Application of satellite and reanalysis precipitation products for hydrological modeling in the data-scarce Porijõgi catchment, Estonia

Moges, Desalew Meseret; Kmoch, Alexander; Uuemaa, Evelyn

doi:10.1016/j.ejrh.2022.101070

Cited by 15 publications

(12 citation statements)

References 83 publications

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“…Of the models with the same soil, those models with the high-resolution Estonian land use data had a smaller decline in NSE during the validation period, indicating that land use parameters were more reliable. Modeled and reanalyzed rainfall data would be available [13], but we decided to refrain from introducing additional large-scale data.…”

Section: Discussionmentioning

confidence: 99%

“…For the general characterization and management of a catchment and to understand and describe the hydrological system, monthly flows are an appropriate scale to identify patterns to see the "big picture". Furthermore, Moges et al [13] reported high daily variation in the detection of rainfall in the observed gauges for the Porijõgi catchment. Thus, we calibrated and validated all models only on a monthly scale to reduce additional noise.…”

Section: Calibration and Validationmentioning

confidence: 97%

“…The main crops grown in the catchment include wheat and rapeseed, with mainly mineral fertilizers used [20]). The average annual precipitation during the research period (2007-2019) is 678 mm, and the mean annual temperature is 6.38 • C [13].…”

Section: Description Of the Study Areamentioning

confidence: 99%

“…In recent years, the sensitivity of the SWAT model to spatial input data has attracted the attention of researchers [6,8,[11][12][13]. The previous studies show somewhat contradicting results, and there is no clear pattern indicating that high-resolution and local data outperform the low-resolution data.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Spatial Input Data Quality on the Performance of the SWAT Model

Kmoch

Moges

Sepehrar

et al. 2022

Water

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Soil and land use information are important inputs for physically-based hydrological modeling such as SWAT. Although fine resolution local or regional data are often preferred for modeling, it is not always reliable that these data can lead to better model performance. In this study, we investigate the effect of input data on the sensitivity and uncertainty of the SWAT model in the Porijõgi catchment in Estonia. We created four model setups using global/regional level data (HWSD soil and CORINE) and local high-resolution spatial data, including the Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). We employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. The results illustrated that models with high-resolution local soil data performed lower than models with global soil data, but in contrast, in the case of land use datasets, the local high-resolution ETAK dataset improved performance over the CORINE data.

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Section: Discussionmentioning

confidence: 99%

Section: Calibration and Validationmentioning

confidence: 97%

Section: Description Of the Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Spatial Input Data Quality on the Performance of the SWAT Model

Kmoch

Moges

Sepehrar

et al. 2022

Water

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“…Precipitation estimates based on satellites with high spatial and temporal resolution and wide coverage of the area offer a potential alternative source of impact data for hydrological models in areas where traditional in situ precipitation observations are not always available. The increasing availability of hhigh-resolution (and near real-time) satellite-based rainfall estimates has significant potential in applications like hydrological analysis for engineering design, assimilation of precipitation data into forecast models, flood forecasting, and water resource management in general (Liu et al 2012;Zambrano-Bigiarini et al 2017;Moges et al 2022). These applications could have far-reaching implications for various developing countries, such as Ethiopia, which lack ground-based rain gauges and does not have radar capabilities to measure representative rainfall magnitude.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Nigussie

Tenfie

Zimale

et al. 2022

Journal of Water and Climate Change

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This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used as Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods such as Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM) on five metrological stations. Six statistical performance measuring techniques were employed. The evaluation was carried out from the year 2003 to 2016 on daily and monthly time scales. The results depicted that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study highlights the benefits of using SRPs and bias correction methods to enhance the distribution of local rainfall data, which is critical for water resource planning and other related sectors.

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