The Maranhão reservoir (~180 hm3) is mainly used for energy production and irrigation of 15360 ha in the Sorraia Valley, in southern Portugal. The necessity to save water in the Autumn/Winter rainfall season to be used for irrigation of Spring/Summer crops creates a sensible management situation regarding the control of floods and their impact on downstream areas in years with extreme precipitation events. Streamflow forecast is then essential to improve the reservoir’s management regarding water storage. This study addresses the estimation of the daily streamflow in the watershed draining to the Maranhão reservoir (2311 km3) following two different approaches. Firstly, the physically based hydrological model, MOHID-Land, was calibrated/validated for estimating daily streamflow in the study area following physically processes and using a finite volume approach, which require considerable amount of input data. Secondly, a data-driven model composed of an artificial neural network (ANN) was used with the same purpose. This ANN model was selected from a previous work where different models (multi-layer perceptron, convolutional neural network, and long short-term memory model) were tested with different scenarios for the combination of input variables. Both models were optimized considering the observed streamflow in the Ponte Vila Formosa station, which drains 30% of the Maranhão watershed, being after applied to the entire domain. The results for the Maranhão watershed were then validated considering a mass balance considering the reservoir’s outflow, level, and water consumed values. The ANN model had a better performance predicting streamflow than the physically based model, and with less calibration effort. However, the physically based model can give much more information about the entire system. Lastly, it is expected that a physically based model can correctly estimate the streamflow for extreme events not considered in the calibration and validation datasets, but for the ANN models this question should be carefully addressed since data-driven models are event-based.

The National Water Model (NWM) currently requires the post-processing of forecast discharges to produce forecast flood inundation maps (FIM) that support the National Weather Service’s mission of protecting life and property. Height Above Nearest Drainage (HAND) is a means of detrending digital elevation models (DEM) by normalizing elevations to the nearest, relevant drainage line (creeks, rivers, etc). It’s worthy of producing high-resolution FIMs at large spatial scales and frequent time steps using reach-averaged synthetic rating curves. Current operational capabilities support 10 meter (1/3 arc-second) spatial resolution DEMs sourced from the National Elevation Dataset (NED). The 3D Elevation Program (3DEP) managed by the United States Geological Survey (USGS) publishes a variety of gridded elevation datasets at 1 m, 3 m (1/3 arc-second), 5 m, and 10 m (1/9 arc-second) among others. While the 1/3 arc-second product provides seamless coverage across CONUS, the remaining products lack full spatial support with respect to that of the NWM. However, 3DEP is actively publishing additional data with national coverage scheduled for 2023. We seek to investigate the efficacy of assimilating higher resolution 1 m and 3 m (1/3 arc-second) data derived from light detection and ranging sensors (Lidar). These Lidar derived datasets not only represent higher horizontal resolution but also have improved vertical accuracy when compared to the NED. We seek to utilize Py3DEP from the HyRiver ecosystem of tools to retrieve 3DEP data. HAND derived FIMs will be evaluated against high-fidelity HEC-RAS 1D inundation maps for 100 year and 500 year events. Possible skill enhancements can be observed from having terrain information that better agrees with those of the benchmark HEC-RAS datasets. Lidar terrain data can better resolve fine scale features that flood inundation extents may be very sensitive to. Additionally, we would investigate mosaicing techniques to deal with processing units (hydrologic unit codes) of heterogeneous data availability. This can involve resampling DEM’s to create seamless rasters within units. Lastly, we can investigate the effect of Lidar data on synthetic rating curves as well as consider the latest hydro-conditioning techniques from GeoFlood for stream line delineation on Lidar data.

The Panola Mountain Research Watershed (PMRW) is a 41-hectare, forested research catchment within the Piedmont Province of the United States (U.S.), located about 25 km southeast of Atlanta Georgia (33˚ 37’ 54” N, 84˚ 10’ 20” W). Annual precipitation (P) averages 1,250 mm (<1% as snow) and annual runoff averages 358 mm, resulting in a runoff ratio of 0.29 (based on water years 1986–2015; annual range 0.13–0.50). The PMRW is seasonally water limited which results in water deficits and long-term actual evapotranspiration (ET) is about 75% of potential ET. Recharge of storage occurs predominantly in September–March when ET is lower. The U.S. Geological Survey (USGS) initiated research at the PMRW in 1985. Early focus on the effects of acid deposition transitioned to investigating processes affecting streamflow generation and water quality as part of a network of five diverse U.S. watersheds in the USGS Water, Energy and Biogeochemical Budgets Program (1991–2016). Current research is funded by the USGS Ecosystems’ Climate Research and Development Program and is focused on the effects of droughts and climatic change on ET, soil moisture, groundwater recharge, and streamflow generation. Collaboration with many Universities have occurred throughout the study. Long-term monitoring includes P, streamflow, groundwater, soil moisture, and meteorological parameters and water quality sampling of P, streamwater, and soil water. Thirty years of monthly water budgets (including actual ET and watershed storage components) and 31 years of atmospheric deposition and streamwater solute fluxes are published in USGS ScienceBase and include supporting data. The PMRW has long water residence times despite its small size, with a volume-weighted mean streamwater transit-time of ~4.7 years but can be >10 years during dry years. The PMRW has a large dynamic (>500 mm) and total (~1,000 mm) watershed storage with a hydrologic persistence of 19-months, which is evident from the water budget response to recurring hydrologic droughts. The dominant flowpath of hillslope recharge to the riparian area and stream is through bedrock. Storm-streamwater quality response was controlled by riparian (not hillslope) groundwater. We welcome opportunities for collaborative studies, cross-site comparisons, and data sharing.

The interpretation and use of scientific models are relevant to public discourse and decision-making about future water scenarios. Tools to use these scientific models aim to facilitate understanding water systems; however, the information in these tools can be so vast, complex, and prone to uncertainties that users may find using some of these tools a cumbersome task. Users can be presented with numerous outputs, visualizations, and vocabulary that are irrelevant or do not align with their perspective (i.e., user role) in the water system under study. To address these issues, the SWIM platform extends the capabilities of traditional Web-based graphical interfaces to foster the use of scientific models, in particular, water sustainability models. With the integration of a recommender system and a dynamic interface, SWIM provides users with a list of prioritized outputs (i.e., modeling results) based on their perspective, to reduce the overload of data presented to users. Users are also provided with a range of output visualization graphs and narrative elements (i.e., contextual natural language descriptions) to support the interpretation of model results. The SWIM interface design provides a seamless high-level workflow for models developed in different modeling software tools and languages. The SWIM platform is designed to foster interoperability by providing open APIs and knowledge bases to access data, metadata, and models (i.e., Model-As-A-Service). This presentation highlights the key elements of SWIM that enable the interpretation of scientific model outputs from different perspectives and the interoperability features of this platform.

Land surface features such as elevation, soils, land use, and vegetation fluctuate on scales ranging from millimeters to hundreds of kilometers. The state of the land surface and many hydrological processes vary accordingly. Land surface temperature (LST) is a crucial factor determining the interactions between the land surface and the atmosphere (i.e., energy, water, and carbon fluxes). Decades of global satellite remote sensed LST fields are now available, constituting an unprecedented opportunity to understand better the factors influencing hydrological variability from regional to global scales. An important under-researched aspect regarding variability, at least over continental extents, is determining the scales for which hydrological variations are spatially and temporally related. These scales would serve as indicators for the required time and spatial resolution for observational systems. This presentation will address this gap in understanding across scales through a comprehensive analysis of spatial and temporal correlation lengths of LST across the contiguous United States (CONUS). Correlation lengths (CLs) are measures of the stationarity of a property distribution both in space and time. They reveal the scales of variability for fields thus, contributing to estimating the stationarity of the property. Temporal correlation lengths (tCLs) express the property changes in time for a fixed location, providing a measure of the persistence or variability of the time series. On the other hand, spatial correlation lengths (sCLs) depict the spatial patterns of the property over a predefined area by representing the distance for which variations are spatially related. As part of our evaluation, we will analyze derived fields of tCLs and sCLs for the ~2x2 km2 GOES-16 LST hourly product over CONUS. A 0.25-degree regular grid over CONUS will be defined, and an hourly time step between 2017 and 2021 will be used for the analysis. The obtained CLs will be assessed in terms of the time of the day and season. Additionally, we propose a comparison of well-known spatiotemporal influencing factors of LST such as land cover, surface thermal properties, topography, incoming solar radiation, and meteorological conditions.

Grid-based spatially distributed hydrological modeling became feasible along with advances in watershed routing scheme, remote sensing technology, and computing resources. Such modeling is expected to be common in routine hydrological analysis and watershed management planning as it can maximize the use of spatial information and provide the detailed picture of transport processes. However, the heavy computational requirement and resulting long running time are still barriers that prevent the spatially detailed modeling practices from being employed widely, particularly in a fine-resolution large-scale study. Parallelizing computational tasks has been successful in mitigating the difficulty. We propose a noble way to improve the simulation efficiency of direct runoff transport processes by carefully grouping watershed areas based on the time-area routing scheme. The proposed parallelization method was applied to simulating the runoff routing processes of three watersheds draining the areas of 3.79 km2, 133.59 km2, and 2,800 km2 respectively at a 30-m resolution. Results demonstrated that the new method could substantially improve the computational efficiency of the time-area routing method with common computing resources. The efficiency of the parallelization scheme was not limited by the hierarchical relationship between upstream and downstream catchments along flow paths, which could be possible with the Lagrangian flow tracking strategy of the time-area routing method.

In Southern Thailand, Prince of Songkla University is the leading public university across five campuses, with Hat Yai being the main one. Science education is strong with several undergraduate programs; among them physics with geophysics electives where students can also do their term project. Groundwater exploration is among the main objectives of the Geophysics Research Center at PSU and undergraduate projects are well integrated into them. During their classes and laboratories, students learn the science behind groundwater and geophysical technologies. This requires working in the field and thus carrying heavy equipment. Female students, often the majority, have to do that in the same way as male students and thus learning it by doing it as a team. Teamwork is one of the essential social skills students learn. Undergraduate term projects in geophysics are always chosen based on a real problem, which is usually coming from outside the department and in most cases from outside the university, with customers ranging from local communities, over companies, to local government agencies. By this, the students learn the importance of their work for other people, thus giving them a motivation. Every project requires the students to initially talk with the customers to identify the problem and explore the area. They then have to decide on the geophysical survey plan with support from their advisor. After finishing the data acquisition and processing, the interpretation will be done in a team with a presentation, usually at the university. After that, students give a shorter presentation and report to the customers, explaining them in a non-scientific language their interpretations and conclusion. They also have to answer questions that came up. For doing this, in most cases the team is going back into the field where the customers are. This overall procedure with community engagement shows the student how important groundwater is in many rural areas and how difficult it can be to find groundwater, thus realizing them the importance of preserving it. If circumstance allow, later field teams might visit earlier projects done and learn about how the customers continued after the undergraduate project was finished. Photo: Student team engaged with local community leaders after a finished project, where a well was drilled.

My earth science focused PhD research included the analysis of annual fluorescent laminae in cave stalagmites, under the supervision of karst hydrologist Peter Smart (Bristol, UK). At the time, the source of this fluorescence was uncertain, opening new research opportunities characterizing what is now understood to be soil-derived, water-soluble fluorescent dissolved and colloidal organic matter that is transported to the cave by vadose zone percolation waters. Aquatic organic matter fluorescence research benefitted at this time from significant laboratory analytical advances that were commercially driven, including Sony’s Blu-ray technology and the use of fluorescent labelling in the biomedical sciences. Faster analyses at increasingly higher energy excitation energies opened opportunities for novel fingerprinting of organic matter in hydrological science, including landfill leachates and sewage contamination. Today, hand-held fluorescence sensors can instantaneously determine microbial water quality. And back in the field of speleothem (cave deposit) science, annual geochemical laminae are now recognized to be widely preserved in regions where there is a seasonality in recharge, providing a precise chronology for the stalagmite paleoenvironmental archive. Currently, we are utilizing this precise chronology to generate high-resolution fire history records, where the fire proxy is water-soluble ash-derived elements transported from soil to cave by vadose zone percolation waters.

The use of modeling tools for integrated water resources management is a complex task due to the large number of processes involved in a basin. Moreover, these modeling tools commonly require information that is not readily available, such as illegal water withdrawals, or other data difficult to obtain, which results in groundwater models that fail to capture the aquifer dynamics. In recent years, machine learning algorithms have shown outstanding performance as prediction tools. Despite being questioned for not having a physical basis, they have been used in areas such as hydrology and hydrogeology (e.g., for flow prediction, rain forecast). Thus, the objective of this research is to estimate groundwater withdrawals using machine learning algorithms and integrated water management models. To achieve this objective, ensembles of groundwater levels were generated with a previously calibrated groundwater/surface water integrated model. Then, these ensembles were used as input parameters for Gaussian process regression (GPR) and artificial neural network (ANN) models to construct time series of water withdrawals throughout a basin. This method was applied in the Petorca and La Ligua basins, in central Chile, as they exhibit a contrasting reality in terms of water availability even when they have geographical proximity. Also, these basins are within an effective extraction monitoring program lead by the Chilean water authority that can be used to validate the users’ water withdrawal. Our results show that the GPR model, compared to ANNs, adequately estimates the spatiotemporal distribution of groundwater withdrawals in the pilot basins. Thus, the use of machine learning algorithms improves the performance of integrated water resources management models.

The increase in world population, added to socioeconomic development and climate change, have highlighted one of the biggest problems worldwide: the depletion of water resources. The La Ligua and Petorca river basins, in central Chile, are an example of this problem, as rainfall has decreased in recent years, while socio-economic activities, mainly agriculture have increase. This situation has led to a severe water stress, and the need for integrated and sustainable river basin management, aimed at understanding the behavior of basins, aquifers, and the exchange of flows between them. Therefore, the main objective of this research is to quantify the impacts of climate change, in terms of groundwater scarcity, in semi-arid basins using integrated modeling of water resources. For this purpose, groundwater/surface waters integrated models of La Ligua and Petorca basins were developed using WEAP and MODFLOW. Both basins present different hydrological, social, and geographical characteristics. Different scenarios were evaluated to quantify groundwater depletion. These scenarios depend on climatic forcings, such as precipitation and temperature, which were obtained from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Results forecast that annual precipitation will decrease, whereas average annual temperature will increase in these semi-arid regions. As a consequence, the aquifer’s recovery rate will reduce, decreasing the number of wells that provide drinking water in rural and agricultural areas. In conclusion, the coupling of hydrological and hydrogeological models is a tool that allows researchers and stakeholders to make opportune and appropriate decisions on the management of basins and aquifers, which is even more important in basins that are expected to be or are already under severe water stress.

Due to the effects of climate change, the USA Southwest is expected to experience increased temperatures and decreased water availability. Extended droughts will likely have important consequences for riparian trees whose growth and habitat are strongly limited by water availability. One consequence of extended water table declines on riparian trees is the development of wood anatomical anomalies. Using techniques developed for quantitative wood anatomy (QWA) it may be possible to quantify these anomalies and reconstruct water table variability through time. We explore this possibility using tree rings from netleaf hackberry (Celtis laevigata var. reticulata) collected along the Upper Santa Cruz River, Arizona. We suggest hackberry has the potential to be used 1) as an indicator of riparian ecosystem health, and 2) to provide improved reconstructions of water availability. Understanding how hackberry responds to periods of low and high flows can provide insight into how to better manage these ecosystems into the future.

Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic amplification. These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the changes taking place, it is useful to have records that help place recent hydrological changes in context. In addition to an existing network of river gauges, extreme flood events can be captured in the wood anatomical features of riparian trees, which help identify the most extreme flood events. Along the Yenisei River, Siberia we collected willow (Salix spp.) samples from a low terrace that occasionally floods when water levels are extremely high. Using these samples, we use an approach known as quantitative wood anatomy to measure variation in radial cell dimensions, including vessel area, wood fiber size and cell wall thickness. We then compare these measurements to observed records of flood stage. We hypothesize that (1) characteristic patterns of wood fiber size and cell wall thickness in Salix rings are present during flood years, (2) these patterns can be quantified by measuring wood fiber size and cell wall thickness, and (3) quantified variations in cell anatomical properties can be related to flood magnitude and duration. Understanding how riparian vegetation responds to extreme flood events can help us better manage riparian ecosystems and understand changes to the Arctic hydrological regime.

Year-to-year variability of precipitation and temperature has significant consequences for water management decision making. “Whiplash” is a term which describes this variability at its most severe, referring to events at various timescales in which the hydroclimate switches between extremes. Tree-rings in semi-arid environments like the Truckee-Carson River Basin (California/Nevada watersheds with headwaters in the Sierra Nevada) can provide proxy records of hydroclimate as their annual growth is tied directly to limitations in water-year rainfall and temperature, but traditional metrics of reporting explained variance do not distinguish a reconstruction’s sensitivity to whiplash events. In this study, a pool of total ring width indices from five snow-adapted conifer species (Abies magnifica, Juniperus occidentalis, Pinus ponderosa, Pinus jeffreyi, Tsuga mertensiana) were used to develop a series of standardized reconstructions of water-year PRISM precipitation (P12) using stepwise linear regression. A nonparametric analysis approach was then used to determine positive and negative whiplash events in reconstructed and instrumental precipitation records. Hypergeometric distribution of the resulting timeseries datasets illustrates relationships between reconstructions and recorded whiplash events and allows for determination of patterns in tree-ring growth response. The results of this study suggest that ring-width indices from the assessed conifer species in the snow-belt of the Sierra Nevada are often able to record consecutive years of opposing extreme precipitation and report such events through derived models. Negative WL events are tracked more consistently across species in site-specific reconstructions of P12 than positive ones. It appears that residual effects of a preceding year’s drought or pluvial do not necessarily suppress records of WL, though sensitivity to precursor conditions in tracking of WL events may differ across species, and the absolute WL events captured in a reconstruction vary.

Compound event research has gained a lot of momentum over the past few years. Traditionally risk assessment studies used to consider only one climatic driver/ process at a time. However, it was then recognized that it is the combination of multiple drivers and their statistical dependencies that lead to aggravated, non-linear impacts. The present study investigated and quantified the preconditioning of precipitation extremes (P) by existing soil moisture (SM) anomalies. Event coincidence analysis (ECA) was employed to investigate the coupling nature between SM & P event series. The datasets used include GLDAS-2.2 CLSM model products for soil moisture and GPM IMERG V06 for gridded rainfall data. Using SM and P data from 2004-2020, we identified hot-spots of SM-P coupling over India. A statistical significance test (α = 0.05) was carried out to ensure that the observed coincidences are not by chance. Our observed results agree with the widely regarded hypothesis of stronger SM-P coupling in transitional regions between wet and dry climates. The temporal evolution of SM-P dependence over the past two decades is also investigated. Results obtained provides critical insights on the complex dynamical relationship between soil moisture and precipitation. The dependence nature unraveled has vast implications for directing future research on coupled hydro-meteorological phenomena.

Tropical coastal communities accommodate critical infrastructure, densely-populated urban regions, tourism-driven economies, and industrial facilities. These communities are prone to multiple flood hazards such as nuisance flooding, tropical cyclones, extreme rainfall events, and sea-level rise (SLR). Thus, governments and stakeholders Are pursuing a range of measures to enhance flood resiliency. These alternatives can be classified into structural (i.e., conventional infrastructure), non-structural, natural and nature-based features (NNBF), and hybrid systems. While there is a large body of published work on coastal risk reduction via conventional infrastructure and NNBF, there is a paucity of information on Hybrid Infrastructure Systems (HIS) in the literature, especially under multi-flood hazard scenarios. This research aims to assess various HIS under multiple flood hazards for flood reduction and wildlife and habitat benefits in the coastal community of Tybee Island (Georgia, US). This community is the most densely populated barrier island in Georgia and receives over 1 million visitors each year. The Interconnected Channel and Pond Routing (ICPR) hydrodynamic model was selected to simulate hydrologic (e.g., rainfall and infiltration) and coastal (e.g., tides, storm surge, and SLR) processes, and various combinations of HIS including several conventional infrastructures (e.g., stormwater drainage system, culverts, pump systems), inland (e.g., bioswales and pocket parks) and coastal (e.g., horizontal levees and retention ponds with smart tidal gates) NNBF. Results show that NNBF can prolong the service life of conventional infrastructure in a HIS by reducing flooding stress on these structures while promoting wildlife habitats and marsh conservation by increasing the hydraulic connectivity in the tidal river system. HIS alternatives were ranked using multi-criteria decision analysis. The local government, residents, and stakeholders will select their preferred alternative for detailed design. Local studies and modeling of multi-hazard flood processes can provide insight into the performance of HIS, thus providing the opportunity to policy-makers and government agencies to improve design standards and permitting procedures for HIS at a regional scale.

Flow measurement is critical in hydraulic engineering for developing and managing water resources. The mean flow velocity and cross-sectional flow area at the measurement location are two major metrics required for discharge estimation. River bathymetric surveys or advanced technologies such as the Acoustic Doppler Current Profiler (ADCP) might be used to determine the cross-sectional area. The mean flow velocity can be measured using different techniques depending on whether the measurements are taken from a distance (non-contact) or a contact method (traditional approach). Non-contact velocity measuring techniques are becoming increasingly common in recent years since they are less time-consuming and easier to utilize while dealing with heavy flows and inclement weather. One such modern approach is the entropy-based concepts (such as Shannon entropy, Tsallis entropy and Renyi entropy) used to calculate the discharge from non-contact observations, yielding superior results to classic approaches such as the velocity area method. Entropy-based velocity distribution depends on the crucial parameter called entropy parameter (a function of the ratio of the mean and maximum velocity), which is linked to the channel and flow characteristics. Its value is surmised to be constant for a particular river reach. Due to this fact, the entropy-based approach was used in this study to evaluate the discharge in case of the adverse bed slope condition that may arise due to several reasons, and one among them is the excessive mining in the particular river reach. This study collected the experimental velocity data for the mild, horizontal and adverse bed slope conditions from a rectangular flume fitted with a mechanical apparatus to change the bed slope. Results concluded that the mild and horizontal slope conditions depicted only a slight variation in entropy parameter value, i.e., almost constant. The same was adopted for finding the mean velocity for the adverse bed slope condition to calculate the discharge. Furthermore, the discharge error analysis presented a substantial justification for the utilized single constant value of the entropy parameter for the whole cross-section, and the same can be employed for future explorations on the same channel stretch.

A challenge to managing water resources is characterizing the scale-dependent heterogeneity created by the interactions among hydrological, ecological and anthropological processes. It is often difficult to collect sufficient empirical data over the range of scales required to construct mathematical models that facilitate robust bottom-up descriptions or predictions. An alternative is identifying emergent properties of complex systems, whose components self-organize into novel structures or processes via their collective interactions with each other and the environment. A new level of organization and complexity emerges that cannot be predicted from or attributed to the components alone. Emergence offers a number of perspectives from which to interpret, if not predict, the behavior of complex water resource systems. One of these is entropy, which maximizes the options for system components to alter their interactions and, thus, permits variability and adaptability. At the scale of watersheds, increased entropy is pertinent because of its relationship to information (as probability functions), which is transmitted through connected components of a watershed in a manner such that the accrued information gives rise to emergent properties. Hence, analyzing the behaviors of a system according to emergence introduces the possibility of evaluating the information content via its interconnected components. Connectivity then assumes an integral role in a hydrologic system’s response to natural or anthropogenic disturbances (e.g., climate change, land use). Replacing the details of multi-scale heterogeneity and causal mechanisms with the functions that watersheds perform allows processes such as stream flow rate/duration and flood frequency to be construed as emergent spatiotemporal patterns. A reductionist or bottom-up approach to assessing the behavior of aquatic systems shifts to a functional or top-down approach that does not depend upon an understanding of all the physical, chemical or biological mechanisms involved. This latter approach could supplement conventional water resource descriptions and predictions via more comprehensively characterizing watershed or aquatic ecosystem functions.

As Earth's ecological landscape continues to change, it will become increasingly important to understand how it has changed, and how it may change in the future. Freely available multispectral remote sensing datasets, such as the Landsat-8 dataset accessed through the USGS EarthExplorer tool, provide large scale, high resolution satellite imagery that can be leveraged by researchers across scientific disciplines for timeseries index analysis. LINDEX provides an extensible framework that automates Landsat-8 timeseries index analysis, resulting in an average ninety-four percent reduction in overall processing time compared to hands-on methods. Traditionally, in order to make use of this historical data, researchers must acquire the data, uncompress each scene, crop each scene to their region of interest, sort each cropped scene by date and cloud cover, and then either use GIS tools to run index analyses or code the index analyses themselves. This process is time consuming, requires extensive computational knowledge, and is prone to human error. In order to address these challenges, we developed LINDEX, a fully containerized end-to-end extensible processing framework for Landsat-8 timeseries index analysis. LINDEX is open source and leverages open source python packages to automate decompression, cropping, cloud cover detection, sorting by date, and index analysis for Landsat-8 data while also providing a customisable and growing library of eleven ecologically useful indices including NDWI, NDMI, NDSI and NDGI. LINDEX is designed to work synergistically with the EarthExplorer Bulk Download Application as well as QGIS, providing a bridge from download through analysis. The LINDEX framework uses a well defined methodology for incorporating custom indices into your workflow, making LINDEX a useful tool for researchers interested in exploring any Landsat-8 multispectral index while saving time, and reducing error.