Evaluation of Land Suitability for Irrigation Development and Sustainable Land Management Using ArcGIS on Katar Watershed in Rift Valley Basin, Ethiopia
Issue:
Volume 9, Issue 3, June 2020
Pages:
56-63
Received:
23 March 2020
Accepted:
17 April 2020
Published:
19 August 2020
Abstract: Evaluating land suitability from the available land and water resources potential for irrigation development is very important for planning sustainable use of limited land and water resources. The critical objective of this study was to evaluate the suitable land resource potential for irrigation development for the Katar River watershed in the Rift Valley Basin in Ethiopia by using ArcGIS based on Multi-Criteria Evaluation (MCE) techniques. The steps undertaken were watershed delineation, characterizing the watershed by suitability parameters such as slope, soil texture, soil depth, drainage classes, proximity to river and urban, and land use land cover. And then, re-classification and mapping according to suitability classification, identification of irrigable land, and estimation of surface water potential and irrigation water requirements were followed. After reclassified, the suitability analyses of the each parameter were classified based on suitability classes for irrigation development. The weighting analysis of all parameters resulted that 34.08% was classified as high suitable (S1), 58.08% moderately suitable (S2), 3.8% marginally suitable (S3), whereas 3.21% not suitable (N) for surface irrigation development. Finally, comparing the required gross irrigation water requirement and available monthly flow of the River, the River has the capacity for the fulfilling irrigation water demand to irrigate command area of the watershed during dry season.
Abstract: Evaluating land suitability from the available land and water resources potential for irrigation development is very important for planning sustainable use of limited land and water resources. The critical objective of this study was to evaluate the suitable land resource potential for irrigation development for the Katar River watershed in the Rif...
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Global Distribution of Surface Water Vapour Density Using in Situ and Reanalysis Data
Emmanuel Israel,
Adedayo Kayode David,
Ojo Olusola Samuel,
Ashidi Ayodeji Gabriel,
Emmanuel Grace Omolara
Issue:
Volume 9, Issue 3, June 2020
Pages:
64-70
Received:
9 January 2020
Accepted:
21 January 2020
Published:
3 September 2020
Abstract: Global spatial and annual distribution of surface water vapour density were estimated using 2005 -2016 monthly air temperature and relative humidity at 1° ×1° resolution obtained from Era interim and NCEP/NCAR database products. Obtained results from reanalysis were statistically tested using in situ data from Tropospheric Data Acquisition Network (TRODAN) of The Center for Atmospheric Research (CAR). Four seasonal variations of surface water vapour density (winter (DJF), spring (MAM), summer (JJA) and autumn (SON)) was examined. Observed result from the two reanalysis follow similar trends with value from Era interim leading. High values ranges between 50 g/m2 and 68 g/m2 were observed in tropical regions and humid sub-tropical regions. Low values ranges between 8 g/m2 and 38 g/m2 were observed in Ice cap, Tundra and arid regions. High warming may be experienced in tropical and sub-tropical regions, similarly, climate change with alarming rate may be experienced in locations with low values. The annual cycle of surface water vapor density is clearly established from two reanalysis across world classified into twelve regions. The statistical test for the reanalysis present good result with a mean bias error, MBE, root mean square error, RMSE and R square of 20.56, 18.29, 0.87 and 5.87, 0.98, 0.93 for Era interim and NCEP/NCAR respectively.
Abstract: Global spatial and annual distribution of surface water vapour density were estimated using 2005 -2016 monthly air temperature and relative humidity at 1° ×1° resolution obtained from Era interim and NCEP/NCAR database products. Obtained results from reanalysis were statistically tested using in situ data from Tropospheric Data Acquisition Network ...
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