. This paper presents a comprehensive review and analysis of the available literature and information on droughts to build a continental, regional and country level perspective on geospatial and temporal variation of droughts in Africa. The study is based on the review and analysis of droughts occurred during 1900–2013, as well as evidence available from past centuries based on studies on the lake sediment analysis, tree-ring chronologies and written and oral histories and future predictions from the global climate change models. Most of the studies based on instrumental records indicate that droughts have become more frequent, intense and widespread during the last 50 years. The extreme droughts of 1972–1973, 1983–1984 and 1991–1992 were continental in nature and stand unique in the available records. Additionally, many severe and prolonged droughts were recorded in the recent past such as the 1999–2002 drought in northwest Africa, 1970s and 1980s droughts in western Africa (Sahel), 2010–2011 drought in eastern Africa (Horn of Africa) and 2001–2003 drought in southern and southeastern Africa, to name a few. The available (though limited) evidence before the 20th century confirms the occurrence of several extreme and multi-year droughts during each century, with the most prolonged and intense droughts that occurred in Sahel and equatorial eastern Africa. The complex and highly variant nature of many physical mechanisms such as El Nino–Southern Oscillation (ENSO), sea surface temperature (SST) and land–atmosphere feedback adds to the daunting challenge of drought monitoring and forecasting. The future predictions of droughts based on global climate models indicate increased droughts and aridity at the continental scale but large differences exist due to model limitations and complexity of the processes especially for Sahel and northern Africa. However, the available evidence from the past clearly shows that the African continent is likely to face extreme and widespread droughts in future. This evident challenge is likely to aggravate due to slow progress in drought risk management, increased population and demand for water and degradation of land and environment. Thus, there is a clear need for increased and integrated efforts in drought mitigation to reduce the negative impacts of droughts anticipated in the future.

/pdf/a-review-of-droughts-on-the-african-continent-a-geospatial-4fj42g8jwj.pdf

A review of droughts on the African continent: a geospatial and long-term perspective

Drought characterization from a multivariate perspective: A review

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2016RG000549

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Droughts are a normal part of the climate, and they can occur in any climate regime around the world, even deserts and rainforests. Like other hazards, droughts can be characterized in terms of their severity, location, duration, and timing. Droughts can arise from a range of hydrometeorological processes that suppress precipitation and/or limit surface water or groundwater availability, creating conditions that are significantly drier than normal or otherwise limiting moisture availability to a potentially damaging extent. Drought risk management involves hazards, exposure, vulnerability and impact assessment, a drought early warning system (DEWS), and preparedness and mitigation. DEWS typically aim to track, assess, and deliver relevant information concerning climatic, hydrologic, and water supply conditions and trends. This chapter discusses some of the most commonly used drought indicators/indices that are being applied across drought-prone regions, with the goal of advancing monitoring, early warning, and information delivery systems in support of risk-based drought management policies and preparedness plans.

/pdf/handbook-of-drought-indicators-and-indices-10lcf98imy.pdf

Handbook of Drought Indicators and Indices

The ability of tree species to cope with anticipated decrease in water availability is still poorly understood. We evaluated the potential of Norway spruce, Scots pine, European larch, black pine, and Douglas-fir to withstand drought in a drier future climate by analyzing their past growth and physiological responses at a xeric and a mesic site in Central Europe using dendroecological methods. Earlywood, latewood, and total ring width, as well as the d13C and d18O in early- and latewood were measured and statistically related to a multiscalar soil water deficit index from 1961 to 2009. At the xeric site, d13C values of all species were strongly linked to water deficits that lasted longer than 11 months, indicating a long-term cumulative effect on the carbon pool. Trees at the xeric site were particularly sensitive to soil water recharge in the preceding autumn and early spring. The native species European larch and Norway spruce, growing close to their dry distribution limit at the xeric site, were found to be the most vulnerable species to soil water deficits. At the mesic site, summer water availability was critical for all species, whereas water availability prior to the growing season was less important. Trees at the mesic were more vulnerable to water deficits of shorter duration than the xeric site. We conclude that if summers become drier, trees growing on mesic sites will undergo significant growth reductions, whereas at their dry distribution limit in the Alps, tree growth of the highly sensitive spruce and larch may collapse, likely inducing dieback and compromising the provision of ecosystem services. However, the magnitude of these changes will be mediated strongly by soil water recharge in winter and thus water availability at the beginning of the growing season.

Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch

Drought is a stochastic natural hazard that is instigated by intense and persistent shortage of precipitation. Following an initial meteorological phenomenon, subsequent impacts are realized on agr...

A review of drought indices

Sewer systems, over their life cycle, suffer deterioration due to aging, aggressive environmental factors, increased demand, inadequate design, third party intervention, and improper operation and maintenance activities. As a result, their state and overall long-term performance can be affected, which often requires costly and extensive maintenance, repair, and rehabilitation. Furthermore, these pressures can enhance the risk of failures (e.g., sewer leakage) which in turn can have serious impacts on the environment, public safety and health, economics, and the remaining service life of these assets. Effective asset management plans must be implemented to address long-term sustainability principles, i.e., economic growth, human health and safety, and environmental protection, simultaneously. The aim of this paper is to evaluate and compare four typical sewer pipe materials [i.e., concrete, polyvinyl chloride (PVC), vitrified clay, and ductile iron] and identify sustainable solutions. Two comprehensive life cycle sustainability assessment (LCSA) frameworks were applied. The first LCSA framework was based on the integration of emergy synthesis, life cycle assessment (LCA), and life cycle costing (LCC). In this framework, emergy synthesis has been applied to integrate the results from environmental analysis (i.e., LCA) and economic analysis (i.e., LCC) to an equivalent form of solar energy: a solar emergy joule. The second LCSA framework was based on a conventional, multi-criteria decision-making technique, i.e., the analytical hierarchy process, to integrate the results from environmental analysis (i.e., LCA) and economic analysis (i.e., LCC) and find the most sustainable solution over the sewer pipe life cycle. The results demonstrate that PVC pipe is the most sustainable option from both environmental and economic view points and can ensure a more sustainable sewer system.

Life cycle sustainability assessment (LCSA) for selection of sewer pipe materials

Improving users' awareness of the imperfections in spatial data has been a research issue explored within Geographic Information Sciences (GIS) for more than 30 years. However, little practical progress has been made toward this objective. Currently, most spatial data producers document information about spatial data quality as part of metadata. However, this information remains largely ignored by GIS users, which leads to the risk of users making poor decisions based on spatial data. As users increasingly make use of various GIS functionalities, GIS still lack the necessary mechanisms to effectively warn the users of the existence of quality issues in the spatial data being used. This becomes even more problematic in a web environment where data and services of unknown qualities can be shared and combined in the same application. In this paper we present an approach which aims at improving the use of quality information by providing it to the users in a more efficient way than existing approaches used for consulting metadata. We use an operation-based approach to link data quality information to the individual operations used in GIS applications. A conceptual framework for associating quality information with GIS operations is presented. Then, a prototype implementing this concept into a GIS software is described and discussed.

An Operation-Based Communication of Spatial Data Quality

Two machine learning methods, support vector machine and K-Nearest Neighbours (KNN) were investigated in this paper to predict the coagulant dosage in water treatment plants (WTPs). Two types of support vector machine regression techniques, e-SVR and v-SVR, using two different kernel functions (radial basis function (RBF) and polynomial function), and KNN were investigated in order to predict coagulant dosage in a large, a medium, and two small-sized WTPs. The results show that these two types of support vector machine regression techniques have good predictive capabilities for the large and medium WTPs as compared to small water systems. The performances of e-SVR with RBF kernel function were compared with that obtained from the KNN algorithm (as baseline) for four WTPs. The comparison shows that the KNN has similar performances as e-SVR for the large and medium- sized WTPs and performs better for two small-sized WTPs. The results show that different machine learning methods have competing predictive abilities.

Machine learning approaches to predict coagulant dosage in water treatment plants

Studying uncertainties in hydrological modeling is necessary because of data scarcity or abundance and quality issues. These uncertainties can have significant effects on environmental decision making. Traditionally, probabilistic methods have been used to study uncertainties; however, recently, more comprehensive methods are used in the treatment of uncertainty. These methods are capable of addressing uncertainty in the form of vagueness, ambiguity, and conflict, which cannot be studied efficiently using probabilistic frameworks. The Dempster-Shafer theory of evidence (DST) is one of the popular methods that can provide a unified platform to address data conflict and incompleteness. In this paper, the use of DST to model and propagate the uncertainty arising from two snow water equivalent data sets with a high degree of conflict (DST conflict k=0.74) is demonstrated. In DST, on the basis of the nature of data, e.g., the degree of conflict, different combination rules are applicable. Here, four DS...

Amin Zargar

Papers

A review of drought indices

Life cycle sustainability assessment (LCSA) for selection of sewer pipe materials

An Operation-Based Communication of Spatial Data Quality

Machine learning approaches to predict coagulant dosage in water treatment plants

Dempster-Shafer Theory for Handling Conflict in Hydrological Data: Case of Snow Water Equivalent