Spatial resolutions in areal rainfall estimation and their impact on hydrological simulations of a lowland catchment

Terink, W.; Leijnse, H.; Eertwegh, G. van den; Uijlenhoet, R.

doi:10.1016/j.jhydrol.2018.05.045

Cited by 44 publications

(17 citation statements)

References 61 publications

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“…However, the spatial resolution of a precipitation dataset has a significant impact on the hydrological simulation. Many studies have shown that in hydrological modeling with high spatial and temporal resolutions, datasets can better characterize streamflow [55]. Thus precipitation datasets with higher spatial and temporal resolution are needed to provide good hydrological simulations.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China

Gao

Zhu

Yang

et al. 2018

Water

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Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period.

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Section: Discussionmentioning

confidence: 99%

Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China

Gao

Zhu

Yang

et al. 2018

Water

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“…In order to successfully carry out high-resolution inundation simulation in time and space, the rainfall feature with high spatiotemporal resolution (i.e., gridded precipitation or high-density gauged rainfall) should be taken into account (e.g., Lovat et al 2019;Try et al 2020;Zanchetta & Coulibaly 2020). Although improving the spatial density of the rain-gauge networks can effectively capture the rainfall features representing the components of the hydrological cycle, the high cost of equipment and associated maintenance require consideration (Terink et al 2018). Therefore, rain-field simulation modeling is required in implementing flood and inundation simulations through the hydrologic/hydraulic model (e.g., Wheater et al 2005;Paschalis et al 2013;Jiang & Tatano 2015;Wu et al 2015;Chang et al 2018;Zanchetta & Coulibaly 2020).…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of gridded short-term rainstorms

Hsu

Chang

2021

Hydrology Research

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This study aims to develop a stochastic method (SM_GSTR) for generating short-time (i.e., hourly) rainstorm events at all grids (named gridded rainstorm events) in a region. The proposed SM_GSTR model is developed by the non-normal correlated multivariate Monte Carlo simulation (MMCS) method (Wu et al. 2006) with the statistical properties and spatiotemporal correlation structures of the four event-based gridded rainfall characteristics. The radar-based rainfall data on 20 typhoon events at 336 grids in a basin located in north Taiwan, Nankan River watershed, are used in the model development and demonstration. The results from the model demonstration indicate that the proposed SM_GSTR model can reproduce a great number of gridded rainfall characteristics, of which, the statistical properties in time and space have a good fit to those from the observations in association with the acceptable deviation; thus, it can reasonably emulate the behavior of the rain field in both time and space. It is expected that the resulting massive rainfall-induced disasters (e.g., inundation and landslide) from the physical-based numerical model with the simulated gridded rainstorms by the proposed SM_GSTR model can be applied to establish an alternative artificial intelligence (AI) model for effectively forecasting the hydrologic variables (e.g., runoff and water-level).

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“…There is a recent trend towards a decrease of hydrometric network density ( Mishra & Coulibaly, 2009 ; Keum & Kaluarachchi, 2015 ). Yet, the uncertainty in average rainfall strongly depends on rain gauge sampling density ( Xu et al, 2013 ; Terink et al, 2018 ). Hence, a reduction of the rain gauge density will increase the uncertainty about the discharge predicted by the rainfall-runoff model.…”

Section: Introductionmentioning

confidence: 99%

Optimization of rain gauge sampling density for river discharge prediction using Bayesian calibration

Wadoux

Heuvelink

Uijlenhoet

et al. 2020

PeerJ

Self Cite

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River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy of discharge predictions. Over the past years, Bayesian calibration has emerged as a suitable method for quantifying uncertainty in model parameters and model structure, where the latter is usually modelled by an additive or multiplicative stochastic term. Recently, much work has also been done to include input uncertainty in the Bayesian framework. However, the use of geostatistical methods for characterizing the prior distribution of the catchment rainfall is underexplored, particularly in combination with assessments of the influence of increasing or decreasing rain gauge network density on discharge prediction accuracy. In this article we integrate geostatistics and Bayesian calibration to analyze the effect of rain gauge density on river discharge prediction accuracy. We calibrated the HBV hydrological model while accounting for input, initial state, model parameter and model structural uncertainty, and also taking uncertainties in the discharge measurements into account. Results for the Thur basin in Switzerland showed that model parameter uncertainty was the main contributor to the joint posterior uncertainty. We also showed that a low rain gauge density is enough for the Bayesian calibration, and that increasing the number of rain gauges improved model prediction until reaching a density of one gauge per 340 km2. While the optimal rain gauge density is case-study specific, we make recommendations on how to handle input uncertainty in Bayesian calibration for river discharge prediction and present the methodology that may be used to carry out such experiments.

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Spatial resolutions in areal rainfall estimation and their impact on hydrological simulations of a lowland catchment

Cited by 44 publications

References 61 publications

Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China

Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China

Stochastic modeling of gridded short-term rainstorms

Optimization of rain gauge sampling density for river discharge prediction using Bayesian calibration

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