Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany

2020 ◽  
Author(s):  
Zheng Duan ◽  
Edward Duggan ◽  
Ye Qing ◽  
Ye Tuo
Keyword(s):  
Spatial Resolution ◽  
Swat Model ◽  
Rain Gauge ◽  
Hydrological Modelling ◽  
Satellite Precipitation ◽  
Precipitation Product ◽  
Monthly Streamflow ◽  
Precipitation Estimates ◽  
Temporal And Spatial ◽  
Streamflow Simulation

<p>Hydrological modelling is an important tool to improve our understanding of hydrological processes of river basins and to predict impacts of climate change and environmental change on water resources. Precipitation is a key component of the hydrological cycle, and the most important driver/input data for hydrological models. Accurate precipitation measurements at desirable temporal and spatial resolution are essential for achieving reasonable performance of hydrological modelling. Compared to the conventional measurements from point-based rain gauge stations, remote sensing of precipitation with satellite sensors and ground-based radar can expand observational coverage and provide regional precipitation at varying temporal and spatial resolutions. Radars can provide sampling at very high resolution but also tend to contain significant errors in precipitation estimates. The Deutscher Wetterdienst (DWD; German Weather Service) developed the RADOLAN (RADar-OnLine-ANeichung) method (a real-time, gauge-adjustment and correction procedure) to generate precipitation estimates (termed as RADOLAN product) from the German Doppler radar network. More recently (2017), the DWD published a reanalysis of radar data to generate RADKLIM (RADarKLIMatologie) precipitation product using upgraded correction algorithms and additional offline gauge adjustment. </p><p> </p><p>This study presents the first assessment of the performance of two high spatial resolution (1 km) radar-based precipitation products (RADOLAN and RADKLIM) in streamflow simulation using the hydrological model SWAT (Soil and Water Assessment Tool) in Germany. We also evaluate the performance of conventional point-based rain gauge data and a satellite precipitation product in driving SWAT for streamflow simulation. The selected satellite product is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) because of its well reported good performance and the relative higher spatial resolution (0.05°). The Vils Basin located in Bavaria, Germany is chosen as the study area. Performance of investigated precipitation product is assessed by comparing simulated streamflow using calibrated SWAT model against measured streamflow at basin outlet at both daily and monthly time scales. The model calibration is performed using the SWAT-CUP program with measured streamflow. Different calibration procedures are also investigated to analyze the influence on model performance. This study presents and discusses the accuracy and uncertainty of using ground-based radar and satellite precipitation products in driving SWAT model for daily and monthly streamflow simulation. Our findings are expected to provide beneficial feedback to product developers for further improvements, and to inform local end-users about the quality of investigated precipitation products.</p>

A review of spatial downscaling of satellite precipitation products

2020 ◽  
Author(s):  
Zheng Duan ◽  
Cheng Chen ◽  
Hongkai Gao ◽  
Jian Peng
Keyword(s):  
Spatial Variability ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Cycle ◽  
Rain Gauge ◽  
Spatiotemporal Resolution ◽  
Great Ability ◽  
Satellite Precipitation ◽  
Temporal And Spatial ◽  
Spatial Downscaling

<p>Precipitation is an important component of the water cycle. Precipitation is characterized with high temporal and spatial variability. Accurate measurements of precipitation at high spatiotemporal resolution are essential for many applications in the fields of hydrology, meteorology and ecology. The traditional rain gauge stations provide direct measurements of rainfall at the surface but at a limited scale; rain gauge measurements are often considered as point-based measurements that are insufficient to represent the spatial variability of rainfall over a certain region, especially in the case of sparse rain gauge network. Satellite remote sensing has been developing with great ability of being used for estimating various water cycle components at different temporal and spatial scales. Considerable efforts have been made to develop satellite precipitation products at different spatial and temporal resolutions over the global or quasi-global scale. The majority of global/quasi-global precipitation products are at the spatial resolution of 0.25° (~25 km) with very few products at 0.05°-0.10° resolution. The usefulness of satellite precipitation products has been increasingly recognized but the relative coarse spatial resolution is still a limitation for many applications such as hydrological modelling at basin scales that generally require precipitation data at a desirable higher spatial resolution (e.g. 1 km). Over recent years, numerous spatial downscaling procedures/methods have been proposed to obtain precipitation products at higher spatial resolution. The relationships between precipitation and various auxiliary land-surface variables were explored and incorporated into spatial downscaling procedures using a large range of regression algorithms. Advanced machine learning and geostatistical methods have also been innovatively used to develop spatial downscaling procedures.</p><p> </p><p>The aim of this study is to present a comprehensive review of studies on spatial downscaling of satellite precipitation products over the recent years. We will summarize the proposed spatial downscaling methods, investigated auxiliary land-surface variables and the evaluation strategy. The performance of spatial downscaling methods in studied regions and their applications will be compared and discussed in terms of advantages and limitations. Finally, we will conclude this paper with outlook on future research needs and associated challenges about spatial downscaling of satellite precipitation products.</p>

scholarly journals Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

2016 ◽  
Vol 8 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Sunil Ghaju ◽  
Knut Alfredsen
Keyword(s):  
Bias Correction ◽  
Rain Gauge ◽  
Extensive Study ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Satellite Precipitation ◽  
Precipitation Estimates ◽  
Set Up ◽  
Rain Gauge Network ◽  
Precipitation Station

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

scholarly journals Quality-Based Combination of Multi-Source Precipitation Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1709 ◽  
Author(s):  
Anna Jurczyk ◽  
Jan Szturc ◽  
Irena Otop ◽  
Katarzyna Ośródka ◽  
Piotr Struzik
Keyword(s):  
Spatial Resolution ◽  
Flash Flood ◽  
Radar Data ◽  
Rain Gauge ◽  
Quality Information ◽  
Precipitation Data ◽  
Spatial Error ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

scholarly journals Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

scholarly journals Life Cycle Characteristics of MCSs in Middle East China Tracked by Geostationary Satellite and Precipitation Estimates

2016 ◽  
Vol 144 (7) ◽  
pp. 2517-2530 ◽  
Author(s):  
Yufei Ai ◽  
Wanbiao Li ◽  
Zhiyong Meng ◽  
Jun Li
Keyword(s):  
Brightness Temperature ◽  
Spatial Resolution ◽  
Geostationary Satellite ◽  
East China ◽  
Study Region ◽  
Convective Systems ◽  
Mesoscale Convective ◽  
Precipitation Estimates ◽  
Temporal And Spatial ◽  
Cloud Tops

Abstract By combining high temporal and spatial resolution Multifunctional Transport Satellite-1R (MTSAT-1R) infrared (IR) images and precipitation data from the Climate Prediction Center morphing technique (CMORPH), this study tracked mesoscale convective systems (MCSs) from May to August in 2008 and 2009 in the middle of east China with an automatic tracking algorithm based on an areal overlapping methodology. This methodology is adjusted to include those MCSs with a relative weak intensity before formation. The unique advantage of combining high temporal and spatial resolution geostationary satellite brightness temperature images and the precipitation measurements for tracking MCSs is that the cloud-top height along with the coverage and the precipitation intensity can be well identified. Results showed that the MCSs formed most frequently in the southwest Henan Province and at the border of four provinces—Shandong, Henan, Anhui, and Jiangsu—which is east of the convergence zone near the terrain’s edge. Locations of the highest cloud tops and of the heaviest precipitation rates did not always match. In addition, the MCSs in the study region tended to first reach the maximum precipitation rate, followed soon by the minimum brightness temperature, then the maximum associated precipitation area, and finally the maximum in system area.

scholarly journals Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau

2018 ◽  
Vol 10 (8) ◽  
pp. 1316 ◽  
Author(s):  
Peng Bai ◽  
Xiaomang Liu
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Distributed Hydrological Model ◽  
Satellite Precipitation ◽  
Rain Gauges ◽  
Upper Yangtze River ◽  
Precipitation Estimates

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.

scholarly journals The Development of a Two-Step Merging and Downscaling Method for Satellite Precipitation Products

2020 ◽  
Vol 12 (3) ◽  
pp. 398 ◽  
Author(s):  
Lu ◽  
Tang ◽  
Wang ◽  
Liu ◽  
Wei ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spline Interpolation ◽  
Auxiliary Variable ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Data Merging ◽  
Coarse Spatial Resolution ◽  
Precipitation Estimates

Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation.

scholarly journals A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model

2020 ◽  
Vol 12 (18) ◽  
pp. 3088
Author(s):  
Yeganantham Dhanesh ◽  
V. M. Bindhu ◽  
Javier Senent-Aparicio ◽  
Tássia Mattos Brighenti ◽  
Essayas Ayana ◽  
...  
Keyword(s):  
Open Access ◽  
Assessment Tool ◽  
Mean Squared Error ◽  
Swat Model ◽  
Hydrologic Model ◽  
Probability Of Detection ◽  
Satellite Precipitation ◽  
Statistical Measures ◽  
Monthly Scale ◽  
Monthly Streamflow

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.

scholarly journals Using SWAT to simulate streamflow in Huifa River basin with ground and Fengyun precipitation data

2015 ◽  
Vol 17 (5) ◽  
pp. 834-844 ◽  
Author(s):  
Honglei Zhu ◽  
Ying Li ◽  
Zhaoli Liu ◽  
Xiaoliang Shi ◽  
Bolin Fu ◽  
...  
Keyword(s):  
River Basin ◽  
Assessment Tool ◽  
Hydrological Processes ◽  
Validation Period ◽  
Mean Values ◽  
Satellite Precipitation ◽  
Calibration And Validation ◽  
Simulation Performance ◽  
Precipitation Product ◽  
Streamflow Simulation

High-resolution satellite precipitation products, which can provide a reasonable depiction of the spatial extent of rainfall, have been increasingly used to model hydrological processes. In this study, we introduced important satellite rainfall data – Fengyun (FY) precipitation product, and evaluated the data through streamflow simulation using the Soil and Water Assessment Tool model in Huifa River basin, China. Three precipitation inputs were conducted to investigate the simulation performance of the FY precipitation product: (1) available rain gauges within the watershed; (2) pixel values of FY-2 precipitation products nearest to the geographic centers of the subbasins; and (3) mean values of FY-2 precipitation pixels within the subbasins. The results showed that good model performance (defined as: NSE > 0.75; Nash–Sutcliffe efficiency: NSE) was achieved for all precipitation inputs both in the calibration and validation period. Best streamflow simulation was obtained when the model was calibrated with the third precipitation input, with NSE 0.86 and 0.84, R2 0.86 and 0.86 in the calibration and validation period. This study reveals that the FY precipitation product is a significant data source in modeling hydrological processes. Moreover, it is reasonable to use the mean values of the satellite precipitation pixels within the subbasins.

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