. (3) ESs in mining areas showed trade-offs in general, mainly between water production services and carbon storage services, with clay as the major negative factor of mineral species, and iron ore mines that have undergone ecological protection construction showed the lowest negative impact on ecology.

E-DBCM: A dynamically coupled upland and in-stream water quality model for watershed water quality simulation

A dynamic bidirectional coupled modeling framework for water environment simulation (E-DBCM), including an upland watershed model (UWSM) and a two-dimensional (2D) downstream waterbody model (DWBM), is proposed. The UWSM is implemented to describe the rainfall-runoff and determine the pollutant load to downstream waterbodies, whereas the DWBM is used to simulate the pollutant transport and flood processes on downstream waterbodies. The UWSM and DWBM are spatially connected through a moving boundary, which can ensure the mass and momentum conservation. The proposed E-DBCM is verified using three case studies and the results indicate that the E-DBCM has satisfactory numerical accuracy, which can effectively reproduce the pollutant transport process and achieve satisfactory results. The water environment in Yanqi River Basin is assessed based on the proposed model. The simulated results are consistent with the measured data, indicating that the E-DBCM is reliable and the prediction accuracy can meet the requirements of engineering practices. Water is seriously polluted in this watershed, especially during peak tourist season when many pollutants are produced. Various measures should be taken to protect the water environment in this basin.

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

Investigation of Ocean Sub-Surface Processes in Tropical Cyclone Phailin Using a Coupled Modeling Framework: Sensitivity to Ocean Conditions

The present study is aimed to investigate sub-surface ocean processes and their contribution to the intensification of a tropical cyclone (TC) from a coupled-modeling perspective. The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model was employed to simulate TC Phailin, which originated over the Bay of Bengal and made landfall on the eastern coast of India in October 2013. Three sub-surface ocean condition datasets—viz., (a) the European Centre for Medium-Range Weather Forecast (ECMWF) Ocean Reanalysis, (b) the Climate Forecast System Version 2 (CFSV2) Operational Analysis, and (c) the Hybrid Coordinate Ocean Model (HYCOM) Reanalysis datasets—were used for the initial and boundary conditions for the oceanic component of the coupled model in three different simulations of TC Phailin. All the simulations showed a delay in intensification compared to the observation, and ECMWF simulated the most intensified TC. CFSV2 simulated a deeper mixed layer (ML) and higher mixing, which hindered the intensification. Furthermore, higher entrainment of cold water in the ML led to cold water reaching the surface and, consequently, decreased sea surface temperature, which acted as negative feedback in the intensification of the storm in the cases of CFSV2 and HYCOM. ECMWF realistically simulated the interactions of the TC with a cold-core eddy before landfall. A sudden increase in ML heat content, the addition of heat in the ML due to entrainment, and the prevention of cold water reaching the surface were indicative of the breaking of the barrier layer (BL) in ECMWF, which was further corroborated by the spatial distribution of BL thickness in the simulation. This acted as positive feedback in the intensification of the TC. The findings of this study strongly suggest that not only the incorporation of physical oceanic sub-surface processes in the modeling of TCs but also the proper representation of prevailing mesoscale features and ocean sub-surface temperature, salinity, and current profiles in datasets is essential for realistic simulations of TCs.

Coupled Carbonate Chemistry - Harmful Algae Bloom Models for Studying Effects of Ocean Acidification on Prorocentrum minimum Blooms in a Eutrophic Estuary

Eutrophic estuaries have suffered from a proliferation of harmful algal blooms (HABs) and acceleration of ocean acidification (OA) over the past few decades. Despite laboratory experiments indicating pH effects on algal growth, little is understood about how acidification affects HABs in estuaries that typically feature strong horizontal and vertical gradients in pH and other carbonate chemistry parameters. Here, coupled hydrodynamic–carbonate chemistry–HAB models were developed to gain a better understanding of OA effects on a high biomass HAB in a eutrophic estuary and to project how the global anthropogenic CO2 increase might affect these HABs in the future climate. Prorocentrum minimum in Chesapeake bay, USA, one of the most common HAB species in estuarine waters, was used as an example for studying the OA effects on HABs. Laboratory data on P. minimum grown under different pH conditions were applied in the development of an empirical formula relating growth rate to pH. Hindcast simulation using the coupled hydrodynamic-carbonate chemistry–HAB models showed that the P. minimum blooms were enhanced in the upper bay where pH was low. On the other hand, pH effects on P. minimum growth in the mid and lower bay with higher pH were minimal, but model simulations show surface seaward estuarine flow exported the higher biomass in the upper bay downstream. Future model projections with higher atmospheric pCO2 show that the bay-wide averaged P. minimum concentration during the bloom periods increases by 2.9% in 2050 and 6.2% in 2100 as pH decreases and 0.2 or 0.4, respectively. Overall the model results suggest OA will cause a moderate amplification of P. minimum blooms in Chesapeake bay. The coupled modeling framework developed here can be applied to study the effects of OA on other HAB species in estuarine and coastal environments.

Applying a Coupled Hydrologic-Economic Modeling Framework: Evaluating Alternative Options for Reducing Impacts for Downstream Locations in Response to Upstream Development

Economic input-output and watershed models provide useful results, but these kinds of models do not use the same spatial units, which typically limits their integration. A modular hydrologic-economic modeling framework is designed to couple the Rectangular Choice-of-Technology (RCOT) model, a physically constrained, input-output (I-O) model, with the Hydrological Simulation Program-Fortran (HSPF). Integrating these two models can address questions relevant to both economists and hydrologists, beyond addressing only administrative or watershed concerns. This framework is utilized to evaluate alternative future development prospects within Fauquier County, northern Virginia, specifically residential build-up, and agricultural intensification in the upstream location of the local watershed. Scenarios are designed to evaluate the downstream impacts on watershed health caused by upstream development and changes made within the economic sectors in response to these impacts. In the first case, an alternative residential water technology is more efficient than the standard for ensuring adequate water supply downstream. For scenarios involving upstream agricultural intensification, a crop shift from grains to fruits and vegetables is the most efficient of the alternatives considered. This framework captures two-way feedback between watershed and economic systems that expands the types of questions one can address beyond those that can be analyzed using these models individually.

Compound flood modeling framework for surface–subsurface water interactions

Abstract. Compound floods are an active area of research in which the complex interaction between pluvial, fluvial, coastal and groundwater flooding are analyzed. A number of studies have simulated the compound flooding impacts of precipitation, river discharge and storm surge variables with different numerical models and linking techniques. However, groundwater flooding is often neglected in flood risk assessments due to its sporadic frequency (as most regions have water tables sufficiently low that do not exacerbate flooding conditions), isolated impacts and considerably lower severity with respect to other types of flooding. This paper presents a physics-based, loosely coupled modeling framework using FLO-2D and MODFLOW-2005 that is capable of simulating surface–subsurface water interactions. FLO-2D, responsible for the surface hydrology and infiltration processes, transfers the infiltration volume as recharge to MODFLOW-2005 until the soil absorption capacity is exceeded, while MODFLOW-2005 returns exchange flow to the surface when the groundwater heads are higher than the surface depth. Three events characterized by short-duration intense precipitation, average tide levels and unusually high water table levels are used to assess the relevance of groundwater flooding in the Arch Creek Basin, a locality in North Miami particularly prone to flooding conditions. Due to limitations in water level observations, the model was calibrated based on properties that have experienced repetitive flooding losses and validated using image-based volunteer geographic information (VGI). Results suggest that groundwater-induced flooding is localized, and high groundwater heads influence pluvial flooding as the shallow water table undermines the soil infiltration capacity. Understanding groundwater flood risk is of particular interest to low-elevation coastal karst environments as the sudden emergence of the water table at ground surface can result in social disruption, adverse effects to essential services and damage to infrastructure. Further research should assess the exacerbated impacts of high tides and sea level rise on water tables under current and future climate projections.

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System

AbstractThe NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.