Application of analytical hierarchy process to assess groundwater potential for a sustainable management in the Menoua Division.

Number of wells drilled by private and public stakeholders, as well as nongovernmental organizations in the Menoua Division are unproductive. This is due to the lack of preliminary surveys assessing groundwater potential (GWP). A combined remote sensing (RS) and analytical hierarchy process (AHP) approach handled on a geographic information system (GIS) environment is efficient for such an investigation. For this article, seven environmental parameters, with significant contribution to groundwater occurrence, are integrated. Those parameters are drainage density, elevation, lineament density, land use/land cover (LULC), rainfall, slope, and topographic wetness index (TWI). RS and GIS techniques said to be quick and simple for exploring GWP whatever the geological settings, have the advantage of investigating large areas with little financial resources. Although these techniques are widely used in the world, this is the first time they are applied in the Menoua Division. The outcome, which is a sound GWP map, has been sorted into five zones: very low potential for 13 %, low potential for 27 %, medium potential also for 27 %, high potential for 23 %, and very high potential for 11 % of the Menoua Division. This may help to reduce the rate of noncompliant hydrogeophysical surveys and the number of unproductive boreholes by converging hydrogeophysical surveys on high GWP sites.

The equatorial rainforest of Central Africa between economic needs and sustainability requirements.

This article sets out to investigate the role played by the rainforest of Central Africa in providing environmental goods and services, regulating and stabilizing the global climate as well as participating in socio-economic development of the riparian countries. This complex role offers a double status, almost confrontational, to this rainforest: it stands as an economic resource and as a major global climate regulator. Hence, there is an urgent need to question certain aspects such as climate trends in this strategic region and the use of local forest resources for economic purpose in order to suggest ecological attitudes to be adopted by policymakers, stakeholders, forest professionals and users for a sustainable development. It is shown that: 1) this rainforest constitutes an economic resource and plays a major socio-cultural role in addition to its global climate regulatory role, 2) an overexploitation of the forest resources for economic purposes exposes the forest to an increased deterioration which can change the ecological and socio-economic balance, or destroy this forest, and by so doing, alter its global climate control power, 3) the climate of the region is experiencing serious variability. Thus, solutions that can satisfy socio-economic needs and give room for sustainable development are proposed.

Erratum to: Lessening the adverse effect of the semivariogram model selection on an interpolative survey using kriging technique.

[This corrects the article DOI: 10.1186/s40064-016-2142-4.].

Lessening the adverse effect of the semivariogram model selection on an interpolative survey using kriging technique.

OBJECTIVE: Many parameters in environmental, scientific and human sciences investigations need to be interpolated. Geostatistics, with its structural analysis step, is widely used for this purpose. This precious step that evaluates data correlation and dependency is performed thanks to semivariogram. However, an incorrect choice of a semivariogram model can skew all the prediction results. The main objectives of this paper are (1) to simply illustrate the influence of the choice of an inappropriate semivariogram model and (2) to show how a best-fitted model can be selected. This may lessen the adverse effect of the semivariogram model selection on an interpolation survey using kriging technique. METHODS: The influence of the semivariogram model selection is highlighted and illustrated by thematic maps drawn using four different models (Gaussian, magnetic, spherical and exponential). Then, a guideline to select the most suitable model, using mean error (ME), mean square error (MSE), root mean square error (RMSE), average standard error (ASE), and root mean square standardized error (RMSSE), is proposed. RESULTS: The choice of a semivariogram model seriously influences the results of a kriging survey at both endpoints and amplitude of the range of the estimated values. However, the direction of variation of the interpolated values is independent of the semivariogram model: different semivariogram models (with the same characteristics) produce different thematic maps but, the areas of minimum and maximum values remain unchanged. Yet, the suitable model can be selected by means of ME, MSE, RMSE, ASE and RMSSE. CONCLUSION: The present article illustrates how the use of an inappropriate semivariogram model can seriously distort the results of an evaluation, assessment or prediction survey. To avoid such an inconveniency, a methodical approach based on the computation and analysis of ME, RMSE, ASE, RMSSE and MSE is proposed.