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Land use, land cover and climate change (CC) can significantly influence the hydrologic balance and biogeochemical processes of watershed systems. These changes can alter interception, evapotranspiration (ET), infiltration, soil moisture, water balance, and biogeochemical cycling of carbon, nitrogen, and other elements. The need to evaluate the combined effect of land use change and CC of watershed systems is a focus of this study. We simulated watershed processes in the SuAsCo River watershed in MA, USA, using a calibrated and validated Hydrological Simulation Program Fortran model. Climatic scenarios included downscaled regional projections from Global Climate Model models. The Land Transformation Model was used to project land use. Combined change in land cover and climate reduce ET with loss of vegetation. Changes in climate and land cover increase surface runoff significantly by 2100 as well as stream discharge. Combined change in land cover and climate cause 10% increase in peak volume with 7% increase in precipitation and 75% increase in effective impervious area. Climate and land use changes can intensify the water cycle and introduce seasonal changes in watershed systems. Understanding dynamic changes in watershed systems is critical for mitigation and adaptation options. We propose restoration strategies that can increase the resilience of watershed systems.

The changes in energy balance resulting from land-use change may significantly affect the amount and timing of water loss to the atmosphere as evapotranspiration (ET). Also, these will impact water fluxes in the watershed system, influencing runoff rate, flow volume, intensity, and frequency of floods. During the past century, land-use change in the SuAsCo (Sudbury-Assabet and Concord) watershed has altered basin hydrology, sediment, and nutrient load that is detrimental to water resources in SuAsCo. This study uses an integrated physically-based model Hydrological Simulation Program-FORTRAN (HSPF), along with Land Transformation Model (LTM), to assess predicted temporal and spatial changes in water, nutrient, and sediment yields for future land-use scenarios of 2035, 2065, and 2100. Results showed that a 75% increase in effective impervious area and a 50% decrease in forest area in 2100 (from 2005 baseline levels) are projected to cause a 3% increase in annual streamflow and a 69% increase in total yearly mean surface runoff. The average annual total suspended solid (TSS) yield at the watershed outlet is estimated to increase by 54% in 2100. An increase of 12% and 13% concentrations of average annual total phosphorus (TP) and total nitrogen (TN) are predicted by 2100 due to urban expansion and increased runoff volume. This integrated modeling approach will inform watershed managers and landowners about critical areas of the SuAsCo watershed to apply best management practices (BMPs) to mitigate the effects of land-use land cover (LULC) change.

Parkinson’s disease (PD) is one of the well-known neurodegenerative diseases. The main reason is the death of dopaminergic neurons that release dopamine in the brain region known as the Substantia Nigra pars Compacta (SNc). In this study, we developed a mathematical model of Parkinson’s disease incorporating a fractal-fractional operator with the Mittag–Leffler kernel to capture the complex, memory-dependent dynamics of the disease. We conduct a qualitative analysis to explore the existence and uniqueness of solutions and examine both disease-free and endemic equilibrium states. Stability conditions are explored using fixed-point theory and Lyapunov functions, while the dynamics are further analyzed through sensitivity analysis to identify the parameters most influential to the basic reproduction number. Additionally, chaos control is investigated using PID feedback strategies, and a Newton polynomial-based numerical method is implemented to simulate the system’s behavior. This approach enhances our understanding of Parkinson’s disease progression and offers a foundation for developing personalized therapeutic strategies.

Accurate measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are essential to know crop water requirements and to improve irrigation scheduling. The eddy covariance (EC) technique is increasingly being used to do so. Precise information on Kc for lowland rice is essential for local- and regional-scale irrigation planning but it is lacking for tropical humid climates such as those found in eastern India. We used the EC technique to measure ETa and Kc—the ratio of ETa to reference potential evapotranspiration (ET0)—of tropical lowland rice in eastern India over 2 years. ET0 was estimated by four different approaches—the Food and Agriculture Organization-Penman–Monteith (FAO-PM) method, the Hargreaves and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements were taken when rice was grown in the dry season (January–May) and wet season (July–November) and in between growing seasons when the field was kept fallow. The magnitude of average ETa during dry seasons (2.86 and 3.32 mm d−1 in 2015 and 2016, respectively) was higher than that of the wet seasons (2.3 and 2.2 mm d−1) in both the study years. Of the four methods tested for ET0 estimation, the FAO-PM method best-represented ET0 in this region of India. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet season (42–48%) and wet fallow (33–69%) periods of both years of study, suggesting that lateral heat transport was an important term in the energy balance. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages, namely, initial, crop development, reproductive, and late-season, were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76, and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in the dry seasons, whereas Epan performed better in wet seasons. The actual Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation scheduling.

Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. The goal of this work was to create, compare, and quantify uncertainty associated with evapotranspiration (ET) maps generated from different conditions and image capture elevations. We collected optical and thermal data from a commercially irrigated potato (Solanum tuberosum) field in the Wisconsin Central Sands using a quadcopter RPA system and combined multispectral/thermal camera. We conducted eight mission sets (24 total missions) during the 2019 growing season. Each mission set included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 min of each RPA mission set. Evapotranspiration values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. We compared HRMET-derived ET estimates to an Eddy Covariance system within the flight domain. Additionally, we assessed uncertainty for each flight using a Monte Carlo approach. Results indicate that the primary source of uncertainty in ET estimates was the optical and thermal data. Despite some additional detectable features at low elevation, we conclude that the tradeoff in resources and computation does not currently justify low elevation flights for annual vegetable crop management in the Midwest USA.

Predicting and forecasting evapotranspiration (ET) and groundwater (GW) variations are essential for sustainable water use in agriculturally intensive areas. Despite its importance in linking energy cycles and water, ET is challenging to measure. Further, to accurately estimate ET and GW dynamics, input uncertainty and deficiencies in hydrologic models pose fundamental challenges. Moreover, for land surface model-based ET, process models GW reanalysis, and remote sensing products, performance varies with the spatiotemporal scale due to the complex nonlinear relationships among meteorological and biophysical predictors of ET and GW dynamics in managed landscapes. Because of complicated boundary conditions, heterogeneous hydrogeological characteristics, groundwater extraction, and nonlinear interactions between these factors, it is proved difficult to predict and forecast ET and GW anomalies over the long term in agricultural areas. Nevertheless, data-driven methods and deep learning have shown promising results when identifying variables' dependencies.Here, this dissertation addresses this gap by 1) evaluating sources of bias in the regional Wisconsin Irrigation and Scheduling Program (WISP) models and developing a correction based on eddy covariance (EC) observations. 2) developing and evaluating the performance of data-driven models such as random forests (RF) and long short-term memory (LSTM) to predict and forecast daily ET on diverse agricultural farms in the Midwest, USA. and 3) utilizing recurrent neural network of LSTM as a method for forecasting GW anomalies two months in advance and for analysis of drivers that affect GW dynamics.To accomplish the first objective of this dissertation, ET, observations were made for five years in agricultural fields in the Wisconsin Central Sands (WCS) region, one of the most productive agricultural regions of the country, using EC systems. WISP model ET bias was traced to the underestimation of net longwave radiation (LWnet) owing to a biased specification of effective clear sky atmospheric emissivity ( , ). Correcting the , reduced the WISP model's bias and error for both LWnet and ET. The second objective was to apply the ET modeling framework developed for Midwest for nineteen fields where eddy covariance ET and meteorological observations are available during the growing season (April-October). In terms of daily ET prediction, a 16 parameter random forest approach outperformed a process-based land surface model. In irrigated crops, vapor pressure and crop coefficients were the most important predictors, while in non-irrigated crops, short wave radiation and enhanced vegetation index were the most important predictors. Finally, for the third objective, a modeling approach to forecast GW anomalies was developed and evaluated in the WCS region in the U.S. Groundwater anomalies showed high spatiotemporal variability, and their responses differed across locations depending on boundary conditions, catchment geology, climate, and topography. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. By understanding the critical processes underlying hydrologic and climatic variability and change over land, these findings may enable improved and more accurate hydrologic and climatic simulations. Using our framework, we can model water cycle components and dynamics in areas with unknown or uncertain subsurface properties.

Widespread occurrence of emerging contaminants (ECs) in water bodies is a major health concern worldwide, both in developing and developed countries. Contaminants from pharmaceutical, personal care products, pest control, and animal operations enter water bodies and can exceed acceptable levels. ECs can cause harmful impacts on aquatic and terrestrial wildlife and human communities. Endocrine disrupting chemicals cause a number of reproductive and sexual abnormalities in wildlife and humans. During prenatal and/or early postnatal life, exposure to these chemicals can impair the development of the endocrine system and of the organs that respond to endocrine signals in organisms. The effects during development are permanent and sometimes irreversible. Managing ECs in water resources is a critical issue that requires attention especially in sensitive ecosystems and in rapidly developing areas. There is a need for a comprehensive framework that aims at system-wide abatement (source-transfer-fate levels) using both structural and non-structural approaches. In this study, we review the state of this problem in developing and developed countries, nature of their impacts on aquatic organisms, terrestrial animals, and on public health. A comprehensive, innovative, and novel approach with multi-level strategies at source, transfer, and sink level is proposed for effective removal of ECs. Some structural approaches at source level for abatement of ECs include the use of best management practices like buffer strips, riparian management, natural, and constructed wetlands. Since these strategies have multi-level applicability, they are cost-effective alternatives to include in wastewater treatment. Among structural approaches at sink level, powdered activated carbon, nanofiltration, and reverse osmosis can remove most of the emerging organic. However, the cost of treatment by these methods is high and it is inevitable for treating drinking water. Besides structural approaches, non-structural approaches play a major role and need to use targeted strategies in dissemination of information, outreach to modify human behavior, incentives for controlling contaminant loads, and improved and updated policy mechanism for compliance to pollutant standards.

Purpose: The aim of this research was to prepare adhesive nanoparticles for the topical application of Minoxidil (MXD). Methods: Thiolated β-CDs were prepared via conjugation of cysteamine with oxidized CDs. MXD was encapsulated within thiolated and unmodified β-CDs. Ionic gelation method was used to prepare nanoparticles (Thio-NP and blank NP) of CDs with chitosan. Nanoparticles were analyzed for size and zetapotential. Inclusion complexes were characterized via FTIR. Drug dissolution studies were carried out. An in vitro adhesion study over human hair was performed. An in vivo skin irritation study was performed. Ex vivo drug uptake was evaluated by using a Franz diffusion cell. Results: Thiolated β-CDs presented 1804.68 ± 25 μmol/g thiol groups and 902.34 ± 25 μmol/g disulfide bonds. Nanoparticles displayed particle sizes within a range of 231 ± 07 nm to 354 ± 13 nm. The zeta potential was in the range of −8.1 ± 02 mV, +16.0 ± 05 mV. FTIR analyses confirmed no interaction between the excipients and drug. Delayed drug release was observed from Thio-NP. Thio-NP retained over hair surfaces for a significantly longer time. Similarly, drug retention was significantly improved. Thio-NP displayed no irritation over rabbit skin. Conclusion: Owing to the above results, nanoparticles developed with MXD-loaded thiolated β-CDs might be a potential drug delivery system for topical scalp diseases.

The current study was conducted to estimate the carbon (C) storage status of agroforestry systems, via a non-destructive strategy. A total of 75 plots (0.405 ha each) were selected by adopting a lottery method of random sampling for C stock estimations for soil, trees and crops in the Mandi-Bahauddin district, Punjab, Pakistan. Results revealed that the existing number of trees in selected farm plots varied from 25 to 30 trees/ha. Total mean tree carbon stock ranged from 9.97 to 133 Mg C ha−1, between 5–10 km away from the brick kilns in the study area. The decreasing order in terms of carbon storage potential of trees was Eucalyptus camaldulensis > Syzygium cumin > Popolus ciliata > Acacia nilotica > Ziziphus manritiana > Citrus sinensis > Azadirachtta Indica > Delbergia sisso > Bambusa vulgaris > Melia azadarach > Morus alba. Average soil carbon pools ranged from 10.3–12.5 Mg C ha−1 in the study area. Meanwhile, maximum C stock for wheat (2.08 × 106 Mg C) and rice (1.97 × 106 Mg C) was recorded in the cultivated area of Tehsil Mandi-Bahauddin. The entire ecosystem of the study area had an estimated woody vegetation carbon stock of 68.5 Mg C ha−1 and a soil carbon stock of 10.7 Mg C ha−1. These results highlight that climate-smart agriculture has great potential to lock up more carbon and help in the reduction of CO2 emissions to the atmosphere, and can be further used in planning policies for executing tree planting agendas on cultivated lands and for planning future carbon sequestration ventures in Pakistan.