Water Availability Project Research Articles

Impact of Asian aerosols on the summer monsoon strongly modulated by regional precipitation biases

Abstract. Reliable attribution of Asian summer monsoon variations to aerosol forcing is critical to reducing uncertainties in future projections of regional water availability, which is of utmost importance for risk management and adaptation planning in this densely populated region. Yet, simulating the monsoon remains a challenge for climate models that suffer from long-standing biases, undermining their reliability in attributing anthropogenically forced changes. We analyze a suite of climate model experiments to identify a link between model biases and monsoon responses to Asian aerosols and associated physical mechanisms, including the role of large-scale circulation changes. The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model's ability to simulate the spatio-temporal variability in the climatological monsoon winds, clouds, and precipitation across Asia, which modulates the magnitude and efficacy of aerosol–cloud–precipitation interactions, an important component of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response. We found a striking contrast between the early- and late-summer aerosol-driven changes ascribable to opposite signs and seasonal evolution of the biases in the two regions. A realistic simulation of the evolution of the large-scale atmospheric circulation is crucial to realize the full extent of the aerosol impact over Asia. These findings provide important implications for better understanding and constraining the diversity and inconsistencies of model responses to aerosol changes over Asia in historical simulations and future projections.

Larger increase in future global terrestrial water availability than projected by CMIP6 models

<p>Climate change and increasing water use associated with socio-economic growth have exacerbated the water crisis in most of the world. However, terrestrial water availability (precipitation minus evapotranspiration, PME) is inadequately represented within Earth System Models, directly affecting the reliability and consistency of future projections. To address this issue, we employ twenty Coupled Model Intercomparison Project Phase 6 (CMIP6) models and multiple observational PME datasets to produce more robust future projections. Most models underestimate the increase in PME over 1982–2011, while over 80% of the models project a continued rise in future PME. We thus constrain future changes in global mean PME (ΔPME) under medium and high greenhouse gas emission scenarios (SSP2-4.5 and SSP5-8.5) using the hierarchical emergent constraint (HEC) approach. The constrained PME shows a significant increase by 33.81 ± 13.72mm, or 13% ± 5%, during the period <styled-content style-type="number">2070</styled-content>–<styled-content style-type="number">2099</styled-content> relative to 1982–2011 under SSP5-8.5. Implementation of the HEC method reduces the uncertainty range by 24% and shows the ΔPME of the raw CMIP6 was underestimated by 37%. The PME change trends are most evident in low-latitude regions, with constrained PME increasing by 75.69 ± 24.35 mm, or 28% ± 9%, in the Northern Hemisphere’s low latitudes and by 34.56 ± 38.91mm, or 8% ± 10% in the Southern Hemisphere’s low latitudes by <styled-content style-type="number">2070</styled-content>–<styled-content style-type="number">2099</styled-content> under SSP5-8.5. Our findings highlight the importance of accurate model representation of precipitation to produce reliable projections of global terrestrial water availability. The observationally-constrained ΔPME is valuable for sustainable management of water resources.</p>

Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment

AbstractHigh Mountain Asia (HMA) is among the most vulnerable water towers globally and yet future projections of water availability in and from its high‐mountain catchments remain uncertain, as their hydrologic response to ongoing environmental changes is complex. Mechanistic modeling approaches incorporating cryospheric, hydrological, and vegetation processes in high spatial, temporal, and physical detail have never been applied for high‐elevation catchments of HMA. We use a land surface model at high spatial and temporal resolution (100 m and hourly) to simulate the coupled dynamics of energy, water, and vegetation for the 350 km2 Langtang catchment (Nepal). We compare our model outputs for one hydrological year against a large set of observations to gain insight into the partitioning of the water balance at the subseasonal scale and across elevation bands. During the simulated hydrological year, we find that evapotranspiration is a key component of the total water balance, as it causes about the equivalent of 20% of all the available precipitation or 154% of the water production from glacier melt in the basin to return directly to the atmosphere. The depletion of the cryospheric water budget is dominated by snow melt, but at high elevations is primarily dictated by snow and ice sublimation. Snow sublimation is the dominant vapor flux (49%) at the catchment scale, accounting for the equivalent of 11% of snowfall, 17% of snowmelt, and 75% of ice melt, respectively. We conclude that simulations should consider sublimation and other evaporative fluxes explicitly, as otherwise water balance estimates can be ill‐quantified.

A physically-based potential evapotranspiration model for global water availability projections

When the projections from the hydrological models conflict with those from the fully-coupled climate model, the vegetation appears to be responsible for the divergence. In the presence of an elevated concentration of atmospheric CO2 (Ca), the leaf-level stomatal conductance (gsl) tends to decrease thereby reducing water demand, and such effects can be partly offset by the increased leaf area (generally represented by leaf area index, LAI) simultaneously. Nonetheless, the impacts of vegetation are rarely explicitly quantified in existing potential evapotranspiration (EP) models. In this study, we proposed a new EP equation that combines a well-established gsl model with a simple bulk upscale model. The new model allows us to investigate the effects of elevated Ca-induced physiological and structural changes in vegetation on EP estimations. The results show that, compared to conventional EP models (e.g., the Penman-Monteith reference crop model), the new EP equation performs better in capturing long-term EP changes. Furthermore, we embedded the new EP equation in a simple hydrologic model, the Budyko framework, to estimate the changes in long-term runoff. By accounting for both physiological and structural responses of vegetation, estimated runoff by the Budyko framework shows a significant increase of ∼ 30 mm/year in 2071–2100 compared to 2006–2035, consistent with projections from fully-coupled climate models. The increase in LAI offset ∼ 30% of the water-saving caused by stomatal closure on a global average. The newly developed EP model provides a fully transparent and easy-to-use way for describing the impacts of climate change-induced vegetation responses on EP and can be effectively employed to detect long-term hydrological changes.

Solutions to agricultural green water scarcity under climate change.

Rain-fed agricultural systems, which solely depend on green water (i.e. soil moisture from rainfall), sustain ∼60% of global food production and are particularly vulnerable to vagaries in temperature and precipitation patterns, which are intensifying due to climate change. Here, using projections of crop water demand and green water availability under warming scenarios, we assess global agricultural green water scarcity-defined when the rainfall regime is unable to meet crop water requirements. With present-day climate conditions, food production for 890 million people is lost because of green water scarcity. Under 1.5°C and 3°C warming-the global warming projected from the current climate targets and business as usual policies-green water scarcity will affect global crop production for 1.23 and 1.45 billion people, respectively. If adaptation strategies were to be adopted to retain more green water in the soil and reduce evaporation, we find that food production loss from green water scarcity would decrease to 780 million people. Our results show that appropriate green water management strategies have the potential to adapt agriculture to green water scarcity and promote global food security.

A Review of Current Capabilities and Science Gaps in Water Supply Data, Modeling, and Trends for Water Availability Assessments in the Upper Colorado River Basin

The Colorado River is a critical water resource in the southwestern United States, supplying drinking water for 40 million people in the region and water for irrigation of 2.2 million hectares of land. Extended drought in the Upper Colorado River Basin (UCOL) and the prospect of a warmer climate in the future pose water availability challenges for those charged with managing the river. Limited water availability in the future also may negatively affect aquatic ecosystems and wildlife that depend upon them. Water availability components of special importance in the UCOL include streamflow, salinity in groundwater and surface water, groundwater levels and storage, and the role of snow in the UCOL water cycle. This manuscript provides a review of current “state of the science” for these UCOL water availability components with a focus on identifying gaps in data, modeling, and trends in the basin. Trends provide context for evaluations of current conditions and motivation for further investigation and modeling, models allow for investigation of processes and projections of future water availability, and data support both efforts. Information summarized in this manuscript will be valuable in planning integrated assessments of water availability in the UCOL.

Understanding wind-driven melt of patchy snow cover

Abstract. The representation of snow processes in most large-scale hydrological and climate models is known to introduce considerable uncertainty into the predictions and projections of water availability. During the critical snowmelt period, the main challenge in snow modeling is that net radiation is spatially highly variable for a patchy snow cover, resulting in large horizontal differences in temperatures and heat fluxes. When a wind blows over such a system, these differences can drive advection of sensible and latent heat from the snow-free areas to the snow patches, potentially enhancing the melt rates at the leading edge and increasing the variability of subgrid melt rates. To get more insight into these processes, we examine the melt along the upwind and downwind edges of a 50 m long snow patch in the Finseelvi catchment, Norway, and try to explain the observed behavior with idealized simulations of heat fluxes and air movement over patchy snow. The melt of the snow patch was monitored from 11 June until 15 June 2019 by making use of height maps obtained through the photogrammetric structure-from-motion principle. A vertical melt of 23 ± 2.0 cm was observed at the upwind edge over the course of the field campaign, whereas the downwind edge melted only 3 ± 0.4 cm. When comparing this with meteorological measurements, we estimate the turbulent heat fluxes to be responsible for 60 % to 80 % of the upwind melt, of which a significant part is caused by the latent heat flux. The melt at the downwind edge approximately matches the melt occurring due to net radiation. To better understand the dominant processes, we represented this behavior in idealized direct numerical simulations, which are based on the measurements on a single snow patch by Harder et al. (2017) and resemble a flat, patchy snow cover with typical snow patch sizes of 15, 30, and 60 m. Using these simulations, we found that the reduction of the vertical temperature gradient over the snow patch was the main cause of the reductions in vertical sensible heat flux over distance from the leading edge, independent of the typical snow patch size. Moreover, we observed that the sensible heat fluxes at the leading edge and the decay over distance were independent of snow patch size as well, which resulted in a 15 % and 25 % reduction in average snowmelt for, respectively, a doubling and quadrupling of the typical snow patch size. These findings lay out pathways to include the effect of highly variable turbulent heat fluxes based on the typical snow patch size in large-scale hydrological and climate models to improve snowmelt modeling.

Projection of Streamflow Change Using a Time‐Varying Budyko Framework in the Contiguous United States

AbstractPredicting future streamflow change is essential for water resources management and understanding the impacts of projected climate and land use changes on water availability. The Budyko framework is a useful and computationally efficient tool to model streamflow at larger spatial scales. This study predicts future streamflow changes in 889 watersheds in the contiguous United States based on projected climate and land use changes from 2040 to 2069. The temporal variability of surface water balance controls, represented by the Budyko ω parameter, was modeled using multiple linear regression, random forest (RF), and gradient boosting. Results show that RF is the optimal model and can explain >85% of the variance in most watersheds. Relative cumulative moisture surplus, forest coverage, crop land and urban land are the most important variables of the time‐varying ω in most watersheds. There are statistically significant increases in mean annual precipitation, potential evapotranspiration, and ω in 2040–2069, as compared to 1950–2005. This leads to a statistically significant decrease in the runoff ratio (Q/P). Streamflow is projected to decrease in the central, southwestern, and southeastern United States and increase in the northeast. These projections of water availability which are based on future climate and land use change scenarios can inform water resources management and adaptation strategies.

Basin management inspiration from impacts of alternating dry and wet conditions on water production and carbon uptake in Murray-Darling Basin

The impacts of alternating dry and wet conditions on water production and carbon uptake at different scales remain unclear, which limits the integrated management of water and carbon. We quantified the response of runoff efficiency (RE) and plant water-use efficiency (PWUE) to a typical shift from dry to wet episode of 2003–2014 in Australia's Murray-Darling basin using good and specific data products for local application, including Australian Water Availability Project, Penman-Monteith-Leuning Evapotranspiration V2 product, MODIS MCD12Q1 V6 Land Cover Type and MODIS MOD17A3 V055 GPP product. The results show that there are significant power function relationships between RE and precipitation for basin and all ecosystems, while the PWUE had a negative quadratic correlation with precipitation and satisfied the significance levels of 0.05 for basin and the ecosystems except the grassland and cropland. The shrubs can achieve the best water production and carbon uptake under dry conditions, while the evergreen broadleaf trees and evergreen needleleaf trees can obtain the best water production and carbon uptake in wet conditions, respectively. These findings help integrated basin management for balancing water resource production and climate change mitigation.

Analysis of River Basin Management in Madagascar and Lessons Learned from Japan

River basins in Madagascar are prone to water scarcity and conflicts of use, which are projected to worsen due to climate change. Therefore, effective integrated water resources management (IWRM) is key. This review examines the current condition of IWRM in Madagascar, evaluates its sustainability, and compares it with the one in Japan. The effects of climate change were analyzed using Aqueduct data. Madagascar has established an IWRM legal framework aligned with the Sustainable Development Goals; however, its application remains limited. For Japan, it was useful to establish IWRM for floods, water utilization, and environmental conservation. This study analyzes the future projections of water availability and demand for the 12 major river basins of Madagascar. Climate change will reduce water availability; the West and the East will experience water stress; the demand will be higher in the highlands, with less water available in the south. The proposed framework and Japan’s experience in river basin management can contribute to solving those problems. The findings of this study serve to enhance the knowledge on IWRM and the impact of climate change in Madagascar; they also suggest actions to be taken by the Malagasy government.

Strong direct and indirect influences of climate change on water yield confirmed by the Budyko framework

Research findings concerning the main processes influencing water resources differ substantially, and so the topic remains controversial. Recent studies indicate that the changes in water yield, expressed through the n-parameter of Budyko framework, are associated with vegetation coverage changes. Here, we use runoff measurements and outputs from 13 dynamic global vegetation models, to investigate the underlying drivers of the n-parameter changes. Unlike previous studies, we instead find that climate change is the primary driver of adjustments on water resources. Changing climatic characteristics, particularly the intensity and seasonality of rainfall, modulates the runoff generation process. Indirect effects of climate change occur through altering vegetation properties, which in turn also impact river flow. We also find that in the arid and sparse vegetation regions, water yield is more sensitive to changes in n-parameter. Thus, the Budyko framework provides a reliable parameter-sparse representation of runoff changes, and reveals that terrestrial water cycle is changing substantially under climate change. This climate forcing requires on-going investigation to generate more refined and reliable projections of future water availability.

Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

Abstract. Physically based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, no snow model comparison has been done in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point over one snow season and to evaluate their sensitivity relative to parameterisation and forcing. For that purpose, the two models are forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing. Results indicate large uncertainties depending on forcing, the snow roughness length z0, albedo parameterisation, and fresh snow density parameterisation. The uncertainty caused by the forcing is directly related to the bias chosen. Even though the models show significant differences in their physical complexity, the snow model choice is of least importance, as the sensitivity of both models to the forcing data was on the same order of magnitude and highly influenced by the precipitation uncertainties. The sublimation ratio ranges are in agreement for the two models: 36.4 % to 80.7 % for SnowModel and 36.3 % to 86.0 % for SNOWPACK, and are related to the albedo parameterisation and snow roughness length choice for the two models.

Evaluation of Extreme Temperatures Over Australia in the Historical Simulations of CMIP5 and CMIP6 Models

AbstractHistorical simulations of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated over 10 Australian regions for their performance in simulating extreme temperatures, among which three models with initial‐condition large ensembles (LEs) are used to estimate the effects of internal variability. Based on two observational data sets, the Australian Water Availability Project (AWAP) and the Berkeley Earth Surface Temperatures (BEST), we first analyze the models' abilities in simulating the probability distributions of daily maximum and minimum temperature (TX and TN), followed by the spatial patterns and temporal variations of the extreme indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Overall, the CMIP6 models are comparable to CMIP5, with modest improvements shown in CMIP6. Compared to CMIP5, the CMIP6 ensemble tends to have narrower interquartile model ranges for some cold extremes, as well as narrower ensemble ranges in temporal trends for most indices. Over southeast, tropical, and southern regions, both CMIP ensembles generally exhibit relatively large deficiencies in simulating temperature extremes. We also confirm that internal variability can affect the trends of the extremes and there is uncertainty in representing the irreducible variability among different LEs in CMIP6. Furthermore, the evaluation based on Perkins' skill score (PSS) and root‐mean‐square error (RMSE) in the three LEs does not directly correlate with the ranges of the trends for extreme temperatures. The findings of this study are useful in informing and interpreting future projections of temperature‐related extremes over Australia.

A methodology to downscale water demand data with application to the Andean region (Ecuador, Peru, Bolivia, Chile)

ABSTRACT Mountainous regions are a hotspot for water scarcity and anthropogenic pressure on water resources. Substantial uncertainty surrounds projections of future climate and water availability. Furthermore, quantitative and distributed data on water demand are generally scarce, dispersed, and highly heterogeneous. This forms a major bottleneck to studying water resources issues and developing strategies to improve water resource management. Here we present a methodology to produce and evaluate high-resolution gridded maps of anthropogenic surface water demand with application to the Andean region. These data are disaggregated according to the major types of water demand: domestic users, irrigated area, and hydropower. This dataset was built by homogenizing, integrating, and interpolating data obtained from various national institutions in charge of water resource management as well as relevant global datasets. The maps can be used to research anthropogenic impacts on water resources, and to guide regional decision-making in regions such as the Andes.

River basin salinization as a form of aridity

Soil-salinization affects, to a different extent, more than one-third of terrestrial river basins (estimate based on the Food and Agriculture Organization Harmonized World Soil Database, 2012). Among these, many are endorheic and ephemeral systems already encompassing different degrees of aridity, land degradation, and vulnerability to climate change. The primary effect of salinization is to limit plant water uptake and evapotranspiration, thereby reducing available soil moisture and impairing soil fertility. In this, salinization resembles aridity and-similarly to aridity-may impose significant controls on hydrological partitioning and the strength of land-vegetation-atmosphere interactions at the catchment scale. However, the long-term impacts of salinization on the terrestrial water balance are still largely unquantified. Here, we introduce a modified Budyko's framework explicitly accounting for catchment-scale salinization and species-specific plant salt tolerance. The proposed framework is used to interpret the water-budget data of 237 Australian catchments-29% of which are already severely salt-affected-from the Australian Water Availability Project (AWAP). Our results provide theoretical and experimental evidence that salinization does influence the hydrological partitioning of salt-affected watersheds, imposing significant constraints on water availability and enhancing aridity. The same approach can be applied to estimate salinization level and vegetation salt tolerance at the basin scale, which would be difficult to assess through classical observational techniques. We also demonstrate that plant salt tolerance has a preeminent role in regulating the feedback of vegetation on the soil water budget of salt-affected basins.

Nossana Spring (Northern Italy) under Climate Change: Projections of Future Discharge Rates and Water Availability

Nossana represents an important pre-Alpine karst spring for drinking supply, sustaining a water distribution system serving 300,000 people. The goal of this study was to project Nossana discharges and evaluate potential supply limits for four future periods (2021–2040, 2041–2060, 2061–2080, 2081–2100). Bias-corrected Regional Climate Models (RCMs), part of the EURO-CORDEX experiment and forced by three emission scenarios (RCP2.6, RCP4.5, RCP8.5), were evaluated, statistically downscaled, and used as input in a calibrated rainfall-runoff model ensemble. For each emission scenario, the calibrated model ensemble considered three RCMs and ten rainfall-runoff parameterizations. Projected ensemble mean discharges are lower than observations (3%–23%) for all RCPs, though they do not show a clear trend between the four time periods. Days characterized by discharges lower than actual water demand are projected to decrease, except for the RCP8.5 emission scenarios and the period 2081–2100. Conversely, the same consecutive days are expected to increase after 2060 for all emission scenarios. These results reflect the projected precipitation trend, characterized by longer, drier summer periods and wetter autumns in comparison to today’s climate. Also, they indicate a possible need for alternative drinking water resources. The proposed methodology was demonstrated to deliver useful quantitative information for water management in the mid- long-term period.

An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia

Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP – Australian Water Availability Project), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, and modified Kling–Gupta efficiency), categorical metrics, and other statistics (wet-day frequency, transition probabilities, and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both the point and grid scale of 5 km. BARRA also more closely reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias, and modified Kling–Gupta efficiency (KGE′) indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms ERA-Interim in terms of the KGE′ score and the components of the KGE′ score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.

An Assessment of Global Precipitation and Evapotranspiration Products for Regional Applications

Precipitation (P) and evapotranspiration (ET) are the key factors determining water availability for water resource management activities in river basins. While global P and ET data products have become more accessible, their performances in river basins with a diverse climate and landscape remain less discussed. This paper evaluated the performance of four representative global P (CHIRPSP, GLDASP, TRMMP and PersiannP) and ET products (CSIROET, GLDASET, MODET and TerraClimateET) against the reference data provided by the Australian Water Availability Project (AWAP) in the Murray Darling Basin (MDB) of Australia. The disparities among the data products both in the period from 2001 to 2016 and across the 22 catchments of MDB were related to a set of catchment characteristics (climate, terrain, etc.) to explore any possible contributors. The results show that the four global P products presented overall high consistency with AWAPP across the MDB catchments except in southeastern catchments with abundant rainfalls and large terrain variations. The Penman–Monteith algorithm based MODET underestimated ET in the MDB, especially in the arid, less vegetation covered catchments. While the CSIROET, which also estimated with the Penman–Monteith method, presented overall better estimations, which can be attributed to the better parameterization of the landscape in the simulation processes. The hydrological model based TerraClimateET showed overall good consistency with AWAPET except in the arid catchments, which might be attributed to the simplified water balance model it applied, however it did not adequately reflect the intensive ground water uses in these catchments. The findings indicated that basin and catchment characteristics had impacts on the accuracy of global products and therefore provided important implications for choosing appropriate product and/or conducting field calibrations for potential users in large basins characterized with diverse rainfall, terrain variations and land use patterns.

What Climate Models Get Wrong About Future Water Availability

Models that accurately represent past and present rainfall provide more accurate projections of water availability, a new study suggests.

Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

Soil moisture forecasts are vital for environmental monitoring, the health of ecological systems, hydrology, agriculture and understanding the soil characteristics. In this study, we design a new multivariate sequential predictive model that utilizes the ensemble empirical mode decomposition (EEMD) algorithm hybridized with extreme learning machines (ELM) to forecast soil moisture (SM) over weekly horizons. The EEMD data pre-processing utility is a self-adaptive tool, which does not require a predefined basis function and avoids frequency-mode mixing issues. The proposed multivariate sequential EEMD model is designed to sequentially demarcate model predictor variables and the target (SM) into analogous intrinsic mode functions (IMFs) and a residue component using the EEMD process, to address the complexities and associated non-linearities in hydrologic-based inputs. To validate the new approach, four diversely characterized sites in Australia's Murray-Darling Basin are purposely selected where 13 weekly hind-casted predictors are collated from the Australian Water Availability Project WaterDyn physical model. After the sequential EEMD transformation process, a two-stage feature selection employing cross-correlation and random forest based Boruta wrapper algorithm is adopted to extract pertinent features from the hydro-meteorological predictor series to construct a hybridized multivariate sequential EEMD-Boruta-ELM model. Comprehensive model evaluation using statistical metrics and diagnostic plots against alternative methods: hybrid multivariate adaptive regression splines (MARS) (EEMD-Boruta-MARS) and classical MARS and ELM, establish the superiority of hybrid EEMD-Boruta-ELM model, yielding relatively low errors and high performance. The study ascertains that the EEMD-Boruta-ELM hybrid model can be explored as a pertinent data-driven tool for relatively short-term soil moisture forecasts, thus advocating its practical use in near real-time hydrological and pedological applications.