Research paperEstimation of hydrological parameters at ungauged catchments
Abstract
Estimates of hydrological parameters at ungauged sites have traditionally been obtained from regression equations. This study investigates alternative methods based on the classification of catchments into groups according to their flow regime, the assignment of ungauged catchments to a group based on physical characteristics of the catchment, and the use of similarity measures to transfer parameters from gauged to ungauged catchments. The paper considers the methods that can be adopted in this type of approach and the many variations that must be considered in their implementation. The methods are examined using a set of 99 catchments from the UK and are seen to be efficacious in estimating the unit hydrograph time to peak and standard percentage runoff, as defined by the UK Flood Studies Report.
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Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches
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Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins
2023, Science of the Total EnvironmentInformation on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.
Bias correcting discharge simulations from the GEOGloWS global hydrologic model
2023, Journal of HydrologyThe Group on Earth Observations (GEO) Global Water Sustainability (GEOGloWS) hydrologic model provides global river discharge hindcasts and daily forecasts at approximately one million subbasins worldwide. The model is meant to sustainably provide discharge data during emergency situations and to underdeveloped countries which do not have sufficient local capacity. The primary model error is biased flow magnitudes which reduce the usefulness of the results. We applied a revised implementation of the SABER bias correction method to correct GEOGloWS model results. SABER uses a combination of watershed clustering with machine learning, geospatial analysis, and statistics to generalize bias patterns in gauged basins so they can also be applied to ungauged basins. We validated the bias corrected data created using the improved SABER method at 12,965 gauges globally and showed that this method reduced the mean error at 90% of gauges. We present an analysis of the improved SABER method using several metrics including mean error, root mean squared error, and Kling Gupta Efficiency. We found that the GEOGloWS model is usually biased high but our results indicate a reduction in the bias of the GEOGloWS model worldwide. We evaluate the varied performance of the bias correction procedure and significance of the improvements which vary based on stream order the watershed classifications derived in our analysis. We provide guidance on the use of bias corrected global data to local scale applications and discuss implications for the GEOGloWS model in the future.
Estimation of flow duration and mass flow curves in ungauged tributary streams
2023, Journal of Cleaner ProductionThe mastery in forecasting the streamflow rates is of great importance in the design, planning and resilience against droughts. Likewise, the application of flow duration and mass flow curves in the design of the reservoir capacity, energy generation, water allocation, etc. especially at the tributary reaches is a great challenge mostly due to the lack of information and data records. In this study, we have developed a methodology to obtain the flow duration curve (FDC) and mass flow curve (MFC) in tributary stream stations with the help of estimated streamflow rates. The procedure suggests using two alternative approaches in the selection of the reference station on the mainstream. The streamflow in the reference station is decomposed into direct runoff (DR) and base flow (BF) using one-parameter digital filter method. Together with the precipitation records in the tributary station, the DR and BF on the reference station are then used to estimate the FDC and MFC. The multivariate adaptive regression spline (MARS) and random forest (RF) methods are used to alternate each other, and the residual of the models are simulated using the autoregressive conditionally heteroscedastic (ARCH) approach to develop the hybrid MARS-ARCH and RF-ARCH models. A data set related to Coruh River Basin, in Turkey is used to confirm the methodology, while results with R2 ≥ 0.92, reasonable bias, and relative error in the estimation of the expected FDC and MFC rates indicated the robustness of the suggested methodology.
Regional streamflow prediction in northwest Spain: A comparative analysis of regionalisation schemes
2023, Journal of Hydrology: Regional StudiesStudy Region: The present study was conducted in 24 watersheds located in the region of Galicia, in the northwest of Spain, covering an extension of approximately 13,000 km.
Study focus: This study is focused on the application and evaluation of different schemes for streamflow Prediction in Ungauged Basins (PUB). The MHIA model (Spanish acronym for Modelo HIdrológico Agregado), is first used to reproduce the observed time series of discharge in several gauged basins. Then, six different regionalisation schemes are applied to transfer the hydrological model parameters to ungauged catchments. For that purpose, we explore and compare two physical similarity, two spatial proximity and two regression-based regionalisation schemes. Output averaging (also known as ensemble modelling) as well as parameter averaging implementations of the physical similarity and spatial proximity methods are analysed.
New hydrological insights: The most efficient methods are those based on output averaging, with acceptable success rates (SR) in 88% of the cases. On the other hand, the parameter averaging-based methods have the lowest SR. The methods based on spatial proximity output averaging provide the best performance when the receptor basin has a sufficient number of nearby donor basins. On the other hand, the methods based on physical similarity output averaging show a better performance in areas where there is a low density of donor catchments. The regression-based methods showed the lowest performance in all cases. The existence of correlations between the performance of the regionalisation schemes and the area of the receptor catchments was observed, with higher performances in large basins than in small basins.
Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds
2023, Journal of Hydrology: Regional StudiesThe study region is 23 different watersheds across Nepal.
This study aims at assessing the strengths and weaknesses of widely used regionalization methods for simulating daily hydrograph and flow duration curve in a comparatively large sample of 23 medium to small-sized watersheds across Nepal. We employed a deductive approach based on extractable watershed properties to test the performance of four regionalization methods: principal component regression (PCR), random forests (RF) under regression-based methods, spatial proximity (SP), and physical similarity (PS) under donor-based methods in a leave-one-out cross-validation (LOOCV) setting.
The GR4J rainfall-runoff model coupled with Cemaneige snow module (GR4J-CN) could provide good simulation for majority of the watersheds with median NSE of 0.76 and 0.74 for calibration and validation periods respectively. Model simulation using parameter values predicted from different regionalization methods showed satisfactory results in majority of the watersheds for daily hydrograph simulation. While there wasn’t a single method that performed well in all of the watersheds, the physical similarity methods was found to be the most robust. Visual comparison of errors in flow duration curve (FDC) also indicated physical similarity method as a better approach in ungauged watersheds of Nepal. Further experiment using multiple donors using the output averaging option was found to increase the performance of donor-based methods while the parameter averaging option resulted in a drop in performance. The study provides a comprehensive assessment of regionalization methods and advocates the use of hydrological model regionalization as a promising tool for streamflow prediction in ungauged Himalayan watersheds.
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On leave from the Department of Civil Engineering, University of Manitoba, Winnipeg, Man., Canada, R3T 2N2.