A document by Yashvardhan Verma. Click on the document to view its contents.

A document by Swarnalee Mazumder. Click on the document to view its contents.

Most studies on the impacts of extreme hydrometeorological events on hydrological processes have focused primarily on surface water systems rather than groundwater systems. This study explores and seeks to untangle the complex nature of groundwater dynamics and resilience across British Columbia (BC) in response to the 2021 heatwave-intensified drought and atmospheric rivers (ARs). Historically, there have been many episodic drought events along with substantially wet periods. However, 2021 marked an unprecedented year for the immediate co-occurrence of intense and extreme drought and deluge. This weather whiplash resulted in the lowest and highest groundwater levels on record for many wells across BC. The record meteorological drought, intensified by a 13-day heatwave in late June, affected the entire province and lasted for over 50 days in the south coast region. This was followed in November by the most intense ARs to make landfall on record in southwestern BC. Groundwater hydrograph anomalies for 2021 were computed relative to their short-term historical mean for 194 provincial observation wells across the province. The 2021 anomalies showed a limited but distinct range of responses to both the drought and ARs, and cluster into three response groups, largely associated with their respective hydroclimatic regime. Many coastal wells showed a strong response to drought; however, nearly all wells in the southern interior responded substantially, with groundwater levels significantly below their historical range by late summer. Presently, groundwater levels seem to have recovered across the province, especially on the coast. This resiliency is attributed in part to the ARs that made landfall since last year along with a particularly wet, La Niña winter. The majority of coastal wells showed a much stronger signal to the ARs compared to the interior or eastern BC wells, likely due to the more rapid and intense rainfall experienced in southwestern BC. Groundwater systems across BC were variably impacted by these hydrometeorological extremes, showcasing the need for focused and area-specific approaches to water allocation decisions in assuring sustainable withdrawal practices.

Temperature is a key physical variable in streams that controls rates of metabolic processes and oxygen availability, and therefore the suitability of aquatic ecosystems. During the summer low flow period, stream temperature can be moderated by contributions from cool water sources, such as groundwater discharge and higher elevation headwaters. However, the relative contribution of these cool water sources can be spatially and temporally varying, particularly in snowmelt-dominated, high-relief watersheds. In this study, in situ and remote sensing methods are used to measure the stream temperature along a low elevation section of the North Alouette River (British Columbia, Canada) that passes through a forested area and into an open agricultural area. The methods include temperature loggers placed at the stream surface and streambed interface, and thermal infrared images acquired using a drone and Landsat 8 and 9 satellites. The drone and in situ measurements of stream temperature show good agreement, while the satellite images show the same temperature distribution (cooler in the forested area and warmer downstream in the open agricultural area) but overall shifted temperatures. Areas of mixing of cool and warm waters are identified within the stream channel using the drone imagery. Waters samples analyzed for stable isotopes are used to identify the different source waters and estimate their relative contribution to stream temperature moderation. This fingerprinting is made possible by a precipitation isotope composition-elevation gradient in the catchment. The isotope data support the observations of mixing identified with the temperature data. Understanding of where and when cool water sources contribute to streamflow will be used to inform groundwater allocation decision-making, to ensure that groundwater pumping is minimized in areas where groundwater discharge is critical for moderating stream temperatures.

In light of climate change, wildfires are concurrently becoming more frequent and devastating worldwide. Though we have a good understanding of fire frequency changes in the past using charcoal analysis, records of the characteristics of these fires, such as fire severity, are lacking. This limits our ability to model how fire severity responds to climate change. Boron isotopes in clay minerals show promise as a novel fire severity proxy, where increased 𝛿11B is correlated with higher fire severity. Through reacting boron leached from experimentally combusted plants with clays, we determine that the observed correlation with fire severity is likely caused by input of isotopically heavier boron from combusted leaves. In contrast, combusted barks lower the 𝛿11B of clays upon reaction. Despite the different results, in both experiments with barks and leaves, similar boron isotope fractionation is observed during boron adsorption onto clays, where the lighter 10B is preferentially adsorbed. Therefore, the different results are likely caused by the different boron isotope composition of leaves and barks, where leaves have a much higher 𝛿11B (~30 ‰) than bark (~9 ‰). Combustion temperature can also affect the 𝛿11B of clays: changes to the 𝛿11B of clays were observed only when reacting with bark combusted at >300 °C, or with leaves combusted at >550 °C. This could be because more boron can be leached into solution from materials combusted at higher temperatures, which in turn results in greater adsorption onto clays during reactions. Clays have higher 𝛿11B in soils affected by high severity fires that consume tree crowns, because these fires combust more leaves that then deposit their isotopically heavier boron content into the soil. This relationship could help complete our fire record and improve our ability to predict future fire characteristics.

Seismology is a data-driven science with a huge amount of data gathered for over a century. Though seismic data recording started in 1900, the growth of seismic data has obviously been exponentially in the last three decades. This data growth can be easily noticed if one takes a close look at just one of the largest seismological data centers in the US, the Data Management Center (DMC) of the Integrated Research Institutions in Seismology (IRIS). Data at the DMC grew from less than 10 Tebibytes in 1992 to about 800 Tebibytes in 2022. With the availability of such a large amount of seismic data, it is paramount to develop new seismic data processing and management tools to help analyze and find new and better seismic models. Developing new big seismic data processing and management tools will be helpful to make the best use of such growing big seismological data sets. The main goal of this investigation is the development of efficient data manipulation and processing tools for retrieval, processing, merging, aggregation, and management of big seismic data from disparate data sources. In this study, such big seismic data processing tools are being developed using python programming language and open source python libraries, and the tools we are developing will be helpful to extract, split, and convert, merge and process big seismic data. In addition, python is very suitable for data science and has powerful libraries to process and manage data and applications. Significant contributions have been made in recent python based libraries for seismic data processing, though there are still some rooms for improvement when it comes to seismic applications to merge, convert, manage  and process big seismic data from disparate data sources and converting different file formats. Seismic data from different networks surrounding the Rio Grande Valley have been collected from different data sources. Our attempt is to test the developed tools and evaluate their performance. This study has made important progress in this regard and the results are promising. 

Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars in losses. Marine earthquakes are the leading cause of large tsunamis which cause deaths, destruction, displacement of population, and a possible nuclear meltdown. Thus, prediction of earthquake or its aftershocks or earthquake early warning system has a great potential to mitigate the loss of life as well as different kinds of damage. Earthquake prediction would mean forecasting the occurrence of an earthquake by providing both its magnitude estimate and accurate location. Earthquake prediction has been an important area of seismology research for quite a while, and it looks like it will continue to be an important area of research. Recently, with the implementation of deep learning in seismology, scientists have been able to detect, predict, and model seismic waves and earthquake aftershocks. Earthquake aftershocks are generally triggered by changes in stress formed by large earthquakes that happen within, or surrounding a given fault network system. The main goal of this study is to investigate the improvement of aftershock pattern predictions with the implementation of tuning and optimizing of deep learning parameters. To achieve these goals, we have developed an algorithm that can help first gather mainshock-aftershock sequence data. Some of the criteria used in identifying earthquakes that initiate an aftershock is to look at earthquakes that happen within a certain radius, the values we attempted are within about 0.5 degrees range, and within a certain period, from few seconds to several weeks of the occurrence of the main shock. For the sequence identification, we have been using seismic data from the United States Geological Survey (USGS)-National Earthquake Information Center (NEIC). We are also looking at different open-source data gathered by researchers for a similar study. The deep neural networks we are implementing make use of Keras python Toolkit, and Theano and Tensorflow libraries, with a plan to use PyTorch python library instead of Theano library in the future because of some maintenance issues. To this point our attempts have shown a good progress.

Inundation is increasing due to extreme weather conditions severely impacting the agricultural sector. Hence, inundation impact on crops and its monitoring is essential for policy and decision-makers to provide timely and precise reimbursement to farmers. This study attempts to estimate paddy crop (a major staple food cultivation of the world) yield under different inundations using multispectral imageries acquired by Unmanned Aerial Vehicle (UAV) in the subtropical region. A field experiment was carried out on paddy crop (MTU-1010) during the Kharif (monsoon) season of 2018 in the research farm of the Agricultural and Food Engineering (AgFE) Department, IIT Kharagpur, India. The experimental design consists of five different types of treatments i.e., treatment 1-20 cm standing water level was maintained for 10 days, treatment 2-20 cm (20 days), treatment 3-30 cm (10 days), treatment 4-30 cm (20 days) and treatment-5 (control)-5 cm standing water level maintained up to maturity stage with three replications. An in-house-quad copter Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera was used to acquire the high-resolution imageries at different inundation periods. The acquired images were radiometrically calibrated and pre-processed using Pix4d-mapper software. Various spectral indices (such as NDVI, NGRDI, RVI, GRVI, NDRE, TNDVI etc.) were evaluated and compared with the different ground truth parameters (SPAD, green seeker). The yield of different treatments was also compared and correlated with the spectral indices. The yield was increased from treatment 1 to treatment 3 but decreased for treatment 4. The highest yield of 5.02 t/ha was observed for treatment 3, and treatment 1 was the lowest yield of 4.55 t/ha compared to the control treatment of 4.92 t/ha. The spectral reflectance of RVI and GRVI were observed to have similar response variations for different treatments with an increase in Days After Transplantation (DAT). The variations of yield and spatial maps generated using UAV-based multispectral imageries for the treatments will be helpful to government agencies for early estimation of yield due to flood inundation within the small farming fields. Ask a question or comment on this session (not intended for technical support questions). Have a question or comment? Enter it here.

India predominately depends on rainfall for its agricultural water requirements. Effective rainfall (ER) is the amount of water needed for crop cultivation without any external water supply support. Hence, ER calculation is necessary for irrigation management. Besides crop water management, ER can also aid in understanding extreme hydrological analysis. This study aims to find the suitability of employing ER as an alternative for meteorological drought severity identification. In this study, one of the most widely used ER calculation methods, the USDA-SC (Smith) method, was employed to calculate ER from 2011 to 2020 throughout India. The Standard Precipitation Index (SPI-3) at 3 months intervals was used to validate the suitability of ER for assessing the drought. Four test sites were selected that cover different climate conditions of India to validate the using ER in alternative to drought. This study reveals that ER values accord with SPI-3 values in all most all 118 months. The ER was classified into 5 classes: extreme drought, moderate to severe drought, normal rainfall, severe wet, and extreme wet to assess the severity of drought. Out of 118 months of the study period, a total of 4, 2, 6 and 5 months fall into extreme drought class in Kundala (Odisha), Mallapur (Telangana), Morbi (Gujarath), and Kishangarh Urf Pharwahi (Punjab), respectively. There are no extreme wet months in Mallapur and moderate to severe drought months in Morbi. This signifies that Mallapur might be experiencing normal rainfall, and Morbi not having drought-prone months during the study period. In the overall analysis, 2011, 2012, 2013, and 2018 were dry years with low ER values. Further, ER can even identify the wet places apart from only severe to extreme dry places. The advantage of using ER as a drought indicator is that it not only classifies the drought character but also gives the magnitude of the available portion of rainfall.

A document by Fanny Lehmann. Click on the document to view its contents.

The Laccadive-Chagos Ridge (LCR) is a prominent aseismic ridge in the Indian Ocean. The origin and nature of the crust beneath the LCR have been debated. Based on Ar-Ar geochronology of the volcanic basement rocks from ODP wells 713 and 715 from Chagos and Maldives ridges, a hotspot trail model was proposed for the genesis of the LCR. On the other hand, based on geophysical studies, the LCR has been inferred as a continental sliver/hyperextended continental crust which has undergone heavy underplating and Reunion hotspot volcanism. Even though these two different hypotheses differ on the genesis and nature of the LCR, both agree that the ridge experienced extensive Reunion hotspot trail volcanism, making the crust's nature challenging to decipher. We report Ar-Ar ages of two rhyolite rock samples from a well drilled on the Padua bank located on the northern extent of the Laccadive ridge. These acidic rocks are at a depth of ~1700m and ~2300m and underlie the Tertiary sediments forming the basement in this well. The previously determined K-Ar geochronology ages are 60.2 Ma and 102 Ma. The precise Ar-Ar dates of these rocks determined in the present study are 76.2 ± 0.4 Ma and 77.5 ± 0.5 Ma (2σ) respectively. These acidic rocks (78-76 Ma) coupled with onshore acidic rocks of the St-Mary's island (87-84 Ma) and Ezhimala (95-93 Ma) suggest continuous extension between the India and Laccadive ridge after the Indo-Madagascar continental breakup (88 ma). Contrary to the earlier hypothesis, the acidic rocks of Laccadive Ridge are older than the Reunion Plume trail volcanism (62-60 Ma). The geophysical and geochronological evidence suggests that the Laccadive ridge is continental in nature, and the volcanism predates the Reunion plume volcanism. These evidences suggest that the Laccadive Ridge is a continental thinned crust that has undergone rift-related volcanism rather than a hotspot trail.

A document by Kavyashree N Kalkura. Click on the document to view its contents.

Numerous deterministic and probabilistic techniques have been utilized extensively to examine the velocity distributions for open channel flows under the various flow and channel characteristics. It is generally known that traditional approaches such as logarithmic velocity laws and power laws furnish vertical velocity profiles for the wide channels (width-to-depth ratio > 5) only. Also, the accuracy of the traditional methods is highly parameter dependent which, in turn, is evaluated using empirical or semi-empirical relationships only. Owing to the limitations of the conventional approaches, the entropy concept is formulated probabilistically and applies to all aspect ratios (width-to-depth ratio). The prominent entropy types explored to date are Shannon entropy, Tsallis entropy, and Renyi entropy. Recently, Fractional entropy has been demonstrated to study the velocity distributions for the open channel flows. Laboratory measurements have been used to demonstrate the applicability and correctness of all the techniques in adverse channel bed slope conditions (Singh & Khosa, 2022). The measurements comprise the velocity data at the different adverse slope values. Finally, a rigorous comparative analysis for the estimated velocity profiles using the four generally used velocity laws and four entropy-based velocity distributions have been conducted, which showed that the information entropy-based methods are far better than the traditional techniques.

A document by Robert L. McGraw. Click on the document to view its contents.

Thawing of permanently frozen ground (permafrost) has increased in recent decades with negative implications for human and non-human adaptation to climate change. Impacts include reduced ground stability, increased transportation risk, and changes in water availability. Direct measurements of permafrost active layer thickness (the depth of thawed ground overlying permafrost) are sparse. Measurements currently exist for a few hundred sites located primarily in the Northern Hemisphere supported by the Circumpolar Active Layer Monitoring (CALM) Program. The sparsity of direct active layer thickness measurements limits broad-scale understanding of changes in permafrost thaw and confidence in future projections. To address the sparsity of direct active layer thickness measurements, we developed a method to estimate active layer thickness change from streamflow measurements, which integrate processes over broad spatial areas and are more common than point-scale active layer thickness measurements. The method uses classical principles of hydraulic groundwater theory and nonlinear baseflow recession analysis, which sets it apart from prior methods based on linear recession analysis. The method is applied to catchments in the continuous and discontinuous permafrost zone of the North American Arctic containing co-located streamflow and CALM active layer thickness measurements. We find good agreement in the magnitude and direction of measured and predicted active layer thickness trends. This suggests that regional-scale estimates of active layer thickness change can be obtained from streamflow measurements, which may open the door to retrospective estimation of active layer thickness change in data sparse Arctic regions with short, sporadic, or even nonexistent ground-based active layer measurements.

Climate change and a growing global population pose ongoing threats to critical resources. As resources required by the agriculture sector continue to diminish, it is critical to leverage the emerging technologies and new solutions within the sector. New cultivation practices have emerged over the years, allowing food to be grown within urban areas. Greenhouses are versatile in the resources needed for their operation, as well as the foods that can be grown. While greenhouses provide a potential for a more constant food supply, there is a lack of optimization between the components. There are benefits to having modular components of a greenhouse, allowing for adjustments or repairs to singular pieces. However, there is inefficiency in the entire system, since each component functions without considering the others. To improve greenhouse efficiency, a closed-loop system can be introduced. A greenhouse is a closed system, and by repurposing, reusing, and recirculating resources, a greenhouse can evolve to have a closed-loop system. This enables the components of a system to share resources more effectively, communicate any systems changes that are required, and minimize waste outputs.This research explores the current technology in the space of agriculture and computer science to create a fully closed-loop system. The most noticeable system components are food waste, nutrient systems, water systems, growing media, and heating and energy. Not all components within a greenhouse can leverage the same artificial intelligence methods and techniques based on existing findings. There are methods in place that allow the components to interpret data gathered from the greenhouse and alter its operational patterns. There remains a lack in communicating this information to other aspects of the system to have it make informed data-driven decisions as well. One can optimize singular components thereby reducing resource reliance, to a certain threshold until it impacts the plant’s development and yield. When all the systems components’ resource needs and outputs converge the functionality of the system can be optimized to utilize resources at a higher efficiency. Results are indicative of very siloed and isolated research, exploring closed-loop systems within greenhouses, but not leveraging its full capabilities.

A document by Antony Joh Moothedan. Click on the document to view its contents.

A document by Zahra Zali. Click on the document to view its contents.