A novel approach to infer streamflow signals for ungauged basins

In this paper, we present a novel paradigm for inference of streamflow for ungauged basins. Our innovative procedure fuses concepts from both kernel methods and data assimilation. Based on the modularity and flexibility of kernel techniques and the strengths of the variational Bayesian Kalman filter and smoother, we can infer streamflow for ungauged basins whose hydrological and system properties and/or behavior are non-linear and non-Gaussian. We apply the proposed approach to two watersheds, one in California and one in West Virginia. The inferred streamflow signals for the two watersheds appear promising. These preliminary and encouraging validations demonstrate that our new paradigm is capable of providing accurate conditional estimates of streamflow for ungauged basins with unknown and non-linear dynamics.     

Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins

Approaches to modeling the continuous hydrologic response of ungauged basins use observable physical characteristics of watersheds to either directly infer values for the parameters of hydrologic models, or to establish regression relationships between watershed structure and model parameters. Both these approaches still have widely discussed limitations, including impacts of model structural uncertainty. In this paper we introduce an alternative, model independent, approach to streamflow prediction in ungauged basins based on empirical evidence of relationships between watershed structure, climate and watershed response behavior. Instead of directly estimating values for model parameters, different hydrologic response behaviors of the watershed, quantified through model independent streamflow indices, are estimated and subsequently regionalized in an uncertainty framework. This results in expected ranges of streamflow indices in ungauged watersheds. A pilot study using 30 UK watersheds shows how this regionalized information can be used to constrain ensemble predictions of any model at ungauged sites. Dominant controlling characteristics were found to be climate (wetness index), watershed topography (slope), and hydrogeology. Main streamflow indices were high pulse count, runoff ratio, and the slope of the flow duration curve. This new approach provided sharp and reliable predictions of continuous streamflow at the ungauged sites tested.     

Intra-annual variability of urban effects on streamflow

While considerable research has established the impacts of urbanization on streamflow, there has been little emphasis on how intra-annual variations in streamflow can deepen the understanding of hydrological processes in urban watersheds. This study fills this critical research gap by examining, at the monthly scale, correlations between land-cover and streamflow, differences in streamflow metrics between urban and rural watersheds, and the potential for the inflow and infiltration (I&I) of extraneous water into sewers to reduce streamflow. We use data from 90 watersheds in the Atlanta, GA region over the 2013–2019 period to accomplish our objectives. Similar to other urban areas in temperate climates, Atlanta has a soil-water surplus in winter and a soil-water deficit in summer. Our results show urban watersheds have less streamflow seasonality than do rural watersheds. Compared to rural watersheds, urban watersheds have a much larger frequency of high-flow days during July–October. This is caused by increased impervious cover decreasing the importance of antecedent soil moisture in producing runoff. Urban watersheds have lower baseflows than rural watersheds during December–April but have baseflows equal to or larger than baseflows in rural watersheds during July–October. Intra-annual variations in effluent data from wastewater treatment plants provide evidence that I&I is a major cause of the relatively low baseflows during December–April. The relatively high baseflows in urban watersheds during July–October are likely caused by reduced evapotranspiration and the inflow of municipal water. The above seasonal aspects of urban effects on streamflow should be applicable to most urban watersheds with temperate climates.     

Spatial patterns of frequent floods in Switzerland

This study investigates the spatial dependence of high and extreme streamflows in Switzerland across different scales. First, using 56 runoff time series from Swiss rivers, we determined the average length of high-streamflow events for different levels of extremeness. Second, a dependence measure that expressed the probability that streamflow peaks would meet or exceed streamflow peaks at a conditioning site was used to describe and map the spatial extent of joint streamflow-peak occurrences across Switzerland. Third, we analysed the spatial patterns of jointly occurring high streamflows using cluster analysis to identify groups that react similarly in terms of flood frequency at different sites. The results indicate that, on a coarse scale, high and extreme streamflows are asymptotically independent in the main Swiss basins. Additionally, mesoscale tributaries in the main basins show distinct flood regions across river systems.     

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58 % of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and 3-h precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions.     

Modeling river/reservoir system management, water allocation, and supply reliability

The state of Texas has implemented a modeling system for assessing the availability and reliability of water resources that consists of a generalized simulation model called the Water Rights Analysis Package (WRAP) and input datasets for the state's 23 river basins. Reservoir/river system management and water allocation practices are simulated using historical naturalized monthly streamflow sequences to represent basin hydrology. Institutional systems for allocating streamflow and reservoir storage resources among numerous water users are considered in detail in evaluating basinwide impacts of water management decisions. The generalized WRAP model is a flexible tool that may be applied to river basins anywhere. The Texas experience in implementing a statewide modeling system illustrates issues that are relevant to water management in many other regions of the world.     

Hydrologic comparison between a forested and a wetland/lake dominated watershed using SWAT

The Soil and Water Assessment Tool (SWAT) is a physically‐based hydrologic model developed for agricultural watersheds, which has been infrequently validated for forested watersheds, particularly those with deep overwinter snow accumulation and abundant lakes and wetlands. The goal of this study was to determine the applicability of SWAT for modelling streamflow in two watersheds of the Ontonagon River basin of northern Michigan which differ in proportion of wetland and lake area. The forest‐dominated East Branch watershed contains 17% wetland and lake area, whereas the wetland/lake‐dominated Middle Branch watershed contains 26% wetland and lake area. The specific objectives were to: (1) calibrate and validate SWAT models for the East Branch and Middle Branch watersheds to simulate monthly stream flow, and (2) compare the effects of wetland and lake abundance on the magnitude and timing of streamflow. Model calibration and validation was satisfactory, as determined by deviation of discharge D and Nash and Sutcliffe coefficient values E that compared simulated monthly mean discharge versus measured monthly mean discharge. Streamflow simulation discrepancies occurred during summer and fall months and dry years. Several snow melting parameters were found to be critical for the SWAT simulation: TIMP (snow temperature lag factor) and SMFMX and SMFMN (melting factors). Snow melting parameters were not transferable between adjacent watersheds. Differences in seasonal pattern of long‐term monthly streamflow were found, with the forest‐dominated watershed having a higher peak flow during April but a lower flow during the remainder of the year in comparison to the wetland and lake‐dominated watershed. The results suggested that a greater proportion of wetland and lake area increases the capacity of a watershed to impound surface runoff and to delay storm and snow melting events. Representation of wetlands and lakes in a watershed model is required to simulate monthly stream flow in a wetland/lake‐dominated watershed. Copyright © 2007 John Wiley & Sons, Ltd.     

Roles of forest disturbance and climate variability on streamflow components in snow-dominated paired watersheds at multiple temporal scales

The paired watershed experimental (PWE) approach has long been used as an effective means to assess the impacts of forest change on hydrology in small watersheds (<100 km²). Yet, the effects of climate variability on streamflow are not often assessed in PWE design. In this study, two sets of paired watersheds, (1) Camp and Greata Creeks and (2) 240 and 241 Creeks located in the Southern Interior of British Columbia, Canada, were selected to explore relative roles of forest disturbance and climate variability on streamflow components (i.e., baseflow and surface runoff) at different time scales. Our analyses showed that forest disturbance is positively related to annual streamflow components. However, this relationship is statistically insignificant since forest disturbance can either increase or decrease seasonal streamflow components, which eventually limited the positive effect on streamflow at the annual scale. Interestingly, we found that forest disturbance consistently decreased summer streamflow components in the two PWEs as forest disturbance can augment earlier and quicker snow-melt processes and hence reduce soil moisture to maintain summer streamflow components. More importantly, this study revealed that climate variability played a more significant role than forest disturbance in both annual and seasonal streamflow components, for instance, climate variability can account for as much as 90% of summer streamflow components variation in Camp, suggesting the role of climate variability on streamflow should be highlighted in the traditional PWE approach to truly advance our understanding of the interactions of forest change, climate variability and water for sustainable water resource management.     

Temporal analysis of Soil and Water Assessment Tool (SWAT) performance based on remotely sensed precipitation products

No study has systematically evaluated streamflow modelling between monthly and daily time scales. This study examines streamflow from seven watersheds across the USA where five different precipitation products were used as primary input into the Soil and Water Assessment Tool (SWAT) to generate simulated streamflow. Time scales examined include monthly, dekad (10 days), pentad (5 days), triad (3 days), and daily. The seven basins studied are the San Pedro (Arizona), Cimarron (north‐central Oklahoma), mid‐Nueces (south Texas), mid‐Rio Grande (south Texas and northern Mexico), Yocano (northern Mississippi), Alapaha (south Georgia), and mid‐St. Francis (eastern Arkansas). The precipitation products used to drive simulations include rain gauge, NWS Multisensor Precipitation Estimator, Tropical Rainfall Measurement Mission (TRMM), Multi‐Satellite Precipitation Analysis, TRMM 3B42‐V6, and Climate Prediction Center Morphing Method (CMORPH). Understanding how streamflow varies at sub‐monthly time scales is important because there are a host of hydrological applications such a flood forecast guidance and reservoir inflow forecasts that reside in a temporal domain between monthly and daily time scales. The major finding of this study is the quantification of a strong positive correlation between performance metrics and time step at which model performance deteriorates. Better performing simulations, with higher Nash–Sutcliffe values of 0.80 and above can support modeling at finer time scales to at least daily and perhaps beyond into the sub‐daily realm. These findings are significant in that they clearly document the ability of SWAT to support modeling at sub‐monthly time steps, which is beyond the capability for which SWAT was initially designed. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows

ABSTRACT

In this study, the distributed catchment-scale model, DiCaSM, was applied on five catchments across the UK. Given its importance, river flow was selected to study the uncertainty in streamflow prediction using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology at different timescales (daily, monthly, seasonal and annual). The uncertainty analysis showed that the observed river flows were within the predicted bounds/envelope of 5% and 95% percentiles. These predicted river flow bounds contained most of the observed river flows, as expressed by the high containment ratio, CR. In addition to CR, other uncertainty indices – bandwidth B, relative bandwidth RB, degrees of asymmetry S and T, deviation amplitude D, relative deviation amplitude RD and the R factor – also indicated that the predicted river flows have acceptable uncertainty levels. The results show lower uncertainty in predicted river flows when increasing the timescale from daily to monthly to seasonal, with the lowest uncertainty associated with annual flows.     

Global sensitivity analysis of hydrologic processes in major snow‐dominated mountainous river basins in Colorado

The performance of watershed models in simulating stream discharge depends on the adequate representation of important watershed processes. In snow‐dominated systems, snow, surface and subsurface hydrologic processes comprise a complex network of nonlinear interactions that influence the magnitude and timing of discharge. This study aims to identify critical processes and interactions that control discharge hydrographs in five major mountainous snow‐dominated river basins in Colorado, USA. A comprehensive watershed model (Soil and Water Assessment Tool) and a variance‐based global sensitivity analysis technique (Fourier Amplitude Sensitivity Test) were used in conjunction to identify critical models parameters and processes that they represent. Average monthly streamflow and streamflow root mean square error over a period of 20 years were used as two separate objective functions in this analysis. Examination of the sensitivity of monthly streamflow revealed the influence of parameters on flow volume, whereas the sensitivity of streamflow root mean square error also exposed the influence of parameters on the timing of the hydrographs. A stability analysis was performed to investigate the computational requirements for a robust sensitivity analysis. Results show that streamflow volume is mostly influenced by shallow subsurface processes, whereas interactions between groundwater and snow processes were the key in the timing of streamflows. A large majority of important parameters were common among all study watersheds, which underlies the prospect for regionalization of process‐based hydrologic modelling in headwater river basins in Colorado. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatio-temporal changes and their relationship in water resources and agricultural disasters across China

Changes in monthly streamflow and the potential influences and feedbacks of agricultural activities are investigated. Significant decreases in streamflow are observed in northern China, including the Yellow, Huaihe and Haihe river basins, while in southern China streamflow increases significantly in the Yangtze, Pearl and South river basins. This spatial pattern of changes in streamflow indicates that the imbalance in water resources between northern (dry) and southern (wet) China has increased during past decades. On the one hand, available water resources are a controlling factor determining the expansion of irrigated land and the structure of crop plantation (i.e. rice, wheat, corn or bean); on the other hand, crop planting structure and effective irrigated areas are important determinants of changes in streamflow. The increasing effective irrigation and rice planting areas in northern China may increase water withdrawal from rivers, causing subsequent decreases in streamflow, while in southeastern China, decreasing effective irrigation areas enhance the increases in streamflow.     

Long‐term trend of precipitation and runoff in Korean river basins

The spatial and temporal variations of precipitation and runoff for 139 basins in South Korea were investigated for 34 years (1968–2001). The Precipitation‐Runoff Modelling System (PRMS) was selected for the assessment of basin hydrologic response to varying climates and physiology. A non‐parametric Mann–Kendall's test and regression analysis are used to detect trends in annual, seasonal, and monthly precipitation and runoff, while Moran's I is adapted to determine the degree of spatial dependence in runoff trend among the basins. The results indicated that the long‐term trends in annual precipitation and runoff were increased in northern regions and decreased in south‐western regions of the study area during the study period. The non‐parametric Mann–Kendall test showed that spring streamflow was decreasing, while summer streamflow was increasing. April precipitation decreased between 15% and 74% for basins located in south‐western part of the Korean peninsula. June precipitation increased between 18% and 180% for the majority of the basins. Trends in seasonal and monthly streamflow show similar patterns compared to trends in precipitation. Decreases in spring runoff are associated with decreases in spring precipitation which, accompanied by rising temperatures, are responsible for reducing soil moisture. The regional patterns of precipitation and runoff changes show a strong to moderate positive spatial autocorrelation, suggesting that there is a high potential for severe spring drought and summer flooding in some parts of Korea if these trends continue in the future. Copyright © 2008 John Wiley & Sons, Ltd.     

Influence of Three Gorges Dam on streamflow and sediment load of the middle Yangtze River, China

Using updated hydrological datasets from three stations, including Cuntan, Yichang and Hankou, covering the period of January 1992–December 2008, the influence of Three Gorges Dam (TGD) on streamflow and sediment load of the Yangtze River was investigated. Results indicated that TGD did not seem to exert a significant influence on streamflow occurring at three stations and changes in streamflow can be mainly attributed to streamflows of tributaries. However, a sharp decrease in the sediment load after the impoundment of TGD was observed. Clear water after the impoundment caused erosion of riverbed and resulted in more sediment at the Hankou station than at the Yichang station. No distinct changes in the annual and monthly maximum sediment loads were observed before and after the impoundment. Therefore, annual and monthly maximum sediment load changes should be subjected mainly to river hydraulics. This study has practical relevance for understanding the influence of large hydraulic structures on the hydrological processes of large rivers.     

Post‐processing of ensemble forecasts in low‐flow period

For water supply, navigational, ecological protection or water quality control purposes, there is a great need in knowing the likelihood of the river level falling below a certain threshold. Ensemble streamflow prediction (ESP) based on simulations of deterministic hydrologic models is widely used to assess this likelihood. Raw ESP results can be biased in both the ensemble means and the spreads. In this study, we applied a modified general linear model post‐processor (GLMPP) to correct these biases. The modified GLMPP is built on the basis of regression of simulated and observed streamflow calculated on the basis of canonical events, instead of the daily values as is carried out in the original GLMPP. We conducted the probabilistic analysis of post‐processed ESP results falling below pre‐specified low‐flow levels at seasonal time scale. Raw ESP forecasts from the 1980 to 2006 periods by four different land surface models (LSMs) in eight large river basins in the continental USA are included in the analysis. The four LSMs are Noah, Mosaic, variable infiltration capacity and Sacramento models. The major results from this study are as follows: (1) a modified GLMPP was proposed on the basis of canonical events; (2) post‐processing can improve the accuracy and reduce the uncertainty of hydrologic forecasts; (3) post‐processing can help deal with the effect of human activity; and (4) raw simulation results from different models vary greatly in different basins. However, post‐processing can always remove model biases under different conditions. Copyright © 2014 John Wiley & Sons, Ltd.     

Predictions in ungauged basins: an approach for regionalization of hydrological models considering the probability distribution of model parameters

Regionalization of model parameters by developing appropriate functional relationship between the parameters and basin characteristics is one of the potential approaches to employ hydrological models in ungauged basins. While this is a widely accepted procedure, the uniqueness of the watersheds and the equifinality of parameters bring lot of uncertainty in the simulations in ungauged basins. This study proposes a method of regionalization based on the probability distribution function of model parameters, which accounts the variability in the catchment characteristics. It is envisaged that the probability distribution function represents the characteristics of the model parameter, and when regionalized the earlier concerns can be addressed appropriately. The method employs probability distribution of parameters, derived from gauged basins, to regionalize by regressing them against the catchment attributes. These regional functions are used to develop the parameter characteristics in ungauged basins based on the catchment attributes. The proposed method is illustrated using soil water assessment tool model for an ungauged basin prediction. For this numerical exercise, eight different watersheds spanning across different climatic settings in the USA are considered. While all the basins considered in this study were gauged, one of them was assumed to be ungauged (pseudo-ungauged) in order to evaluate the effectiveness of the proposed methodology in ungauged basin simulation. The process was repeated by considering representative basins from different climatic and landuse scenarios as pseudo-ungauged. The results of the study indicated that the ensemble simulations in the ungauged basins were closely matching with the observed streamflow. The simulation efficiency varied between 57 and 61 % in ungauged basins. The regional function was able to generate the parameter characteristics that were closely matching with the original probability distribution derived from observed streamflow data.     

Climate change and future flows of Rocky Mountain rivers: converging forecasts from empirical trend projection and down‐scaled global circulation modelling

To predict future river flows, empirical trend projection (ETP) analyses and extends historic trends, while hydroclimatic modelling (HCM) incorporates regional downscaling from global circulation model (GCM) outputs. We applied both approaches to the extensively allocated Oldman River Basin that drains the North American Rocky Mountains and provides an international focus for water sharing. For ETP, we analysed monthly discharges from 1912 to 2008 with non‐parametric regression, and extrapolated changes to 2055. For modelling, we refined the physical models MTCLIM and SNOPAC to provide water inputs into RIVRQ (river discharge), a model that assesses the streamflow regime as involving dynamic peaks superimposed on stable baseflow. After parameterization with 1960–1989 data, we assessed climate forecasts from six GCMs: CGCM1‐A, HadCM3, NCAR‐CCM3, ECHAM4 and 5 and GCM2. Modelling reasonably reconstructed monthly hydrographs (R² about 0·7), and averaging over three decades closely reconstructed the monthly pattern (R² = 0·94). When applied to the GCM forecasts, the model predicted that summer flows would decline considerably, while winter and early spring flows would increase, producing a slight decline in the annual discharge (?3%, 2005–2055). The ETP predicted similarly decreased summer flows but slight change in winter flows and greater annual flow reduction (?9%). The partial convergence of the seasonal flow projections increases confidence in a composite analysis and we thus predict further declines in summer (about ? 15%) and annual flows (about ? 5%). This composite projection indicates a more modest change than had been anticipated based on earlier GCM analyses or trend projections that considered only three or four decades. For other river basins, we recommend the utilization of ETP based on the longest available streamflow records, and HCM with multiple GCMs. The degree of correspondence from these two independent approaches would provide a basis for assessing the confidence in projections for future river flows and surface water supplies. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin,China

Variations in streamflows of five tributaries of the Poyang Lake basin, China, because of the influence of human activities and climate change were evaluated using the Australia Water Balance Model and multivariate regression. Results indicated that multiple regression models were appropriate with precipitation, potential evapotranspiration of the current month, and precipitation of the last month as explanatory variables. The NASH coefficient for the Australia Water Balance Model was larger than 0.842, indicating satisfactory simulation of streamflow of the Poyang Lake basin. Comparison indicated that the sensitivity method could not exclude the benchmark‐period human influence, and the human influence on streamflow changes was overestimated. Generally, contributions of human activities and climate change to streamflow changes were 73.2% and 26.8% respectively. However, human‐induced and climate‐induced influences on streamflow were different in different river basins. Specifically, climate change was found to be the major driving factor for the increase of streamflow within the Rao, Xin, and Gan River basins; however, human activity was the principal driving factor for the increase of streamflow of the Xiu River basin and also for the decrease of streamflow of the Fu River basin. Meanwhile, impacts of human activities and climate change on streamflow variations were distinctly different at different temporal scales. At the annual time scale, the increase of streamflow was largely because of climate change and human activities during the 1970s–1990s and the decrease of streamflow during the 2000s. At the seasonal scale, climate change was the main factor behind the increase of streamflow in the spring and summer season. Human activities increase the streamflow in autumn and winter, but decrease the streamflow in spring. At the monthly scale, different influences of climate change and human activities were detected. Climate change was the main factor behind the decrease of streamflow during May to June and human activities behind the decrease of streamflow during February to May. Results of this study can provide a theoretical basis for basin‐scale water resources management under the influence of climate change and human activities. Copyright © 2016 John Wiley & Sons, Ltd.     

Streamflow simulations for continental-scale river basins in a global atmospheric general circulation model

Streamflow simulations for 23 major river basins from the third-generation general circulation model (GCM) of the Canadian Centre for Climate Modelling and Analysis are assessed. Precipitation and runoff data are used from the AMIP II simulation in which the GCM is integrated for a 17-yr period with specific sea surface temperatures and sea-ice concentrations. Compared to the observations, the components of the global hydrological cycle and, the globally averaged precipitation and runoff over land, are well simulated. There remain, however, discrepancies in the simulation of regional precipitation and consequently runoff amounts, which lead to differences in basin-wide averaged quantities. Mean annual model precipitation is within 20% of the observed estimates for 13 out of 23 river basins considered. Model mean annual runoff is within 20% of the observed estimates for only 4 out of these 13 river basins. Analysis of basin-wide averaged monthly precipitation and streamflow data, and the errors associated with the mean, and amplitude and phase of the annual cycles, indicate that model streamflow simulations improve with improvement in GCM precipitation.     

River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model

Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins.