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 links between climate variability, depicted by time series of oceanic indices, and changes in total water and groundwater storage are investigated across nine large aquifer basins of the African continent. The Gravity Recovery and Climate Experiment (GRACE) mission’s observations represent a remarkable tool that can provide insight into the dynamics of terrestrial hydrology in areas where direct in situ observations are limited. In order to evaluate the impact of interannual and multidecadal climate variability on groundwater resources, this study assesses the relationship between synoptic controls on climate and total water storage estimates from (i) GRACE from 2002 to 2013 and (ii) a two-variable climate-driven model that is able to reconstruct past storage changes from 1982 to 2011. The estimates are then compared to time series of groundwater levels to show the extent to which total water storage covaries with groundwater storage. Results indicate that rainfall patterns associated with the El Niño Southern Oscillation (ENSO) are the main driver of changes in interannual groundwater storage, whereas the Atlantic MultiDecadal Oscillation (AMO) plays a significant role in decadal to multidecadal variability. The combined effect of ENSO and AMO could trigger significant changes in recharge to the aquifers and groundwater storage, in particular in the Sahel. These findings could help decision-makers prepare more effective climate-change adaptation plans at both national and transboundary levels.

The determination of space–time variation in groundwater accumulation in Colombia’s Eastern Llanos foreland basin from 2003 to 2014 was done using terrestrial water storage (TWS) anomalies identified in two versions of the Gravity Recovery and Climate Experiment (GRACE) data—from the Global Data Center for Space Research (CSR) at the University of Texas at Austin (USA) and from the Institute of Geodesy at the Graz University of Technology (ITSG, Austria)—and also soil moisture storage (SMS) data from the Global Land Data Assimilation System (GLDAS). These data were compared to changes in groundwater storage obtained using the water-budget equation, calculated based on recorded data from hydrometeorological stations. This study confirmed the viability of using satellite information to understand and monitor temporal variation in groundwater recharge in the study area. Temporal variations in TWS, SMS, and groundwater level were shown to correspond to regional rain and drought periods, which are sensitive to climate phenomena such as El Niño and La Niña. Comparing changes in TWS and groundwater level to changes in infiltration and recharge revealed correlation coefficients of 0.56 and 0.98 with CSR data and 0.71 and 0.86 with ITSG data, respectively.