Evaluation of multiple satellite precipitation products for rainfed maize production systems over Vietnam

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Sridhar Gummadi ◽  
Tufa Dinku ◽  
Paresh B. Shirsath ◽  
M. D. M. Kadiyala
Keyword(s):  
Production Systems ◽  
Simulation Models ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Maize Production ◽  
Rain Gauges ◽  
Crop Simulation Models ◽  
Maize Yields ◽  
Satellite Rainfall ◽  
Rainfall Estimates

AbstractHigh-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

scholarly journals Spatial and Temporal Evaluation of Satellite Rainfall Estimates Over Vietnam

2021 ◽  
Author(s):  
Sridhar Gummadi ◽  
Tufa Dinku ◽  
Paresh B. Shirsath ◽  
Dakshina Murthy Kadiyala
Keyword(s):  
Simulation Models ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Rain Gauges ◽  
Crop Simulation Models ◽  
Highly Sensitive ◽  
Maize Yields ◽  
Satellite Rainfall ◽  
Rainfall Estimates ◽  
Better Than

Abstract High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

scholarly journals Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

2014 ◽  
Vol 15 (6) ◽  
pp. 2347-2369 ◽  
Author(s):  
Matthew P. Young ◽  
Charles J. R. Williams ◽  
J. Christine Chiu ◽  
Ross I. Maidment ◽  
Shu-Hua Chen
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Shallow Convection ◽  
Rain Gauges ◽  
The Mean ◽  
Satellite Rainfall ◽  
Convective Cells ◽  
Rainfall Estimates

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

scholarly journals Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?

2016 ◽  
Vol 192 ◽  
pp. 1-12 ◽  
Author(s):  
Francisco J. Morell ◽  
Haishun S. Yang ◽  
Kenneth G. Cassman ◽  
Justin Van Wart ◽  
Roger W. Elmore ◽  
...  
Keyword(s):  
Simulation Models ◽  
Corn Belt ◽  
Total Production ◽  
Crop Simulation ◽  
Crop Simulation Models ◽  
Maize Yields ◽  
The U.S

scholarly journals Verification of APHRODITE Precipitation Data Set in Bangladesh

2021 ◽  
Vol 12 (1) ◽  
pp. 51-56
Author(s):  
Md Atiqul Islam ◽  
Asif Ahmed ◽  
Md Munirujjaman Munir ◽  
Zarif Zaman Khandakar
Keyword(s):  
Extreme Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
False Alarms ◽  
Data Set ◽  
Study Region ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Precipitation Threshold

We investigated the preformance of Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) of water resources precipitation products in Bangladesh taking rain gauge data as reference for a 3-year period (2003-2005). Various statistical and categorical indices such as coefficient of correlation (CC), bias, relative bias (RB), mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and false alarm ratio (FAR), were applied to measure the performance of the product. With CC value of 0.85, bias of 0.91, RB of -9.5%, MAE of 7.7 mm, and RMSE of 15.2 mm the product tended to underestimate rainfall values during the study period. Although, the POD score of 1.00 demonstrated very good skill in detecting the occurrence of rainfall events, FAR value of 0.25 indicated a considerable amount of false alarms. Moreover, as the precipitation threshold increased, the underestimation became more prominent over the study region. Analysis on the basis of location of the rain gauges also showed that APHRODITE consistently underestimated rainfall values with the increase of extreme rainfall thresholds. Journal of Engineering Science 12(1), 2021, 51-56

scholarly journals Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

2018 ◽  
Vol 378 ◽  
pp. 59-65 ◽  
Author(s):  
Thomas Matingo ◽  
Webster Gumindoga ◽  
Hodson Makurira
Keyword(s):  
Flash Flood ◽  
Daily Rainfall ◽  
Flash Floods ◽  
Rain Gauge ◽  
Hydrological Modelling ◽  
Probability Of Detection ◽  
Time Step ◽  
Flood Modelling ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff) and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs) for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013–2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD) of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC) was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System) daily model calibration Nash Sutcliffe efficiency (NSE) for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015–2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized hydrological processes such as flash floods for sub-daily rainfall, because of improved spatial and temporal resolution.

Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning

2019 ◽  
Vol 21 (3) ◽  
pp. 678-694 ◽  
Author(s):  
Graham R. Jeffries ◽  
Timothy S. Griffin ◽  
David H. Fleisher ◽  
Elena N. Naumova ◽  
Magaly Koch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Simulation Models ◽  
Remote Sensing Imagery ◽  
Crop Simulation ◽  
Crop Simulation Models ◽  
Maize Yields

scholarly journals Effectiveness of using representative subsets of global climate models in future crop yield projections

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Budong Qian ◽  
Qi Jing ◽  
Alex J. Cannon ◽  
Ward Smith ◽  
Brian Grant ◽  
...  
Keyword(s):  
Spring Wheat ◽  
Climate Models ◽  
Crop Yields ◽  
Global Climate ◽  
Simulation Models ◽  
Absolute Error ◽  
Global Climate Models ◽  
Climate Projections ◽  
Crop Simulation Models ◽  
Change Impact

AbstractRepresentative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040–2069 and 2070–2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9–4.7% for canola and 1.5–2.2% for spring wheat, and covers 61.8–91.1% and 66.1–80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

Characterizing Weed Community Seedling Emergence for a Semiarid Site in Colorado

1994 ◽  
Vol 8 (2) ◽  
pp. 245-249 ◽  
Author(s):  
R. L. Anderson
Keyword(s):  
Cultural Practices ◽  
Production Systems ◽  
Seedling Emergence ◽  
Simulation Models ◽  
Planting Dates ◽  
Emergence Pattern ◽  
Crop Simulation Models ◽  
Wheat Stubble ◽  
Herbicide Use ◽  
Two Peaks

This study characterized the emergence pattern of a weed community of 16 species between April 1 and August 31 over a 7-yr period. Weed seedlings were counted weekly in quadrats established in winter wheat stubble within no-till and conventional-till production systems. Weed emergence showed two peaks, the first between April 25 and May 9, and the second between May 30 and June 13. Tillage did not affect the weed community emergence pattern. Knowledge of weed community emergence pattern in conjunction with crop simulation models could be used to suggest cultural practices such as optimal planting dates that favor a crop over weeds, and possibly reduce herbicide use for within-crop weed control.

scholarly journals Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1722
Author(s):  
José L. Bruster-Flores ◽  
Ruperto Ortiz-Gómez ◽  
Adrian L. Ferriño-Fierro ◽  
Víctor H. Guerra-Cobián ◽  
Dagoberto Burgos-Flores ◽  
...  
Keyword(s):  
Absolute Error ◽  
Probability Of Detection ◽  
Rain Gauges ◽  
Critical Success ◽  
Lack Of Information ◽  
Precipitation Estimates ◽  
Global Coverage ◽  
Climate Prediction Center ◽  
Critical Success Index ◽  
Precipitation Events

Satellite-based precipitation (SBP) products with global coverage have the potential to overcome the lack of information in places where there are no rain gauges to perform hydrological analyses; however, it is necessary to evaluate the reliability of the SBP products. In this study, we evaluated the performance of the Climate Prediction Center morphing technique with corrected bias (CMORPH-CRT) product in 14 sites in Mexico. The evaluation was carried out using two approaches: (1) using categorical metrics that include indicators of probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and frequency bias index (FBI); and (2) through statistical indicators such as the mean absolute error (MAE), root mean square error (RMSE), relative bias (RB), and correlation coefficient (CC). The analysis was carried out with two levels of temporal aggregation: 30 min and daily. The results indicate that the CMORPH-CRT product overestimates the number of precipitation events in most cases since FBI values greater than 1 in 78.6% of analyzed stations were obtained. Also, we obtained CC values in the range of 0.018 to 0.625, which implied weak to moderate correlations, and found that in all stations, the CMORPH-CRT product overestimates the precipitation (RB > 0).

scholarly journals Evaluating Detection Skills of Satellite Rainfall Estimates over Desert Locust Recession Regions

2010 ◽  
Vol 49 (6) ◽  
pp. 1322-1332 ◽  
Author(s):  
Tufa Dinku ◽  
Pietro Ceccato ◽  
Keith Cressman ◽  
Stephen J. Connor
Keyword(s):  
Information Service ◽  
Region Of Interest ◽  
Estimation Algorithm ◽  
Probability Of Detection ◽  
Semiarid Region ◽  
Desert Locust ◽  
Detection Capabilities ◽  
Satellite Rainfall ◽  
Rainfall Detection ◽  
Rainfall Estimates

Abstract This paper evaluates rainfall detection capabilities of seven satellite rainfall estimates over the desert locust recession regions of the world. The region of interest covers the arid and semiarid region from northwestern Africa to northwestern India. The evaluated satellite rainfall products are the African rainfall climatology (ARC), rainfall estimation algorithm (RFE), Tropical Rainfall Measuring Mission 3B42 and its real-time version (3B42RT), NOAA/Climate Prediction Center morphing technique (CMORPH), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP-MVK and GSMaP-MVK+). The reference data were obtained from the Desert Locust Information Service of the United Nations Food and Agriculture Organization (FAO). The FAO data are qualitative information collated by desert locust survey teams from different countries during field campaigns. Such data can only be used to assess the rainfall detection capabilities of the satellite products. The validation region is divided into four subregions and validations statistics are computed for each region. The probability of detection varies from 70% for the relatively wet part of the validation region to less than 20% for the driest part. The main weakness of the satellite products is overestimation of rainfall occurrences. The false-alarm ratio was as high as 84% for the driest part and as high as 57% for the wetter region. The satellite products still exhibit positive detection skill for all of the subregions. A comparison of the different products shows that no single product stands out as having the best or the worst overall performance.

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