Comparative study of semi-distributed and 2D-distributed hydrological models for streamflow prediction in a data scarce mountainous watershed

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

Download Full-text

Model Uncertainty Analysis Methods for Semi-Arid Watersheds with Different Characteristics: A Comparative SWAT Case Study

Water ◽

10.3390/w11061177 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1177 ◽

Cited By ~ 2

Author(s):

Lufang Zhang ◽

Baolin Xue ◽

Yuhui Yan ◽

Guoqiang Wang ◽

Wenchao Sun ◽

...

Keyword(s):

Uncertainty Analysis ◽

River Basin ◽

Model Uncertainty ◽

Assessment Tool ◽

Rapid Development ◽

Swat Model ◽

Observation Data ◽

Hydrological Models ◽

Distributed Hydrological Models ◽

Different Characteristics

Distributed hydrological models play a vital role in water resources management. With the rapid development of distributed hydrological models, research into model uncertainty has become a very important field. When studying traditional hydrological model uncertainty, it is very common to use multisite observation data to evaluate the performance of the model in the same watershed, but there are few studies on uncertainty in watersheds with different characteristics. This study is based on the Soil and Water Assessment Tool (SWAT) model, and uses two common methods: Sequential Uncertainty Fitting Version 2 (SUFI-2) and Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis. We compared these methods in terms of parameter uncertainty, model prediction uncertainty, and simulation effects. The Xiaoqing River basin and the Xinxue River basin, which have different characteristics, including watershed geography and scale, were used for the study areas. The results show that the GLUE method had better applicability in the Xiaoqing River basin, and that the SUFI-2 method provided more reasonable and accurate analysis results in the Xinxue River basin; thus, the applicability was higher. The uncertainty analysis method is affected to some extent by the characteristics of the watershed.

Download Full-text

Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

Journal of Hydrometeorology ◽

10.1175/2007jhm862.1 ◽

2008 ◽

Vol 9 (1) ◽

pp. 132-148 ◽

Cited By ~ 106

Author(s):

Andrew W. Wood ◽

John C. Schaake

Keyword(s):

River Basin ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Streamflow Prediction ◽

Summer Period ◽

Streamflow Forecast ◽

Ensemble Mean ◽

Ensemble Streamflow Prediction ◽

Deterministic Components ◽

Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

Download Full-text

Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation

SN Applied Sciences ◽

10.1007/s42452-019-1764-x ◽

2019 ◽

Vol 1 (12) ◽

Cited By ~ 2

Author(s):

Nagaveni Chokkavarapu ◽

Venkata Ravibabu Mandla

Keyword(s):

Climate Change ◽

Comparative Study ◽

Climate Change Impact ◽

Future Climate ◽

Future Climate Change ◽

Hydrological Models ◽

Change Impact

Download Full-text

Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons

Earth Interactions ◽

10.1175/2013ei000519.1 ◽

2013 ◽

Vol 17 (25) ◽

pp. 1-22 ◽

Cited By ~ 14

Author(s):

Satish Bastola ◽

Vasubandhu Misra ◽

Haiqin Li

Keyword(s):

United States ◽

South Carolina ◽

Climate Model ◽

Southeastern United States ◽

Boreal Summer ◽

Hydrological Models ◽

State University ◽

Streamflow Prediction ◽

Seasonal Climate ◽

Hydrological Prediction

Abstract The authors evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts [the Florida Climate Institute–Florida State University Seasonal Hindcasts at 50-km resolution (FISH50)] is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall–runoff models. The hydrological models are forced with rainfall from FISH50, (quantile based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogs of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, the authors find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root-mean-square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that, over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. The authors contend that, given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.

Download Full-text