scholarly journals Assessment of SWAT Model Performance in Simulating Daily Streamflow under Rainfall Data Scarcity in Pacific Island Watersheds

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1533 ◽  
Author(s):  
Olkeba Leta ◽  
Aly El-Kadi ◽  
Henrietta Dulai ◽  
Kariem Ghazal
Keyword(s):  
Spatial Variability ◽  
Swat Model ◽  
Model Performance ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Small Scale ◽  
Pacific Island ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Low Performance

Evaluating the performance of watershed models is essential for a reliable assessment of water resources, particularly in Pacific island watersheds, where modeling efforts are challenging due to their unique features. Such watersheds are characterized by low water residence time, highly permeable volcanic rock outcrops, high topographic and rainfall spatial variability, and lack of hydrological data. The Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the Nuuanu area watershed (NAW) and Heeia watershed on the Island of Oahu (Hawaii). The NAW, which had well-distributed rainfall gauging stations within the watershed, was used for comparison with the Heeia watershed that lacked recoded rainfall data within the watershed. For the latter watershed, daily rain gauge data from the neighboring watersheds and spatially interpolated 250 m resolution rainfall data were used. The objectives were to critically evaluate the performance of SWAT under rain gauge data scarce conditions for small-scale watersheds that experience high rainfall spatial variability over short distances and to determine if spatially interpolated gridded rainfall data can be used as a remedy in such conditions. The model performance was evaluated by using the Nash–Sutcliffe efficiency (NSE), the percent bias (PBIAS), and the coefficient of determination (R2), including model prediction uncertainty at 95% confidence interval (95PCI). Overall, the daily observed streamflow hydrographs were well-represented by SWAT when well-distributed rain gauge data were used for NAW, yielding NSE and R2 values of > 0.5 and bracketing > 70% of observed streamflows at 95PCI. However, the model showed an overall low performance (NSE and R2 ≤ 0.5) for the Heeia watershed compared to the NAW’s results. Although the model showed low performance for Heeia, the gridded rainfall data generally outperformed the rain gauge data that were used from outside of the watershed. Thus, it was concluded that finer resolution gridded rainfall data can be used as a surrogate for watersheds that lack recorded rainfall data in small-scale Pacific island watersheds.

scholarly journals Influence of small scale rainfall variability on standard comparison tools between radar and rain gauge data

2014 ◽  
Vol 138 ◽  
pp. 125-138 ◽  
Author(s):  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer ◽  
Alma Schellart ◽  
Alexis Berne ◽  
...  
Keyword(s):  
Rainfall Variability ◽  
Rain Gauge ◽  
Small Scale ◽  
Rain Gauge Data ◽  
Gauge Data

scholarly journals Point-Based Rainfall Intensity Information System in Mt. Merapi Area by X-Band Radar

2019 ◽  
Vol 14 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Santosa Sandy Putra ◽  
Banata Wachid Ridwan ◽  
Kazuki Yamanoi ◽  
Makoto Shimomura ◽  
Sulistiyani ◽  
...  
Keyword(s):  
Satellite Data ◽  
Early Warning Systems ◽  
Radar Data ◽  
Rain Gauge ◽  
Data Grid ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Rain Gauge Data ◽  
X Band ◽  
Gauge Data

An X-band radar was installed in 2014 at Merapi Museum, Yogyakarta, Indonesia, to monitor pyroclastic and rainfall events around Mt. Merapi. This research aims to perform a reliability analysis of the point extracted rainfall data from the aforementioned newly installed radar to improve the performance of the warning system in the future. The radar data was compared with the monitored rain gauge data from Balai Sabo and the IMERG satellite data from NASA and JAXA (The Integrated Multi-satellitE Retrievals for GPM), which had not been done before. All of the rainfall data was compared on an hourly interval. The comparisons were conducted based on 11 locations that correspond to the ground rainfall measurement stations. The locations of the rain gauges are spread around Mt. Merapi area. The point rainfall information was extracted from the radar data grid and the satellite data grid, which were compared with the rain gauge data. The data were then calibrated and adjusted up to the optimum state. Based on January 2017–March 2018 data, it was obtained that the optimum state has a NSF value of 0.41 and R2value of 0.56. As a result, it was determined that the radar can capture around 79% of the hourly rainfall occurrence around Mt. Merapi area during the chosen calibration period, in comparison with the rain gauge data. The radar was also able to capture nearby 40–50% of the heavy rainfall events that pose risks of lahar. In contrast, the radar data performance in detecting drizzling and light rain types were quite precise (55% of cases), although the satellite data could detect slightly better (60% of cases). These results indicate that the radar sensitivity in detecting the extreme rainfall events must receive higher priority in future developments, especially for applications to the existing Mt. Merapi lahar early warning systems.

scholarly journals A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 118 ◽  
Author(s):  
Kreklow ◽  
Tetzlaff ◽  
Kuhnt ◽  
Burkhard
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Additional Information ◽  
Weather Radars ◽  
Gauge Data ◽  
Quality Checks ◽  
Quantitative Precipitation Estimates ◽  
Rainfall Statistics

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially and temporally highly resolved rainfall data. However, they are also subject to systematic and random bias and various potential uncertainties and therefore require thorough quality checks before usage. The dataset described in this paper is a collection of precipitation statistics calculated from the hourly nationwide German RADKLIM and RADOLAN QPEs provided by the German Weather Service (Deutscher Wetterdienst (DWD)), which were combined with rainfall statistics derived from rain gauge data for intercomparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The resulting two point shapefiles are readable with all common GIS and constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 to 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, the publication of this data collection has the potential to benefit other users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

scholarly journals Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

2020 ◽  
Vol 12 (3) ◽  
pp. 363 ◽  
Author(s):  
Qingtai Qiu ◽  
Jia Liu ◽  
Jiyang Tian ◽  
Yufei Jiao ◽  
Chuanzhe Li ◽  
...  
Keyword(s):  
Northern China ◽  
Rain Gauge ◽  
Bayesian Regression ◽  
Small Scale ◽  
Regression Kriging ◽  
Storm Events ◽  
Rain Gauge Data ◽  
Bias Adjustment ◽  
Gauge Data

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

scholarly journals Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1661 ◽  
Author(s):  
Mohd. Rizaludin Mahmud ◽  
Aina Afifah Mohd Yusof ◽  
Mohd Nadzri Mohd Reba ◽  
Mazlan Hashim
Keyword(s):  
High Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Daily Rainfall Data ◽  
Gauge Data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

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