Research ArticleCLIMATE CHANGE EFFECTS

Increasing drought in Jordan: Climate change and cascading Syrian land-use impacts on reducing transboundary flow

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Science Advances  30 Aug 2017:
Vol. 3, no. 8, e1700581
DOI: 10.1126/sciadv.1700581
  • Fig. 1 Major river basins and projected climate change in Jordan.

    (A) Map of Jordan with the major surface water basins color-coded according to the regional basin groups they belong to and locations of the major cities, reservoirs, and stream gauges. (B) Temperature deviation from the baseline period average for RCP4.5 and RCP8.5 scenarios. The gray shaded area represents the baseline period (1981–2010). Although Jordan is expected to experience an increase of 2°C in the annual average temperature by 2071–2100 under RCP4.5, a significant increase of 4.5°C is expected under RCP8.5. (C) Annual precipitation (essentially the same as the winter precipitation because summers are devoid of rainfall) deviation from the baseline period average under RCP8.5. Any deviation that falls below the −1 SD level from the average is considered to indicate a dry year, as represented by the red shaded area. If, for a given year, the annual average precipitation falls below the −2 SD level, then it is considered to be an extremely dry year. The plotted time series include the bias-corrected precipitation simulations for the three chosen climate models (HadGEM, ECHAM, and IPSL) for 1981–2100 and the observed precipitation (1981–2015). The distribution mapping–based bias correction for the climate simulations was based on the observations during the baseline period (1981–2010). The verification period (2011–2015) is a comparison between bias-corrected climate simulations and the observed precipitation post-baseline period. (D) Annual precipitation deviation from the baseline period average for RCP4.5.

  • Fig. 2 Increasing agricultural and hydrologic drought conditions.

    (A) Annual soil moisture deviation from the baseline period average for RCP8.5. (B) Annual soil moisture deviation from the baseline period average for RCP4.5. (C) Annual streamflow deviation from the baseline period average for RCP8.5. (D) Annual streamflow deviation from the baseline period average for RCP4.5. The red shaded region indicates the dry zone. The two red lines indicate 1 and 2 SD levels below the baseline period average.

  • Fig. 3 More warm years and concurrent warm and dry periods.

    (A) Number of years with a positive temperature anomaly that is greater than 1, 2, and 3 SD levels from the baseline period average for RCP8.5. (B) Percent chance of occurrence of high temperature (>1 SD) and different drought types for RCP8.5. (C) Percent chance of occurrence of high temperature (>1 SD) and different drought types for RCP4.5. (D) Number of events resulting in concurrent occurrence of multiple drought types for each 30-year time slice.

  • Fig. 4 Drought properties for RCP4.5 and RCP8.5.

    Droughts are defined by the number of events, average duration, and maximum severity for each 30-year time slice. The gray shaded area represents the baseline period (see figs. S1 and S2 for details).

  • Fig. 5 Progression of multiple drought types in each river basin.

    Spatial maps of the (A) number, (B) duration in months, and (C) maximum drought severity (of a single event) of meteorological, agricultural, and hydrologic droughts during the baseline period, 2071–2100 (RCP4.5), and 2071–2100 (RCP8.5), respectively. The blue, green, and red maps represent meteorological, agricultural, and hydrologic droughts, respectively. We define a drought event as a continuous period of one or more months with precipitation, soil moisture, or streamflow less than −1 SD from the baseline average. The drought duration is in months (see Fig. 1A to identify the locations of basins).

  • Fig. 6 Wet event properties for RCP4.5 and RCP8.5.

    When the variable of interest falls above +1 SD from the baseline average, it is considered as a wet event. Wet events are defined by the number of events, average duration, and maximum severity for each 30-year time slice. The gray shaded area represents the baseline period. The severity of each wet event is defined as the sum of monthly deviations of the variable of interest exceeding +1 SD from the baseline period over a continuous period of one or more months (see figs. S1 and S2 for details). Duration of a wet event is defined as the number of months of a continuous period over which the monthly deviation of the variable of interest is above +1 SD.

  • Fig. 7 Probability distributions show shift toward drier conditions.

    Kernel density functions of the (A) meteorological drought indicator, (B) agricultural drought indicator, and (C) hydrologic drought indicator for the baseline period, RCP4.5, and RCP8.5, respectively. The red shaded portion of the density functions indicate dry events, and the blue shaded portion of the density functions indicate wet events. The time periods considered are 1981–2010 for the baseline period and 2071–2100 for RCP4.5 and RCP8.5 scenarios. The pdfs are obtained using a well-established nonparametric kernel density estimation approach (52).

  • Fig. 8 Transboundary flow reduced due to climate and land-use changes.

    (A) Annual projected inflow in MCM to Al-Wehda dam under different climate and conflict scenarios. (B) Increase in annual flow generation at Al-Wehda dam due to land-use change. (C) Increase in annual flow generation at Al-Wehda dam due to climate change. (D) Land-use map for year 2014 indicating the reduced irrigated agricultural land use under continuing conflict scenario. (E) Land-use map for year 2009 indicating the increase in irrigated agricultural land use under recovery to pre-conflict scenario. The green shaded region indicates the irrigated agricultural land area. The annual incremental flows were calculated by taking the difference between the continuing conflict and recovery to pre-conflict scenarios and RCP4.5 and RCP8.5, respectively, to identify independent impacts of conflict and climate change.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/8/e1700581/DC1

    table S1. List of climate models considered for subset selection.

    table S2. Agricultural land-use area for Yarmouk under different conflict scenarios.

    table S3A. Flow generation to Jordan under different land-use change scenarios.

    table S3B. Flow generation to Jordan under different climate change scenarios.

    table S4A. Performance metrics for the reanalysis data at the 10 station locations.

    table S4B. Summary statistics of the difference between reanalysis and observed monthly precipitation for all the 10 stations in Jordan.

    table S4C. Two sample t test for monthly precipitation at the 10 stations.

    table S5A. SWAT model inputs and sources.

    table S5B. Land-use maps used for different scenarios.

    table S6. Validation of estimated irrigation water requirement in the Yarmouk basin.

    table S7. Results of monthly streamflow calibration and validation.

    table S8. Climate model selection criteria.

    table S9. Bias correction statistics.

    table S10. Drought and wet event classification.

    fig. S1. Scatterplots for extreme event severity (wet and dry) for meteorological, agricultural, and hydrologic droughts under RCP4.5 and RCP8.5.

    fig. S2. Monthly drought indicator values for meteorological, agricultural, and hydrologic droughts under RCP4.5 and RCP8.5.

    fig. S3. Trends in the annual reservoir inflows in the future under RCP8.5 plotted on Jordan’s relief map.

    fig. S4. Performance analysis of reanalysis data set.

    fig. S5. Comparison of empirical CDF for observed and reanalysis monthly precipitation at the 10 stations.

    fig. S6. Correlograms for observed and reanalysis monthly precipitation time series at the selected stations.

    fig. S7. Box plots showing difference between reanalysis and observed monthly precipitation.

    fig. S8. Calibration and validation results for SWAT monthly streamflow simulations.

    fig. S9. Distribution mapping technique and climate model performance in simulating temperature during baseline period.

    fig. S10. Effect of bias correction of precipitation at monthly and annual scales.

    fig. S11. Comparison of observed and bias-corrected climate model precipitation for all rainy season months for a randomly selected grid.

    References (5362)

  • Supplementary Materials

    This PDF file includes:

    • table S1. List of climate models considered for subset selection.
    • table S2. Agricultural land-use area for Yarmouk under different conflict scenarios.
    • table S3A. Flow generation to Jordan under different land use change scenarios.
    • table S3B. Flow generation to Jordan under different climate change scenarios.
    • table S4A. Performance metrics for the reanalysis data at the 10 station locations.
    • table S4B. Summary statistics of the difference between reanalysis and observed monthly precipitation for all the 10 stations in Jordan.
    • table S4C. Two sample t-test for monthly precipitation at the 10 stations.
    • table S5A. SWAT model inputs and sources.
    • table S5B. Land-use maps used for different scenarios.
    • table S6. Validation of estimated irrigation water requirement in the Yarmouk basin.
    • table S7. Results of monthly streamflow calibration and validation.
    • table S8. Climate model selection criteria.
    • table S9. Bias correction statistics.
    • table S10. Drought and wet event classification.
    • fig. S1. Scatterplots for extreme event severity (wet and dry) for meteorological, agricultural, and hydrologic droughts under RCP4.5 and RCP8.5.
    • fig. S2. Monthly drought indicator values for meteorological, agricultural, and hydrologic droughts under RCP4.5 and RCP8.5.
    • fig. S3. Trends in the annual reservoir inflows in the future under RCP8.5 plotted on Jordan’s relief map.
    • fig. S4. Performance analysis of reanalysis data set.
    • fig. S5. Comparison of empirical CDF for observed and reanalysis monthly precipitation at the 10 stations.
    • fig. S6. Correlograms for observed and reanalysis monthly precipitation time series at the selected stations.
    • fig. S7. Box plots showing difference between reanalysis and observed monthly precipitation.
    • fig. S8. Calibration and validation results for SWAT monthly streamflow simulations.
    • fig. S9. Distribution mapping technique and climate model performance in simulating temperature during baseline period.
    • fig. S10. Effect of bias correction of precipitation at monthly and annual scales.
    • fig. S11. Comparison of observed and bias-corrected climate model precipitation for all rainy season months for a randomly selected grid.
    • References (53–62)

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