Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran

Nikroo, Leila; Kompani-Zare, Mazda; Sepaskhah, Ali Reza; Shamsi, Seyed Rashid Fallah

doi:10.1007/s10661-009-1010-x

Cited by 58 publications

(30 citation statements)

References 32 publications

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“…Spatial interpolation of the water table depth from the original ground surface at monthly scale averaging over the 2 years (2014 and 2015) was done over a year using the ordinary kriging option in the Spatial Analyst Tool of ArcGIS. This method was selected because ordinary kriging showed the most acceptable results in groundwater depth interpolation (Nikroo, Kompani‐Zare, Sepaskhah, & Shamsi, ). The interpolation was done for the part of the watershed where groundwater level data was collected, excluding the upper part of the watershed.…”

Section: Methodsmentioning

confidence: 99%

Establishing irrigation potential of a hillside aquifer in the African highlands

Tilahun

Yilak

Schmitter³

et al. 2020

Hydrological Processes

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Feeding 9 billion people in 2050 will require sustainable development of all water resources, both surface and subsurface. Yet, little is known about the irrigation potential of hillside shallow aquifers in many highland settings in sub-Saharan Africa that are being considered for providing irrigation water during the dry monsoon phase for smallholder farmers. Information on the shallow groundwater being available in space and time on sloping lands might aid in increasing food production in the dry monsoon phase. Therefore, the research objective of this work is to estimate potential groundwater storage as a potential source of irrigation water for hillside aquifers where lateral subsurface flow is dominant. The research was carried out in the Robit Bata experimental watershed in the Lake Tana basin which is typical of many undulating watersheds in the Ethiopian highlands. Farmers have excavated more than 300 hand dug wells for irrigation. We used 42 of these wells to monitor water table fluctuation from April 16, 2014 to December 2015. Precipitation and runoff data were recorded for the same period. The temporal groundwater storage was estimated using two methods: one based on the water balance with rainfall as input and baseflow and evaporative losses leaving the watershed as outputs; the second based on the observed rise and fall of water levels in wells. We found that maximum groundwater storage was at the end of the rain phase in September after which it decreased linearly until the middle of December due to short groundwater retention times. In the remaining part of the dry season period, only wells located close to faults contained water. Thus, without additional water sources, sloping lands can only be used for significant irrigation inputs during the first 3 months out of the 8 months long dry season. K E Y W O R D SEthiopia, groundwater, highlands, irrigation, Lake Tana, water budget, water storage, water table fluctuation method

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Section: Methodsmentioning

confidence: 99%

Establishing irrigation potential of a hillside aquifer in the African highlands

Tilahun

Yilak

Schmitter³

et al. 2020

Hydrological Processes

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“…Ahmadi and Sedghamiz (2008) used OK to evaluate the spatial and temporal variations of groundwater level and found that groundwater level variations have strong spatial and temporal structure. Nikroo et al (2010) conducted OK analysis on water table elevation of non-uniformly spaced observation wells in Iran and found a relatively strong spatial relationship between the water table elevations of the wells. Dash et al (2010) used kriging for optimizing data collection and utility in a regional groundwater investigation and showed that OK is a useful tool to elucidate those areas lacking enough data for developing a water table management network.…”

Section: Introductionmentioning

confidence: 99%

Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth

Adhikary

Dash

2014

Appl Water Sci

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Accurate and reliable interpolation of groundwater depth over a region is a pre-requisite for efficient planning and management of water resources. The performance of two deterministic, such as inverse distance weighting (IDW) and radial basis function (RBF) and two stochastic, i.e., ordinary kriging (OK) and universal kriging (UK) interpolation methods was compared to predict spatio-temporal variation of groundwater depth. Pre-and postmonsoon groundwater level data for the year 2006 from 110 different locations over Delhi were used. Analyses revealed that OK and UK methods outperformed the IDW method, and UK performed better than OK. RBF also performed better than IDW and OK. IDW and RBF methods slightly underestimated and both the kriging methods slightly overestimated the prediction of water table depth. OK, RBF and UK yielded 27.52, 27.66 and 51.11 % lower RMSE, 27.49, 35.34 and 51.28 % lower MRE, and 14.21, 16.12 and 21.36 % higher R 2 over IDW. The isodepth-area curves indicated the possibility of exploitation of groundwater up to a depth of 20 m.

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“…In this case, a spherical semivariogram resulted in the lowest mean error, average standard error, and root mean square error when compared to pentaspherical and exponential semivariograms. We followed the procedures outlined in [84,85] for semivariogram selection. One kriged map was created for each year.…”

Section: The Neural Network Approach and Input Datamentioning

confidence: 99%

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

2018

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NASA's Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km 2 ). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km 2 ). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km 2 ) dataset of groundwater storage changes over a portion of California's Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model's simulated maps have the potential for application to local groundwater management initiatives in the region.

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Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran

Cited by 58 publications

References 32 publications

Establishing irrigation potential of a hillside aquifer in the African highlands

Establishing irrigation potential of a hillside aquifer in the African highlands

Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

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