Influence of multisite calibration on streamflow estimation based on the hydrological model with CMADS inputs

Due to the spatial heterogeneity, the hydrological model calibration results only at the total outlet of the basin may not represent the whole basin. To more accurately simulate the historical streamflow process within the Qujiang River Basin, we set up three calibration strategies (single-site, S1; multisite simultaneous, S2; and multisite sequential, S3) for four hydrological stations based on the SWAT (Soil and Water Assessment Tool) model driven by CMADS (China Meteorological Assimilation Driving Datasets for the SWAT model). In addition, the implications of these calibration issues are extended to future streamflow projections using multimodel ensemble data in CMIP6 (Coupled Model Intercomparison Project Phase 6). In the model calibration phase, the SWAT model achieved very satisfactory results in the study area. Compared with S1 and S2, S3 can effectively improve the accuracy of streamflow simulation of stations within the basin and reduce the simulation deviation. Especially at the daily scale, the average NSE values of the four stations with S3 increased by 0.26 and 0.07, and the overall deviation decreased by 0.25 and 6.43%, respectively. Parameter sensitivity analysis also shows that spatial heterogeneity can be more adequately considered when using S3 to calibrate the model. As for the results of future streamflow projection, when using the S3, the average annual streamflow of four stations in the three climate scenarios from 2021 to 2050 is about 44.21, 130.00, 321.55 and 713.24 m3/s, respectively. Correspondingly, the use of S1 and S2 would bring certain risks to future water resource management.

Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India

One of the challenges in rainfall-runoff modeling is the identification of an appropriate model spatial resolution that allows streamflow estimation at customized locations of the river basin. In lumped modeling, spatial resolution is not an issue as spatial variability is not accounted for, whereas in distributed modeling grid or cell resolution can be related to spatial resolution but its application is limited because of its large data requirements. Streamflow estimation at the data-poor customized locations is not possible in lumped modeling, whereas it is challenging in distributed modeling. In this context, semi-distributed modeling offers a solution including model resolution and estimation of streamflow at customized locations of a river basins with less data requirements. In this study, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model is employed in semi-distribution mode on river basins of six different spatial resolutions. The model was calibrated and validated for fifteen and three selected flood events, respectively, of three types, i.e., single peak (SP), double peak (DP)- and multiple peaks (MP) at six different spatial resolution of the Sabari River Basin (SRB), a sub-basin of the Godavari basin, India. Calibrated parameters were analyzed to understand hydrologic parameter variability in the context of spatial resolution and flood event aspects. Streamflow hydrographs were developed, and various verification metrics and model scores were calculated for reference- and calibration- scenarios. During the calibration phase, the median of correlation coefficient and NSE for all 15 events of all six configurations was 0.90 and 0.69, respectively. The estimated streamflow hydrographs from six configurations suggest the model’s ability to simulate the processes efficiently. Parameters obtained from the calibration phase were used to generate an ensemble of streamflow at multiple locations including basin outlet as part of the validation. The estimated ensemble of streamflows appeared to be realistic, and both single-valued and ensemble verification metrics indicated the model’s good performance. The results suggested better performance of lumped modeling followed by the semi-distributed modeling with a finer spatial resolution. Thus, the study demonstrates a method that can be applied for real-time streamflow forecast at interior locations of a basin, which are not necessarily data rich.

Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Dynamical bias correction procedure to improve global gridded daily streamflow data for local application in the Upper Blue Nile basin

Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent global streamflow products for assessment of global challenge such as climate change. Like any other products, the newly developed global streamflow products have limitations accurately represent the dynamics of local streamflow hydrographs. There is a need to locally evaluate and apply correction factors for better representation and make use of the data. This research focuses on the evaluation and correction of the bias embedded in the global streamflow product (WRR, 0.25°) developed by WaterGAP3 hydrological model in the upper Blue Nile basin part of Ethiopia. Three spatiotemporal dynamical bias correction schemes (temporal-spatial variable, temporal-spatial constant and spatial variable) tested in twelve watersheds of the basin. The temporal-spatial variable dynamical bias correction scheme significantly improves the streamflow estimation. The Nash-Sutcliffe coefficient (NSCE) improves by 30% and bias decreases by 19% for the twelve streamflow gauging stations applying leave one out cross-validation approach in turn. Therefore, the temporal-spatial variable scheme is applicable and can use as one method for the bias correction to use the global data for local applications in the upper Blue Nile basin.

Streamflow estimation at partially gaged sites using multiple dependence conditions via vine copulas

Reliable estimates of missing streamflow values are relevant for water resources planning and management. This study proposes a multiple dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin-Pee Dee River Basin, the eastern U. S. Both results inform that the proposed model is better suited for infilling missing values. After that, the performance of the vine copula is compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.