Groundwater dependence and drought within the southern African development community

A groundwater situation analysis of the SADC region has been undertaken as part of the World Bank GEF Programme as a basis for ensuring equitable use of groundwater resources, particularly during periods of drought, both for human needs and for sustaining ecosystems. Much of the groundwater in the region occurs in weathered crystalline rocks suitable for dispersed supply to rural communities, although there are several aquifers capable of sustaining urban demand that contribute to the supply of several major cities and towns. A number of SADC Member States, such as Botswana, Namibia and South Africa, are very dependent on groundwater, whereas the Democratic Republic of Congo is least dependent. Groundwater dependence and groundwater demand, together providing an indication of drought vulnerability, have been assessed from the availability and coverage of groundwater data, but it is very apparent that reliable and comprehensive groundwater data are major deficiencies throughout the SADC region. Few attempts have thus been made to calculate renewable groundwater resource volumes or develop optimum use of groundwater, despite the fact that susceptibility of many Member States to drought requires them to consider mitigation strategies to lessen the hardships imposed largely on their rural population. Such strategy requires long-term intervention and not short-term emergency responses, a process that is directly related to availability of comprehensive groundwater datasets. Considerable effort in groundwater assessment and monitoring and the accumulation, evaluation and dissemination of essential datasets will thus be required to maintain population livelihoods in future years when water supply is projected to be in deficit in over half of the SADC Member States

Solar variability, weather, and climate

Advances in the understanding of possible effects of solar variations on weather and climate are most likely to emerge by addressing the subject in terms of fundamental physical principles of atmospheric sciences and solar-terrestrial physis. The limits of variability of solar inputs to the atmosphere and the depth in the atmosphere to which these variations have significant effects are determined

South Asian Regional Study on Climate Change Impacts and Adaptation: Implications for Human Development

human development, climate change

Science for Disaster Risk Reduction

This thematic report describes JRC's activities in support to disaster management. The JRC develops tools and methodologies to help in all phases of disaster management, from preparedness and risk assessment to recovery and reconstruction through to forecasting and early warning.JRC.A.6-Communicatio

Implications of and possible responses to climate change

Climate change is expected to worsen food insecurity and seriously undermines rural development prospects. It makes it harder to achieve the Millenium Development Goals and ensure a sustainable future beyond 2015. Findings from the recent 4th assessment report of IPCC, Working Group II indicate that already towards 2050 with respect to food crops yield losses between 10 and 30 % can be expected as compared to current conditions in large parts of Africa, including Western, Eastern and southern Africa. Climate change is likely to increase disparities between developed and the developing world, while many uncertainties remain. It is, for instance, estimated that developing countries would need to bear 75-80 % of the costs of damages caused by a changing climate. The prevention of such threats cannot rely on economic growth, but requires climate policies that combine enhancement of development with reduction of vulnerabilities and effective financing mechanisms that support the transition to low-carbon economics. The major strategies to reduce the potentially harmful effects of global changes, especially climate change are 1) adaptation of food and farming systems to climate change, 2) enhancing their resilience and adaptive capacity to changes in climate variability and extremes that are difficult to predict, and to global change more generally (including socio-economic changes), and 3) mitigation of climate change and trading the options to mitigate in low-income countries on the global carbon markets to create a substantial financial flow from the North to the South

Wykorzystanie głębokich sieci neuronowych w ograniczaniu zmian klimatycznych związanych z konfliktem farmerów i pasterzy w ramach inicjatywy na rzecz zrównoważonej integracji społecznej

Peaceful coexistence of farmers and pastoralists is becoming increasingly elusive and has adverse impact on agricultural revolution and global food security. The targets of Sustainable Development Goal 16 (SDG 16) include promoting peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. As a soft approach and long term solution to the perennial farmers-herdsmen clashes with attendant humanitarian crisis, this study proposes a social inclusion architecture using deep neural network (DNN). This is against the backdrop that formulating policies and implementing programmes based on unbiased information obtained from historical agricultural data using intelligent technology like deep neural network (DNN) can be handy in managing emotions. In this vision paper, a DNN-based Farmers-Herdsmen Expert System (FHES) is proposed based on data obtained from the Nigerian National Bureau of Statistics for tackling the incessant climate change-induced farmers-herdsmen clashes, with particular reference to Nigeria. So far, many lives have been lost. FHES is modelled as a deep neural network and trained using farmers-herdsmen historical data. Input variables used include land, water, vegetation, and implements while the output is farmers/herders disposition to peace. Regression analysis and pattern recognition performed by the DNN on the farmers-herdsmen data will enrich the inference engine of FHES with extracted rules (knowledge base). This knowledge base is then relied upon to classify future behaviours of herdsmen/farmers as well as predict their dispositions to violence. Critical stakeholders like governments, service providers and researchers can leverage on such advisory to initiate proactive and socially inclusive conflict prevention measures such as people-friendly policies, programmes and legislations. This way, conflicts can be averted, national security challenges tackled, and peaceful atmosphere guaranteed for sustainable development.   Pokojowe współistnienie rolników i pasterzy staje się coraz mnie realne, co ma negatywny wpływ na rewolucję rolniczą i globalne bezpieczeństwo żywnościowe. Cele zrównoważonego rozwoju (SDG 16) obejmują promowanie tworzenia pokojowych i zintegrowanych społeczeństw na rzecz zrównoważonego rozwoju, zapewnienie wszystkim dostępu do uczciwego wymiaru sprawiedliwości i tworzenie skutecznych, odpowiedzialnych i integrujących instytucji na wszystkich poziomach. W ramach łagodnego podejścia i długofalowego podejścia do problemu konfliktów rolników-pasterzy w kontekście kryzysu humanitarnego, w niniejszym artykule zaproponowano architekturę integracji społecznej wykorzystującą głęboką sieć neuronową (DNN). Formułowanie polityki i wdrażanie programów w oparciu o obiektywne informacje uzyskane z historycznych danych przy użyciu inteligentnej technologii, takiej jak głęboka sieć neuronowa (DNN), może być przydatne w zarządzaniu emocjami. W niniejszym artykule zaproponowano oparty na danych uzyskanych od Nigeryjskiego Narodowego Urzędu Statystycznego system ekspercki rolników-pasterzy (FHES) oparty na DNN w celu przeciwdziałaniu nieustannym starciom rolników-pasterzy wywołanych zmianami klimatu, ze szczególnym uwzględnieniem Nigerii. Do tej pory wiele było ofiar. System FHES jest modelowany jako głęboka sieć neuronowa, przy użyciu danych historycznych hodowców-pasterzy. Zastosowane zmienne wejściowe obejmują ziemię, wodę, roślinność i narzędzia, podczas gdy zmienne wyjściowe to rolnicy-pasterze skłonni do pokoju. Analiza regresji i rozpoznawanie wzorców przeprowadzone przez DNN na danych rolników-pasterzy wzbogaci mechanizm wnioskowania systemu FHES o wyodrębnione reguły (baza wiedzy). Podstawą tej wiedzy jest klasyfikacja przyszłych zachowań pasterzy/rolników, a także przewidywanie ich skłonności do przemocy. Krytyczni interesariusze, tacy jak rządy, dostawcy usług i naukowcy, mogą wykorzystać takie doradztwo do zainicjowania proaktywnych i społecznie włączających środków zapobiegania konfliktom, takich jak przyjazne dla ludzi polityki, programy i prawodawstwo. W ten sposób można uniknąć konfliktów, stawić czoła wyzwaniom bezpieczeństwa narodowego i zagwarantować pokojową atmosferę dla zrównoważonego rozwoju

Spatial Aided Decision-making System for E-Government

Non