Non-polarized electrodes are used to measure the potential differences between two points of ionic conductors. They have been widely used in geoelectric measurements,e.g., magnetotelluric (MT), induced polarization (IP), and spontaneous potential (SP). In practical applications, non-polarized electrodes have the following shortcomings: (1) Regular maintenance is required, and the electrodes must be placed in a special solution after use. (2)The working life is limited due to improper maintenance (3)The electrode is large and generally weighs more than 0.5kg. (4) The electrode temperature coefficient is usually 0.1~1mv/℃ which is required to be buried deep (>1m) for long-term observation. We have developed a maintenance-free solid Pb-PbCl2 non-polarized electrode. The electrode has the following advantages. (1) No maintenance is required, the bottom of the electrode is sealed with a rubber sheath. (2)The potential of electrodes is stable, with a monthly drift of less than 50 μV. (3)The temperature coefficient is 10-20 μV/℃. (4) For 100m electrode distance, the signal-to-noise ratio relative to the natural induced electric field is >20 dB at periods of 10,000 s and 10 dB at periods of 100,000 s, comparing 10 dB and -10 dB for PMS 9000 electrodes, respectively. We developed five types of electrodes for different measurements. The smallest diameter is 2.5cm and 10cm in height, and the weight is 0.1 kg which can be also used in the lab. All types of electrodes are waterproof and can be used for the measurement of the electric field in waters, e.g., lake bottom. The field test shows that our electrode can significantly improve the data quality at dead-band (0.1-10 s) and low-frequency for MT measurement, e.g., one can obtain smooth MT impedance with a period of more than 50,000 s for a month acquisition.

The Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS) is a bulk microphysics framework that calculates process rates using generalized power laws of the moments of the droplet size distribution. Formula parameters are inferred with Monte Carlo methods, evaluating a proposed set of parameter values by comparing model outputs to observations. In this study, we produce a set of 2- and 3-moment BOSS schemes that effectively emulate the behavior of a bin microphysics model, using an idealized 1D driver for various forcing conditions that produce either non-precipitating or drizzling cloud. In this driver, BOSS is fully responsible for collision-coalescence, condensation/evaporation, and calculation of hydrometeor fall speeds, with only dynamical forcing and droplet activation handled externally. Comparing BOSS schemes with different terms and different numbers of prognostic cloud moments allows us to evaluate whether the model benefits from the added complexity of these changes. BOSS also has an advantage over black-box machine-learning methods in that the process rate formulas are “human-readable”, allowing us to analytically identify potential sources of instability.

Inspired by the AAHE Assessment Forum’s Principles for good practice for assessing students’ learning, the author has generated Ten Steps (1992) to Promote a Stronger Emphasis on Student Learning in a Hydrology–Fluid Mechanics Course. Student-Learning must not be focused only about making the connections initially. Instead it should concentrate more about maintaining those connections in the long run. Student learning is fundamentally about a strong bond between the academic establishment and the student citizens of the entire community. Student-Learning is enhanced by the environment. It should take place in the context of a compelling situation that balances curiosity, challenge and opportunity. Student-Learning should have an active search for meaning by the learner – constructing knowledge rather than passively delivering it or receiving it. In other words one should create a Concept Mapping Model instead of a Structured Content Model. Student-Learning is developmental. In other words, it is a cumulative process involving the whole person, who is capable of integrating the new with the old. The settings, the surroundings, the influences of others contribute to successful accomplishments. Student-Learning should be viewed as an effort promoted by individuals who are intrinsically tied to others as social beings, actually interacting as collaborators. Student-Learning is strongly influenced by the educational climate in which learning achievements takes place. Student-Learning requires Action, Communication, Ownership, Reflection and Nurture (ACORN) as suggested by Hawkins and Winter (1997). Student-Learning in reality aims at an educational experience that takes place informally and incidentally, beyond explicit teaching in the lecture hall. Student-Learning is grounded in particular contexts and individual experiences, requiring effort to transfer specific knowledge and skills to other citizens. Student-Learning involves the ability of individuals not only to monitor their own learning, but also be able to enhance learning through collaboration and cooperation. References: Hawkins, P., & Winter, J. (1997). Mastering change: Learning the lessons of the enterprise in higher education initiative. London: Department for Education and Employment.

Accentuating Student Performance in a Hydrology Class The author has successfully used student centered performance pedagogy to enhance student learning in a Fluid Mechanics and Hydrology Class. In this particular research activity, he has used Socratic Inquisition Techniques to enhance student learning. He presents detailed analysis of the data he has collected to examine and determine how student performance can be accentuated and improved. Socratic Inquisition is based on Critical thinking and Critical thinking is the objective analysis of facts to form a judgment. The subject is complex, and several different definitions exist. However, one can generally view it as “unbiased analysis or expressive evaluation of factual evidence.” Scholars agree that Critical thinking must be self-directed, self-disciplined, self-monitored, and self-corrective procedure. This helps the students to become independent learners and be enthusiastic to expand their knowledge base. Fluid Mechanics and Hydrology is a junior level course that is based on providing a mathematical base for building the needed knowledge for a variety of subsequent engineering courses. The main objective has been to instill effective communication skills among students and to enhance problem-solving abilities. The author is satisfied with the results gathered, however he sees lots of areas for improvement of student learning. Based on the research data collected, the author has concluded that the method of Critical Thinking actually motivates the students to take ownership of learning. This is definitely a useful path for Accentuating Student Performance in a Fluid Mechanics and Hydrology Class. References 1. Gardner, Howard (2000). Intelligence Reframed: Multiple Intelligences for the 21st Century. New York: Basic. 2. Keefe, J. W. (1991). Learning style: Cognitive and thinking skills. Reston, VA: National Association of Secondary School Principals. 3. Lage, M. J.,Platt, G. J. & Treglia, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. Journal of Economic Education.

Mangrove ecosystems are an essential component of tropical and subtropical urban coastal regions where they provide critical ecosystem services and ensures climate mitigation while playing a pivotal role in the livelihoods of coastal communities. However, growing anthropogenic pressures from rampant urbanization and infrastructural demands are leading to an unparalleled loss and degradation of mangrove cover especially in coastal cities of the global south. Addressing the immediate need for monitoring, protection and restoration of the ecologically stressed mangroves, this study uses earth observations, machine learning and cloud computing methods for advancing timely and accurate spatiotemporal mangrove mapping and change detection. Image classification through four different models i.e. Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boost (GTB) and Support Vector Machine (SVM) was performed using Google Earth Engine to classify mangrove extent along the coastal regions of Mumbai, India. Spatially explicit temporal trend in mangrove extent was studied and used to estimate the rate of change of mangrove extent over a period of 30 years. Accuracy assessment was conducted to validate the robustness of trained classifier models alongside their comparative performance. Classification accuracies on the order of 95% were achieved through the machine learning-based classifier models in distinguishing mangrove areas from other land cover types. The time-series analysis combined with image classification reveals the pattern and causes of spatiotemporal changes in mangrove cover and highlights the hotspots of mangrove loss and gain. This approach can aid stakeholders in the management and restoration of mangrove ecosystems through periodic and cost-effective monitoring of mangrove cover particularly in data deficient coastal cities. The outcomes of the study will contribute towards efficient decision-making in achieving the localization of Sustainable Development Goals 6 and 11 of the United Nations.

The atmospheric drag and the Radiation Pressure are the dominant forces acting on LEO satellites. Many different approaches have been followed for the modelling of these non-gravitational forces, based on the physics and the satellite characteristics, but in many cases large inconsistencies are present between the models and the accelerometer measurements. Atmospheric drag is considered as the most difficult force to model, and the Radiation Pressure models show large deviations from the measurements depending on the b′ angle and the position of the satellite near the entrance and the exit from the Earth’s shadow. Numerous models have been presented for GRACE satellites but none for GRACE-FO. The innovation of this study is the development of an atmospheric drag and a Radiation Pressure data-driven model based only on the accelerometer measurements of GRACE-C satellite, using least squares principles. The atmospheric drag is modelled using accelerometer measurements from the shadow segment of the orbit. An additional weighted constraint is that near the middle of the sun segment of the orbit, the drag in the x-direction should be equal to the actual measurements due to Radiation Pressure being nearly zero. Subsequently, we subtract the modelled drag from the real measurements in order to estimate the Radiation Pressure which, consequently, is modelled using a least squares frequency-domain analysis. The residual series proceeded from the subtraction of these two models from the actual measurements of GRACE-C accelerometer, are analyzed by taking into consideration the local time, the spatial information and the variations of b΄ angle, as well as their connection with electromagnetic changes in the upper atmosphere. The proposed models have been tested for different time periods in the last three years of GRACE C and the rms of the residual series along the x and the z axes of the accelerometer is ~2.5 nm/s2, while the y-axis exhibits an rms of ~1 nm/s^2.

GRACE-FO (GFO) and Swarm are two LEO missions that, among others, provide non-gravitational acceleration measurements required for geopotential model development and modelling of non-gravitational forces acting upon them. Unfortunately, the performance of the accelerometers on board for both missions is not the expected. Measurements from both missions present dominant bias jumps that occur on all accelerometer axes and they have been linked to the satellites’ entrance to and exit from the Earth’s shadow. These jumps are estimated and corrected at Level 1A of GFO C and at Level 2 of Swarm C in an optimal way using Least Squares methodology. The corresponding variances of the jumps are also calculated. Furthermore, the measurements contain spurious signals and dominant spikes mostly connected with thruster activation, mainly in the equatorial region or high temperature sensitivity. These disturbances have a significant impact on the data analysis. We propose an alternative weighting filter methodology to generate Level 1B data from Level 1A for GFO C that includes the attenuated spikes and their corresponding variances and does not involve the removal of the spikes nor does it include any interpolation to fill data gaps. This methodology is used for Swarm C accelerometer Level 2 dataset as well. Using spectral domain methods, we show that the newly generated GFO Level 1B and Swarm Level 2 data are not contaminated by the presence of spikes and data jumps. In the polar regions, mostly at the South pole, spikes in the measurements are connected to magnetic disturbances when the satellites enter these regions. Our proposed methodology contains an optimal and unbiased dataset of non-gravitational acceleration measurements that can be used for the estimation of geopotential models and also for the investigation of the accelerometer’s response to electromagnetic disturbances and the modelling of other non-gravitational accelerations to derive thermospheric neutral densities.

Dryland play a major role in the global carbon cycle. The US Southwest is experiencing fewer, larger precipitation events and longer dry intervals between rainfalls. These longer droughts are likely driving physiological, phenological, morphological, and community-level responses of dryland vegetation with unknown feedbacks to atmospheric CO2. It remains unclear how seasonal drought intensity and duration affect the magnitude, duration, and direction of dryland vegetation carbon cycling and atmospheric feedbacks. To address this question, we integrated the measurements of soil hydrology, plant community, and carbon fluxes from a new rainfall manipulation experiment site (RainManSR) in the Santa Rita Experimental Range of Southeast Arizona, US into the Community Land Model (CLM5). This field experiment imposed four precipitation treatments (S1–S4), each with the same summer growing season total rainfall (205 mm) but packaged into a range of many/small to few/large rainfall events. This experiment enabled a comprehensive evaluation and parameterization of drought tolerance of semiarid grassland plant functional types (i.e. deep-rooted perennials and shallow-rooted annuals) and their effects on climate extreme-carbon cycles feedbacks. The ability of the improved CLM model to capture dryland productivity and carbon fluxes was then validated at larger scales with observed carbon fluxes from closeby AmeriFlux sites in the US Southwest, such as the semi-arid Kendall grassland site (US-WKG). Applying this model in the Arizona grassland sites indicated that high tolerances of dryland plants to relatively low soil water potential maintains the growing season length of the dryland ecosystem under drought conditions, whereas the acclimation of carbon assimilation and root dynamics to drought mitigate drought effects on vegetation productivity and interannual variability of carbon exchange.

Radiation damage in zircon directly impacts diffusion of He from the crystal lattice and is a key factor in defining the kinetics of the zircon (U-Th)/He system. Damage accumulates within a crystal as a function of time and U and Th concentration, but can be thermally annealed as well. The total level of radiation damage in a zircon crystal is governed by a thermally-activated, kinetic process, which in turn influences the interpretation of zircon (U-Th)/He dates for thermal histories. Several annealing models have been defined for the zircon system based on measurements in natural crystals; however, few studies have investigated how multiple levels of radiation damage due to zonation of actinides within a crystal may influence the annealing process. Here we use Raman spectroscopy to map the full width half maximum (FWHM) of then (SiO) band, a proxy for radiation damage, in zircon crystals from the Lucerne pluton (Maine, USA) with heterogeneous distributions of U and Th. We compare FWHM maps before and after annealing these crystals at laboratory times and temperatures. These maps show that each damage zone within a single zircon acts as an isolated domain that is dictated by an independent set of annealing kinetics. Thermally activated annealing decreases radiation damage in all radiation damage zones; however, the rate of annealing is not consistent across all zones. We identify specific modes of damage in probability density plots of all measured FWHM in a crystal that are not present in pre-annealed imagery, but persist in post-annealing Raman maps, regardless of laboratory time temperature conditions: FWHM modes at 2-5 cm, 10-15 cm, and25-30 cm. We attribute these persistent damage modes to variable annealing kinetics that are partially dependent on the level of pre-annealing damage, combined with the inability of high-damage crystals, or zones within crystals, to fully recover their crystallinity. That is, some damage is permanent. These findings therefore show that zircon crystals with non-uniform distributions of U and Th can anneal to create long-lived damage zones at specific damage levels, which has implications for treating the zircon (U-Th)/He chronometer as a multi-domain diffusion system.

A substantial amount of dust aerosol was transported to the central Himalayas during the multiple dust storm event that occurred from 12 to 15 June 2018 in the Thar desert, northwestern India. In this study, we implemented a cloud-resolving Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to assess the impact of transported Thar-related dust aerosols on precipitation processes over the central Himalayas. We isolated the effect of the transported dust on the precipitation distribution by zeroing out dust aerosols from lateral boundary condition and keeping other species of aerosols. Results show the noticeable impacts of transported dust aerosols on regional precipitation and cloud properties over the central Himalayas. When transported dust is included, spatially averaged (25°N-31°N and 78°E-89°E) AOD increases by 0.36. Over the Himalayas foothills with the dust, precipitation is enhanced by 7.5% (0.63mm), while surface temperature is reduced. The presence of mineral dust aerosols increased the cloud droplets and ice crystal number concentration, which is further facilitated by the mountainous terrain by orographic lifting, leading to enhanced precipitation. We diagnosed the role of cloud microphysics and that of the cloud dynamics in the simulated precipitation sensitivity. This study highlights the effect of remote dust aerosol on the perturbation of cloud microphysical properties, which can significantly influence precipitation over the Himalayas and impact the regional hydrology.

Climate patterns in the agricultural zones of the Indus basin are predicted to undergo undesirable changes in the hydrological cycle. These changes are a threat to the widespread agricultural activity and associated livelihoods of the underlying population. Livestock, an essential sector for human sustenance in the basin, is also a major source of greenhouse gas emissions thereby contributing towards climate change. However, it is also a recipient of climate impacts, thus introducing feedbacks and uncertainties that are further accentuated by the Water-Energy-Food Nexus. Here we model and simulate the farm-level dairy operations of a single dairy farm by introducing informatics-driven precision measurements of water, energy, food, and carbon emissions in a system dynamics framework. We analyze the simulated trajectories for energy, water, and waste fluxes to under different interventive scenarios to identify actions that enhance productivity and minimize environmental impact. The model is constructed based on data gathered from two dairy farms located in rural Punjab, Pakistan. The farms have a livestock capacity of 300 and 134 animals respectively, with data related to water, energy, food, and climate gathered over a duration of two years. The simulated results may be used to uncover structural changes in dairy-farm operations which improve the economic structure of the farm while remining within the thresholds defined by Sustainable Development Goals (SDG) 3, 7 and 13 set by the United Nations. The model itself also helps in unravelling the complex interactions among water-energy-food flows along with their coupling to land-climate interactions in context of the dairy farm operations. Beyond the climate change adaptation measures extracted from this study, the system dynamics model that we construct in the process, can help develop economic tools that leverage the advantages of water/climate informatics driven data services and decisions under large variabilities to devise sound agricultural policy.

Inverse thermal history modeling is an effective tool to explore plausible time-temperature (t-T) histories that can be used to describe the geologic history of a sample. Although in some inverse modeling exercises the input thermochronology data is consistent with a single set of t-T histories with similar heating and cooling trends, more commonly inverse models identify a range of paths with different and distinct heating and cooling histories but similarly good fits to the data. Each set of these “path families” typically requires a different geologic interpretation to explain the observed heating and cooling trend, so it is important to identify and consider all possible path families consistent with the regional geology that fit the modelled dataset before selecting a preferred geologic interpretation. Although the inverse model results are always consistent with measured data, a model’s ability to detect all possible path families is partly controlled by the model design – for example the choice of initial conditions, monotonicity settings, and forced time-temperature windows. In this exercise using the thermal history modeling program HeFTy, we illustrate the effects of model design on the inverse model results of a set of multi-chronometer datasets from the southern Patagonian Andes. We use model design to maximize the number of path families identified through inverse modeling. Once individual paths are classified according to path families, we use independently constrained regional geology to discriminate among the diverse plausible set of path families and evaluate different available geologic scenarios. Our exercise illustrates that models restricting exploration of all path families may not identify the true cooling history of the sample. Initially, it may appear challenging to interpret inverse model results that include multiple path families, but we argue that iterating between independent geologic data and modeling provides an effective tool to test the geologic plausibility of alternative heating and cooling histories. Although this exercise is executed using HeFTy, maximizing the identification of all possible path families should be an important component of model design in inverse modeling exercises using all inverse modeling programs.

Gas hydrates have been reported to exist in marine sediments from various parts of the world ocean. The hydrates start decomposing soon after recovery of the sediments through coring operations due to changes in ambient pressure and temperature. This decomposition leads to changes in sedimentary structures, and thus complicates physical property related measurements of the sediments by conventional methods. In this study, we used a medical X-ray CT scanner to quickly scan the recovered cores, and then used raw data from the CT, and thus avoided image processing steps, to estimate porosity and density of the sediments. The raw data were in terms of CT numbers, which were obtained by drawing a circular region of interest (ROI) to cover most of the sediments visible in a cross section XCT image of the sediments. The data were weighted for relative contribution of liquid and solid in sediments before estimating porosity. On the other hand, density was estimated by using an average CT number that was automatically calculated by the Osirix software used for drawing the ROI on an XCT image, and by using a calibration equation based on a set of standards. Although some uncertainty in estimation of relative volumes of solid, liquid and gas could not be avoided, the results obtained by this new procedure were in good agreement with those obtained by conventional methods. Since porosity and density estimates by the new procedure can be made in a matter of minutes after core recovery, it can guide progress of coring operation and further processing of hydrate-bearing sediments.

Quantification of river flow is a significant component of water resource management needed to develop infrastructure, prepare for drought or flood, and ensure equitable resource use. Yet, these data have often been deficient in rural regions generally dependent on smaller river systems, especially in low- and middle- income countries, and in places where no remote gaging capabilities currently exist. A relationship between river flow and width has been previously demonstrated in attempts to record and monitor flow with only satellite-based measurements; however, those methods have focused on rivers with a width of > 30m and have shown extreme errors when compared to historical gaging records. A new technique for monitoring river flow remotely is presented here, which uses Manning’s equation for open channel flow coupled with width measurements from satellites. This technique allows for the creation of a remotely-sensed historical river flow record and uses the minimum number of ground-based measurements for the calibration of Manning’s equation. Cross-river depth profile, discharge, and slope are necessary for initial calibration. This method used four-band satellite images with 3m-resolution for width measurements, with images being adjusted for the Normalized Difference Water Index (NDWI) for better delineation of the water’s edge. Buffalo Creek, in Freeport, Pennsylvania and the Mutale River, in the Limpopo Province of South Africa, were used as study locations. An estimation of flow at each site was obtained using every available satellite image of those transects. Preliminary results indicated successful calibration of Manning’s equation for the individual river transects, and verification of this method yielded flow estimation with an average of 74% error at Buffalo Creek and 48% error at the Mutale River, when compared to historical data from gages present near the study sites. The primary challenge of using this method on smaller rivers is the determination of width from the satellite images. Automated techniques to delineate the water’s edge are presented as recommendations to increase accuracy. This method may be used as an additional tool in river flow monitoring for water resource management.

A hybrid Linear Programming (LP) and Nonlinear Programming (NLP) optimization model is developed for California’s hydropower operations. Built on top of Pyomo library, a high optimization modeling language in Python, the model can connect to several freely available, state-of-the-art solvers. In this model, fast evaluation of LP and detailed model representation of NLP are fully utilized. The hybrid model solves the same problem with linear approximation (a simplified objective function representation) and with NLP solver, where no simplification is made to objective function. Outputs from LP model are used as initial values (warmstart) for NLP model’s decision variables, which reduce number of iterations for convergence and so runtime. The model is capable of representing large network of hydropower plants that are in serial or parallel, or fixed and variable head plants. The model is used to evaluate effects of increased solar photovoltaic (PV) generation in California. California has a goal of generating electricity from renewable resources at least 33% by 2020, and 50% by 2030, and solar PV generation supplies most of renewable generation portfolio during daytime. This expanded use of solar PV changes generation pattern from one daily peak system to two daily peak system. Due to excess generation of solar PV, negative prices can occur during daytime. Therefore, evaluating effects of solar PV on hydropower operations and adapting to new conditions are essential.

Based on a large database of Wideband Imaging Camera (WIC), which is a part of Far Ultraviolet (FUV) instrument, onboard the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) satellite during 5 years between 2000 and 2005, we found a new morphological type of transpolar arcs (TPAs), which are identified as “nightside distorted TPAs”. The nightside ends of the dawnside (duskside) TPAs got distorted toward pre- (post-) midnight sector; These TPAs look like the shape of an alphabetical letter of “J” or “L”. We identified 17 nightside distorted TPAs from our database. The 12 events out of 17 nightside distorted TPA events were the dawnside TPA with the nightside end distorted toward the pre-midnight sector (“J”-shaped TPA), and the TPAs in the duskside, whose nightside parts got distorted toward the post-midnight sector, were found in the remnant 5 events (“L”-shaped TPA). Statistically, the nightside distorted TPAs can dominantly be found under the northward Interplanetary Magnetic Field (IMF) conditions. Furthermore, when the IMF-By component pointed to the dawnward (duskward) direction, the “J” (“L”)-shaped TPAs were dominantly observed, suggesting that the relation between the IMF-Bz and By orientations, and the locations of the nightside distorted TPAs is consistent with that between the IMF conditions and regular TPA previously reported. We also followed the time sequence of the nightside distorted TPA evolution with the IMAGE FUV-WIC imager and additional Polar UVI data. In most cases, the “J” and “L”-shaped TPAs started to grow from the nightside main auroral oval, and protruded to the dayside region with being distorted. In this presentation, we will introduce several selected cases of the nightside distorted TPA among our database, and discuss why and how the nightside end of the TPA became distorted toward pre- or post-midnight sectors based on the in-situ satellite nightside plasma sheet observations and the electron drift velocity distribution calculated utilizing a simple electromagnetic field model in magnetotail, together with the auroral imager data.

Mangrove forests, occupying tropical and subtropical coastal areas, serve as coast protector, water filter, spot of attraction and an exceptional carbon reservoir. Despite these extraordinary ecological and economic functions, our understanding of the mangroves is limited because field survey is hard to conduct in the intertidal mangrove habitats. While satellite remote sensing provides good spatial and temporal coverages globally, data availability at the tropical latitudes is limited due to frequent cloud contamination. The quickly emerging unmanned aerial vehicle (UAV) technique enables data collection under almost all weather conditions, thus provides new opportunities. By mounting light detection and ranging (LiDAR) sensors on UAVs, the 3D structure of mangroves can be accurately characterized even at individual tree level. The basic tree parameters such as tree height and crown size can be easily extracted, which then enables the estimation of biomass, carbon stock and other important ecological indices. This study uses high density (>100 pt/m2) UAV-LiDAR data collected at four consecutive years to detect the growth rate and pattern of mangroves. The change detection is done at individual tree level. The dependence of mangrove growth pattern on tree species and clumpingness are analyzed. The output will allow scientists and environment managers to know mangroves better and to manage them effectively and efficiently.

Land use/land cover (LULC) change could adversely affect watershed health by elevating nutrients and sediment levels and intensifying the risk of flooding. In this study, a spatially-explicit LULC change modeling framework was coupled with the Chesapeake Bay Watershed Model (CBWM) to investigate the impact of LULC change on nutrients (total nitrogen and total phosphorous), sediment and runoff volume in the watersheds surrounding Virginia’s Shenandoah National Park, U.S. Four stakeholder-informed scenarios alongside a Recent Trends LULC change scenario were studied. The stakeholder-informed LULC change scenarios, which differed in consideration of future planning and population growth, were developed through several meetings with stakeholders. To develop the Recent Trends, the historical LULC trend from 2001 to 2011 was analyzed. Using 2011 as a baseline scenario, the spatio-temporal patterns of LULC change were estimated as influenced by physiographic and socio-economic drivers 50 years in the future (2061). The projected LULCs were fed into the CBWM to predict the change in average annual loading of nutrients, sediment and runoff volume. While the changes in loads at the full study area were not substantial (< 0.9%), changes became more pronounced at finer spatial scales. Expectedly, the LULC change scenario with ad-hoc planning and high population growth resulted in the largest increase in runoff volume. However, the scenario with ad-hoc planning and low population growth showed the largest increase in the simulated pollutants. This was because while this scenario projected less development, it projected more increases in agricultural LULCs that export more nutrients and sediment than other changing LULCs. This implied that sole land use planning based on urban development is not sufficient for watershed protection and agricultural LULCs need to be incorporated in concert in our future planning. This further suggested that land use planning plays a more critical role than population growth rate in water quality management. The results have implications for the Chesapeake Bay total maximum daily load and could help well-informed future land use planning and watershed protection by incorporating the impact of future LULC change on water quality and quantity.