A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia.

The aim of this paper is to use a knowledge-driven expert-based geographical information system (GIS) model coupling with remote-sensing-derived parameters for groundwater potential mapping in an area of the Upper Langat Basin, Malaysia. In this study, nine groundwater storage controlling parameters that affect groundwater occurrences are derived from remotely sensed imagery, available maps, and associated databases. Those parameters are: lithology, slope, lineament, land use, soil, rainfall, drainage density, elevation, and geomorphology. Then the parameter layers were integrated and modeled using a knowledge-driven GIS of weighted linear combination. The weightage and score for each parameter and their classes are based on the Malaysian groundwater expert opinion survey. The predicted groundwater potential map was classified into four distinct zones based on the classification scheme designed by Department of Minerals and Geoscience Malaysia (JMG). The results showed that about 17% of the study area falls under low-potential zone, with 66% on moderate-potential zone, 15% with high-potential zone, and only 0.45% falls under very-high-potential zone. The results obtained in this study were validated with the groundwater borehole wells data compiled by the JMG and showed 76% of prediction accuracy. In addition statistical analysis indicated that hard rock dominant of the study area is controlled by secondary porosity such as distance from lineament and density of lineament. There are high correlations between area percentage of predicted groundwater potential zones and groundwater well yield. Results obtained from this study can be useful for future planning of groundwater exploration, planning and development by related agencies in Malaysia which provide a rapid method and reduce cost as well as less time consuming. The results may be also transferable to other areas of similar hydrological characteristics

Estimation of quantitative measures of total water storage variation from GRACE and GLDAS-NOAH satellites using geospatial technology

This study represents the first attempt to examine spatial and seasonal variations of the surface water budget by using the Gravity Recovery and Climate Experiment (GRACE) by measuring gravity anomalies on earth to estimate changes in Total Water Storage (TWS) content over the north-western region of the India including New Delhi and states of Rajasthan, Uttar Pradesh and Haryana, covering an area of 676,917 km(2). The TWS (surface plus ground) and its changes were estimated from 2003 to 2012. Additionally, Global Land Data Assimilation System (GLDAS) variables were used to infer as to how TWS was partitioned into canopy water and soil moisture components. To evaluate monthly accumulated rainfall, Tropical Rainfall Measuring Mission (TRMM) data, processed by the Global Precipitation Climatology Center (GPCC) were used. By computing storage changes in GRACE, TWS, GLDAS land surface state variables and terrestrial-based water balance approach, we calculated groundwater storage changes. The time-series comparisons show good agreement between the GRACE satellite data, GLDAS model data and computed groundwater data. The change in soil moisture storage is less than that in saturated storage. Both the GRACE and calculated groundwater storage changes indicate storage loss in the range of 86.43 km(3)/y +/- average of 10 years data (in terms of equivalent water thickness). The average groundwater loss for was calculated as 9.7 +/- km(3)/y, states of Haryana as 9.7 +/- km(3)/y, Rajasthan as 33.199 +/- km(3)/y and Uttar Pradesh as 44.4827 +/- km(3)/y. Our results are convincing of a credible GRACE hydrology data which can be handy in monitoring storage dynamics and water availability at regional scale. As GRACE data are available for virtually every region of the world, their application in conjunction with hydrological models will improve applications of hydrological studies which may lead not only to water balance closures, but also to sustainable water resource management at regional scale. (C) 2017 Elsevier Ltd and INQUA. All rights reserved

Evaluation of In Situ Rainwater Harvesting as an Adaptation Strategy to Climate Change for Maize Production in Rainfed Africa

Stabilizing smallholder crop yields under changing climatic conditions in sub-Saharan Africa will require adaptation strategies focused on soil and water management. Impact studies of climate change on crop yields often ignore the potential of adaptation strategies such as rainwater harvesting (RWH). While RWH is bringing benefits to agricultural systems today, it is still unclear which regions could increasingly benefit from RWH under changing climatic conditions. Here we employ a continental scale modelling strategy using the latest CMIP5 data and explicitly take into account design factors of RWH to show that it is a valuable adaptation strategy to climate change in Africa for maize (Zea mays L.). We find that RWH can bridge up to 40 % of the yield gaps attributable to water deficits under current conditions and 31 % under future (2050s) climatic conditions during the main growing season for maize, hence providing an alternative to irrigation from scarce or inaccessible groundwater resources. RWH could increase maize yields by 14–50 % on average for the 2050s across Africa, by bridging water deficits. While in situ RWH strategies show great biophysical potential as an adaptation strategy to climate change, there remain locally specific barriers to their adoption, which will need to be addressed to ensure their successful implementation at a larger scale