Climate change is at the top of the development agenda in Central America. This region, together with the Caribbean, is highly vulnerable to the effects of climate change in Latin America. Climate change is manifesting itself through higher average temperatures and more frequent droughts that result in higher water stress, and through the rising frequency of extreme weather events such as tropical storms, hurricanes, floods and landslides, all of which pose significant challenges in the rural water supply and sanitation sector. The paper starts with a review of the historic data on temperature and precipitation trends in Central America and particularly at the regional level in Nicaragua. The data reveal a clear trend of the growing climate variability, increased water stress for crops, and greater frequency of extreme weather events. The rising intensity and frequency of ex-treme weather events is among the most critical risks to the region's development agenda, and they translate into high economic losses. This paper examines the impacts and implications of potential climate change on water resources in Nicaragua and makes key recommendations to integrate climate change and rural water supply and sanitation policies and programs in a way that increase resilience to current and future climate conditions. | 0

The study was conducted during the summer of 2002. The treatments were monocropping and intercropping the common bean Canario 107, Bayomex (determinate type), Michoacan (indeterminate type), and sunflower cv. Victoria. Sowing took place on May 25, 2002 at a population density of 4.2 (bean) and 8.3 (sunflower) plants m(-2), respectively, in a clay texture soil, with a pH of 7.8 and was fertilized with 100-100-00 of NPK. The experimental design was randomized blocks with four replications. In neither of the crops the phenology, heat units nor evapotranspiration were affected by the sowing system. The heat units (HU) and evapotranspiration (ETc) of de sown crops at physiological maturity were 1521 HU and 279.6 mm for cv. Victoria, 658 HU and 201.3 mm for Canario 107, 811 HU and 213.7 for Bayomex, and 1041 HU and 241.7 mm for Michoacan. The combined agrosystem of sunflower and bean was more efficient in the use of resources in agricultural production. The combination of the Victoria sunflower and the Michoacan bean showed higher efficiency in the use of water and in the use of radiation and, as a result, greater production of biomass and yield. The land equivalent ratio for seed yield obtained with the combination of Victoria plus Canario 107, Victoria plus Michoacan, and Victoria plus Bayomex was 1.6, 1.9, and 3.0, respectively. This shows an advantage for the yield of combined crops over monocrops by 60, 90, and 200%, respectively.

La respuesta productiva del algodonero (Gossypium barbadense L.) cv. Tanguis, se analizo en cinco (5) ensayos de campo, en el Valle de Lluta, Arica. El diseno empleado en cada uno de ellos fue de bloques completos al azar. Los factores estudiados fueron nitrogeno (dos ensayos), densidad de plantacion (dos ensayos) y carga de agua para lixiviacion de sales (un ensayo). Las variables analizadas fueron rendimiento de algodon en rama (RAR) e indice de precocidad (IP). Tanto el RAR como el IP, no mostraron diferencias significativas para ninguno de los factores estudiados en los distintos ensayos. Pese a las dificultades para establecer el cultivo. asociadas basicamente al efecto toxico del alto contenido de boro en el agua y suelo, el cultivo mostro buena adaptabilidad y rendimientos entre 2638 y 4493 kg/ha-1 de algodon en rama, en las condiciones ecologicas del valle de Lluta

Groundwater resources are important sources of drinking water in Africa, and they are hugely important in sustaining urban livelihoods and supporting a diverse range of commercial and agricultural activities. Groundwater has an important role in improving health in sub-Saharan Africa (SSA). An estimated 250 million people (40% of the total) live in urban centres across SSA. SSA has experienced a rapid expansion in urban populations since the 1950s, with increased population densities as well as expanding geographical coverage. Estimates suggest that the urban population in SSA will double between 2000 and 2030. The quality status of shallow urban groundwater resources is often very poor due to inadequate waste management and source protection, and poses a significant health risk to users, while deeper borehole sources often provide an important source of good quality drinking water. Given the growth in future demand from this finite resource, as well as potential changes in future climate in this region, a detailed understanding of both water quantity and quality is required to use this resource sustainably. This paper provides a comprehensive assessment of the water quality status, both microbial and chemical, of urban groundwater in SSA across a range of hydrogeological terrains and different groundwater point types. Lower storage basement terrains, which underlie a significant proportion of urban centres in SSA, are particularly vulnerable to contamination. The relationship between mean nitrate concentration and intrinsic aquifer pollution risk is assessed for urban centres across SSA. Current knowledge gaps are identified and future research needs highlighted.

Introducing the time variable in groundwater vulnerability assessment is an innovative approach to study the evolution of contamination by non-point sources and to forecast future trends. This requires a determination of the relationship between temporal changes in groundwater contamination and in land use. Such effort will enable breakthrough advances in mapping hazardous areas, and in assessing the efficacy of land-use planning for groundwater protection. Through a Bayesian spatial statistical approach, time-dependent vulnerability maps are derived by using hydrogeological variables together with three different time-dependent datasets: population density, high-resolution urban survey, and satellite QuikSCAT (QSCAT) data processed with the innovative dense sampling method (DSM). This approach is demonstrated extensively over the Po Plain in Lombardy region (northern Italy). Calibrated and validated maps show physically consistent relations between the hydrogeological variables and nitrate trends. The results indicate that changes of urban nitrate sources are strongly related to groundwater deterioration. Among the different datasets, QSCAT-DSM is proven to be the most efficient dataset to represent urban nitrate sources of contamination, with major advantages: a worldwide coverage, a continuous decadal data collection, and an adequate resolution without spatial gaps. This study presents a successful approach that, for the first time, allows the inclusion of the time dimension in groundwater vulnerability assessment by using innovative satellite remote sensing data for quantitative statistical analyses of groundwater quality changes.

Groundwater management decisions require robust methods that allow accurate predictive modeling of pollutant occurrences. In this study, random forest regression (RFR) was used for modeling groundwater nitrate contamination at the African continent scale. When compared to more conventional techniques, key advantages of RFR include its nonparametric nature, its high predictive accuracy, and its capability to determine variable importance. The latter can be used to better understand the individual role and the combined effect of explanatory variables in a predictive model. In the absence of a systematic groundwater monitoring program at the African continent scale, the study used the groundwater nitrate contamination database for the continent obtained from a meta-analysis to test the modeling approach; 250 groundwater nitrate pollution studies from the African continent were compiled using the literature data. A geographic information system database of 13 spatial attributes was collected, related to land use, soil type, hydrogeology, topography, climatology, type of region, and nitrogen fertilizer application rate, and these were assigned as predictors. The RFR performance was evaluated in comparison to the multiple linear regression (MLR) methods. By using RFR, it was possible to establish which explanatory variables influence the occurrence of nitrate pollution in groundwater (population density, rainfall, recharge, etc.). Both the RFR and MLR techniques identified population density as the most important variable explaining reported nitrate contamination. However, RFR has a much higher predictive power (R² = 0.97) than a traditional linear regression model (R² = 0.64). RFR is therefore considered a very promising technique for large-scale modeling of groundwater nitrate pollution.