7 research outputs found

    Using precipitation, vertical root distribution, and satelliteā€retrieved vegetation information to parameterize water stress in a Penmanā€Monteith approach to evapotranspiration modeling under Mediterranean climate

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    Abstract Recent studies have shown that global Penmanā€Monteith equation based (PMā€based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satelliteā€retrieved vegetation information to simulate water stress in a PMā€based model (RSā€WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PMā€based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RSā€WBPM is successful. The daily ET resulting from RSā€WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 ( RMSEĀ = 18.72 WĀ māˆ’2) for all 27 sites and R2=0.62 ( RMSEĀ = 18.21 WĀ māˆ’2) for 25 nonagricultural sites. However, combined results from the optimum older PMā€based models at specific sites show R2Ā valuesĀ ofĀ onlyĀ 0.50 ( RMSEĀ = 20.74 WĀ māˆ’2) for all 27 sites. RSā€WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RSā€WBPM are globally available, the results from RSā€WBPM are encouraging and imply the potential of its implementation on a regional and global scale

    Assessment of Global Solar Radiation at Selected Points in Nigeria Using Artificial Neural Network Model (ANNM)

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    In this study, spatial distribution, temporal variations, annual distribution, estimation and prediction of solar radiation in Nigeria was carried out using ANNs. Levenberg-Marquardt backpropagation algorithms was used for the training of the network using solar radiation data along the years (1979-2014). The data records were divided into three portions (training, testing and validation). The network processed the available data by dividing it into three portions randomly: 70% for the training, 15% for validation and the remaining 15% for testing. Input parameters were chosen as latitude, longitude, day of the year, year while observed solar radiation was chosen as targeted data (from a processed file). The output parameter was the estimated solar radiation. The network designs were tested with root mean square error and then the most successful network (taken to be best network) which is network with less error was used to carry out the study. The hyperbolic tangent sigmoid transfer function was also used between the input and the hidden layers as activation function, while the linear transfer function was used from hidden layers to the output layer as the activation function. The performance of ANNs was validated by; estimating the difference between the annual measured and estimated values were determined using coefficient of determination (R2). Results revealed that the R2 result was 0.82 (82%). The result of spatial variations indicated that both wet and dry seasons have their highest concentration in North-East of Nigeria. It is pertinent to also note that the lowest concentration occurred in North-West during wet season, while the lowest occurred at the South-South and South-West of Nigeria in dry season. In addition, the lowest in dry season is about 25W/m2, while that of wet season is about 15W/m2. The agreement between the temporal and annual variation of observed and estimated solar radiation reveals that the model exhibits good performance in studying solar radiation. The model was further used to predict two years ahead of the years of study

    Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

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    Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria

    Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El NiƱoā€“Southern Oscillation (ENSO)

    No full text
    Droughts are one of the worldā€™s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982ā€“2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1ā€“12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPIā€“12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1ā€“12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008ā€“2010, 2000ā€“2003, and 1984ā€“1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997ā€“1998, 1993ā€“1994, and 1991ā€“1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote dā€™Ivoire, Burkina Faso, Niger, and Nigeria

    Spatial Multi-Criterion Decision Making (SMDM) Drought Assessment and Sustainability over East Africa from 1982 to 2015

    No full text
    Droughts are ranked among the most devastating agricultural disasters that occur naturally in the world. East Africa is the most vulnerable and drought-prone region worldwide. In this study, four drought indices were used as input variables for drought assessment from 1982 to 2015. This work applied the SMDM algorithm to the integrated approach of OLR and Hurst exponent. The Detrended Fluctuation Analysis (DFA) and Ordinary Least Square (OLR) were merged to compute the trend and persistence (Hurst exponent) of the drought indices. Result indicates that the OLR at time scale 1, 6, and 12 shows a similar distribution with positive (negative) trends scattered in the Northwest (Northeast and Southern) parts of the study area which differs with the OLR aggregated at a 3-month time scale. The percentage pixel distribution for OLR-1, OLR-3, OLR-6, and OLR-12 is 18.2 (81.8), 72.5 (27.5), 32.9 (67.1), and 36.9 (63.1) for increasing (decreasing) trends respectively. Additionally, results indicate that DFA-1 is highly persistent with few random pixels scattered around Ethiopia, South Sudan and Tanzania, with percentage pixels as 88.7, 11.3 and 0.1 representing h > 0.5, h = 0.5, and h h > 0.5 (h > 1), respectively. Meanwhile, for DFA-3 and DFA-12, the distribution shows persistence and a random walk, respectively. Drought conditions may eventually persist, reverse or vary drastically in an unpredictable manner depending on the driving forces. Overall, the drought risk map at 1-, 3-, and 6-month aggregates has shown severe degradation in Southern Kenya and Tanzania while noticeable improvements are seen in western Ethiopia and South Sudan

    Spatial Multi-Criterion Decision Making (SMDM) Drought Assessment and Sustainability over East Africa from 1982 to 2015

    No full text
    Droughts are ranked among the most devastating agricultural disasters that occur naturally in the world. East Africa is the most vulnerable and drought-prone region worldwide. In this study, four drought indices were used as input variables for drought assessment from 1982 to 2015. This work applied the SMDM algorithm to the integrated approach of OLR and Hurst exponent. The Detrended Fluctuation Analysis (DFA) and Ordinary Least Square (OLR) were merged to compute the trend and persistence (Hurst exponent) of the drought indices. Result indicates that the OLR at time scale 1, 6, and 12 shows a similar distribution with positive (negative) trends scattered in the Northwest (Northeast and Southern) parts of the study area which differs with the OLR aggregated at a 3-month time scale. The percentage pixel distribution for OLR-1, OLR-3, OLR-6, and OLR-12 is 18.2 (81.8), 72.5 (27.5), 32.9 (67.1), and 36.9 (63.1) for increasing (decreasing) trends respectively. Additionally, results indicate that DFA-1 is highly persistent with few random pixels scattered around Ethiopia, South Sudan and Tanzania, with percentage pixels as 88.7, 11.3 and 0.1 representing h > 0.5, h = 0.5, and h < 0.5, respectively. DFA-6 shows high (low) pixels representing h > 0.5 (h > 1), respectively. Meanwhile, for DFA-3 and DFA-12, the distribution shows persistence and a random walk, respectively. Drought conditions may eventually persist, reverse or vary drastically in an unpredictable manner depending on the driving forces. Overall, the drought risk map at 1-, 3-, and 6-month aggregates has shown severe degradation in Southern Kenya and Tanzania while noticeable improvements are seen in western Ethiopia and South Sudan
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