“…Assuming a threshold depth to water table of 20 m, and including a comparison of remotely sensed vegetation indices with the surrounding areas (details of the method are given in Klock 2002), the portion of study area likely to be influenced by secondary transpiration is estimated to be approximately 350 km 2 . Although the transpiration rate for well watered conditions is in the order of 5-7 mm d −1 worldwide (Gerten et al 2004), for the Kalahari it is thought to be significantly lower at 0.05-0.1 mm d −1 (De Vries et al 2000) and 0.4 mm d −1 (Bauer et al 2006). Using the latter figure reveals a total loss of groundwater by secondary evapotranspiration of 51 Mio m 3 a −1 , which is equivalent to 0.23 mm a −1 as an area mean.…”
Groundwater is the only source of drinking water for the inhabitants of the Kalahari. Thus understanding spatial and temporal variations in groundwater recharge is very important and a regional-scale water balance model has therefore been set up for a 209,149 km 2 catchment in north-eastern Namibia and north-western Botswana. The model has a spatial resolution of 1.5×1.5 km, daily model time-steps, and climatic input parameters for 19 years are used. The distributed, GIS-based, process-oriented, physical water balance model (MODBIL) used in this study considers the major water balance components: precipitation, evapotranspiration, groundwater recharge, and surface runoff/interflow. Mean precipitation for the study area is 409 mm a −1 , while mean actual evapotranspiration is 402 mm a −1 and mean groundwater recharge is 8 mm a −1 (2% of mean annual precipitation). The recharge pattern is mainly influenced by the distribution of soil and vegetation units. Groundwater recharge shows a high inter-and intra-annual variability, but not only the sum of annual precipitation is important for the development of groundwater recharge; a large amount of precipitation in a relatively short period is more important. Published independent data from the Kalahari in Namibia, Botswana and the Southern African region under similar climatic conditions are used to verify the modelling results.
“…Assuming a threshold depth to water table of 20 m, and including a comparison of remotely sensed vegetation indices with the surrounding areas (details of the method are given in Klock 2002), the portion of study area likely to be influenced by secondary transpiration is estimated to be approximately 350 km 2 . Although the transpiration rate for well watered conditions is in the order of 5-7 mm d −1 worldwide (Gerten et al 2004), for the Kalahari it is thought to be significantly lower at 0.05-0.1 mm d −1 (De Vries et al 2000) and 0.4 mm d −1 (Bauer et al 2006). Using the latter figure reveals a total loss of groundwater by secondary evapotranspiration of 51 Mio m 3 a −1 , which is equivalent to 0.23 mm a −1 as an area mean.…”
Groundwater is the only source of drinking water for the inhabitants of the Kalahari. Thus understanding spatial and temporal variations in groundwater recharge is very important and a regional-scale water balance model has therefore been set up for a 209,149 km 2 catchment in north-eastern Namibia and north-western Botswana. The model has a spatial resolution of 1.5×1.5 km, daily model time-steps, and climatic input parameters for 19 years are used. The distributed, GIS-based, process-oriented, physical water balance model (MODBIL) used in this study considers the major water balance components: precipitation, evapotranspiration, groundwater recharge, and surface runoff/interflow. Mean precipitation for the study area is 409 mm a −1 , while mean actual evapotranspiration is 402 mm a −1 and mean groundwater recharge is 8 mm a −1 (2% of mean annual precipitation). The recharge pattern is mainly influenced by the distribution of soil and vegetation units. Groundwater recharge shows a high inter-and intra-annual variability, but not only the sum of annual precipitation is important for the development of groundwater recharge; a large amount of precipitation in a relatively short period is more important. Published independent data from the Kalahari in Namibia, Botswana and the Southern African region under similar climatic conditions are used to verify the modelling results.
“…Three well-documented and widely-used USGS models were coupled to form the core of this conjunctive model: MODFLOW, DAFLOW MOC3D. Bauer et al (2005) used SEAWAT software package for coupled flow/transport simulations for the Shashe River Valley in Botswana. They found that the salinity distribution in and around the Shashe River Valley as well as its temporal dynamics can be satisfactorily reproduced if the transpiration is modelled as a function of groundwater salinity.…”
The Balasore coastal groundwater basin in Orissa, India is under a serious threat of overdraft and seawater intrusion. The overexploitation resulted in abandoning many shallow tubewells in the basin. The main intent of this study is the development of a 2-D groundwater flow and transport model of the basin using the Visual MODFLOW package for analyzing the aquifer response to various pumping strategies. The simulation model was calibrated and validated satisfactorily. Using the validated model, the groundwater response to five pumping scenarios under existing cropping conditions was simulated. The results of the sensitivity analysis indicated that the Balasore aquifer system is more susceptible to the river seepage, recharge from rainfall and interflow than the horizontal and vertical hydraulic conductivities and specific storage. Finally, based on the modeling results, salient management strategies are suggested for the long-term sustainability of vital groundwater resources of the Balasore groundwater basin. The most promising management strategy for the Balasore basin could be: a reduction in the pumpage from the second aquifer by 50% in the downstream region and an increase in the pumpage to 150% from the first and second aquifer at potential locations.
“…(3) Evaporation and transpiration are modeled as a single process which does not depend on salinity (cf. [3]). (4) Salt precipitation and its feedback on a decreasing hydraulic conductivity below the island is not modeled explicitely.…”
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