Assessment of Irrigation Performance in Large River Basins Under Data Scarce Environment - A Case of Kabul River Basin, Afghanistan

• DOI: 10.20944/preprints201804.0133.v1
• Publication Date: 2018-04-13T06:55:39Z
• Authors: Fazlullah Akhtar; Usman Khalid Awan; Bernhard Tischbein; Umar Waqas Liaqat
• Keywords: 2. Zero hunger; surface energy balance; Science; Q; 0208 environmental biotechnology; data scarcity; actual evapotranspiration; surface energy balance; irrigation; performance evaluation; remote sensing; 0207 environmental engineering; 02 engineering and technology; atmospheric_science; irrigation; 6. Clean water; performance evaluation; remote sensing; data scarcity; 13. Climate action; actual evapotranspiration

The Kabul River basin (KRB) of Afghanistan, a lifeline around 10 million people, has multiplicity governance, management and development related challenges leading to inequity, inadequacy unreliability irrigation water distribution. Prior any uplifting intervention, there is need evaluate the performance system on long term basis identify existing bottlenecks. Although are several indicators used for evaluation schemes, but we coefficient variation (CV) actual evapotranspiration (ETa) in space (basin, sub-basin, provincial level), relative (RET) temporal CV RET assess equity, adequacy reliability distribution respectively, from 2003 2013. ETa was estimated through surface energy balance (SEBS) algorithm estimates were validated using advection aridity (AA) method with R2 value 0.81 0.77 at Nawabad Sultanpur stations respectively. global land data assimilation (GLDAS) moderate-resolution imaging spectroradiometer (MODIS) products as main inputs SEBS. Results show that mean seasonal sub-based values during summer (May – September) (0.37 ± 0.06) winter (October April) (0.40 0.08) below target (RET ≥0.75) 2003-2013. within sub-basins provinces entire study period equitable October-January (0.09±0.04) whereas highest inequity (0.24±0.08) early summer. range (0.04-0.06) monthly shows supplies or sub-basins. analysis highlights unreliable across basin. maximum Shamal sub-basin (552±43mm) while among Kunar experienced (544±39mm). This dire interventions improve time space. proposed methodology can be framework monitoring implementing plans future.