Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys

A. Akramkhanov, D.J. Brus, D.J.J. Walvoort

    Research output: Contribution to journalArticleAcademicpeer-review

    20 Citations (Scopus)

    Abstract

    Soil salinization in the lower reaches of Amudarya is a constant threat High seepage losses in irrigation water delivery network and deteriorated drainage network result in rising groundwater tables. The shallow groundwater table contributes to salinization of the rooting zone which is tackled by leaching at the end or beginning of the vegetation season. However, there is growing concern that the efficiency of the leaching with application of high amounts of water is low, and besides that the leaching effects are not long-lasting. To monitor local soil salinity an efficient strategy is developed, using electromagnetic induction (EMI) meter measurements. The monitoring strategy is applied and evaluated in a case study in Uzbekistan. The study area of 80 ha was surveyed in 2008-2011, at the end of the vegetation season (October-November), with the EMI in vertical dipole mode. In addition, at 142 calibration locations (including 28 revisited) both EMI was recorded and the soil was sampled to determine electrical conductivity (ECe) in laboratory. Log-transformed values of EMI and ECe were used to fit a simple linear regression model. Maps of ECe at the four time points were obtained by simulating multiple maps of log(EMI) using the ordinary kriging model. Besides, multiple vectors of regression coefficients were simulated, which were used to transform the simulated log(EMI) maps. Finally, simulated maps of regression residuals were added to the transformed log(EMI) fields, and backtransformed. Besides maps with predicted ECe the simulations were used to derive maps of the probability that ECe exceeds the critical threshold of 8 dSm(-1), and a map of the predicted linear temporal trend in ECe. The results show that in 2008-2011 most of the area was not saline, with only several spots reaching a predicted ECe of 6 dSm(-1). The probabilities that ECe exceeds the critical threshold of 8 dSm(-1) were small, and the predicted percentage of the area where ECe exceeds this threshold was very small. At most places a slight positive linear trend in ECe was predicted, but this predicted local trend was not significant at most places. Two areas of concern could be distinguished. First, spots occur displaying pronounced fluctuations in salinity from year to year with peaks close to the critical threshold, suggesting that these areas might be prone to soil salinization over short periods of time. Second, in the central part of the area a gradual build-up of soil salinity was seen, which calls for interventions to halt or reverse the build-up. (C) 2013 Elsevier B.V. All rights reserved.
    LanguageEnglish
    Pages600-607
    JournalGeoderma
    Volume213
    DOIs
    Publication statusPublished - 2014

    Fingerprint

    Uzbekistan
    electromagnetic survey
    soil salinization
    soil salinity
    leaching
    water table
    monitoring
    salinization
    vegetation
    kriging
    seepage
    irrigation water
    electrical conductivity
    rooting
    drainage
    calibration
    salinity
    case studies
    groundwater
    soil

    Keywords

    • groundwater
    • dynamics

    Cite this

    @article{be2593c9c3f74f98bb718fd176d2a30c,
    title = "Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys",
    abstract = "Soil salinization in the lower reaches of Amudarya is a constant threat High seepage losses in irrigation water delivery network and deteriorated drainage network result in rising groundwater tables. The shallow groundwater table contributes to salinization of the rooting zone which is tackled by leaching at the end or beginning of the vegetation season. However, there is growing concern that the efficiency of the leaching with application of high amounts of water is low, and besides that the leaching effects are not long-lasting. To monitor local soil salinity an efficient strategy is developed, using electromagnetic induction (EMI) meter measurements. The monitoring strategy is applied and evaluated in a case study in Uzbekistan. The study area of 80 ha was surveyed in 2008-2011, at the end of the vegetation season (October-November), with the EMI in vertical dipole mode. In addition, at 142 calibration locations (including 28 revisited) both EMI was recorded and the soil was sampled to determine electrical conductivity (ECe) in laboratory. Log-transformed values of EMI and ECe were used to fit a simple linear regression model. Maps of ECe at the four time points were obtained by simulating multiple maps of log(EMI) using the ordinary kriging model. Besides, multiple vectors of regression coefficients were simulated, which were used to transform the simulated log(EMI) maps. Finally, simulated maps of regression residuals were added to the transformed log(EMI) fields, and backtransformed. Besides maps with predicted ECe the simulations were used to derive maps of the probability that ECe exceeds the critical threshold of 8 dSm(-1), and a map of the predicted linear temporal trend in ECe. The results show that in 2008-2011 most of the area was not saline, with only several spots reaching a predicted ECe of 6 dSm(-1). The probabilities that ECe exceeds the critical threshold of 8 dSm(-1) were small, and the predicted percentage of the area where ECe exceeds this threshold was very small. At most places a slight positive linear trend in ECe was predicted, but this predicted local trend was not significant at most places. Two areas of concern could be distinguished. First, spots occur displaying pronounced fluctuations in salinity from year to year with peaks close to the critical threshold, suggesting that these areas might be prone to soil salinization over short periods of time. Second, in the central part of the area a gradual build-up of soil salinity was seen, which calls for interventions to halt or reverse the build-up. (C) 2013 Elsevier B.V. All rights reserved.",
    keywords = "groundwater, dynamics",
    author = "A. Akramkhanov and D.J. Brus and D.J.J. Walvoort",
    note = "WOS:000329594800066",
    year = "2014",
    doi = "10.1016/j.geoderma.2013.07.033",
    language = "English",
    volume = "213",
    pages = "600--607",
    journal = "Geoderma",
    issn = "0016-7061",
    publisher = "Elsevier",

    }

    Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys. / Akramkhanov, A.; Brus, D.J.; Walvoort, D.J.J.

    In: Geoderma, Vol. 213, 2014, p. 600-607.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys

    AU - Akramkhanov, A.

    AU - Brus, D.J.

    AU - Walvoort, D.J.J.

    N1 - WOS:000329594800066

    PY - 2014

    Y1 - 2014

    N2 - Soil salinization in the lower reaches of Amudarya is a constant threat High seepage losses in irrigation water delivery network and deteriorated drainage network result in rising groundwater tables. The shallow groundwater table contributes to salinization of the rooting zone which is tackled by leaching at the end or beginning of the vegetation season. However, there is growing concern that the efficiency of the leaching with application of high amounts of water is low, and besides that the leaching effects are not long-lasting. To monitor local soil salinity an efficient strategy is developed, using electromagnetic induction (EMI) meter measurements. The monitoring strategy is applied and evaluated in a case study in Uzbekistan. The study area of 80 ha was surveyed in 2008-2011, at the end of the vegetation season (October-November), with the EMI in vertical dipole mode. In addition, at 142 calibration locations (including 28 revisited) both EMI was recorded and the soil was sampled to determine electrical conductivity (ECe) in laboratory. Log-transformed values of EMI and ECe were used to fit a simple linear regression model. Maps of ECe at the four time points were obtained by simulating multiple maps of log(EMI) using the ordinary kriging model. Besides, multiple vectors of regression coefficients were simulated, which were used to transform the simulated log(EMI) maps. Finally, simulated maps of regression residuals were added to the transformed log(EMI) fields, and backtransformed. Besides maps with predicted ECe the simulations were used to derive maps of the probability that ECe exceeds the critical threshold of 8 dSm(-1), and a map of the predicted linear temporal trend in ECe. The results show that in 2008-2011 most of the area was not saline, with only several spots reaching a predicted ECe of 6 dSm(-1). The probabilities that ECe exceeds the critical threshold of 8 dSm(-1) were small, and the predicted percentage of the area where ECe exceeds this threshold was very small. At most places a slight positive linear trend in ECe was predicted, but this predicted local trend was not significant at most places. Two areas of concern could be distinguished. First, spots occur displaying pronounced fluctuations in salinity from year to year with peaks close to the critical threshold, suggesting that these areas might be prone to soil salinization over short periods of time. Second, in the central part of the area a gradual build-up of soil salinity was seen, which calls for interventions to halt or reverse the build-up. (C) 2013 Elsevier B.V. All rights reserved.

    AB - Soil salinization in the lower reaches of Amudarya is a constant threat High seepage losses in irrigation water delivery network and deteriorated drainage network result in rising groundwater tables. The shallow groundwater table contributes to salinization of the rooting zone which is tackled by leaching at the end or beginning of the vegetation season. However, there is growing concern that the efficiency of the leaching with application of high amounts of water is low, and besides that the leaching effects are not long-lasting. To monitor local soil salinity an efficient strategy is developed, using electromagnetic induction (EMI) meter measurements. The monitoring strategy is applied and evaluated in a case study in Uzbekistan. The study area of 80 ha was surveyed in 2008-2011, at the end of the vegetation season (October-November), with the EMI in vertical dipole mode. In addition, at 142 calibration locations (including 28 revisited) both EMI was recorded and the soil was sampled to determine electrical conductivity (ECe) in laboratory. Log-transformed values of EMI and ECe were used to fit a simple linear regression model. Maps of ECe at the four time points were obtained by simulating multiple maps of log(EMI) using the ordinary kriging model. Besides, multiple vectors of regression coefficients were simulated, which were used to transform the simulated log(EMI) maps. Finally, simulated maps of regression residuals were added to the transformed log(EMI) fields, and backtransformed. Besides maps with predicted ECe the simulations were used to derive maps of the probability that ECe exceeds the critical threshold of 8 dSm(-1), and a map of the predicted linear temporal trend in ECe. The results show that in 2008-2011 most of the area was not saline, with only several spots reaching a predicted ECe of 6 dSm(-1). The probabilities that ECe exceeds the critical threshold of 8 dSm(-1) were small, and the predicted percentage of the area where ECe exceeds this threshold was very small. At most places a slight positive linear trend in ECe was predicted, but this predicted local trend was not significant at most places. Two areas of concern could be distinguished. First, spots occur displaying pronounced fluctuations in salinity from year to year with peaks close to the critical threshold, suggesting that these areas might be prone to soil salinization over short periods of time. Second, in the central part of the area a gradual build-up of soil salinity was seen, which calls for interventions to halt or reverse the build-up. (C) 2013 Elsevier B.V. All rights reserved.

    KW - groundwater

    KW - dynamics

    U2 - 10.1016/j.geoderma.2013.07.033

    DO - 10.1016/j.geoderma.2013.07.033

    M3 - Article

    VL - 213

    SP - 600

    EP - 607

    JO - Geoderma

    T2 - Geoderma

    JF - Geoderma

    SN - 0016-7061

    ER -