Probabilistic seasonal streamflow forecasts of the Citarum River, Indonesia, based on general circulation models

Sahu, Netrananda; Robertson, Andrew W.; Boer, Rizaldi; Behera, Swadhin K.; DeWitt, David G.; Takara, Kaoru; Kumar, Manish; Singh, R. B.

doi:10.1007/s00477-016-1297-4

Cited by 25 publications

(18 citation statements)

References 56 publications

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“…Ultimately, the most informative forecasts of flood hazard at the seasonal scale could be seasonal streamflow forecasts using hydrological models calibrated for individual river basins (Sahu et al, 2016). While this is more computationally and resource intensive, investments in better forecasts of seasonal flood risk could be of immense use to the disaster preparedness community.…”

Section: Discussionmentioning

confidence: 99%

Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

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Section: Discussionmentioning

confidence: 99%

Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Hydrol. Earth Syst. Sci.

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“…For the statistical prediction of drought, predictors are generally obtained from historical observations (or reanalysis) that are already known prior to the prediction period. With advances in the weather and climate forecast, predictors may also be obtained from dynamical forecast for the prediction of hydroclimatic variables (Chowdhury & Sharma, 2009;Foster & Uvo, 2010;Lang & Wang, 2010;Marcos et al, 2017;Sahu et al, 2016;Schick et al, 2017;Stephenson et al, 2005). This is also related to the Model Output Statistics (MOS) of climate forecast, which will be introduced afterward in section 5.1.2.…”

Section: Selection Of Predictorsmentioning

confidence: 99%

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Hao

Singh

Xia

2018

Reviews of Geophysics

389

232

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Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

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“…In view of the above, a number of studies have attempted to investigate the trend of climatic variables for the country. These studies have looked at the trends on the country scale (Kumar et al, ; Athar, ; Fan and Chen, ; Szabó et al, ), regional scales (Bhutiyani et al, ; Elnesr et al, ; Karpouzos and Kavalieratau, ; Duhan and Pandey, ; Duan et al, ), and at the individual stations (Sahu et al, , , , ; Beyene, ). In fact, local and regional scale analysis (Fischer and Ceppi, ; Babar and Ramesh, ) is more relevant to devise‐specific development and adaptation plans to mitigate negative effects of climate change.…”

Section: Introductionmentioning

confidence: 99%

Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India

Panda

Sahu

2019

Atmospheric Science Letters

Self Cite

180

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Climate variability, particularly that of the annual air temperature and rainfall, has received a great deal of attention worldwide. The magnitude of the variability or fluctuations of the factors varies according to locations. Hence, examining the spatiotemporal dynamics of meteorological variables in the context of changing climate, particularly in countries where rainfed agriculture is predominant, is vital to assess climate-induced changes and suggest feasible adaptation strategies. To that end, the present study examines long-term changes and short-term fluctuations in monsoonal rainfall and temperature over Kalahandi, Bolangir and Koraput (hereafter KBK) districts in the state of Odisha. Both rainfall and temperature data for period of 1980-2017 were analyzed in this study. Statistical trend analysis techniques namely Mann-Kendall test and Sen's slope estimator were used to examine and analyze the problems. The detailed analysis of the data for 37 years indicate that the annual maximum temperature and annual minimum temperature have shown an increasing trend, whereas the monsoon's maximum and minimum temperatures have shown a decreasing trend. Statistically significant trends are detected for rainfall and also the result is statistically significant at 99% confidence limit during the period of 1980-2017. Rainfall is showing a quite good increasing trend (Sen's slope = 4.034) for JJAS season. In the case of maximum temperature for the observed period, it showed a slight warming or increasing trend (Sen's slope = 0.29) while the minimum temperature trend showed a cooling trend (Sen's slope = −0.006) but result of maximum temperature trend analysis is statistically significant at 95% confidence limit, on the contrary, the trend analysis result of minimum temperature is not statistically significant.

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Probabilistic seasonal streamflow forecasts of the Citarum River, Indonesia, based on general circulation models

Cited by 25 publications

References 56 publications

Should seasonal rainfall forecasts be used for flood preparedness?

Should seasonal rainfall forecasts be used for flood preparedness?

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India

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