Many waterbodies around the world are adversely impacted by harmful algal blooms (HABs). One primary driver of these blooms is often high concentrations of anthropogenic phosphorus loading. The phosphorus mitigation plans require accurate information on nutrient sources and transport, to and through water bodies, including the stream network. Diffuse sources, are particularly difficult to quantify due to the cost of in situ monitoring, and is often supplemented using various water quality models. SWAT, a comprehensive watershed-scale model, is widely used to assess and improve downstream water quality using QUAL2E equations. EPA developed QUAL2E can model phytoplankton growth but has a limited capacity to model benthic algae. Although SWAT requires a lesser number of parameters while simulating water quality outputs, unlike, HSPF, INCA, SPARROW, WASP, and MIKE-SHE, the water quality algorithm within SWAT needs modifications for simulating phosphorus legacy within the waterbodies. This study reviews the existing water quality models to improve the water quality algorithm within SWAT. Most of the water quality models can simulate processes, including the proliferation of fixed and floating algal biomass and phosphorus cycling (QUAL2E/K, WASP, HSPF). Some water quality models are better in simulating the time-dependent factors, such as light attenuation, form and concentration of nutrients, and water temperature (HSPF, INCA). There are a few water quality algorithms that can simulate both horizontal stream flow and shallow flow (SHETRAN, INCA). Both horizontal and shallow flow takes into account the anisotropy and variable biogeochemistry impacts on the turbulence of water, thus, the water quality. Some water quality models simulate the non-linear relationship between nutrient concentration and discharge timing and magnitude (SPARROW). There are some commercialized models like MIKE-SHE that simulate reasonably good results, but the water quality algorithm/equation/process is not publically available. Our review of the existing water quality models will help in identifying, modifying, and implementing the SWAT source code revisions required to improve and mitigate water quality degradation from a finer spatial scale, including small ditches and streams, to the large-scaled watershed over time.

The science linking anthropogenic greenhouse gas emissions with our changing climate, and the resulting impacts, has been well established for decades. During that time one of the focal points of international negotiations was to establish a common target for action to address climate change. These negotiations culminated in the Paris Agreement at COP21 in 2015 which seeks to limit the global mean surface temperature (GMST) rise to well below 2C above pre-industrial, and to pursue efforts to limit it to 1.5C. Our research seeks to assess the climate justice implications of using global mean surface temperature as a metric for climate action by combining data from ice sheet models and fully coupled global climate model simulations in conjunction with a literature review spanning fields including international relations, political economy, critical geography, and history. Considering the political and scientific history of the development of the temperature target alongside global impacts of climate change we gain a new understanding of spatial, temporal, and procedural aspects of climate justice.

The so-called regularized Biot-Savart laws (RBSLs, Titov et al. 2018) provide an efficient and flexible method for modeling pre-eruptive magnetic configurations whose characteristics are constrained by observational image and magnetic-field data. This method allows one to calculate the field of magnetic flux ropes (MFRs) with small circular cross-sections and an arbitrary axis shape. The field of the whole configuration is constructed as a superposition of (1) such a flux-rope field, (2) an ambient potential field determined, for example, by the radial field component of an observed magnetogram, and (3) a so-called compensating potential field that counteracts deviations of the radial field caused by the axial current of the MFR. The RBSL kernels are determined from the requirement that the MFR field for a straight cylinder must be exactly force-free. For a curved MFR, however, the magnetic forces are generally unbalanced over the whole path of the MFR. To reduce this imbalance, we apply a modified Gauss-Newton method to minimize the magnitude of the residual magnetic forces per unit length and the unit axial current of the MFR. This is done by iteratively adjusting the MFR axis path and axial current. We then try to relax the resulting optimized configuration in a subsequent line-tied zero-beta MHD simulation toward a force-free equilibrium. By considering several examples, we demonstrate how this approach works depending on the initial parameters of the MFR and the ambient magnetic field. Our method will be beneficial for both the modeling of particular eruptive events and theoretical studies of idealized pre-eruptive magnetic configurations. This research is supported by NSF, NASA’s HSR, SBIR, and LWS Programs, and AFOSR

Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. Required forest carbon data consist of integrated: (1) baseline mapping of contemporary carbon stocks, (2) projections of future carbon stocks for planning, and (3) annual monitoring for assessment. Previously, we developed high-resolution mapping of contemporary carbon stocks and 300-yr projections of annual carbon sequestration potential (CSP) for Maryland at 90m resolution by integrating airborne LiDAR with mechanistic ecosystem modeling (Ecosystem Demography (ED) model). Here we extend this work to Delaware and present the first consistent, annual monitoring results for both states (Maryland and Delaware). For monitoring, we intersect annual carbon stock estimates with 30m Landsat-derived changes in forest area to compute realized carbon gains and losses over the period 2011—2019. Moving forward, we expect to extend this pilot system developed for Maryland and Delaware to an additional 9 U.S. states. As the framework is flexible, developing a nationwide or global system is increasingly feasible, particularly with the recent availability of GEDI LiDAR observations from space.

Arctic summer sea ice has decreased dramatically over the last few decades, with a substantial part of this decline attributed to internal variability. However, models show large differences in their simulated internal variability, increasing projection uncertainty and complications with model-observation comparisons. Here we will present results that aim to quantify the contribution of internal variability in different models which provide large ensemble simulations, and compare them with estimates from observations. In particular, we are comparing five models from the CLIVAR multi-model large ensemble (CanESM2, CESM1, CSIRO MK36, GFDL ESM2M, and MPI ESM1) with observations. So far, we have found a large range in simulated pan-Arctic sea ice area standard deviation from 0.35 million km2 (CSIRO MK36) to 0.74 million km2 (CESM1) for mean September areas between 4.00-4.25 million km2. Spatially, the detrended standard deviation in the central Arctic is consistently over-represented in models compared to observations. Conversely, the marginal seas are simulated to have slightly below to several times below observed detrended standard deviation. Further analysis on a more regional scale will be done over the coming months to further characterize the realism of simulated internal variability in Arctic sea ice.

The Gravity Recovery and Climate Experiment (GRACE) mission and the Follow-On mission (GRACE-FO) have been widely used to estimate Groundwater (GW) at different scales. With improved releases and data longevity of these missions it becomes possible to analyze changes on GW and its link with global climate patterns such as El Niño-Southern Oscillation (ENSO), which can contribute to better management of this resource, especially in zones where it is crucial for the livelihoods and food security of the community. In the current study, TWS-derived from GRACE, Soil Moisture and Snow simulated by the Global Land Data Assimilation System (GLDAS) were used in the period 2002-2020 to estimate fluctuations in High Plains Aquifer (HPA). Trend and annual cycle were analyzed, and then Principal Component Analysis (PCA) was carry out to observe spatio-temporal oscillation modes and its relationship with Oceanic Niño Index (ONI) through Spectral Analysis. Furthermore, to validate GRACE-derived GW and evaluate a larger period pattern such as Pacific Decadal Oscillation (PDO), Depth to Water data from United States Geological Survey (USGS) wells distributed in the aquifer were selected with a longer period looking for teleconnections. Results shows increasing trend in Northern High Plains while a decreasing in the central and southern part of it. GW peak occurs in August in most of HPA, three month later than rainfall peak. First four components explain 77% of total variance. GW derived from GRACE and GRACE-FO as well as GW from wells could detect periodicities that suggest connections of GW variability with PDO more than with ENSO in HPA.

Ceboruco (2280 m.a.s.l.), in the western Trans-Mexican Volcanic Belt, is considered among the most hazardous volcanoes in Mexico. Some 55,000 people and important infrastructure (e.g. hydroelectric dams; highways; railways) lie within the area covered by deposits of Holocene eruptions. A diverse activity over the past 1000 years spans from effusive (e.g. andesite lava flows, dacitic domes) to explosive (e.g. Strombolian, Vulcanian and Plinian eruptions). With a poor monitoring network, a first hazard map was published in 2019. Here we present the first probabilistic hazard maps for Ceboruco, which constitute a progression towards a more quantitative hazard assessment. We conduct a probabilistic hazard assessment using the pyBET_VH (Bayesian Event Tree for Volcanic Hazard) tool (i.e. software implementation of the event-tree scheme), which allows the user to estimate and visualize the probabilities and uncertainties associated with volcanic phenomena. pyBET_VH merges information from eruptive history, expert elicitation, and the output of other computer models to produce probabilistic hazard maps (i.e. absolute and conditional probabilities and associated uncertainties). We present the probability hazard maps for each eruptive scenario (i.e. Scenario 1 – small magnitude effusive eruption; Scenario 2 – medium magnitude effusive and/or explosive eruption (VEI<3), and Scenario 3 – large magnitude Plinian eruption) and the associated uncertainties. Such maps can be used by civil authorities and stakeholders for the purpose of crisis management as well as for long-term development strategies by visualizing the probabilities of areas around the volcano likely to be impacted by volcanic phenomena. Using pyBET_VH has advantages and disadvantages: the reliability of the output maps is directly related to the quality of the input data, but the tool allows easy estimation and visualization of the uncertainties; being an interactive tool, the user can continuously update the probability maps as new information becomes available.

Every year millions of people in the US are affected by power outages, disrupting the economy and daily life. Many of these outages are caused by events such as strong winds, heavy rains, thunderstorms, floods, tropical storms and hurricanes. At the University of Connecticut an outage prediction model (OPM) has been developed for forecasting outages during storms. The OPM has been operational since 2015 serving utilities in the Northeastern US. It uses variables describing weather events, infrastructure, land cover and elevation. Non-parametric machine learning (ML) ensembles generate the predictions. The first version of the model served Connecticut exclusively and was characterized by large uncertainty in predictions due to the dataset limitations of a small service territory and limited historical dataset. Over time, the model expanded to include utility territories in Massachusetts and New Hampshire, the dataset grew, the understanding between environmental forcing and outages improved, and probabilistic operational forecasts began to be produced. The relationship between UConn and the utility stakeholders has grown to where operational forecasts are now used as part of response planning to storm events by the utility. This work leverages knowledge from the UConn OPM and utilizes a similar ML framework in combination with non-utility-owned customer outage data to build a community OPM for predicting customer outages along the US Eastern Seaboard for large scale events. Proxies for proprietary infrastructure are used including road and publicly available transmission line data. Variables including tree type and ecoregion data are used to account for regional diversity of the larger domain. To validate the customer outage reference data, correlations are shown between customer outages and utility trouble spots in the Northeast where outage data from utilities is known. Model performance evaluated at county and state levels shows that the model is capable of predicting the peak number of customer outages with great accuracy, demonstrating promise for the ultimate goal of determining return periods of outages under current and future climate scenarios to help the public and utilities with resiliency and response planning.

The Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework considers the interactions between the environment, societal impact, human decision-making, and technology design to support decision making. EVDT has been expanded to include a public health model in the Vida Decision Support System, which will help local leaders understand the relationships between societal factors relating to COVID-19. Key to the development of Vida are collaborative design and mutual learning with international and interdisciplinary teams. Collaborations with researchers and government officials (including public health, economics, environmental, and demographic data collection officials) in Angola, Brazil, Chile, Indonesia, Mexico and the United States provide in-depth understanding of local contexts. Lessons learned from these collaborations include the value of dialogues with teams from the same region but different topic areas (such as a space agency compared to a public health agency), allowing for time to learn the best way to combine diverse data types and find the tools each collaborator prefers, and encouraging the use of the preferred language of collaborators. During Vida’s development, each collaborator has worked to create their own version of Vida using local data sources, the US team has provided prototype analyses and models, and collaborators have shared individual insights among the whole network. These partnerships have yielded promising initial results to support decision making, with prototype tools incorporating local data on COVID cases, the environment, and socio-economic factors from Rio De Janeiro and Chile being evaluated. This collaborative design process will develop insights for decision-making, create a network of international collaborators that can exchange technical methods beyond the pandemic, and emphasize the principles of inclusive innovation and decoloniality by submitting to the preferences of local leaders in each country.

We study the consistency of long-term trends in the surface water balance of 63 sub-catchments of the Amazon River basin. Monthly time series of precipitation, evaporation, runoff and soil water storage are obtained from remote sensors and flow stations (CHIRPS, ETR-Amazon, ANA-Brazil and JPL-GLDAS). Missing data during the period 1995-2015 are reconstructed applying an adaptation of the methodology proposed by Kondrashov & Ghil (2006). Empirical mode decomposition (Huang et al., 1998) is applied to filter out different modes of natural variability, with the aim to isolate the long-term trend of time series. The Mann-Kendall and Sen tests are applied to the residue and the sign and magnitude of the trends was obtained. No generalized unidirectional trends were found for the Amazon basin for any of the variables studied (Table 1), although some unidirectional trends were found in groups of sub-catchments that belong to the same stream. The consistency of the general water balance equation [dS/dt=P(t)-E(t)-R(t)], and its long-term approximation [\overline{R} = \overline{P} - \overline{E}] were evaluated. The general water balance does not close for 37 sub-catchments, while in the long-term the error in the balance tends asymptotically to a constant value, different from zero, which indicates that in the period of 20 years studied the long-term condition is fulfilled, but there is no closure for the long-term water balance either. The consistency of the surface water balance equation was also studied regarding the signs of the trends, finding that in 32 (51%) of sub-catchments studied the trend signs are not consistent with the water balance equation. Finally, for the remaining 31 sub-catchments (with trends consistent in signs), the consistency of the water balance equation was evaluated regarding the magnitude of trends, finding closure errors on water balance of trends up to 281% of the average of the magnitudes. We discuss several reasons for the lack of closure of the water balance, including the very existence of trends that violate the stationary hypothesis underlying the long-term approximation of the surface water balance. The used data and some of the results are available on the supplemental files. The Raw Data CSV file contains the spatial average timeseries at the basin that drain to the flow stations for the water balance equation variables. The Filled Data CSV file contains those timeseries after appliying the reconstruction. MKtrends and SenTrendMagnitude CSV files contains the results of the appliying the Mann-Kendall and Sen trend tests to the filled data.

The British Oceanographic Data Centre (BODC) celebrated its 50th anniversary in 2019. It holds data collected from 1773 to the present day. Holdings are multidisciplinary, heterogeneous data reflecting the full range of disciplines, platforms, temporal and spatial fieldwork scales typically encountered in oceanographic research and monitoring. These collections vary in granularity and contain data which are at different stages of curation ranging from raw data to standardised data products. BODC need to improve data services to meet the developing the expectations of the research community. These include the FAIR data principles, TRUSTed repository guidelines and CoreTrustSeal accreditation. This is a significant challenge within the constraints of resource available (both financial and human). The initial focus for BODC is making holdings citable with the following aspirations: Application of DOIs to data at the point of receipt by BODC. Publication of data papers and publication of DOIs for data products. Application of persistent identifiers to low level data granules where DOIs are not feasible. Application of persistent identifiers to datasets included in BODC API services and versioning of these data. Work with organisations or groups who include data curated by BODC in their products to enable the provenance of data to be unambiguous. Work with communities on joint data papers where BODC are a partner organisation. This will enable each type of data served by BODC to be unambiguously citable. The initial effort is being directed towards the application of DOIs to data submissions and publication of data papers for BODC curated data products.

In the northeastern Atlantic Ocean, the eastward-flowing Azores Current converges towards the southwestward-flowing Canary Current, following the African coastline and leading the Eastern Boundary Current System of the North Atlantic Subtropical Gyre. Based on satellite altimetry measurements (1992-2006) and in situ observations (1998, 2002), Sangrà et al. (2009) described for the first time the Canary Eddy Corridor in the northeastern Atlantic Ocean (22.1oN - 29.1oN) as a zonal long-lived mesoscale eddy corridor. In this work, we extend the study area and redefine five zonal corridors that expand from 12oN to 40oN. The aim is to assess the potential role of these eddy corridors as zonal conveyors of mass and ocean properties. To ease its identification, we name these corridors following their generation sites: Cape Verde, Cape Blanc, Canary Islands, Madeira, and the Azores Front. We do this using an altimeter-based dataset of global mesoscale ocean eddies (1992-2018). The analyses are performed in terms of the eddy demography and eddy geometric properties. Generally, we find that cyclonic eddies (CEs) are the most frequent type of long-lived eddies (> 6 months) everywhere but in the Cape Verde Corridor and the Azores Front Corridor, where anticyclonic eddies (AEs) and CEs are of similar number. On average, CEs propagate the farthest away due to the combination of longer lifetimes and higher track stabilities, although AEs also reach on occasions as far as 1000 km away from their generation site. Zonally, results agree well with theory and show recurrent patterns about their geometric eddy properties, where both anticyclonic (AEs) and cyclonic (CEs) eddies display significant differences. Both eddy types increase (decrease) its radius and decrease (increase) its amplitude and rotational speeds when their corridor locates at lower (higher) latitudes. Also, observed eddies propagate west at approximately the phase speed of nondispersive baroclinic Rossby waves, according to their latitude. These results highlight the implications of the geographical location (latitude) of the eddy corridors, suggesting that low latitude eddy corridors may generate the best candidates to convey ocean properties to remote places while these candidates are significantly less frequent than eddies of higher latitude corridors.

Black carbon (BC), organic carbon (OC) and dust (or Absorbing Aerosols - AA) strongly absorb visible solar radiation, leading to impacts on the atmospheric radiation budget, climate, water cycle, and more. Recent attempts have been made to elucidate the spatial-temporal concentrations and radiative forcing of AA over East Asia using the UV band OMAERUV product from OMI. The product provides global coverage of AAOD in the UV bands, limiting the results to where retrievals can be made, and to cases where the average aerosol size is not too small. For these reasons, this approach cannot estimate the magnitude of total AAOD and/or radiative forcing. To achieve this, we include relevant data from multiple bands in the visible and NIR in tandem with the UV, so that a more complete relationship can be made to understand the magnitude and properties of AA globally. We employ a MIE model to simulate the absorption of core-shell coated mixtures of AA (specifically a mixture of BC core with sulfate shell (MBS) and OC core with sulfate shell (MOS) across multiple individual wavelengths. These values are then merged with individual inversions of SSA from AERONET at each individual wavelength across the spectrum from the UV through the NIR. Fitting is done based on the temporally varying magnitude band of the measured AOD and the inverted SSA incorporating all individual data points where both calculations exist at each station, from 2010 to 2016. The relationship between core and shell sizes that is consistent with AERONET is then fitted to OMI measurements that overlap AERONET in space and time. A sensitivity matrix of optical uncertainties is made to compute the robustness of the constrained aerosol size, chemical composition and refractive indices. Initial results show that retrieved aerosol properties of MBS and MOS are consistent with known properties over urban areas, biomass burning areas, and those regions frequently impacted by long-range transport events, as observed over Asia. A few interesting scientific findings include mixing between these different sources and detection of otherwise missing sources. It is hoped that ongoing calculations allowing our approximation to be extended spatially away from sites where AERONET measurements exist will also be ready to present.

A fire-spotting parameterization was developed for the WRF-Fire component of the WRF model version 4.0.1. The parameterization uses a Lagrangian particle transport framework and is coupled to the fire component of the WRF-ARW model as an independent Fortran module. When fires are active, the fire-spotting module identifies areas at risk of fire spotting by modeling transport and physical processes of individual firebrands released from fire locations. Firebrands are released at varying heights, from locations with higher emission potential, defined as a function of fire rate of spread and fuel load. Firebrands are transported with the atmospheric flow, and physical properties (temperature, mass, and terminal velocity) are updated at the default model timestep. The particles may either burnout before settling or deposit at a grid point when carried below a specified height threshold. The number and spatial distribution of deposited firebrands correspond to the flow-dependent risk component of new fire ignitions due to fire spotting. The flow-dependent component is then combined with the risk associated with local fuel properties (load and moisture) to yield the fire spotting spatial likelihood. In this presentation, the fire-spotting parameterization is assessed through a qualitative analysis of wildfires in Colorado. Uncertainties in fire ignition observations, used to initialize fires in the WRF-Fire model, often limit the ability to accurately model fire area, which in turn controls the firebrands’ emission location. Limited spotting observations are also a challenge to an objective verification of the module skill. We expect that the most recent remote sensing products will improve the representation of surface properties and accuracy of ignition parameters for WRF-Fire, which will directly transfer to the fire-spotting module capability. Direct enhancements to the parameterization may be incorporated into the module as laboratory experiments and field campaigns provide data to improve our ability to model firebrands’ initial properties (e.g. firebrand size and ejection height) and physical processes (burnout and terminal velocity).

For the past five+ years, The GLOBE Program has hosted an International Virtual Science Symposium (IVSS). This annual event invites primary through undergraduate GLOBE (Global Learning and Opportunities to Benefit the Environment) students to showcase their Earth science research in an entirely online platform. With GLOBE, students learn the practices of science through hands-on investigations in their own communities, sparking their curiosity and interest in science. This often leads to inquiries that help solve real-world problems and further understanding of our global environment. Students from around the world submit their research projects to a panel of international scientists, teachers, subject matter experts, graduate students, and other interested community members for scoring and valuable feedback which will help them improve as scientists. As well, students are awarded digital badges if they demonstrate specific scientific practices such as collaboration, data science, and engineering. Badges are also awarded to students who work with STEM professionals, explore STEM careers, demonstrate community impacts, and share their research in creative ways such as through storytelling. By the time the pandemic started shutting down schools across the world, the 2020 IVSS was already underway and was able to continue with few interruptions. This helped to reinforce the IVSS as a model for virtual student engagement on a global scale. As well, we now have five years of data to help assess the efficacy of the IVSS, analyze its growth, and help direct future engagement opportunities. Planning for the 2021 IVSS is already underway and we are currently working on how to incorporate COVID-19-related restrictions into the model by encouraging a focus on using previously collected GLOBE data and creating student resources aimed at improving data science skills.

Use of ensemble rainfall forecasts has gained popularity in providing detailed uncertainty information and improving hydrologic prediction skill. India Meteorological Department (IMD) provides medium-range multi-model ensemble (MME) forecasts for all over India with 1- to 5-day lead time. In this study, we bias correct the IMD MME rainfall forecasts using a modified version of Kohonen Self-Organizing Maps (KSOM) and analyse the effect of bias correction on the streamflow prediction skill of MIKE 11 Hydrodynamic (HD) model for the years 2012-2014. We have selected the upper region of the Mahanadi River basin as the test bed. The results indicate improvement of rainfall forecasts after bias correction. Subsequently, use of bias corrected rainfall forecasts as input forcing to the MIKE 11 HD model provides better streamflow forecasts at all the lead times (Nash Sutcliffe Efficiency, NSE ranging from 0.89 to 0.41) compared to the use of raw forecasts (NSE ranging from 0.89 to -0.51). To further improve the streamflow forecasts, we have applied the recently developed robust wavelet-based non-linear autoregressive with exogenous inputs dynamic neural network model, WNARX. This post-processing operation tends to improve the streamflow forecast quality to an acceptable range (NSE = 0.92-0.71). The results encourage us to conclude that the IMD MME forecasts has great potential to improve streamflow prediction skill of a hydrodynamic model.

Nowadays, many of the world cities and its peripheral areas are under constant threat of flooding due to high rainfall events. Increased rate of severe flood hazards world-wide have created a demand for understanding the flooding behaviour across various urban and peri-urban catchments. However, simulating the flooding scenarios under limited hydrological data condition is a great challenge for the urban water planners and managers, especially in developing countries. To this, in this study, a combined modelling approach is proposed with the physically based Variable Parameter Muskingum Stage (VPMS) routing model to develop a local rating curve for generating the discharge data from the available stream stage data, which was subsequently used as an input to the Storm Water Management Model (SWMM). The coupled methodology is applied in a typical ungauged urban and peri-urban catchment of eastern India. The proposed SWMM-VPMS model is calibrated for the monsoon (rainy) season of the year 2009, and validated for the monsoon seasons of 2011 and 2014. The performance of the model is satisfactory with the Nash-Sutcliffe efficiency (NSE) estimates of 0.82, 0.89, and 0.89 for the years 2009, 2011, and 2014, respectively. The proposed SWMM-VPMS model can be used for a catchment where the tributary is ungauged while the main river is gauged at a downstream location of the confluence point. This methodology can also be adopted in catchments with missing rating curves. Additionally, the information regarding spatial distribution of channel flow depth at different time can be utilised to have an initial assessment of the flood-vulnerable areas. Keywords: VPMS; SWMM; Urban and peri-urban catchment; Ungauged catchment

Water Balance Components (WBCs), the most fundamental processes of a river basin, are getting disturbed by climate change and land-use alterations in the present scenario. These two factors are the major driving force behind the spatio-temporal variation of the WBCs that creates an alarming concern for fulfilling water demand by different sectors. The popular hydrological model Soil and Water Assessment Tool (SWAT) is applied to the Baitarani River basin (12,095 km2) of eastern India for evaluating the dominance of the above factors on the WBCs deviations at a decadal scale (1980-1989, 1990-1999, and 2000-2009). A quantile regression-based stochastic approach has been used to analyze the uncertainties resulting from different simulations accounting the combined responses of climate change and land-use alterations as well as model parameters. However, such analysis has not been explored more for the present study area. The model performance results reveal that the statistical performance indices (NSE and R2) are within the acceptable limit. The WBCs in terms of evapotranspiration, surface runoff, lateral flow, water yield, soil moisture storage, and deep aquifer recharge has been quantified at a decadal scale from the simulated model outcomes. The result shows that the water yield component is more (680.36 mm) for mid-decade (1990-1999) as compared to other decades, which is favourable to fulfill sectoral water demand. Further, the uncertainty analysis explains that climate change impact plays a vital role in WBCs variation. The developed approach can portray the variability of WBCs of other river basins that are vulnerable to climate change. The outcomes of this study can be used to maintain an appropriate balance between water availability and demand to avoid water scarcity. Keywords: Water balance components; Climate change; Land-use Alterations, Brahmani; Baitarani