Your search found 3 records
1 Ahmadisharaf, E.; Kalyanapu, A. J.; Chung, E.-S.. 2016. Spatial probabilistic multi-criteria decision making for assessment of flood management alternatives. Journal of Hydrology, 533:365-378. [doi: https://doi.org/10.1016/j.jhydrol.2015.12.031]
Flood control ; Decision making ; Hydrology ; Hydraulics ; Probabilistic models ; Risk analysis ; Uncertainty ; Water resources ; Watersheds ; Rain ; Maps ; Geographical information systems / USA / Buncombe / Swannanoa River Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H047554)
(3.23 MB)
Flood management alternatives are often evaluated on the basis of flood parameters such as depth and velocity. As these parameters are uncertain, so is the evaluation of the alternatives. It is thus important to incorporate the uncertainty of flood parameters into the decision making frameworks. This research develops a spatial probabilistic multi-criteria decision making (SPMCDM) framework to demonstrate the impact of the design rainfall uncertainty on evaluation of flood management alternatives. The framework employs a probabilistic rainfall–runoff transformation model, a two-dimensional flood model and a spatial MCDM technique. Thereby, the uncertainty of decision making can be determined alongside the best alternative. A probability-based map is produced to show the discrete probability distribution function (PDF) of selecting each competing alternative. Overall the best at each grid cell is the alternative with the mode parameter of this PDF. This framework is demonstrated on the Swannanoa River watershed in North Carolina, USA and its results are compared to those of deterministic approach. While the deterministic framework fails to provide the uncertainty of selecting an alternative, the SPMCDM framework showed that in overall, selection of flood management alternatives in the watershed is ‘‘moderately uncertain”. Moreover, three comparison metrics, F fit measure, j statistic, and Spearman rank correlation coefficient (q), are computed to compare the results of these two approaches. An F fit measure of 62.6%, j statistic of 15.4–45.0%, and spatial mean q value of 0.48, imply a significant difference in decision making by incorporating the design rainfall uncertainty through the presented SPMCDM framework. The SPMCDM framework can help decision makers to understand the uncertainty in selection of flood management alternatives.
2 Mohsenipour, M.; Shahid, S.; Chung, E.-s.; Wang, X.-j. 2018. Changing pattern of droughts during cropping seasons of Bangladesh. Water Resources Management, 32(5):1555-1568. [doi: https://doi.org/10.1007/s11269-017-1890-4]
Drought ; Seasonal variation ; Crops ; Growth period ; Climate change ; Precipitation ; Rain ; Evapotranspiration ; Temperature ; Estimation ; Spatial distribution / Bangladesh
(Location: IWMI HQ Call no: e-copy only Record No: H048511)
(1.45 MB)
There has been a growing concern on temporal variations on drought characteristics due to climate change. This study compares meteorological drought characteristics for two different periods to quantify the temporal changes in seasonal droughts of 18 weather stations of the country. Fifty-five years rainfall and temperature data are divided into two different thirty-year periods, 1961–1990 and 1985–2014 and standardized precipitation evapotranspiration index (SPEI) for those periods are calculated to assess the changes. Four seasons in this study are selected as two major crop growing seasons namely, Rabi (November to April) and Kharif (May to October) and two critical periods for crop growth in term of water supply namely critical Rabi (March–April) and critical Kharif (May). Results show that moderate, extreme, and severe Rabi droughts has increased in 11, 9, and 4 stations out of 18 stations, respectively, and Kharif severe and extreme droughts has increased in 8 and 9 stations, respectively, In addition, the frequency analysis shows that the return periods have decreased during 1985–2014 at the stations where it was high during 1961–1990 and vice versa. This has made the spatial distribution of return periods of droughts more uniform over the country for most of the seasons. Increased return period of droughts in highly drought prone north and northwest Bangladesh has caused decrease in average frequency of droughts. Consequently, this result corresponds that Bangladesh experiences fewer droughts in recent years. Trend analysis of rainfall and temperature data reveals that significant increase of mean temperature and no significant change in rainfall in almost all months have increased the frequency of droughts in the regions where droughts were less frequent.
3 Ahmed, K.; Shahid, S.; Chung, E.-S.; Wang, X.-j.; Harun, S. B. 2019. Climate change uncertainties in seasonal drought severity-area-frequency curves: case of arid region of Pakistan. Journal of Hydrology, 570:473-485. [doi: https://doi.org/10.1016/j.jhydrol.2019.01.019]
Climate change ; Uncertainty ; Drought ; Precipitation ; Forecasting ; Arid zones ; Climatic seasons ; Agriculture ; Models ; Performance evaluation / Pakistan / Balochistan
(Location: IWMI HQ Call no: e-copy only Record No: H049138)
(1.96 MB)
The uncertainty assessment of the changes in drought characteristics due to climate change has caught the attention of the scientific community. This study used gauge-based gridded precipitation data obtained from Global Precipitation Climatology Centre (GPCC) to reconstruct historical droughts and downscale future precipitation projected by seven general circulation models (GCMs) of Coupled Model Inter-comparison Project phase 5 (CMIP5) under four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Support vector machine (SVM) and quantile mapping were used for downscaling and GCM bias correction, respectively. The model performances were assessed based on statistical measures. The historical and future projected precipitation data were finally used to characterize the seasonal droughts using Standardized Precipitation Index (SPI) for different crop growing periods. The drought severity-area-frequency (SAF) curves for the historical (1961–2010) and three future periods (2010–2039, 2040–2069, and 2070–2099) were developed. The uncertainty band of future drought SAF curves was estimated using Bayesian bootstrap (BB) at a 95% confidence level. As a result, SVM was successful in downscaling the precipitation of all selected CMIP5 GCMs. The seasonal ensemble of GCMs projected an increase in precipitation ranging from 8% to 41% under all scenarios. The historical SAF curves revealed that for equal drought severity, larger areas are affected by droughts having higher return periods. Future projections of droughts revealed the increase in affected area for lower severity and return period droughts and the decrease for higher severity and return period droughts. The uncertainty bands of drought SAF curves with higher return periods were found much wider compared to those with lower return periods which indicates more uncertainty in the projection of higher severity and return period droughts.
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