Effects of different precipitation inputs on streamflow simulation in the Irrawaddy River Basin, Myanmar

Sirisena, T. A. J. G.; Maskey, Shreedhar; Ranasinghe, Roshanka; Babel, Mukand S.

doi:10.1016/j.ejrh.2018.10.005

Cited by 40 publications

(38 citation statements)

References 39 publications

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“…Hydrologic simulation performance evaluation result of SREs showed that accurate characterization of rainfall variability is very critical for reliable hydrological predictions. This finding is consistent with studies that reported that different precipitation datasets influence model performance, parameter estimation and uncertainty in streamflow predictions (Sirisena et al, 2018;Goshime et al, 2019). Overestimation of streamflow for all SREs products could be attributed to uncertainty of SREs for extreme rainfall events at daily scale (Zhao et al, 2017).…”

Section: Discussionsupporting

confidence: 91%

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Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Wedajo¹,

Muleta²,

Gessesse³

2020

Preprint

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Abstract. Precipitation is a crucial driver of hydrological processes. Ironically, reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurements using rain gauges can be more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) can be good alternatives, especially for data-scarce basins like in Ethiopia. However, SREs rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SREs products (i.e., CHIRPS2, IMERG6, TAMSAT3, and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrologic modelling approaches were used for performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SREs products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal, and monthly timescales. The hydrologic simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrologic response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products can be reliable rainfall data sources for hydrologic analysis of the Dhidhessa River Basin.

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Section: Discussionsupporting

confidence: 91%

“…The performance of the rainfall products were evaluated using SWAT-CUP at monthly time steps. predictions (Sirisena et al, 2018;Goshime et al, 2019). Relative sensitivity of the parameters also varied between the rainfall datasets.…”

Section: Hydrological Modelling Performance Evaluationmentioning

confidence: 99%

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Wedajo¹,

Muleta²,

Gessesse³

2020

Preprint

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“…To enable the evaluation of precipitation datasets over data-sparse regions, several studies have used hydrological modeling constrained with measurements from climatic variables that are directly dictated by precipitation such as streamflow (Grimes and Diop 2003;Bitew et al 2012;Thiemig et al 2012;Tong et al 2014;Sirisena et al 2018) and soil moisture (Pan et al 2010;Azarderakhsh et al 2011;Martens et al 2017). This has enabled the assessment of global precipitation datasets over regions with sparse P observations with more confidence.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure

Hobeichi

Abramowitz

Contractor

et al. 2020

Journal of Hydrometeorology

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Evaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-sample testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-sample testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.

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“…We based our simulations on a model calibration conducted by Ivanov et al [29,49] as part of the distributed model intercomparison project. Ivanov et al [49] obtained a correlation coefficient of 0.763 and a Nash-Sutcliffe coefficient of 0.565 (which can be considered as satisfactory following the guidelines exposed in Sirisena et al [50]) for the hourly simulated streamflow at the outlet of Baron Fork basin (in which PC is located), compared to the hourly observed streamflow from April of 1994 to July of 2000. To make the experiment approachable, the computational load was balanced by using parallel computing techniques.…”

mentioning

confidence: 67%

Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

Gabriel-Martín

Sordo‐Ward

Garrote

et al. 2019

Water

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This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.

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Effects of different precipitation inputs on streamflow simulation in the Irrawaddy River Basin, Myanmar

Cited by 40 publications

References 39 publications

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure

Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

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