Search Results (22)

Agronomic practices and future climate change lead to divergent responses in crop growth and greenhouse gas (GHG) emissions, which challenge a sustainable low-carbon agricultural economy. Therefore, this study developed a simulation-optimization hybrid model to identify long-term best management practices (BMPs) for economic and social benefits under the effects of future climate change. This model, i.e., RZWQM2 coupled with an orthogonal optimization algorithm (RZWQM2-OOA), integrates four core components, including an orthogonal sampling module, climate prediction module, RZWQM2 simulation module, and optimization analysis module. The model enabled a high-fidelity simulation of crop growth and carbon emissions across complex management practice-climate combinations, while efficiently identifying BMPs and circumventing dimensionality challenges through orthogonality and balanced dispersion mechanisms. To validate the applicability of the developed model, it was applied to a real-world, irrigated, continuous corn (Zea mays L.) production system in the USA. Results indicated that the maximum increases in direct and indirect economic benefits (F₁ and F₂) and potential social benefits (F₃) were 35.7%, 42.6%, and 155.5%, respectively, compared to the actual practice. Fertilization amount was the key regulating factor for direct economic and potential social benefits, which exhibited the largest contribution rates (44.3% for direct economic benefit and 53.9% for potential social benefit). Irrigation exerted the most significant influence on indirect economic benefits (Contribution rate = 53.9%). This study provides a replicable and scalable methodology for policy-makers to balance the trade-offs between the economy and carbon emissions in agricultural sustainability. Full article

Optimizing irrigation and nitrogen (N) management in saline soils is critical for sustainable cotton production in arid regions that have been subjected to climate change. In this study, a two-year factorial field experiment (3 salinity levels × 3 N rates × 3 irrigation quotas) is integrated with the RZWQM2 model to (1) identify water–N–salinity thresholds for cotton yield and (2) to project climate change impacts under SSP2.4-5 and SSP5.8-5 scenarios (2031–2090) in Xinjiang, China, a global cotton production hub. The results demonstrated that a moderate salinity (6 dS/m) combined with a reduced irrigation (3600 m³/hm²) and N input (210 kg/hm²) achieved a near-maximum yield (6918 kg/hm²), saving 20% more water and 33% more fertilizer compared to conventional practices. The model exhibited a robust performance (NRMSE: 5.94–12.88% for soil–crop variables) and revealed that warming shortened the cotton growing season by 1.2–9.5 days per decade. However, elevated CO₂ (832 ppm by 2090) levels under SSP5.8-5 increased yields by 22.6–42.1%, offsetting heat-induced declines through enhanced water use efficiency (WUE↑27.5%) and biomass accumulation. Critically, high-salinity soils (9 dS/m) required 25% additional irrigation (4500 m³/hm²) and a full N input (315 kg/hm²) to maintain yield stability. These findings provide actionable strategies for farmers to optimize irrigation schedules and nitrogen application, balancing water conservation with yield stability in saline-affected arid agroecosystems that have been subjected to climate change. Full article

Maize is an important crop used as food, feed, and industrial raw material. Therefore, it is critical to maximize maize yield on available land by using optimal inputs and adapting to challenges posed by climate change. The Leaf Area Index (LAI) is a key parameter that provides significant assistance in forecasting maize yields. This study focuses on modeling the Leaf Area Index for maize. Specifically, it compiles and systematizes the main findings of papers published over the past approximately 10–15 years. Our results are organized and presented based on the five most commonly used models: CERES-Maize, AquaCrop, WOFOST, APSIM, and RZWQM2. The limitations of these models’ applicability are also discussed. We present the limitations of these models and compare their minimum climate input requirements. Additionally, we evaluate the performance of the models across different climate zones, explore how the integration of remote sensing data sources can enhance model estimation accuracy, and examine the potential for spatial scalability in maize LAI modeling. Full article

Two years of field experiments were conducted at the National Field Observation Experiment Station for Efficient Agricultural Water Use in the Wuwei Oasis, Gansu Province. Based on the eddy correlation system, the evapotranspiration (ET) of the cabbage agroecosystem during the growth period was obtained and the main driving factors of ET changes were determined. The Root Zone Water Quality Model 2.0 version (RZWQM2 model) was used to simulate ET during the growth period. The results showed the following: (1) The ET of cabbage during the growth period was 260. 1 ± 24.2 mm, which was basically lower than other crops planted in this area. (2) Through partial correlation analysis and principal component analysis, it can be found that environmental and physiological factors jointly drive changes in ET. The main driving factors include gross primary productivity, net radiation, and water use efficiency. (3) The RZWQM2 model can simulate the ET of the cabbage agroecosystem well, especially in simulating the total ET value and its trend. The growth period ET_s were 7.3% lower than the ET_m. Cabbage is an important cash crop in Northwest China, and ET is an important component of the water cycle in the agroecosystem. Determining the main driving factors of ET is of great significance for the sustainable utilization of agricultural water resources in Northwest China. Our results can provide a scientific basis for the cultivation of cabbage as a cash crop and the development of water saving agriculture. Full article

The Root Zone Water Quality Model (RZWQM) is a one-dimensional process-based model used for simulating major physical, chemical, and biological processes in agricultural systems. To date, the model has not been applied to potato production systems for simulating nitrate leaching. In this study, 35 datasets collected between 2009 and 2016 at a field under a three-year potato (potato–barley–red clover) rotation in Prince Edward Island (PEI), Canada, have been employed for calibrating and validating the water, nitrogen (N) cycling, and plant growth routines of RZWQM and for subsequently estimating nitrate leaching. The model fitness, evaluated using univariate and bivariate indicators, was rated as high for most of the parameters tested. As a result of the combined influence of higher infiltration and reduced plant uptake, the model showed that the highest leaching at the rotation level occurred between September and December. A secondary leaching period occurred in spring, when residual soil nitrate was mobilized by increased percolation due to snowmelt. Most of the nitrate leaching occurred during the potato year (89.9 kg NO₃–N ha⁻¹ y⁻¹), while leaching for barley and red clover years had comparable values (28.6 and 29.7 kg NO₃–N ha⁻¹ y⁻¹, respectively). The low N use efficiency of the entire rotation (i.e., 30.2%), combined with the high NO₃–N concentration in leachate (i.e., 34.9 mg NO₃–N L⁻¹ for potato and 16.3 mg NO₃–N L⁻¹ for the complete rotation), suggest that significant efforts are required for adapting management practices to ensure sustainability of potato production systems. Full article

Improving cotton (Gossypium hirsutum L.) yield and water use efficiency (WUE) under future climate scenarios by optimizing irrigation regimes is crucial in hyper-arid areas. Assuming a current baseline atmospheric carbon dioxide concentration (${\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}$) of 380 ppm (baseline, BL_0/380), the Root Zone Water Quality Model (RZWQM2) was used to evaluate the effects of four climate change scenarios—S_1.5/380 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=1.5 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=0$), S_2.0/380 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=2.0 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=0$), S_1.5/490 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=1.5 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=+110 \mathrm{p}\mathrm{p}\mathrm{m}$) and S_2.0/650 ($∆{\mathrm{T}}_{\mathrm{a}\mathrm{i}\mathrm{r}}^{°}=2.0 °\mathrm{C},∆{\left[{\mathrm{C}\mathrm{O}}_2\right]}_{\mathrm{a}\mathrm{t}\mathrm{m}}=+270 \mathrm{p}\mathrm{p}\mathrm{m}$) on soil water content (θ), soil temperature (${\mathrm{T}}_{\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}}^{°}$), aboveground biomass, cotton yield and WUE under full irrigation. Cotton yield and irrigation water use efficiency (IWUE) under 10 different irrigation management strategies were analysed for economic benefits. Under the S_1.5/380 and S_2.0/380 scenarios, the average simulated aboveground biomass of cotton (vs. BL_0/380) declined by 11% and 16%, whereas under S_1.5/490 and S_2.0/650 scenarios it increased by 12% and 30%, respectively. The simulated average seed cotton yield (vs. BL_0/380) increased by 9.0% and 20.3% under the S_1.5/490 and S_2.0/650 scenarios, but decreased by 10.5% and 15.3% under the S_1.5/380 and S_2.0/380 scenarios, respectively. Owing to greater cotton yield and lesser transpiration, a 9.0% and 24.2% increase (vs. BL_0/380) in cotton WUE occurred under the S_1.5/490 and S_2.0/650 scenarios, respectively. The highest net income ($3741 ha⁻¹) and net water yield ($1.14 m⁻³) of cotton under climate change occurred when irrigated at 650 mm and 500 mm per growing season, respectively. These results suggested that deficit irrigation can be adopted in irrigated cotton fields to address the agricultural water crisis expected under climate change. Full article

Crop evapotranspiration (ET_C) water demands are critical decision support information for the sustainable use of water resources for optimum crop productivity. When measurements of ET_C at all locations are not feasible, the prediction of ETC and crop growth from weather and soil–water–crop management data using state-of-the-science cropping system simulations is a viable alternative. This study compared soybean (Glycine max (L.) Merr.) ET_C quantified using the eddy covariance (EC) method against simulations from two models, (i) the CSM-CROPGRO-soybean module within the Decision Support System for Agroecology Transfer (DSSAT) and (ii) CSM-CROPGRO-soybean module within the Root Zone Water Quality Model v2.0 (RZWQM) for a grower’s field in the Mississippi Delta, USA, during 2017, 2018, and 2019 growing seasons. The measured soybean grain yields during the three seasons, respectively, were 4979 kg ha⁻¹, 5157 kg ha⁻¹, and 5665 kg ha⁻¹. The DSSAT and RZWQM simulated yields deviated from the measured yields by −10.8% and 15.4% in 2017, −24.0% and 1.56% in 2018, and −6.22%, and 9.98% in 2019. Simulated daily ET_C values were less than EC estimates by 0.33 mm, 0.29 mm, and 0.23 mm for DSSAT and 0.05 mm, 0.42 mm, and 0.24 mm for RZWQM, respectively, for the three seasons. EC-quantified seasonal values of ET_C were 584 mm, 532 mm, and 566 mm, respectively, for three seasons. Similarly, simulated seasonal ET_C values were less than EC estimates by 40 mm, 31 mm, and 16 mm by DSSAT, and 7 mm, 46 mm, and 29 mm by RZWQM. The results obtained demonstrated that accuracy in the prediction of ETC varied among models and growing seasons. When the magnitude of errors in daily ET_C simulations does not deter its applications in tactical irrigation water management decisions, a higher degree of agreement between measured and simulated ET_C values at a seasonal scale is more promising for strategical irrigation water management planning decision support. Further improvement of the models for more accurate simulations of daily ET_C can help in more confident applications of these models for tactical crop-water management applications. Full article

Strategic management of nitrogen fertilizers can not only mitigate agricultural nitrogen pollution but also significantly enhance crop yield and nitrogen use efficiency. This study was designed to determine the optimal nitrogen fertilizer management strategy for the Yellow River irrigation area. Leveraging two years of field data related to soil water nitrogen and summer maize growth indices, parameters for the Root Zone Water Quality Model 2 (RZWQM2) were calibrated and validated. Subsequently, various scenarios were generated to simulate the impacts of different nitrogen application rates and basal chasing ratios on summer maize yield, nitrogen agronomic efficiency, nitrogen physiological efficiency, and nitrogen apparent recovery rate. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was employed for a comprehensive evaluation. RZWQM2 can effectively simulate the dynamic changes in soil moisture and nitrogen in the Yellow River irrigation area, and the results indicated that the mean relative error (MRE) between the simulated and observed values varied from 5.77% to 14.09%, and 4.36% to 33.01%, while the root mean square error (RMSE) ranged from 0.016 to 0.037 cm³/cm³, and 0.111 to 1.995 mg/kg. The normalized root mean square error (NRMSE) varied between 6.20% to 14.42% and 5.24% to 17.84%, respectively. The results validate the model’s effectiveness in simulating summer maize yields and nitrogen metrics under varying nitrogen fertilizer management practices. A nitrogen application rate of 180–200 kg/hm² (expressed in terms of pure nitrogen) in the Yellow River irrigation area could adequately meet the requirements for summer maize production. The recommended nitrogen fertilizer management strategy in the Yellow River irrigation area involves applying 200 kg/hm² of nitrogen in a 1:2:1 ratio during the sowing, trumpeting, and anthesis stages. Full article

Water security in the ecotone between semi-humid and semi-arid regions (EHA) is very vulnerable and sensitive to climate change and human interferences. Urban turf irrigation is a primary consumer of urban water resources in the EHA, which places huge pressures on water security by substantial irrigated water use due to the expansion of urban turf planting. Based on a 2-year (2020–2021) turf experiment in Zhangjiakou City, a typical water-deficit city in the EHA of northern China, the water budget for turf was measured and analyzed. Furthermore, the Root Zone Water Quality Model (RZWQM2) was employed to evaluate the optimal irrigation scheme for turf. The results showed that the average volumetric water content in the 0–40 cm soil layer was maintained above 23% in 2020–2021. The evapotranspiration in growth period of turf accounted for more than 70% of the annual evapotranspiration, and the deep seepage in turf soil accounted for 49.67% and 60.28% of the total precipitation and irrigation in 2020 and 2021, respectively, during the vigorous growth period of the turf from May to September. The calibrated RZWQM2 showed a robust ability to simulate the water changes in turf. The d-values (consistency index) between the simulated and observed volumetric water contents and evapotranspiration were both greater than 0.90. In the aspects of irrigation scenarios, the T_60%-12 scenario (T_A-B, where A is 100%, 80%, 60% or 40% of the total irrigation amount and B is the number of irrigation events corresponding to A) was determined as the best irrigation schedule in our study area because of lower evapotranspiration, seepage and higher turf soil water storage under this irrigation scenario, also resulting from the comparison of different irrigation scenarios using the entropy-weight-TOPSIS method. In such an optimal scenario, T_60%-12 irrigation treatment reduced the irrigated water requirement of turf by 40% (142.06 mm) and the seepage amount by 28.07% (39.05 mm), and had the lowest negative impacts on the turf growth. Full article

Agricultural pollution models are a valuable tool for researchers and managers to predict and assess the potential contamination from the use of fertilizers and pesticides in the field. RZWQM2 is a comprehensive software package developed by the US EPA to predict environmental pollution after agrochemical application. The aim of the present study was to predict, using RZWQM2, the nitrogen and pesticides contents in soil of a monocrop and a tree-crop agroforestry system, and evaluate the effect of trees in reducing pollutants. Soil, weather, and agrochemical parameters for each setup were used as inputs in the model. Soil samples were collected at various depths and distances from the olive trees and were analyzed in the laboratory for nitrogen and pesticide contents. From the analysis of the results, it can be concluded that the model could identify the positive impact of the tree-crop agroforestry system in pollution reduction. Comparing the estimates with the relevant field data, the model presented some overestimation of the pesticide levels, particularly for the high-adsorptive and persistent pendimethalin herbicide, and slightly underestimated the concentrations of nitrates in the soil profile, while ammonium concentrations were well described. Overall, the model can be considered a useful and powerful tool for assessing the positive impacts of agroforestry systems in reducing soil pollution. Full article

The drip fertigation technique is a modern, efficient irrigation method to alleviate water scarcity and fertilizer surpluses in crop production, while the precise quantification of water and fertilizer inputs is difficult for drip fertigation systems. A field experiment of maize (Zea mays L.) in a solar greenhouse was conducted to meet different combinations of four irrigation rates (I₁₂₅, I₁₀₀, I₇₅ and I₅₀) and three nitrogen (N) fertilizer rates (N₁₂₅, N₁₀₀ and N₇₅) under surface drip fertigation (SDF) systems. The Root Zone Water Quality Model (RZWQM2) was used to assess the response of soil volumetric water content (VWC), leaf area index (LAI), plant height and maize yield to different SDF managements. The model was calibrated by the I₁₀₀N₁₀₀ scenario and validated by the remaining five scenarios (i.e., I₁₂₅N₁₀₀, I₇₅N₁₀₀, I₅₀N₁₀₀, I₁₀₀N₁₂₅ and I₁₀₀N₇₅). The predictions of VWC, LAI and plant height were satisfactory, with relative root mean square errors (RRMSE) < 9.8%, the percent errors (PBIAS) within ±6%, indexes of agreement (IoA) > 0.85 and determination of coefficients (R²) > 0.71, and the relative errors (RE) of simulated yields were in the range of 1.5–7.2%. The simulation results showed that both irrigation and fertilization had multiple effects on water and N stresses. The calibrated model was subsequently used to explore the optimal SDF scenarios for maximizing yield, water use efficiency (WUE) or nitrogen use efficiency (NUE). Among the SDF managements of 21 irrigation rates × 31 N fertilizer rates, the optimal SDF scenarios were I₁₂₀N₁₃₀ for max yield (10516 kg/ha), I₅₀N₇₀ for max WUE (47.3 kg/(ha·mm)) and I₁₂₅N₇₅ for max NUE (30.2 kg/kg), respectively. The results demonstrated that the RZWQM2 was a promising tool for evaluating the effects of SDF management and achieving optimal water and N inputs. Full article

Irrigated cotton (Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content (θ), soil temperature (${\mathrm{T}}_{\mathrm{soil}}^{°}$) and plant transpiration (T), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and ${\mathrm{T}}_{\mathrm{soil}}^{°}$, as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha⁻¹) and net income (US $3165 ha⁻¹), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha⁻¹ mm⁻¹) and net water production (0.94 $ m⁻³). These results provided farmers guidelines to adopt deficit irrigation strategies. Full article

Nitrogen (N) fertilization is critical for crop growth; however, its effect on crop growth and evapotranspiration (ETc) behaviors under different amounts of rainfall is not well understood. As such, there is a need for studying the impact of nitrogen application rates and rainfall amounts on crop growth and ETc components. Agricultural system models help to fill this knowledge gap, e.g., the Root Zone Water Quality Model (RZWQM2), which integrates crop growth-related processes. The objective of this study is to investigate the effect of the nitrogen application rate on crop growth, soil water dynamics, and ETc behavior under different rainfall amounts by using experimental data and the RZWQM2. A field study was conducted from 2016 to 2019 with three nitrogen application rates (0, 70, and 130 kg N ha⁻¹) for unirrigated winter wheat (Triticum aestivum L.), and two nitrogen application rates (0 and 205 kg N ha⁻¹) for unirrigated corn (Zea mays L.). For the period of 1986–2019, the amounts of actual rainfall during each crop growth period are categorized into four groups. Each rainfall group is used as a rainfall scenario in the RZWQM2 to explore the interactions between the rainfall amounts and N levels on the resulting crop growth and water status. The results show that the model satisfactorily captures the interaction effects of nitrogen application rates and rainfall amounts on the daily ETc and soil water dynamics. The nitrogen application rate showed a noticeable impact on the behavior of soil water dynamics and ETc components. The 75% rainfall scenario yielded the highest nitrogen uptake for both crops. This scenario revealed the highest water consumption for wheat, while corn showed the highest water uptake for the 100% rainfall scenario. The interaction between a high nitrogen level and 50% rainfall yielded the highest water use efficiency, while low nitrogen and 125% rainfall yielded the highest nitrogen use efficiency. A zero nitrogen rate yielded the highest ETc and lowest soil water content among all treatments. Moreover, the impacts of the nitrogen application rate on ETc behavior, crop growth, and soil water dynamics differed depending on the received rainfall amount. Full article

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions. Full article

To be able to compare many agricultural models, a general framework for model comparison when field data may limit direct comparison of models is proposed, developed, and also demonstrated. The framework first calibrates the benchmark model against the field data, and next it calibrates the test model against the data generated by the calibrated benchmark model. The framework is validated for the modeling of the soil nutrient nitrogen (N), a critical component in the overall agriculture system modeling effort. The nitrogen dynamics and related carbon (C) dynamics, as captured in advanced agricultural modeling such as RZWQM, are highly complex, involving numerous states (pools) and parameters. Calibrating many parameters requires more time and data to avoid underfitting. The execution time of a complex model is higher as well. A study of tradeoff among modeling complexities vs. speed-up, and the corresponding impact on modeling accuracy, is desirable. This paper surveys soil nitrogen models and lists those by their complexity in terms of the number of parameters, and C-N pools. This paper also examines a lean soil N and C dynamics model and compares it with an advanced model, RZWQM. Since nitrate and ammonia are not directly measured in this study, we first calibrate RZWQM using the available data from an experimental field in Greeley, CO, and next use the daily nitrate and ammonia data generated from RZWQM as ground truth, against which the lean model’s N dynamics parameters are calibrated. In both cases, the crop growth was removed to zero out the plant uptake, to compare only the soil N-dynamics. The comparison results showed good accuracy with a coefficient of determination (R2) match of 0.99 and 0.62 for nitrate and ammonia, respectively, while affording significant speed-up in simulation time. The lean model is also hosted in MyGeoHub cyberinfrastructure for universal online access. Full article