SAFARI 2000 SOLAR SPECTRAL FLUX RADIOMETER DATA, SOUTHERN AFRICA, DRY SEASON 2000

2007 ◽  
Author(s):  
P. PILEWSKIE
Keyword(s):  
Southern Africa ◽  
Dry Season ◽  
Spectral Flux

SAFARI 2000 MOPITT TROPOSPHERIC CARBON MONOXIDE, SOUTHERN AFRICA, DRY SEASON 2000

2006 ◽  
Author(s):  
M. N. DEETER ◽  
J. R. DRUMMOND ◽  
D. P. EDWARDS ◽  
J. C. GILLE ◽  
D. MAO
Keyword(s):  
Carbon Monoxide ◽  
Southern Africa ◽  
Dry Season

The efficacy of seasonal terrestrial water storage forecasts for predicting vegetation activity over Africa

2021 ◽  
Author(s):  
Benjamin I Cook ◽  
Kimberly Slinski ◽  
Christa Peters-Lidard ◽  
Amy McNally ◽  
Kristi Arsenault ◽  
...  
Keyword(s):  
Regression Model ◽  
Southern Africa ◽  
Water Storage ◽  
Dry Season ◽  
Forecast Skill ◽  
Terrestrial Water Storage ◽  
Wet Season ◽  
Area Index ◽  
Vegetation Activity ◽  
Terrestrial Water

AbstractTerrestrial water storage (TWS) provides important information on terrestrial hydroclimate and may have value for seasonal forecasting because of its strong persistence. We use the NASA Hydrological Forecast and Analysis System (NHyFAS) to investigate TWS forecast skill over Africa and assess its value for predicting vegetation activity from satellite estimates of leaf area index (LAI). Forecast skill is high over East and Southern Africa, extending up to 3–6 months in some cases, with more modest skill over West Africa. Highest skill generally occurs during the dry season or beginning of the wet season when TWS anomalies from the previous wet season are most likely to carry forward in time. In East Africa, this occurs prior to and during the transition into the spring “Long Rains” from January–March, while in Southern Africa this period of highest skill starts at the beginning of the dry season in April and extends through to the start of the wet season in October. TWS is highly and positively correlated with LAI, and a logistic regression model shows high cross-validation skill in predicting above or below normal LAI using TWS. Combining the LAI regression model with the NHyFAS forecasts, 1-month lead LAI predictions have high accuracy over East and Southern Africa, with reduced but significant skill at 3-month leads over smaller sub-regions. This highlights the potential value of TWS as an additional source of information for seasonal forecasts over Africa, with direct applications to some of the most vulnerable agricultural regions on the continent.

scholarly journals Changes in number and distribution of hippoptamus (Hippopotamus amphibius) in the Sabie River, Kruger National Park, during the 1992 drought

Koedoe ◽  
1995 ◽  
Vol 38 (2) ◽  
Author(s):  
P.C. Viljoen
Keyword(s):  
Southern Africa ◽  
National Park ◽  
Dry Season ◽  
Kruger National Park ◽  
Population Densities ◽  
Drought Period ◽  
Aerial Surveys ◽  
Hippopotamus Amphibius ◽  
Reduced Flow

The 1991/92 drought in Southern Africa and the effect of the resultant reduced flow of the Sabie River on hippopotami was investigated. Hippopotami are counted annually in the Kruger National Park's (KNP) major rivers as part of the park's monitoring pro- gramme. Two additional aerial surveys were conducted to document changes in hippopotamus population densities in the Sabie River during the drought period. The hippopotamus population decreased during the drought by 12.6 to 672 animals between July 1991 and October 1992. The highest and lowest hippopotamus densities recorded were 11.6 and 2.2 animals/km river length respectively in different river sections. Only 12 hippopotamus mortalities were recorded at the end of the 1992 dry season (October).

scholarly journals Examining the Value of Global Seasonal Reference Evapotranspiration Forecasts to Support FEWS NET’s Food Insecurity Outlooks

2017 ◽  
Vol 56 (11) ◽  
pp. 2941-2949 ◽  
Author(s):  
Shraddhanand Shukla ◽  
Daniel McEvoy ◽  
Mike Hobbins ◽  
Greg Husak ◽  
Justin Huntington ◽  
...  
Keyword(s):  
Food Insecurity ◽  
Central Asia ◽  
East Africa ◽  
Central America ◽  
Southern Africa ◽  
Early Warning ◽  
Reference Evapotranspiration ◽  
Dry Season ◽  
Wet Season ◽  
Skill Evaluation

AbstractThe Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ET0) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by FEWS NET to support food insecurity early warning. The ET0 reforecasts span the 1982–2009 period and are calculated following the American Society for Civil Engineers formulation of the Penman–Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from the National Centers for Environmental Prediction CFSv2 model and the National Aeronautics and Space Administration GEOS-5 model. The skill evaluation, using deterministic and probabilistic scores, focuses on the December–February (DJF), March–May (MAM), June–August (JJA), and September–November seasons. The results indicate that ET0 forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation > 0.35 at leads of 2 months) include the western United States, northern parts of South America, parts of the Sahel region, and southern Africa. The FEWS NET regions where forecasts are most skillful (correlation > 0.35 at lead 3) include northern sub-Saharan Africa (DJF; dry season), Central America (DJF; dry season), parts of East Africa (JJA; wet season), southern Africa (JJA; dry season), and central Asia (MAM; wet season). A case study over parts of East Africa for the JJA season shows that ET0 forecasts in combination with the precipitation forecasts would have provided early warning of recent severe drought events (e.g., in 2002, 2004, 2009) that contributed to substantial food insecurity in the region.

The composition and health of fishes in residual dry season habitats in southern Africa (Strauch et al. 2015)

2017 ◽  
Vol 68 (2) ◽  
pp. 203
Author(s):  
Brian Marshall ◽  
Albert Chakona ◽  
Denis Tweddle ◽  
Paul Skelton ◽  
Roger Bills ◽  
...  
Keyword(s):  
Data Analysis ◽  
Southern Africa ◽  
Dry Season ◽  
Regional Literature ◽  
Zambezi River

A recent paper on the composition and health of fish in refugia habitats in seasonal tributaries of the Zambezi River in southern Africa contains several errors. These include the misidentification of species and a misunderstanding of the zoogeography of the Zambezi River. There were also several weaknesses in the data analysis and some conclusions were based on misinterpretations of their own data and the literature. The authors should have considered regional literature and worked with southern African ichthyologists to prevent these errors.

scholarly journals Evaluation of the JULES land surface model in simulating catchment hydrology in Southern Africa

2013 ◽  
Vol 10 (8) ◽  
pp. 11093-11128 ◽  
Author(s):  
N. C. MacKellar ◽  
S. J. Dadson ◽  
M. New ◽  
P. Wolski
Keyword(s):  
Southern Africa ◽  
Land Surface ◽  
River Discharge ◽  
Water Resource Management ◽  
River Flow ◽  
Land Surface Model ◽  
Dry Season ◽  
Surface Model ◽  
Runoff Generation ◽  
Subsurface Runoff

Abstract. Land surface models (LSMs) are advanced tools which can be used to estimate energy, water and biogeochemical exchanges at regional scales. The inclusion of a river flow routing module in an LSM allows for the simulation of river discharge from a catchment and offers an approach to evaluate the response of the system to variations in climate and land-use, which can provide useful information for regional water resource management. This study offers insight into some of the pragmatic considerations of applying an LSM over a regional domain in Southern Africa. The objectives are to identify key parameter sensitivities and investigate differences between two runoff production schemes in physically contrasted catchments. The Joint UK Land Environment Simulator (JULES) LSM was configured for a domain covering Southern Africa at a 0.5° resolution. The model was forced with meteorological input from the WATCH Forcing Data for the period 1981–2001 and sensitivity to various model configurations and parameter settings were tested. Both the PDM and TOPMODEL sub-grid scale runoff generation schemes were tested for parameter sensitivities, with the evaluation focussing on simulated river discharge in sub-catchments of the Orange, Okavango and Zambezi rivers. It was found that three catchments respond differently to the model configurations and there is no single runoff parameterization scheme or parameter values that yield optimal results across all catchments. The PDM scheme performs well in the upper Orange catchment, but poorly in the Okavango and Zambezi, whereas TOPMODEL grossly underestimates discharge in the upper Orange and shows marked improvement over PDM for the Okavango and Zambezi. A major shortcoming of PDM is that it does not realistically represent subsurface runoff in the deep, porous soils typical of the Okavango and Zambezi headwaters. The dry-season discharge in these catchments is therefore not replicated by PDM. TOPMODEL, however, simulates a more realistic seasonal cycle of subsurface runoff and hence improved dry-season flow.

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