Although adequately detailed kerosene chemical-combustion Arrhenius reaction-rate suites were not readily available for combustion modeling until ca. the 1990’s (e.g., Marinov ), it was already known from mass-spectrometer measurements during the early Apollo era that fuel-rich liquid oxygen + kerosene (RP-1) gas generators yield large quantities (e.g., several percent of total fuel flows) of complex hydrocarbons such as benzene, butadiene, toluene, anthracene, fluoranthene, etc. (Thompson ), which are formed concomitantly with soot (Pugmire ). By the 1960’s, virtually every fuel-oxidizer combination for liquid-fueled rocket engines had been tested, and the impact of gas phase combustion-efficiency governing the rocket-nozzle efficiency factor had been empirically well-determined (Clark ). Up until relatively recently, spacelaunch and orbital-transfer engines were increasingly designed for high efficiency, to maximize orbital parameters while minimizing fuels and structural masses: Preburners and high-energy atomization have been used to pre-gasify fuels to increase (gas-phase) combustion efficiency, decreasing the yield of complex/aromatic hydrocarbons (which limit rocket-nozzle efficiency and overall engine efficiency) in hydrocarbon-fueled engine exhausts, thereby maximizing system launch and orbital-maneuver capability (Clark; Sutton; Sutton/Yang). The combustion community has been aware that the choice of Arrhenius reaction-rate suite is critical to computer engine-model outputs. Specific combustion suites are required to estimate the yield of high-molecular-weight/reactive/toxic hydrocarbons in the rocket engine combustion chamber, nonetheless such GIGO errors can be seen in recent documents. Low-efficiency launch vehicles also need larger fuels loads to achieve the same launched mass, further increasing the yield of complex hydrocarbons and radicals deposited by low-efficiency rocket engines along launch trajectories and into the stratospheric ozone layer, the mesosphere, and above. With increasing launch rates from low-efficiency systems, these persistent (Ross/Sheaffer ; Sheaffer ), reactive chemical species must have a growing impact on critical, poorly-understood upper-atmosphere chemistry systems.
Although adequately detailed kerosene chemical-combustion Arrhenius reaction-rate suites were not readily available for combustion modeling until ca. the 1990’s (e.g., Marinov ), it was already known from mass-spectrometer measurements during the early Apollo era that fuel-rich liquid oxygen + kerosene (RP-1) gas generators yield large quantities (e.g., several percent of total fuel flows) of complex hydrocarbons such as benzene, butadiene, toluene, anthracene, fluoranthene, etc. (Thompson ), which are formed concomitantly with soot (Pugmire ). By the 1960’s, virtually every fuel-oxidizer combination for liquid-fueled rocket engines had been tested, and the impact of gas phase combustion-efficiency governing the rocket-nozzle efficiency factor had been empirically well-determined (Clark ). Up until relatively recently, spacelaunch and orbital-transfer engines were increasingly designed for high efficiency, to maximize orbital parameters while minimizing fuels and structural masses: Preburners and high-energy atomization have been used to pre-gasify fuels to increase (gas-phase) combustion efficiency, decreasing the yield of complex/aromatic hydrocarbons (which limit rocket-nozzle efficiency and overall engine efficiency) in hydrocarbon-fueled engine exhausts, thereby maximizing system launch and orbital-maneuver capability (Clark; Sutton; Sutton/Yang). The rocket combustion community has been aware that the choice of Arrhenius reaction-rate suite is critical to computer engine-model outputs. Specific combustion suites are required to estimate the yield of high-molecular-weight/reactive/toxic hydrocarbons in the rocket engine combustion chamber, nonetheless such GIGO errors can be seen in recent documents. Low-efficiency launch vehicles (SpaceX, Hanwha) therefore also need larger fuels loads to achieve the same launched/transferred mass, further increasing the yield of complex hydrocarbons and radicals deposited by low-efficiency rocket engines along launch trajectories and into the stratospheric ozone layer, the mesosphere, and above. With increasing launch rates from low-efficiency systems, these persistent (Ross/Sheaffer ; Sheaffer ), reactive chemical species must have a growing impact on critical, poorly-understood upper-atmosphere chemistry systems.
The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2021. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2021 was 1.89 K for the minimum daily temperature, 1.27 K for the mean, and 1.72 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 49% of the mean square error for the minimum temperature and 87% for the maximum. In a national application, we report a stronger relationship between minimum temperature in a heatwave and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.
Salt marshes are ecosystems with significant economic and environmental value. With accelerating rate in sea-level rise, it is not clear whether salt marshes will be able to retain their resilience. Field and numerical investigations have shown that storms play a significant role in marsh accretion and that they might be crucial to salt marsh survival to sea-level rise. Here we present the results from two studies (Pannozzo et al., 2021a,b; Pannozzo et al., 2022) that used numerical and field investigations to quantify the impact of storm surges on the sediment budget of salt marshes within different sea-level scenarios and to investigate how sediment transport pathways determine marsh response to storm sediment input. The Ribble Estuary, North-West England, was used as a test case. The hydrodynamic model Delft3D was used to simulate the estuary morpho-dynamics under selected storm surge and sea-level scenarios. In addition, sediment samples collected with a monthly frequency from different areas of the marsh were analysed with sediments collected from possible sources to integrate field observations with the numerical investigation of sediment transport pathways during stormy and non-stormy conditions. Results showed that, although sea-level rise threatens the estuary and marsh stability by promoting ebb dominance and triggering a net export of sediment, storm surges promote flood dominance and trigger a net import of sediment, increasing the resilience of the estuary and salt marsh to sea-level rise, with the highest surges having the potential to offset sea-level effects on sediment transport and sediment budget of the system. However, although storm sediment input resulted to be significant for the accretion of the marsh platform and particularly for the marsh interior, data showed that storms mainly remobilise sediments already present in the intertidal system and only to a minor extent transport new sediment from external sources.ReferencesPannozzo N. et al., 2021. Salt marsh resilience to sea-level rise and increased storm intensity. Geomorphology, 389 (4): 107825.Pannozzo N. et al., 2021. Dataset of results from numerical simulations of increased storm intensity in an estuarine salt marsh system. Data in Brief, 38 (6): 107336.Pannozzo N. et al., 2022. Sediment transport pathways determine the sensitivity of salt marshes to storm sediment input. In preparation.
Anthropogenic litter is omnipresent in terrestrial and freshwater systems, and can have major economic and ecological impacts. Monitoring and modelling of anthropogenic litter comes with large uncertainties due to the wide variety of litter characteristics, including size, mass, and item type. It is unclear as to what the effect of sample set size is on the reliability and representativeness of litter item statistics. Reliable item statistics are needed to (1) improve monitoring strategies, (2) parameterize litter in transport models, and (3) convert litter counts to mass for stock and flux calculations. In this paper we quantify sample set size requirement for riverbank litter characterization, using a database of more than 14,000 macrolitter items (>0.5 cm), sampled for one year at eight riverbank locations along the Dutch Rhine, IJssel and Meuse rivers. We use this database to perform a Monte Carlo based bootstrap analysis on the item statistics, to determine the relation between sample size and variability in the mean and median values. Based on this, we present sample set size requirements, corresponding to selected uncertainty and confidence levels. Optima between sampling effort and information gain is suggested (depending on the acceptable uncertainty level), which is a function of litter type heterogeneity. We found that the heterogeneity of the characteristics of litter items varies between different litter categories, and demonstrate that the minimum required sample set size depends on the heterogeneity of the litter category. More items of heterogeneous litter categories need to be sampled than of heterogeneous item categories to reach the same uncertainty level in item statistics. For example, to describe the mean mass the heterogeneous category soft fragments (>2.5cm) with 90% confidence, 990 items were needed, while only 39 items were needed for the uniform category metal bottle caps. Finally, we use the heterogeneity within litter categories to assess the sample size requirements for each river system. All data collected for this study are freely available, and may form the basis of an open access global database which can be used by scientists, practitioners, and policymakers to improve future monitoring strategies and modelling efforts.
The El Niño-Southern Oscillation (ENSO) in the equatorial Pacific is the dominant mode of global air-sea CO2 flux interannual variability (IAV). Air-sea CO2 fluxes are driven by the difference between atmospheric and surface ocean pCO2, with variability of the latter driving flux variability. Previous studies found that models in Coupled Model Intercomparison Project Phase 5 (CMIP5) failed to reproduce the observed ENSO-related pattern of CO2 fluxes and had weak pCO2 IAV, which were explained by both weak upwelling IAV and weak mean vertical DIC gradients. We assess whether the latest generation of CMIP6 models can reproduce equatorial Pacific pCO2 IAV by validating models against observations-based data products. We decompose pCO2 IAV into thermally and non-thermally driven anomalies to examine the balance between these competing anomalies, which explain the total pCO2 IAV. The majority of CMIP6 models underestimate pCO2 IAV, while they overestimate SST IAV. Thermal and non-thermal pCO2 anomalies are not appropriately balanced in models, such that the resulting pCO2 IAV is too weak. We compare the relative strengths of the vertical transport of temperature and DIC and evaluate their contributions to thermal and non-thermal pCO2 anomalies. Model-to-observations-based product comparisons reveal that modeled mean vertical DIC gradients are biased weak relative to their mean vertical temperature gradients, but upwelling acting on these gradients is insufficient to explain the relative magnitudes of thermal and non-thermal pCO2 anomalies.
The Central Highlands of Vietnam is the biggest Robusta coffee (Coffea canephora Pierre ex A.Froehner) growing region in the world. This study aims to identify the most important climatic variables that determine the current distribution of coffee in the Central Highlands and build a “coffee suitability” model to assess changes in this distribution due to climate change scenarios. A suitability model based on neural networks was trained on coffee occurrence data derived from national statistics on coffee-growing areas. Bias-corrected regional climate models were used for two climate change scenarios (RCP8.5 and RCP2.6) to assess changes in suitability for three future time periods (i.e., 2038-2048, 2059-2069, 2060-2070) relative to the 2009-2019 baseline. Average expected losses in suitable areas were 62% and 27% for RCP8.5 and RCP2.6, respectively. The loss in suitability due to RCP8.5 is particularly pronounced after 2060. Increasing mean minimum temperature during harvest (October-November) and growing season (March-September) and decreasing precipitation during late growing season (July-September) mainly determined the loss in suitable areas. If the policy commitments made at the Paris agreement are met, the loss in coffee suitability could potentially be compensated by climate change adaptation measures such as making use of shade trees and adapted clones.
Water quality in rivers is influenced by natural factors and human activities that interact in complex and nonlinear ways, which make water quality modelling a challenging task. The concepts of complex networks (CN), a recent development in network theory, seem to provide new avenues to unravel the connections and dynamics of water quality phenomenon, including clandestine teleconnections. This study aims to explore the spatial patterns of water quality using the CN concepts, at both catchment scale and larger national scale. Three major water quality parameters, i.e. dissolved oxygen (DO), permanganate index (COD Mn), and ammonia nitrogen (NH 3-N) are considered for analysis. Weekly data over a period of 12 years (since 2006) from 91 monitoring stations across China are analysed. Degree centrality and clustering coefficient methods are employed. The results show that the degree centrality and clustering coefficients values for water quality indicators is DO > NH 3-N > COD Mn at both basin scale and national scale. Since COD Mn is more sensitive to the upstream point source pollution, as it depends upon the locality and human activities, it leads to a higher heterogeneity of CN indexes even among spatially closer stations. NH 3-N comes next due to the identical pollution level and degradation process in a certain spatial extension. Meanwhile, DO shows good regional connectivity in line with the strong diffusivity. However, the CN characteristic is relatively inconspicuous in large basins and nationwide scale, which indicates the regional impact on water quality fluctuation and CN analysis. These original findings boost a comprehensive understanding of water quality dynamics and enlighten novel methods for environment system analysis and watershed management.
Rock glaciers manifest the creep of mountain permafrost occurring in the past or at present. Their presence and dynamics are indicators of permafrost distribution and changes in response to climate forcing. Knowledge of rock glaciers is completely lacking in the West Kunlun, one of the driest mountain ranges in Asia, where widespread permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active rock glaciers based on satellite Interferometric Synthetic Aperture Radar (InSAR) and Google Earth images. Then we trained DeepLabv3+, a deep learning network for semantic image segmentation, to automate the mapping task. The well-trained model was applied for a region-wide, extensive delineation of rock glaciers from Sentinel-2 images to map the landforms that were previously missed due to the limitations of the InSAR-based identification. Finally, we mapped 413 rock glaciers across the West Kunlun: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,389 m and 5,541 m with an average size of 0.26 km2 and a mean slope angle of 17°. The mean and maximum surface downslope velocities of the active ones are 24 cm yr-1 and 127 cm yr-1, respectively. Characteristics of the rock glaciers of different categories hold implications on the interactions between glacial and periglacial processes in the West Kunlun.
Freeboardelevation of a structure above the base flood elevation (BFE)is a critical component in mitigating or avoiding flood losses. However, the unrevealed benefits and savings of freeboard installation have prevented communities from adopting this approach. To improve decision-making for flood-vulnerable communities and enhance flood risk mitigation strategies, this study presents the methodology underlying a new webtool, FloodSafeHome, that estimates comprehensively the economic benefits and savings of freeboard installation for new construction of residential buildings. Specifically, the proposed evaluation framework has been designed to calculate monthly savings for individual buildings by assessing freeboard cost, insurance savings per year, and expected annual flood loss. This new evaluation method is built into a web-based, decision-making tool for use by the public and community leaders in three southeastern Louisiana parishes, to identify expected future benefits of building residences with freeboard and enhance their decision-making processes with interactive risk/benefit analysis features. For example, results indicate the levels of freeboard that optimize the costbenefit ratio for flood-insured homes in the study area. This approach is expected to improve long-term flood resilience and provide cost-efficient flood mitigation strategies particularly in disaster vulnerable regions.
Manganese (Mn) is an essential element for photosynthetic life, yet concentrations in Southern Ocean open waters are very low, resulting from biological uptake along with limited external inputs. At southern latitudes, waters overlying the Antarctic shelf are expected to have much higher Mn concentrations due to their proximity to external sources such as sediment and sea ice. In this study, we investigated the potential export of Mn-rich Antarctic shelf waters toward depleted open Southern Ocean waters. Our results showed that while high Mn concentrations were observed over the shelf, strong biological uptake decreased dissolved Mn concentrations in surface waters north of the Southern Antarctic Circumpolar Current Front (< 0.1 nM), limiting export of shelf Mn to the open Southern Ocean. Conversely, in bottom waters, mixing between Mn-rich Antarctic Bottom Waters and Mn-depleted Low Circumpolar Deep Waters combined with scavenging processes led to a decrease in dissolved Mn concentrations with distance from the coast. Subsurface dissolved Mn maxima represented a potential reservoir for surface waters (0.3 – 0.6 nM). However, these high subsurface values decreased with distance from the coast, suggesting these features may result from external sources near the shelf in addition to particle remineralization. Overall, these results imply that the lower-than-expected lateral export of trace metal-enriched waters contributes to the extremely low (< 0.1 nM) and potentially co-limiting Mn concentrations previously reported further north in this Southern Ocean region.
Heatwaves occurred frequently in summers, severely harming natural environment and human society. While a few long-term spatiotemporal heatwave studies have been conducted in China at the grid scale, their shortcomings involve discrete distribution and poor spatiotemporal continuity. We used daily data of 691 meteorological stations to obtain torridity index (TI) and heatwave index (HWI) datasets (0.01°), to evaluate the spatiotemporal distribution of heatwaves in Chinese mainland for 1990-2019. The results were as follows: (1) TI rose but with fluctuations. The largest increase occurred in North China in July. Areas with hazard levels of medium and above accounted for 22.16%, mainly in the eastern and southern provinces of China, South Tibet, East and South Xinjiang, and Chongqing. The hazard indicators in Chongqing and central Zhejiang were at especially high levels, which is concerning. (2) Average heatwave frequency, intensity, and duration reached relatively high levels of 6-8, 20-25, and 11-16, respectively, in East and South Xinjiang and Southeast Tibet. (3) The study areas were divided into four categories according to the spatiotemporal distribution of hazards. The “high hazard and rapidly increasing” and “low hazard and keep increasing” areas accounted for 8.71% and 41.33%, respectively. (4) The proportions of units with significantly increased average hazard (AH) at city and county levels were 57% and 68%, respectively. Jinhua, Zhengzhou, Nanchang, Wuhan, Shaoxing, Changsha, Shijiazhuang, Nanjing, Wuxi, and Changzhou accounted for the top 10 AH among the 49 first-tier, new first-tier, and second-tier cities. “Ten Furnaces” at the top of the provincial capitals were Zhengzhou, Nanchang, Wuhan, Changsha, Shijiazhuang, Nanjing, Hangzhou, Haikou, Chongqing, and Hefei. Suzhou’s AH rose the fastest. While the strategy of west development and of revitalizing northeast China progressed, and the urbanization level and population aging of developed areas were further developed, the continuously increasing heatwave hazard should be fully considered.
The distribution of a mosaic of biological soil crusts (BSCs) and shrubs is a common landscape surface feature in temperate deserts. With the continued climatic change, the desert shrub experiences varying rates of mortality which has serious negative impacts on soil structure and functions. However, it is not clear whether BSCs, which develop extensively in areas under shrub canopies, can mitigate the effects of shrub mortality on soil nutrient multifunctionality. Therefore, in this study, the Gurbantungut Desert, a typical temperate desert in northern China, was selected as the study area, and the dominant shrubs, Ephedra przewalskii shrub, and the moss crust were used as the study objects. Soil samples were collected from the bare sand and moss crusts under the living shrub and the dead shrub and analyzed to determine their carbon, nitrogen, phosphorus, and potassium contents. The results showed that the shrub mortality reduced the soil moisture content, pH, electric conductivity, and carbon, nitrogen, phosphorus, and potassium contents in the bare sand compared with the bare sand under the living shrub. The presence of the moss crust greatly mitigated the negative impacts of shrub mortality on soil carbon, nitrogen, phosphorus, and potassium contents, and the nutrient multifunctionality of the moss crust was only reduced by 4.01% compared with the reduction by bare sand (67.42%) after shrub mortality. The results of SEM analysis showed that with the coexistence of shrubs and crust, the effect of shrubs on soil multifunctionality was much stronger than that of the moss crust; compared with available nutrients, the total nutrient content was the most important factor driving changes in soil nutrient multifunctionality. In conclusion, in desert ecosystems with degraded shrubs, moss crusts can mitigate the reduction in soil nutrient contents caused by shrub degradation and, therefore, maintain the soil stability and nutrient multifunctionality as a “substitute”.
Arctic-Boreal lakes emit methane (CH₄), a powerful greenhouse gas. Recent studies suggest ebullition may be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed, but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial datasets of lake area, lake perimeter, permafrost, landcover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R² = 0.211), followed by lake surface area (adj. R² = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes may explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R² = 0.176) and temperature (adj. R² = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R² values are typical and informative for methane attribution studies. A multiple regression model combining LWF, area, and temperature performs best (adj. R² = 0.325). Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-Boreal lake methane emissions to readily available environmental variables.
The propagating muons deposit their energies in the volume-of-interest (VOI) within the tomographic configurations, and this energy loss directly indicates that there is a difference in terms of the kinetic energy between the incoming muons and the the outgoing muons. In this study, by using the GEANT4 simulations, we first elaborate this energy difference over the nuclear waste barrels that contain cobalt, strontium, caesium, uranium, and plutonium. We show that the deposited energy through these VOIs is not negligible for the initial energy bins. Then, we suggest a correction factor for the image reconstruction codes where the initial kinetic energy of the entering muons is coarsely predicted in accordance with the deflection angle through the hodoscope sections, thereby renormalizing the deflection angle in the bottom hodoscope depending on the intrinsic properties of the corresponding VOIs. This correction factor encompasses useful information about the target volume traversed by the muons since it is related to the intrinsic features of the VOI. Therefore, it might be utilized in order to complement the scattering information as an input to the image reconstruction.
Dust devils play an important role in dust transport by carrying it from surface into the atmosphere, especially in summer. However, information on how dust devil changed in the past decades and what caused these changes remains lacking. Based on thermodynamic criterion and ERA5 reanalysis dataset, this study investigated long-term variation of dust devil in East Asia over the past 60 years. We found the annual mean frequency of dust devil in East Asia was approximately 8.2×105 hours for 1959 to 2021. In particular, it is shown that an overall downward trend in dust devil frequency over the study period in East Asia, among which strong dust devils declined significantly at the fastest rate while the weak ones dropped insignificantly. Moreover, dust devil frequency in the Gobi Desert (GD) increased obviously but they decreased in the Taklimakan Desert (TD). It is represented that there was a peak for monthly variation of dust devil, shifting from June to July, over the past decades. For the diurnal changes, dust devils mainly occurred between 10:00 and 16:00 at Local Time (LT), with over 60% of them happening between 12:00 LT and 14:00 LT. We also found a negative correlation between precipitation and dust devil frequency. This study provides a comprehensive understanding of dust devils in East Asia over the past decades, which is of great importance to further evaluate its impact on climate, environment as well as ecosystem.
To integrate temporal and spatial dimensions of seasonal cycles, we combine two conceptual frameworks: ecological calendars and the “3Hs” model of the biocultural ethic. The latter values the vital links between human and other-than-human co-inhabitants, their life habits (e.g., cultural practices of human communities or life cycles of other-than-human species) and the structure, patterns and processes of their shared habitats. This integration enhances an understanding of core links between cultural practices and the life cycles of biocultural keystone species. As a synthesis, we use the term biocultural calendars to emphasize the co-constitutive nature of calendars that result from continuous interactions between dynamic biophysical and cultural processes. We apply biocultural calendars to examine cultural practices and socio-environmental changes in southwestern South America, specifically in Chile, spanning from (1) Cape Horn at the southern of the Americas in sub-Antarctic habitats inhabited by the Yagan indigenous community, (2) artisanal fisher communities in Chiloe; archipelagoes, (3) coastal regions of central-southern Chile inhabited by Lafkenche and Williche indigenous communities, to (4) high Andean habitats in northern Chile co-inhabited by Aymara communities along with domesticated camelids and a rich biodiversity. To illustrate biocultural calendars, we designed analemma diagrams that show the position of the Sun in the sky as seen from a fixed time and location, and linked to continuous renewal of astronomical, biological and cultural, seasonal cycles that sustain life. These biocultural calendars enhance the integration of indigenous and scientific knowledge to confront complex challenges of climate change faced by local communities and global society.
Urban flooding is caused due to poor drainage design, extreme weather, and excessive rain. Such flooding severely affects the road infrastructure. While there are a number of hydrologic software (e.g., TR-55, HydroCAD, TR-20, HEC-RAS, StreamStats, L-THIA, SWMM, WMOST, MAST, HY-8) available to examine extent of urban flooding, the softwares primarily require walking through a series of manual steps and address each study area individually preventing a collective view of an urban area in an efficient manner for hydrologic analysis. Furthermore, the softwares have no ability to recommend optimal culver pipe sizes to minimize flooding. In this paper, we develop a non-linear optimization formulation to minimize urban flooding using underdrain pipe size as a decision variable. We propose a solution algorithm in an integrated GIS and Python environment. Monte Carlo Simulation is used to simulate rainfall intensity by using empirical data on extreme weather from the National Oceanic and Atmospheric Administration. An example using the storm-drain system for the Baltimore County is performed. The results show that the model is effective in identifying storm-drain deficiencies and correcting them by choosing appropriate storm-drain inlet types to minimize flooding. The proposed method eliminates the need to examine each study area manually using existing hydrologic tools. Future works may include expanding the methodology for large datasets. They may also include a more sophisticated modeling approach for estimating rainfall intensity based on extreme weather patterns.