Hydroclimatic Data Research Articles

Suitability of Flood Hazard Assessment Methods for Tanzania: A Case of Little Ruaha and Upper Ngerengere Catchment

Understanding the applicability of flood quantile estimation methods in flood hazard assessment is fundamental for planning, prevention, and management of flood risks. Therefore, this study evaluates and compares three hydrological methods, namely Hydrologiska Byråns Vattenbalansavdelning (HBV), Soil Conservation Service-Curve Number (SCS-CN), and regional regression equation (RRE), to estimate flood quantiles embedded in the existing flood damage assessment framework by applying them to two different river catchments, Little Ruaha (LR) and Upper Ngerengere (UN), Tanzania. The evaluation of method performance was carried out using three standard statistical measures for data from 1954 to 2010 and the 1971–1988 period in LR and UN catchments (LRC and UNC). The findings indicated that no single approach could fit all catchments and return periods for these case studies. Overall performance indicated that the RRE method provides more accurate and consistent quantile estimates than other approaches. These findings indicate that spatial scale, model structure, parameters, and hydro-climatic data condition are the most important elements influencing the suitability of the supplied methods for flood risk assessments, which serve as the foundation for developing an improved flood damage assessment framework.
 Keywords: Flood Quantiles; Estimate Methods; Flood Risk Management; Little Ruaha; Ngerengere

Predicting Daily Suspended Sediment Load Using Machine Learning and NARX Hydro-Climatic Inputs in Semi-Arid Environment

Sediment transport in basins disturbs the ecological systems of the water bodies and leads to reservoir siltation. Its evaluation is crucial for managing water resources. The practical application of the process-based model can confront some limitations noticed in the lower accuracy during the validation process due to the lack of reliable physical datasets. In this study, we attempt to apply machine-learning-based modeling (ML) to predict the suspended sediment load, using hydro-climatic data as input variables in the semi-arid Bouregreg basin, Morocco. To that end, data for the years 2016 to 2020 were used for the training process, and the validation was performed with 2021 data. The results showed that most ML models have good accuracy, with a Nash–Schiff efficiency (NSE) ranging from 0.47 to 0.80 during the validation phase, which indicates satisfactory performances in predicting the SSL. Furthermore, the models were ranked against their generalization ability (GA), which revealed that the developed models are good to excellent in terms of GA. Overall, the present study provides new insight into predicting the SSL in a semi-arid environment, such as the Bouregreg basin.

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters.

Long‐Term Water Imbalances of Watersheds Resulting From Biases in Hydroclimatic Data Sets for Water Budget Analyses

AbstractAssessing the water budget closures and source of water budget imbalances is fundamental to improving the understanding of changes in the hydrological system and their associated impacts. We analyzed the long‐term (1982–2016) water budget for 1,561 watersheds by using various observed data sets for precipitation (P), evapotranspiration (ET), observed streamflow (Q), and total water storage change (∆TWS). The results show that 93%, 79%, 44%, and 20% of watersheds show water imbalances ratio less than 30%, 20%, 10%, and 5% of their corresponding precipitation. The average absolute water imbalance ratio for all watersheds is 14.2% of P. Watersheds showing large water imbalance ratio values are mostly located in biomes of Tropical and Subtropical Moist Broadleaf Forests, Boreal Forests/Taiga, and Tundra. Different P, ET, and Q data set combinations result in different degrees of water imbalance. The water budget imbalance ratio shows a significant negative relationship with humidity index and vegetation coverage, while a positive relationship with the proportions of irrigation area and watershed area. Showing small water imbalances for most watersheds, reanalysis precipitation data set (ERA5 and MSWEP), MTE evapotranspiration data set performed better than other data sets in water budget analyses in most biomes. The uncertainties of P, ET, Q, and ∆TWSGRACE contribute to 40.1%, 14.3%, 26.6%, and 19% of the water budget imbalance on average, respectively. Improving the accuracy of P and ET estimates, and streamflow measurements are critical to better understanding the water budget and improves modeling of hydrological processes.

Navigating Great Lakes Hydroclimate Data

Despite the fact that the Great Lakes contain roughly 20% of the world's surface freshwater, there is a relatively limited body of recent work in peer reviewed literature that addresses recent trends in lake levels. This work is largely coming from a handful of authors who are most well-versed in the complexities of monitoring and modeling in a basin that spans an international border and contains vast areas of surface water connected by both natural and managed connecting channel flows. At the same time, the recent dramatic changes from record low water levels in the early 2010's to record high water levels across the Great Lakes in 2019 and 2020 have brought significant attention to the hydroclimatic conditions in the basin, underscoring the need to bring new approaches and diverse perspectives (including from outside the basin) to address hydroclimate research challenges in the Great Lakes. Significant effort has led to advancements in data and model coordination among U.S. and Canadian federal agencies throughout the decades, and at the same time research from the broader community has led to higher resolution gridded data products. In this paper, we aim to present the current state of data and models for use in hydrological simulation with the objective of providing a guide to navigating the waters of Great Lakes hydroclimate data. We focus on data for use in modeling water levels, but we expect the information to be more broadly applicable to other hydroclimate research. We approach this by including perspectives from both the Great Lakes water management community and the broader earth science community.

The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction

ABSTRACT This paper focuses on the development of a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters of a machine learning model. To do so, the simulated annealing (SA) algorithm is integrated with the mayfly optimization algorithm (MOA) as SAMOA to determine the optimal hyper-parameters of support vector regression (SVR) to overcome the exploration weakness of the MOA method. The proposed method is compared with the classical SVR and hybrid SVR-MOA. To examine the accuracy of the selected methods, monthly hydroclimatic data from Jhelum River Basin is used to predict the monthly streamflow on the basis of RMSE, MAE, NSE, and R2 indices. Test results show that the SVR-SAMOA outperformed the SVR-MOA and SVR models. SVR-SAMOA reduced the prediction errors of the SVR-MOA and SVR models by decreasing the RMSE and the MSE from 21.4% to 14.7% and from 21.7% to 15.1%, respectively, in the test stage.

Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products

Study RegionAlaska and hydrologically connected portions of Canada Study FocusRugged terrain and harsh conditions make observational hydroclimatic data, especially runoff data, extremely limited across most of Alaska. Consequently, many previous hydrologic studies in Alaska have focused on smaller-domain trends, where pockets of hydroclimatic observations are clustered. Using a combination of reanalysis and remotely sensed gridded products, we develop a calibration framework for a monthly resolution water budget model, over the entire Alaskan domain, that does not rely on runoff. The domain is comprised of distributed model response units (MRUs), each representing a Hydrologic Unit Code 12 basin. We use a Fourier Amplitude Sensitivity Test (FAST) to inform a two-step calibration process; first calibrating to actual evapotranspiration and then to fractional snow-covered area (fSCA), resulting in a range of parameter solutions for each MRU. New Hydrological Insights for the RegionUsing this framework, the model performs best in the moderate to dry Alaskan interior; in the absence of glaciation and where permafrost is generally not continuous. Modelled runoff for recent decades shows increased runoff trends at lower latitudes and slightly decreased runoff trends at higher latitudes within the Alaskan domain. These findings reveal that reliance upon non-runoff fluxes for model calibration is viable in this cold region and that better characterization of hydroclimatic data is necessary to improve confidence in cold region modeling studies.

Impact of climate change and human factors on water resources variations in reservoir dams (case study: Lighvan River, Iran)

In recent years, runoff of basins has changed due to climate change and human activities. Since river flow changes play a key role in reservoirs water resources planning purposes, the analysis of the role of climatic and human factors in river flow changes is of great importance in reservoirs development plans. In this study, the effect of climate change and human activities on the runoff responses was examined using hydrologic sensitivity analysis in the Lighvan basin located in the northwest of Iran. The Vanyar dam is located in downstream of the Lighvan basin. The Mann–Kendall test and the Pettitt test were applied to identify the monotonic and rapid trends respectively, in hydro-climatic data series. The results showed that there was negative trend in discharge data series. The effect percentages of the human factors and climatic factors on runoff reduction were 65%-84% and 16%-35%, respectively. Therefore, in addition to the role of climate change, there is a need to pay more attention to the impacts of human activities in the upstream of dams. Also, according to the rate of changes of climatic and human factors, the design and planning of water resources of reservoirs should be improved and adapted.

A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

IMPACT ASSESSMENT OF SUBSTANTIVE HYDRO-CLIMATIC VARIABLES ON 2012 FLOOD EXTENT IN YOLA AND ENVIRONS

Flood is a seasonal phenomenon which is natural in it hazardous implication and occurs when there is relative high flow over the banks of the streams as a combine consequence of high recorded data of hydro-climatic related variables in a given geographical area. Yola North LGA, of Adamawa state had experienced an unprecedented flood in the year 2012 over the past decade which might have been influenced by some hydro-climatic variables and caused devastating effects on lives, properties, farmland and buildings respectively. This study focused on the impact assessment of substantive hydro-climatic variables on 2012 flood event in Yola -North and its environs. The hydro-climatic variables data were obtained from Meteorological station at UBRBDA, Yola for a decade. The amount of rainfall experienced was found to be highest (1085.2mm) in the year 2012 than any other year under consideration (2008-2017) except that of 2016, number of rainy days was highest (81 days) in the year 2012. Similarly, in the month of August in the year 2012 evaporation rate was lowest with about 69 mm than any other month of August in the decade, the annual value of water discharge was highest in the year 2012 over the decade with about 6,340(m3/s), the gauge height was found to be highest with about 7.33 m in the year 2012 and the water level was highest in the month of June, July and September with the corresponding values of 3.37 m, 3.49 m and 6.58 m compared to similar months in the years of the decade respectively. These increased changes in some hydro-climatic data analyzed might be the fundamental natural factor that causes the unique flooding than any other factor in the year 2012 in the study area and over time posed negative impact on agricultural lands. Therefore, the study recommends the urgent need to carry out a comprehensive seasonal hydro-climatic data record simulation analysis and variations with a view of taking them as a recipe and strategies of forecasting and predicting the reoccurrence of such phenomenon. The additional meteorological station should be provided by the government agencies in all agricultural zones of the state for adequate and wide range of hydro-climatic data recording for appropriate prediction of weather indices in future.
 
 

Influences of climate variability on regional precipitation and temperature associations

ABSTRACT Joint variations of precipitation (P) and temperature (T) under the influence of climate variability are examined in this study. Individual and joint influences of Atlantic multidecadal oscillation (AMO) and El Niño Southern Oscillation (ENSO) on P and T variability in a tropical region (i.e. Florida) are investigated. A rank-based association measure, nonparametric hypothesis tests, and Latin hypercube bootstrap sampling are used for assessments of hydroclimatic data at 78 observation stations. Spatially non-uniform influences of these two oscillations on the P-T associations are evident. While P-T associations were stronger during La Niña compared to El Niño, relatively lower precipitation is observed during La Niña. Temperature range-specific variations in the frequency and magnitude of precipitation are noted in two phases of AMO and ENSO. Climate variability has a stronger influence than local meteorological conditions in generating higher precipitation. The changes in P-T associations due to climate variability are mostly elevation invariant.

Trends in pluviometric precipitation and climatic water balance in the hydrographic region of Paraguaçu – BA

Trend analysis of hydroclimatic data is essential in the development of water resources management, as it can envisage changes in the pattern of behaviour, helping develop strategies for adaptation in the face of imminent climate change. This study aimed to investigate possible annual and seasonal trends in rainfall and climatological water balance in the hydrographic region of Paraguaçu - BA. From the historical series of precipitation, deficiency and water surpluses, between 1989 and 2018, two analysis scenarios were conducted: the first to verify the annual and seasonal trends of each station, using the traditional Mann-Kendall (MK) methods and Sen’s estimator; and the second for each sub-region of Paraguaçu, by comparing MK with the Innovative Trend Analysis (ITA). The results of the annual series, regardless of the methodology adopted, point to negative trends in rainfall, positive trends in deficit and negative trends in water surplus. Seasonally, in the autumn and winter seasons, generally considered to be drought, there were more trends of increasing rainfall and decreasing water deficiency. A comparison between the MK and ITA models showed that both have similar results for indicating trends in the sub-regions of Paraguaçu. However, the ITA has shown a higher number of significant trends.

Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling

AbstractLarge‐scale covariations of freshwater fluxes and storages on land can critically regulate the balance of green (evapotranspiration) and blue (runoff) water fluxes, and related land‐atmosphere interactions and hydroclimatic hazards. Such large‐scale covariation patterns are not evident from smaller‐scale hydrological studies that have been most common so far, and remain largely unknown for various regions and climates around the world. To contribute to bridging the large‐scale knowledge gaps, we synthesize and decipher hydroclimatic data time series over the period 1980–2010 for 6,405 catchments around the world. From observation‐based data, we identify dominant large‐scale linear covariation patterns between monthly freshwater fluxes and soil moisture (SM) for different world parts and climates. These covariation patterns are also compared with those obtained from reanalysis products and Earth System Models (ESMs). The observation‐based data sets robustly show the strongest large‐scale hydrological relationship to be that between SM and runoff (R), consistently across the study catchments and their different climate characteristics. This predominantly strongest covariation between monthly SM and R is also the most misrepresented by ESMs and reanalysis products, followed by that between monthly precipitation and R. Comparison of observation‐based and ESM results also shows that an ESM may perform well for individual monthly variables, but fail in representing the patterns of large‐scale linear covariations between them. Observation‐based quantification of these patterns, and ESM and reanalysis improvements for their representation are essential for fundamental understanding, and more accurate and reliable modeling and projection of large‐scale hydrological conditions and changes under ongoing global and regional change.

Quantifying the actual sediment load flux into Lake Baikal: A case study of the main tributary – The Selenga River (Russia)

The significant reduction of sediment load flux into Lake Baikal from its main tributary – the Selenga River began in the mid-1970s, and can be explained by climate change and socio-economic activities. Integrated analysis was done of changes in hydro-meteorological parameters (water discharge, sediment load, air temperature, and precipitation) and their tendencies over periods of 1946–1975 (baseline) and 1976–2017 (warming). Changes in natural processes and human activity were negligible during the baseline period. In the warming period, against the background of an increase in temperature, the water discharge had a slight decreasing trend (−13%) whereas the sediment load has significantly decreased (−53%) these are consist with the precipitation change (−9.4%). Analysis of hydro-climatic data using statistical methods showed that in the warming period the greatest reduction in river sediment runoff occurred in the interval 1996–2017. In this period the sediment load was 768 × 103 t/yr, which is less than the average value during the warming period – 1048 × 103 t/yr. Considering sedimentation in the Selenga River delta, the actual sediment load flux from the Selenga River into Lake Baikal amounted to 515 × 103 t/yr, which is three times less than the average multiyear value 1535 × 103 t/yr for observation period (1946–2017).

Hydroclimatic Data Prediction using a New Ensemble Group Method of Data Handling Coupled with Artificial Bee Colony Algorithm

Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it provides more robust, accurate, and efficient results.

Tropical Cyclones’ Contribution to Seasonal Precipitation and Streamflow Over the Southeastern and Southcentral United States

AbstractStudies have quantified the contribution of tropical cyclones (TCs) toward seasonal precipitation, but limited analysis is available on TC contribution toward seasonal streamflow over the southeastern and southcentral (SESC) United States (U.S.). Using an extensive network of hydroclimatic data that consists of 233 TC tracks and daily precipitation and streamflow, we estimate TC contribution toward precipitation and streamflow during the hurricane season over the SESC U.S. We found that TCs account for 12% of seasonal streamflow and 6% of seasonal precipitation over the region. Florida, North Carolina, and Louisiana have the highest fractional occurrence of TC‐generated annual maximum precipitation (∼20%–32%) and streamflow (∼15%–27%). We also found the fractional occurrence of TCs associated with peak‐over threshold precipitation (streamflow) events ranges from 5% to 8% in coastal regions (10%–20% over FL and 5%–10% over coastal NC). Increased antecedent moisture results in increased TCs contribution to streamflow leading to different land‐surface responses for similar hurricane events.

Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data

Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data

Quantifying the effects of urban green space on water partitioning and ages using an isotope-based ecohydrological model

Abstract. The acceleration of urbanization requires sustainable, adaptive management strategies for land and water use in cities. Although the effects of buildings and sealed surfaces on urban runoff generation and local climate are well known, much less is known about the role of water partitioning in urban green spaces. In particular, little is quantitatively known about how different vegetation types of urban green spaces (lawns, parks, woodland, etc.) regulate partitioning of precipitation into evaporation, transpiration and groundwater recharge and how this partitioning is affected by sealed surfaces. Here, we integrated field observations with advanced, isotope-based ecohydrological modelling at a plot-scale site in Berlin, Germany. Soil moisture and sap flow, together with stable isotopes in precipitation, soil water and groundwater recharge, were measured over the course of one growing season under three generic types of urban green space: trees, shrub and grass. Additionally, an eddy flux tower at the site continuously collected hydroclimate data. These data have been used as input and for calibration of the process-based ecohydrological model EcH2O-iso. The model tracks stable isotope ratios and water ages in various stores (e.g. soils and groundwater) and fluxes (evaporation, transpiration and recharge). Green water fluxes in evapotranspiration increased in the order shrub (381±1mm)<grass(434±21mm)<trees(489±30 mm), mainly driven by higher interception and transpiration. Similarly, ages of stored water and fluxes were generally older under trees than shrub or grass. The model also showed how the interface between sealed surfaces and green space creates edge effects in the form of “infiltration hotspots”. These can both enhance evapotranspiration and increase groundwater recharge. For example, in our model, transpiration from trees increased by ∼ 50 % when run-on from an adjacent sealed surface was present and led to groundwater recharge even during the growing season, which was not the case under trees without run-on. The results form an important basis for future upscaling studies by showing that vegetation management needs to be considered within sustainable water and land use planning in urban areas to build resilience in cities to climatic and other environmental change.

Regional mass variations and its sensitivity to climate drivers over glaciers of Karakoram and Himalayas

ABSTRACT The regional scale mass change provides a substantial understanding to the influence of climate change on the glacier surface. However, the scarce ground observations and large spatial extent limit the use of field-based mass balance estimation. In this study, the Gravity Recovery and Climate Experiment (GRACE) satellite data were used to estimate the regional glacier mass variation over the Karakoram and Himalayas (KH) region during 2002–2019 hydrological years. Our study showed that the total mass change rate over the KH region was – 55.0 ± 8.7 Gt yr–1 for the study period. We have identified four positive/negative clusters of mass change over the KH region, with the eastern part of the Karakoram (KK) and part of the Central Himalaya (CH) regions have experienced a positive mass change. In contrast, the junction area between the Western Himalaya (WH) and CH region observed an extreme negative mass change. The results demonstrated that the annual mass change for WH become negative or nearly zero from 2007/08 onwards, while for CH and Eastern Himalaya (EH) regions, it became negative since 2005/06. The GRACE-derived mass change was validated with the published in-situ mass balance data, which reveals a high correlation (r = 0.77) over the region. The performance of obtained results were also compared with Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1) data for the period 2003–2009, indicating a correlation of 0.87 and 0.91 for monthly and annual scale, respectively. Moreover, the interannual mass change in the glaciated region was analyzed using hydro-climatic data and energy fluxes, suggesting a higher correlation of mass change with total precipitation and snowfall over the spatiotemporal scale. However, this correlation fails at locations where evaporation, runoff, and radiation have prominently influenced the mass loss. The uncertainty and sensitivity test for mass change demonstrated that snowfall and temperature have a strong influence on the response of mass change. Overall, this study provided regional mass change and their relationship with hydro-climatic variables, which will provide a better understanding related to the water availability in the downstream areas.

Assessing the snow cover dynamics and its relationship with different hydro-climatic characteristics in Upper Ganges river basin and its sub-basins

Snowmelt is an important source of water in upstream part of the Ganges river basin (GRB), which provides water for different purposes to its 655 million inhabitants. However, studies assessing relationship between snow cover dynamics and changes in hydro-climatic variables are limited within this region, motivating the current research. In this study, MODIS snow cover product (MOD10A1) was used to assess the snow cover area (SCA) dynamics within the Upper Ganges river basin (UGRB) and its sub-basins for the time period of 2002–2014; available climate and hydrological data were used to assess the hydrological characteristics within three selected sub-basins in Nepal; and relationships between snow cover and different hydro-climatic variables are established for three sub-basins owing to availability of hydro-climatic data. Results show that the average annual maximum SCA is around 24.6–47.5% for UGRB and its sub-basins. Upper Yamuna river basin (UYRB) with lowest mean elevation among the sub-basins shows a single SCA peak in spring within an annual cycle, whereas UGRB and the higher sub-basins show an additional lower peak in fall mainly resulted from snow sublimation. During 2002–2014, SCA shows slight decreasing trends for UGRB (Kendall's Tau τ = −0.039) and the higher elevation zones B (3001–4500 m a.s.l.) and C (>4500 m a.s.l.) of most sub-basins, with significance in Zone C of SaRB (τ = −0.070) and KoRB (τ = −0.062). Annual discharge for Gandaki river basin (GaRB) and Koshi river basin (KoRB) shows non-significant decreasing trends (τ = −0.182, −0.303) which are resulted from decreasing discharge in different seasons in different sub-basins. Seasonal correlation analysis indicates an important water supply from rainfall in GaRB and combined water supply from rainfall and snowmelt in KoRB, along with dominant contribution of precipitation in monsoon months and snowmelt in non-monsoon months for all the three sub-basins.