Multi-scale Evaluation Research Articles

Comparison of ecosystem services provided by an urban and a riverine wetland: a multi-scale evaluation from lower Gangetic plain, Eastern India.

Wetland ecosystems (WE) are considered the most productive ecosystems on the globe which provide a range of basic and critical ecosystem services (ES) to humans. However, WE are constantly being degraded because of a poor understanding of social benefits at a local level and the lack of recognition of economic values for ES at sub-national and national levels. This study aims to compare a peri-urban wetland with a riverine wetland from lower Gangetic plain, Eastern India, to identify the bundle of ES based on its perceived importance from multiple perspectives at a local level. Participatory research tools have been employed (i.e., key informant interviews, household survey, and field observation) for data collection. Based on rapid assessment of wetland ecosystem services (RAWES) approach, a priority ranking analysis was used to examine ES. A Mann-Whitney U test was performed to find out significant differences in RAWES approach between riverine and peri-urban wetlands. Multiple regression model (MRM) was also performed to explore the impact of socio-demographic factors towards the perception of WES. Results showed that (i) the utilizations of WES largely vary between riverine and peri-urban wetland; (ii) provisioning and regulating ES were attributed relatively higher importance by the people of riverine and peri-urban wetlands in comparison to other ES; (iii) as per RAWES approach, the ESI value for riverine wetland was higher (0.58) than the peri-urban wetland (0.44) indicating the higher importance and dependence on WES; and (iv) population growth, expansion of settlement, climate change, and agricultural expansion were attributed as significant drivers of WE change (particularly for urban wetland). This study suggests the much-needed policy interventions and future pathways of research for developing effective measures to ensure livelihood sustainability of the dependent communities and to sustain (active) the ecosystem health of riverine and peri-urban wetlands.

Multi-scale classification and evaluation of shale reservoirs and ‘sweet spot’ prediction of the second and third members of the Qingshankou Formation in the Songliao Basin based on machine learning

Owing to the unique structure of shale reservoirs intercalated with thin siltstone in the second and third members of the Qingshankou Formation (K1qn2+3) in the north of the Central Depression of the Songliao Basin, it is difficult to objectively predict and evaluate multi-scale (i.e., from the micro to macro scale) physical properties of the reservoir and the ‘sweet spot’ area (the area with the best reservoir quality). Using machine learning, we present herein a new machine learning framework (GCA-CE-MGPK) specific to reservoirs for studying the shale reservoirs in K1qn2+3. According to the results of a high-pressure mercury-injection experiment, organic geochemical analysis and scanning electron microscopy. Through grey correlation analysis, clustering ensemble and the Kriging model combined with macro geological parameters, an efficient, accurate and objective multi-scale evaluation of the shale reservoirs and the prediction of the ‘sweet spot’ area were realised. This method can be used to overcome difficulty in parameter selection, time-consuming classification of a large amount of data and difficulty in macro-scale reservoir prediction in the absence of seismic data with an average accuracy of 82.4%. The reservoir prediction results showed that the Class-I reservoir, the ‘sweet spots’ area, is mainly distributed in the north of the study area with an area of 3.15 × 108 m2; the Class-II reservoir is mainly distributed in the north of the study area with an area of 9.88 × 108 m2; the Class-III shale reservoir is the most widely distributed reservoir type with an area of 4.90 × 109 m2. Overall, compared with K1qn1, K1qn2+3 offers more realistic oil and gas exploration potential and advantages.

Multi-scale Evaluation of Aging and Damage-Healing Performance of Asphalt Mixtures of Various Gradations

Abstract This study aimed to explore the changes in the aging and self-healing properties of styrene butadiene styrene-modified asphalt mixtures of different gradations under different aging conditions, healing temperatures, healing times, and damage degrees. The internal pore structure of the mixture was analyzed using the nuclear magnetic resonance technique. The flexural creep healing test was used, and the self-healing performance of modified asphalt mixtures of AC-13, AC-16, and SMA-13 was evaluated by calculating the cumulative dissipated energy. The results show that the original asphalt mixture with the same gradation has the largest pore size, which decreases with the degree of aging; the smallest porosity is that of thermal-ultraviolet composite aging. High temperatures can improve the fluidity of asphalt, partly filling the pores inside the asphalt mixture and thereby reducing the porosity. Furthermore, with the increase in the degree of aging and damage, the healing rate becomes slower. In addition, the healing rate of the three asphalt mixtures under different influencing factors shows that the SMA-13 asphalt mixture performs the best and the AC-13 asphalt mixture is the worst. The basalt fiber plays a bridging role, as it improves the adhesion between asphalt and other aggregates. The increase in the degree of aging reduces the deformation ability of the asphalt mixture and lowers the fluidity of the asphalt and the healing rate. The SMA-13 asphalt mixture shows better self-healing performance when applied to a bituminous pavement.

Presenting a comprehensive multi-scale evaluation framework for participatory modelling programs: A scoping review.

Systems modelling and simulation can improve understanding of complex systems to support decision making, better managing system challenges. Advances in technology have facilitated accessibility of modelling by diverse stakeholders, allowing them to engage with and contribute to the development of systems models (participatory modelling). However, despite its increasing applications across a range of disciplines, there is a growing need to improve evaluation efforts to effectively report on the quality, importance, and value of participatory modelling. This paper aims to identify and assess evaluation frameworks, criteria, and/or processes, as well as to synthesize the findings into a comprehensive multi-scale framework for participatory modelling programs. A scoping review approach was utilized, which involved a systematic literature search via Scopus in consultation with experts to identify and appraise records that described an evaluation framework, criteria, and/or process in the context of participatory modelling. This scoping review is registered with the Open Science Framework. The review identified 11 studies, which varied in evaluation purposes, terminologies, levels of examination, and time points. The review of studies highlighted areas of overlap and opportunities for further development, which prompted the development of a comprehensive multi-scale evaluation framework to assess participatory modelling programs across disciplines and systems modelling methods. The framework consists of four categories (Feasibility, Value, Change/Action, Sustainability) with 30 evaluation criteria, broken down across project-, individual-, group- and system-level impacts. The presented novel framework brings together a significant knowledge base into a flexible, cross-sectoral evaluation effort that considers the whole participatory modelling process. Developed through the rigorous synthesis of multidisciplinary expertise from existing studies, the application of the framework can provide the opportunity to understand practical future implications such as which aspects are particularly important for policy decisions, community learning, and the ongoing improvement of participatory modelling methods.

Participatory Systems Modelling for Youth Mental Health: An Evaluation Study Applying a Comprehensive Multi-Scale Framework.

The youth mental health sector is persistently challenged by issues such as service fragmentation and inefficient resource allocation. Systems modelling and simulation, particularly utilizing participatory approaches, is offering promise in supporting evidence-informed decision making with limited resources by testing alternative strategies in safe virtual environments before implementing them in the real world. However, improved evaluation efforts are needed to understand the critical elements involved in and to improve methods for implementing participatory modelling for youth mental health system and service delivery. An evaluation protocol is described to evaluate the feasibility, value, impact, and sustainability of participatory systems modelling in delivering advanced decision support capabilities for youth mental health. This study applies a comprehensive multi-scale evaluation framework, drawing on participatory action research principles as well as formative, summative, process, and outcome evaluation techniques. Novel data collection procedures are presented, including online surveys that incorporate gamification to enable social network analysis and patient journey mapping. The evaluation approach also explores the experiences of diverse stakeholders, including young people with lived (or living) experience of mental illness. Social and technical opportunities will be uncovered, as well as challenges implementing these interdisciplinary methods in complex settings to improve youth mental health policy, planning, and outcomes. This study protocol can also be adapted for broader international applications, disciplines, and contexts.

Characterization of soundscape perception of preschool children

To characterize their soundscape perception, preschool children were subjected to inclusive soundscape exposure conditions (both indoor and outdoor) and were asked to evaluate the soundscape in this study comprising two experiments. In Experiment 1, reliable soundscape perception dimensions for preschool children were formulated using semantic differential analysis. In Experiment 2, the relationship between the dimensions and the overall soundscape evaluation and acoustic characteristics was investigated. The principal component analysis resulted in three factors (eigenvalue> 1), namely pleasantness, peacefulness, and regularity, explaining 69.96% of the variance (48.70%, 13.13%, and 8.13%, respectively). Among them, pleasantness and peacefulness significantly contributed to the overall soundscape evaluation (preference), as the improvement attitude of these two factors led to a higher chance of a positive attitude towards the overall soundscape preference (i.e., “like”). LA50 and LA10 contributed significantly to the regression for pleasantness and peacefulness, accounting for 41.2% and 92.6% of the prediction variance, respectively. Regularity had no significant effect on the overall soundscape evaluation. In addition, no acoustic characteristics significantly predicted this factor. The outcome showed that preschool children are capable of multi-scale evaluation of diverse soundscapes, embodying their awareness of both the quality and influence of the acoustic environment. Their soundscape perception dimensions pertain to the features of the preschool period and are thus inconsistent with previous adults' soundscape dimensions. In addition, preschool children seem to desire mild and peaceful soundscapes under control.

Data-Driven Selection of Land Product Validation Station Based on Machine Learning

Validation is a crucial technique used to strengthen the application capabilities of earthobservation satellite data and solve the quality problems of remote-sensing products. Observing land-surface parameters in the field is one of the key steps of validation. Therefore, the demand for long-term stable validation stations has gradually increased. However, the current location-selection procedure of validation stations lacks a systematic and objective evaluation system. In this research, a data-driven selection of a land product validation station (DSS-LPV) based on Machine Learning is proposed. Firstly, we construct an evaluation indicator system in which all factors affecting the location of validation stations are divided into surface characteristics, atmospheric conditions and the social environment. Then, multi-scale evaluation grids are constructed and indicators are allocated for spatial evaluation. Finally, four Machine Learning (ML) methods are used to learn the established reliable stations, and different data-driven scoring models are constructed to explore the intrinsic relationship between evaluation indicators and station locations. In this article, the reliability of DSS-LPV is effectively validated by the example of China using the national-level land product validation station that has been established. After a comparison between the four ML models, the random forest (RF) with the highest accuracy was selected as the modeling method of DSS-LPV. The correlation between the regression value of test stations and the target value is 0.9133. The average score of test stations is 0.8304. The test stations are generally located within the calculated hot-spot area of the score density map, which means that it is highly consistent with the location of the built stations. Research results indicate that DSS-LPV is an effective method that can provide a reasonable geographical distribution of the stations. The location-selection results can provide scientific decision-making support for the construction of land product validation stations.

Study on temporal and spatial differentiation of biocapacity in Shenyang from a multi-scale perspective.

Biocapacity of a region exhibits spatial differences owing to the limitations of regional scale and natural conditions. Based on the multi-scale perspective, we comprehensively studied and analyzed the temporal and spatial differences of the biocapacity of a region in an attempt to establish the groundwork for optimizing urban development and its utilization framework. By adopting the ecological footprint model along with multi-scale difference evaluation method, the municipal and county scales are incorporated into a unified analysis framework in this paper, thereby facilitating the exploration of the temporal and spatial differences in the biocapacity of Shenyang—a city in China—from 2005 to 2019. The results demonstrated that: 1) At the municipal scale, the biocapacity per capita fluctuated between 1.35 hm2/person and 2.22 hm2/person. It revealed an “up-down-up” trend, which appeared consistent with the Kuznets cycle; at the county scale, the biocapacity depicted spatial differences, while those of downtown and surrounding districts/counties developed a two-level ascending hierarchical structure. 2) The time series of footprint size and depth first ascended and then declined, and can be classified into four types: closed type, inverted U-type, S-type, and M-type. Among them, S-type and M-type have the phenomenon of over-utilizing the stock capital. 3) For a long time, the regional difference of biocapacity has mostly dwelt on two scales with an evident scale effect, and the biocapacity of Liaozhong District was the worst.

Multiscale evaluation of CMIP5 models using wavelet-based descriptive and diagnostic techniques

Time–frequency localization of model-data discrepancies may provide useful information for climate models inter-comparison, and especially for the goals of climate model refinement and improvement. CMIP5 models of the long-term historical (1850–2005) run experiment are compared using wavelet-based multiscale descriptive and diagnostic techniques with interesting results. Wavelet coherence maps can visualize the ability of alternative CMPI5 models to capture the observed climate variability at different time scales, while the performance of each CMIP5 model is assessed using goodness of fit relative measures on a scale-by-scale basis. Finally, the plots of wavelet decompositions of CMIP5 models and observed temperature series at different scales can detect and locate model/data disagreements across frequencies and over time, thus providing useful information to researchers for model diagnostic refinement and improvement.

Validation of Satellite Rainfall Estimates over Equatorial East Africa

Abstract Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–18, this study performs a multiscale evaluation of gauge-calibrated SREs, namely, IMERG, TMPA, CHIRPS, and MSWEP (v2.2 and v2.8). Skills were assessed from daily to annual time scales, for extreme daily precipitation, and for TMPA and IMERG near-real-time (NRT) products. Results show that 1) the SREs reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best for shorter temporal scales while MSWEPv2.2 and CHIRPS perform best at the monthly and annual time steps, respectively; 3) the performance of all the SREs varies spatially, likely due to an inhomogeneous degree of gauge calibration, with the largest variation seen in MSWEPv2.2; 4) all the SREs miss between 79% (IMERG-NRT) and 98% (CHIRPS) of daily extreme rainfall events recorded by the rain gauges; 5) IMERG-NRT is the best regarding extreme event detection and accuracy; and 6) for return values of extreme rainfall, IMERG, and MSWEPv2.2 have the least errors while CHIRPS and MSWEPv2.8 cannot be recommended. The study also highlights improvements of IMERG over TMPA, the decline in performance of MSWEPv2.8 compared to MSWEPv2.2, and the potential of SREs for flood risk assessment over East Africa.

Multiscale Evaluations of Global, National and Regional Digital Soil Mapping Products in France Across Regions and Soil Properties

Multiscale Evaluations of Global, National and Regional Digital Soil Mapping Products in France Across Regions and Soil Properties

Multi-scale evaluation of ecological restoration effects in the riparian zone using Landsat series images from 1980 to 2019

Quantitative evaluation of ecological restoration effects (EREs) is essential to provide operational feedback and implement adaptive management for future riparian restoration. This manuscript aims to develop a multi-scale evaluation method of EREs based on remote sensing observations, which uses the composite index of physical structural integrity (PSI) to indicate riparian condition. The riparian EREs were quantitatively assessed by comparing the post-restoration PSI values with the ecological reference status derived from pre-restoration surveys. In addition, ecological partitioning was adopted to identify the vulnerable riparian areas and explore the influence indicators of ecological restoration. In this paper, we used the case study of the Liaohe River, the first protected area aimed to recover the riparian ecosystem in China, to evaluate the riparian EREs at three spatial scales using Landsat time series images from 1980 to 2019. Results indicated that: (1) the pre-restoration riparian ecosystem exhibited an clear degradation trend and ecological conservation was urgent; (2) riparian restoration is a continuous and heterogeneous ecological process, the project had not yet met its objectives in 2016 with a low ERE value of 49.1 (Poor), while achieved a significant success in 2019 with the ERE value of 68.8 (Very good); (3) the limited restoration partitions with a ERE value below 60 accounted for 42.8% of riparian zones, which were the fragile riparian ecosystems and mainly distributed in reach D and F; and (4) the dominant factors for riparian restoration were canopy coverage, human disturbance and natural vegetation percent. This study is expected to provide a practical and feasible approach for evaluating the riparian EREs and supporting the future river management, in particular for rivers lack of long-term monitoring data.

Multiscale evaluation of the efficiently separation of phenols using a designed cationic functionalized ionic liquid based on Brønsted/Lewis coordination

Phenolic compounds (PCs) are value-added materials in producing medicines and disinfectants, which were enriched in the coal tar oils. However, the commonly industrial applied methods exposed several environmental concerns. Thus, the utilization separation of PCs using a green and sustainable technology present both commercial and biological value. In the present work, a type of simple and easy prepared carboxyl functionalized ionic liquid, 1-carboxyethyl-3-methylimidazolium nitrate ([CeMIM][NO3]), was designed based on the Brønsted/Lewis coordination theory and prepared to effective separation of PCs from the oil mixtures, taking m-cresol as the example. The total nonbonded interaction energy (Etotal), spatial distribution function (SDF) and averaged noncovalent interaction (aNCI) were calculated to reveal that the formed H-bonds were enhanced by the functional group of the adopted IL. Moreover, FT-IR was also performed to prove the H-bonds interaction and acid-base coordination with the IL. Afterwards, the performance was explored by measuring the variation of the extraction efficiency (E) under different separation conditions such as separation temperature (ST), the mass ratio of IL to model oil (mIL/moil), and extraction time (ET). Meanwhile, the separation performance of [CeMIM][NO3] on other phenols was also investigated and followed by the regeneration of [CeMIM][NO3]. The E values are 98.77 % for m-cresol, 97.65 % for o-cresol, 98.03 % for p-cresol and 98.14 % for phenol with mIL/moil = 1:5 and extraction time 30 min at 298.15 K.

Multiscale evaluation method of the drag effect on shallow water flow through coastal forests based on 3D numerical simulations

AbstractThis study presents a method for determining the drag parameter in the 2D shallow water (SW) equation for flows through a coastal forest by conducting a series of 3D numerical simulations (3D NSs). Following the theory of multiscale modeling, an evaluation method procedure is proposed. We first prepare a local test domain that contains a sufficient number of trees to constitute part of a coastal forest. Then, 3D NSs are conducted in this test domain with various inflow conditions. Based on the corresponding results, the momentum losses over the test domain are converted into the drag parameter of the global SW equation. A response surface of the drag parameter is constructed as a function of the flow conditions. The stabilized finite element method is employed for both the local and the global NSs, and the phase‐field method is utilized to represent 3D free surfaces. Comparisons between the 2D SW calculation results and the 3D NS results are also performed to verify the validity of the proposed method.

Multi-scale evaluation of diesel commuter rail fuel use, emissions, and eco-driving

• Diesel commuter rail fuel use and emission rates (FUERs) are quantified. • Key sources of variability in diesel commuter rail FUERs are quantified. • Diesel commuter rail FUERs are 7 to 8 times greater for hotspots than non-hotspots. • Eco-driving can reduce system-wide diesel commuter rail FUERs by 10% annually. • Mesoscopic eco-driving can mitigate or worsen FUERs hotspots. Diesel commuter rail emissions affect populations near rail corridors. An approach is demonstrated to quantify mesoscale and microscale diesel commuter rail fuel use and emission rates (FUERs) of CO 2 , CO, NO x , particulate matter , and total hydrocarbons based on two U.S. systems. A speed trajectory simulator, an energy model, and an emissions model were calibrated, evaluated, and applied. FUERs and potential reductions from eco-driving were quantified based on simulated trajectories. Hotspots were defined as sections with ≥ 90 th percentile of section-average FUERs by species. A few key variables explain 74–80% of variability in mesoscopic and microscopic FUERs. On average, FUERs are 7–8 times greater for hotspots than non-hotspots. Eco-driving was estimated to reduce segment-average FUERs by 3–33% and eliminate 2–11% hotspots. However, mesoscale oriented eco-driving can, at some locations, increase microscopic FUERs. The approach is adaptable to other diesel commuter rail systems.

Characterizing the features of precipitation for the Tibetan Plateau among four gridded datasets: Detection accuracy and spatio-temporal variabilities

Evaluation of the gridded products in the detection accuracy and spatio-temporal variabilities of precipitation over the Tibetan Plateau (TP) is crucial to the research of climate change and extreme events. Previous evaluation works mainly focus on the amount of precipitation and the capability in event detection. This study presents a multi-scale evaluation of four gridded precipitation datasets with relatively high resolutions against stations data in the variabilities of precipitation over the TP from 2004 to 2017. The four gridded products are the fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5), the Global Land Data Assimilation Systems (GLDAS), the High Asian Refined Analysis version 2 (HAR v2), and the Tropical Rainfall Measuring Mission (TRMM). Results show that TRMM and GLDAS have good performances in the occurrence frequency of daily precipitation over the TP but cannot precisely detect the daily precipitation events. In contrast, ERA5 can well detect the daily precipitation events although it overestimates the frequency of daily precipitation. Regarding the longer time scales, ERA5 outperforms the other three gridded products. It succeeds in reproducing the interannual and decadal variabilities of precipitation and reflecting the spatio-temporal patterns of precipitation over the whole TP. Thus, this study highlights that there are different optimal choices of precipitation products for different study purposes, and demonstrates the potential capability of ERA5 for being used in the studies of climate and hydrological change over the TP.

Comparing Micro- and Macro-Level Rheological Properties of Polymeric and Reclaimed Asphalt Pavement-Modified Asphalt Binders

Multi-scale evaluation of the rheological and mechanical properties of asphalt binder has substantial importance in understanding the binder’s micro- and macro-scale properties. This study compares the macro- and micro-scale mechanistic properties of asphalt binders. Test samples used in this study include performance grade binders (PG 64-22) from two different sources along with their modified counterparts. The modifiers include polyphosphoric acid (PPA), styrene-butadiene-styrene (SBS), a combination of SBS and PPA, and reclaimed asphalt pavement. To achieve the goal of this study, atomic force microscope technology was utilized to estimate the asphalt binder’s micro-mechanical properties (e.g., Derjaguin, Muller, Toropov modulus and deformation). On the other hand, data on the macro-scale properties—such as rutting factor (G*/sinδ), consistency and penetration—of the selected binders were analyzed and compared with the aforementioned micro-level properties. The comparative analyses indicated that the micro-mechanical properties of asphalt binders followed a linear trend with the macro-scale properties. The findings of this study are expected to help researchers and pavement professionals in modeling asphalt materials when multi-scale effects are deemed to be necessary.

Multi-scale evaluation of the effect of saturated steam on the micromechanical properties of Moso bamboo

Abstract The changes in chemical composition and micro-mechanical properties of Moso Bamboo fiber cells were evaluated by applying saturated steam heat treatment at 160, 170, and 180 °C for periods of 4, 6, and 8 min, and subsequent analysis by X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopic, and nanoindentation methods. The hemicellulose and cellulose content decreased as expected, while the relative lignin content showed an upward trend. Significant changes in the bamboo micro-structure were detected by scanning electron microscope under the action of high-temperature and saturated steam. Both temperature and time were shown to affect micromechanical properties of the bamboo cell wall. In addition, higher cell wall elastic modulus and hardness were observed (a rise from 16.1 to 19.2 GPa and from 0.6 to 0.8 GPa, respectively), as compared with those of the untreated sample. Meanwhile, the creep ratio decreased after saturated steam heat treatment.

Multi-scale evaluation of fractured carbonate reservoir and its implication to sweet-spot optimization: A case study of Tazhong oilfield, Central Tarim Basin, China

Ordovician limestone and Cambrian dolomite are the main oil and gas producing layers in eastern Tazhong field, Tarim basin. Industrial oil flow has been obtained from drilling in this zone, proving that the Lower Paleozoic strata is important for oil and gas exploration and development. However, the main controlling factors and distribution of high-quality reservoirs remain unclear. Based on the logging constrained seismic inversion , FMI (Fullbore Formation MicroImager) log interpretation and core analysis of six wells, the main controlling factors of Paleozoic carbonate reservoirs in the study area were investigated. The results reveal that the two buried cuestas control the distribution of the Cambrian and Ordovician strata, the ratio of mudstone in north buried cuesta (NBC) is more than that in south buried cuesta (SBC), and the average porosity of ▪3xq dolomite formation is more than that of O1−2y limestone formation. FMI logging interpretation results show that fracture density of well TZ-1 is significantly higher than that of well TZ-2, combined with core observation and microscopic analysis we can believe that reconstructed dolomite reservoir has developed dissolution pores and vugs. By the means of seismic impedance reconstruction, the most favorable production areas, so-called sweet-spots, are karst residual mounds that formed independent small anticlinal high points and have been depicted. Finally, total 28 traps in the Cambrian–Ordovician​ carbonate buried hill are characterized, covering an area of 66.4 km2. 17 anticline traps with an area of 26.9 km2 in dolomite reservoirs are optimized to be the next exploration targets.

(Invited) Multiscale Evaluation of Light Absorption, Transport and Kinetics in Z-Scheme Photocatalytic Reactors for Solar Hydrogen Production

Z-scheme photocatalytic particles in an aqueous solution with soluble redox shuttles and present in vertically stacked reaction compartments present a promising pathway for solar water splitting to produce hydrogen1–4. Key advantages of this approach include efficient utilization of the incident solar spectrum with light-absorbers that are optically connected in series while facilitating hydrogen and oxygen separation. In recent work, we theoretically demonstrated the feasibility of attaining up to 4% solar-to-hydrogen efficiencies with state-of-the-art photocatalysts and redox shuttles with passive diffusion-driven species transport5. To attain larger solar-to-hydrogen efficiencies, it is crucial to achieve selective and functional interfaces that can inhibit undesired reactions of the redox shuttle, and enhanced rates of redox shuttle transport between the two reaction compartments. Herein, I report on the development of a multiscale model-based approach, supplemented with experiments, to evaluate the coupled effects of light absorption, species transport, reaction kinetics and heat transfer on the overall performance. At the zero-dimensional scale (or the lumped system), we have developed an expanded equivalent-circuit modeling framework with notable innovations to model competing redox reactions, in addition to the desired reactions, while also accounting for mass-transfer limitations. Predictions from this model provides important insights for the design of selective coatings to mitigate competing redox reactions and identifies key physical parameters that are crucial to optimize for the efficiency of hydrogen production. Next, at the length scale of an individual particle, we assess the effects of particle size and the transport and kinetic parameters on the internal and the external quantum yields. Finally, at the reactor-scale with ensembles of particles, we assess whether natural convective currents can aid in redox shuttle transport. Two-dimensional, coupled flow and heat-transfer models of the reactor have been developed in conjunction with experimental measurements in a benchtop reactor to obtain the spatio-temporal temperature and concentration profiles. On the whole, these results provide new insights and strategies to realize highly effective materials-to-device scale designs for Z-scheme photocatalytic solar fuel reactors with soluble redox shuttles. References Pinaud, B. A. et al. Technical and economic feasibility of centralized facilities for solar hydrogen production via photocatalysis and photoelectrochemistry. Energy Environ. Sci. 6, 1983 (2013).Fabian, D. M. et al. Particle suspension reactors and materials for solar-driven water splitting. Energy Environ. Sci. (2015) doi:10.1039/C5EE01434D.Keene, S., Bala Chandran, R. & Ardo, S. Calculations of theoretical efficiencies for electrochemically-mediated tandem solar water splitting as a function of bandgap energies and redox shuttle potential. Energy Environ. Sci. 12, 261–272 (2019).Wang, Q. et al. Scalable water splitting on particulate photocatalyst sheets with a solar-to-hydrogen energy conversion efficiency exceeding 1%. Nat. Mater. 15, 611–615 (2016).Bala Chandran, R., Breen, S., Shao, Y., Ardo, S. & Weber, A. Z. Evaluating particle-suspension reactor designs for Z-scheme solar water splitting via transport and kinetic modeling. Energy Environ. Sci. 11, 115–135 (2018).