scholarly journals Hydrological Evaluation of PERSIANN-CDR Rainfall over Upper Senegal River and Bani River Basins

2018 ◽  
Vol 10 (12) ◽  
pp. 1884 ◽  
Author(s):  
Khalidou Bâ ◽  
Luis Balcázar ◽  
Vitali Diaz ◽  
Febe Ortiz ◽  
Miguel Gómez-Albores ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Climate Data ◽  
Senegal River ◽  
Precipitation Estimation ◽  
Main Input ◽  
Three Rivers ◽  
Climate Data Record

This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R2). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.

scholarly journals Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

2020 ◽  
Vol 12 (21) ◽  
pp. 3550
Author(s):  
Jie Chen ◽  
Ziyi Li ◽  
Lu Li ◽  
Jialing Wang ◽  
Wenyan Qi ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Tropical Rainfall Measuring Mission ◽  
Transformation Process ◽  
Hydrologic Model ◽  
Hydrological Models ◽  
Random Errors ◽  
Climate Data ◽  
Precipitation Estimation ◽  
Real Performance ◽  
Climate Data Record

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Assessment of TMPA 3B42V7 and PERSIANN-CDR in Driving Hydrological Modeling in a Semi-Humid Watershed in Northeastern China

2020 ◽  
Vol 12 (19) ◽  
pp. 3133
Author(s):  
Lu Zhang ◽  
Zhuohang Xin ◽  
Huicheng Zhou
Keyword(s):  
Hydrological Modeling ◽  
Assessment Tool ◽  
Surface Flow ◽  
Northeastern China ◽  
Base Flow ◽  
Hydrological Processes ◽  
Superior Performance ◽  
Climate Data ◽  
Satellite Precipitation ◽  
Precipitation Estimation

Recent developments of satellite precipitation products provide an unprecedented opportunity for better precipitation estimation, and thus broaden hydrological application. However, due to the errors and uncertainties of satellite products, a thorough validation is usually required before putting into the real hydrological application. As such, this study aims to provide a comprehensive evaluation on the performances of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), as well as their adequacies in simulating hydrological processes in a semi-humid region in the northeastern China. It was found that TMPA 3B42V7 showed a superior performance at the daily and monthly time scales, and had a favorable capture of the rainfall-intensity distribution. Intra-annual comparisons indicated a better representation of TMPA 3B42V7 from January to September, whereas PERSIANN-CDR was more reliable from October to December. The Soil and Water Assessment Tool (SWAT) driven by gauge precipitation data performed excellently with NSE > 0.9, while the performances of TMPA 3B42V7- and PERSIANN-CDR-based models are satisfactory with NSE > 0.5. The performances varied under different flow levels and hydrological years. Water balance analysis indicated a better performance of TMPA 3B42V7 in simulating the hydrological processes, including evapotranspiration, groundwater recharge and total runoff. The runoff compositions (i.e., base flow, subsurface flow, and surface flow) driven by TMPA 3B42V7 were more accordant with the actual hydrological features. This study will not only help recognize the potential satellite precipitation products for local water resources management, but also be a reference for the poor-gauged regions with similar hydrologic and climatic conditions around the world, especially the northeastern China and western Russia.

scholarly journals Performance Evaluation of Satellite-Based Rainfall Products over Nigeria

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 103
Author(s):  
Kingsley N. Ogbu ◽  
Nina Rholan Hounguè ◽  
Imoleayo E. Gbode ◽  
Bernhard Tischbein
Keyword(s):  
Agricultural Development ◽  
Rain Gauge ◽  
Local Economy ◽  
Climate Data ◽  
Climatic Zones ◽  
Rainfall Characteristics ◽  
Precipitation Estimation ◽  
Climate Data Record ◽  
Local Rainfall

Understanding the variability of rainfall is important for sustaining rain-dependent agriculture and driving the local economy of Nigeria. Paucity and inadequate rain gauge network across Nigeria has made satellite-based rainfall products (SRPs), which offer a complete spatial and consistent temporal coverage, a better alternative. However, the accuracy of these products must be ascertained before use in water resource developments and planning. In this study, the performances of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), were evaluated to investigate their ability to reproduce long term (1983–2013) observed rainfall characteristics derived from twenty-four (24) gauges in Nigeria. Results show that all products performed well in terms of capturing the observed annual cycle and spatial trends in all selected stations. Statistical evaluation of the SRPs performance show that CHIRPS agree more with observations in all climatic zones by reproducing the local rainfall characteristics. The performance of PERSIANN and TAMSAT, however, varies with season and across the climatic zones. Findings from this study highlight the benefits of using SRPs to augment or fill gaps in the distribution of local rainfall data, which is critical for water resources planning, agricultural development, and policy making.

scholarly journals Hydrological modeling of Upper Ribb watershed, Abbay Basin, Ethiopia.

2020 ◽  
Keyword(s):  
Surface Runoff ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Simulated Data ◽  
Coefficient Of Determination ◽  
Climate Data ◽  
Water Balance Components ◽  
Discharge Data ◽  
Forested Areas

<p>Hydrological modeling of a watershed is necessary for water resources planning and management. The hydrology of upper Ribb watershed has been analyzed using spatially semi-distributed Soil and water assessment tool (SWAT) model. This study aimed to determine the water balance components and its relation with the rainfall which reaches to the surface of the earth. Different spatio-temporal (land use, soil, digital elevation model, climate data, river discharge) data were used for hydrological modelling of Upper Ribb watershed. The applicability of SWAT model in Upper Ribb watershed has been evaluated using coefficient of determination (R2) and Nash Sutcliff efficiency (NSE) parameters. The calibration results revealed the observed data showed a very good agreement with the simulated data with the R2 and NSE values of 0.90 and 0.84 respectively. Similarly, the validation results of streamflow were acceptable with the R2 and NSE values of 0.80 and 0.82 respectively. The monthly average streamflow from Upper Ribb watershed were found 13.39 m3/s. The major portion of the rainfall contributes to the surface runoff due to the major percentage of the watershed is covered with agricultural lands. The groundwater flow was high in forested areas, while evapotranspiration was found very high in water bodies (Ribb reservoir). In this study area the rainfall showed a direct relationship with the streamflow. The ratio of streamflow and evapotranspiration with rainfall was 0.61 and 0.36 respectively. Due to the presence of high amount of surface runoff and evapotranspiration the deep recharge which contributes to the ground water is not that much significant.</p>

Evaluation of the Performance of Remotely Sensed Rainfall Datasets for Flood Monitoring in the Transboundary Mono River Catchment, Togo and Benin

2021 ◽  
Author(s):  
Rholan Houngue ◽  
Kingsley Ogbu ◽  
Adrian Almoradie ◽  
Mariele Evers
Keyword(s):  
Remotely Sensed ◽  
Climate Data ◽  
Global Precipitation Climatology Centre ◽  
Daily Data ◽  
Flood Monitoring ◽  
Precipitation Estimation ◽  
Daily Scale ◽  
Annual Scale ◽  
Global Precipitation ◽  
Climate Data Record

&lt;p&gt;The variability and changes noted in the climate over the past decades emphasizes the importance of climate information such as precipitation datasets in the management of flood risks in Benin and Togo. The lack of extensive and functional ground observation networks, introduces satellite-based rainfall datasets as a better alternative which needs however to be evaluated beforehand. This study investigated the performance of four satellite and gauge-based rainfall products &amp;#8211;Climate Hazards Group Infrared Precipitation with Station data version v2.0 (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT) and the Global Precipitation Climatology Centre full daily data (GPCC) &amp;#8211; at gauge point level over the Mono River basin which is stretched over Benin and Togo territories. Three synoptic stations located in Tabligbo, Atakpam&amp;#233; and Sokod&amp;#233; were considered because of the completeness of their time series during the study period 1983-2012. The assessments were conducted at daily, dekadal (10-day period), seasonal and annual scale using both continuous and categorical statistics. Results show poor performances at daily and annual temporal scales while the seasonal cycles were well reproduced with Nash-Sutcliffe efficiency equal or higher than 0.94, and correlation coefficient above 0.9. At Tabligbo, CHIRPS and GPCC showed the best statistical results whereas the performance of PERSIANN and TAMSAT varies with the temporal scale and the station. The probability of rainfall detection (POD) and the capability of reproducing extreme daily maxima indicate GPCC as the best product for flood monitoring purposes at daily scale. However, all assessed products exhibited high POD and low false alarm ratio (FAR) at dekadal scale.&lt;/p&gt;

scholarly journals Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan

2020 ◽  
Vol 21 (1) ◽  
pp. 17-37 ◽  
Author(s):  
Khalil Ur Rahman ◽  
Songhao Shang ◽  
Muhammad Shahid ◽  
Yeqiang Wen ◽  
Zeeshan Khan
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Mean Bias Error ◽  
Bias Error ◽  
Climate Data ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
Climate Data Record ◽  
Climate Prediction Center

AbstractMerged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25° using extensively evaluated SPPs and a global atmospheric reanalysis–precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil’s U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day−1, respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil’s U). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).

scholarly journals Evaluation and Calibration of Alternative Methods for Estimating Reference Evapotranspiration in the Senegal River Basin

Hydrology ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 24 ◽  
Author(s):  
Papa Malick Ndiaye ◽  
Ansoumana Bodian ◽  
Lamine Diop ◽  
Abdoulaye Deme ◽  
Alain Dezetter ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Reference Evapotranspiration ◽  
Water Cycle ◽  
Irrigation Management ◽  
Alternative Methods ◽  
Coefficient Of Determination ◽  
Climate Variables ◽  
Senegal River ◽  
Senegal River Basin

Reference evapotranspiration (ET0) is a key element of the water cycle in tropical areas for the planning and management of water resources, hydrological modeling, and irrigation management. The objective of this research is to assess twenty methods in computing ET0 in the Senegal River Basin and to calibrate and validate the best methods that integrate fewer climate variables. The performance of alternative methods compared to the Penman Monteith (FAO56-PM) model is evaluated using the coefficient of determination (R2), normalized root mean square error (NRMSE), percentage of bias (PBIAS), and the Kling–Gupta Efficiency (KGE). The most robust methods integrating fewer climate variables were calibrated and validated and the results show that Trabert, Valiantzas 2, Valiantzas 3, and Hargreaves and Samani models are, respectively, the most robust for ET0 estimation. The calibration improves the estimates of reference evapotranspiration compared to original models. It improved the performance of these models with an increase in KGE values of 45%, 32%, 29%, and 19% for Trabert, Valiantzas 2, Valiantzas 3, and Hargreaves and Samani models, respectively. From a spatial point of view, the calibrated models of Trabert and Valiantzas 2 are robust in all the climatic zones of the Senegal River Basin, whereas, those of Valiantzas 3 and Hargreaves and Samani are more efficient in the Guinean zone. This study provides information on the choice of a model for estimating evapotranspiration in the Senegal River Basin.

scholarly journals Calibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Vicente de Paulo Rodrigues da Silva ◽  
Roberta Araújo e Silva ◽  
Girlene Figueiredo Maciel ◽  
Célia Campos Braga ◽  
José Luiz Cabral da Silva Júnior ◽  
...  
Keyword(s):  
Model Performance ◽  
Absolute Error ◽  
Efficiency Index ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Yield Response ◽  
Statistical Parameters ◽  
Yield Data ◽  
Climate Conditions ◽  
Aquacrop Model

ABSTRACT: The water-driven AquaCrop model to simulate yield response has been calibrated and validated for soybean cultivated under different water levels irrigation in Matopiba region, Brazil. The crop was submitted to seven irrigation treatments during the dry season and a dry treatment in the rainy season. The model was parameterized and calibrated by using soybean yield data collected at field level. Model performance was evaluated by using the following statistical parameters: prediction error (Pe), Nash-Sutcliffe efficiency index (E), coefficient of determination (R2), mean absolute error (MAE), root mean square error normalized (RMSEN) and Willmott’s index (d). The statistical analyses of the AquaCrop model calibrated for the Matopiba region disclosed error acceptable for yield prediction of soybean grown under tropical climate conditions. Results also indicated that the C2 soybean cultivar is more resistant to water stress than the C1 soybean grown in the Matopiba region, Brazil. In the treatments when the crop was well supplied with water, at least in one phase, the yield was greater than those with drought stress at last in one phase.

scholarly journals Evaluating Four Multisatellite Precipitation Estimates over the Diaoyu Islands during Typhoon Seasons

2016 ◽  
Vol 17 (5) ◽  
pp. 1623-1641 ◽  
Author(s):  
Bin Yong ◽  
Jingjing Wang ◽  
Liliang Ren ◽  
Yalei You ◽  
Pingping Xie ◽  
...  
Keyword(s):  
Climate Data ◽  
Rain Belt ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Monitoring And Forecasting ◽  
Precipitation Analysis ◽  
Precipitation Gauge ◽  
Climate Data Record ◽  
The Kuroshio

Abstract The Diaoyu Islands are a group of uninhabited islets located in the East China Sea between Japan, China, and Taiwan. Here, four mainstream gauge-adjusted multisatellite precipitation estimates [TRMM Multisatellite Precipitation Analysis, version 7 (TMPA-V7); CPC morphing technique–bias-corrected product (CMORPH-CRT); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR); and Global Satellite Mapping of Precipitation–gauge adjusted (GSMaP_Gauge)] are adopted to detect the rainfall characteristics of the Diaoyu Islands area with a particular focus on typhoon contribution. Out of the four products, CMORPH-CRT and GSMaP_Gauge show much more similarity both in terms of the spatial patterns and error structures because of their use of the same morphing technique. Overall, GSMaP_Gauge performs better than the other three products, likely because of denser in situ observations integrated in its retrieval algorithms over East Asia. All rainfall products indicate that an apparent rain belt exists along the northeastern 45° direction of Taiwan extending to Kyushu of Japan, which is physically associated with the Kuroshio. The Diaoyu Islands are located on the central axis of this rain belt. During the period 2001–09, typhoon-induced rainfall accounted for 530 mm yr−1, and typhoons contributed on average approximately 30% of the annual precipitation budget over the Diaoyu Islands. Higher typhoon contribution was found over the southern warmer water of the Diaoyu Islands, while the northern cooler water presented less contribution ratio. Supertyphoon Chaba, the largest typhoon of 2004, recorded 53 h of rainfall accumulation totaling 235 mm on the Diaoyu Islands, and this event caused severe property damage and human casualties for Japan. Hence, the Diaoyu Islands play an important role in weather monitoring and forecasting for the neighboring countries and regions.

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