Six Centuries of Upper Indus Basin Streamflow Variability and Its Climatic Drivers

Rao, Mukund Palat; Cook, Edward R.; Cook, Benjamin I.; Palmer, Jonathan; Uriarte, María; Devineni, Naresh; Lall, Upmanu; D’Arrigo, Rosanne; Woodhouse, Connie A.; Ahmed, Moinuddin; Zafar, Muhammad Usama; Khan, Nasrullah; Khan, Ali Haider; Wahab, Muhammad

doi:10.1029/2018wr023080

Cited by 47 publications

(35 citation statements)

References 75 publications

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“…Ho et al (2016), a study that also reconstructed streamflow in the Missouri River Basin, found that vast majority of the RE values in cross-validation mode were significantly positive. Rao et al (2018) found similar results in their reconstructions for the Indus River Basin. Hence, our findings indicate that our model is at least as successful in cross-validation mode as other major studies in streamflow reconstruction.…”

Section: Water Resources Researchsupporting

confidence: 66%

“…We conclude that the BSM model improves upon the PCR model in cross validation with the CE statistic. Cook et al (), Devineni et al (), Ho et al () and Rao et al () all found results that were similar in their reconstructions; in particular, it was a bit more difficult to achieve positive values with the CE statistic than with the RE statistic, and hence a slightly greater number of negative values were encountered in the validation with this statistic.…”

Section: Resultsmentioning

confidence: 77%

“…Devineni et al () and Rao et al () reconstructed flows at multiple streamflow sites or reservoirs within a basin simultaneously, with explicitly modeled spatial dependence of the flows, to produce reconstructions whose outputs were probabilistic in nature; that is, posterior distributions as opposed to conditional means, giving explicit uncertainty estimates in the reconstructions. The hierarchical Bayesian regression used partial pooling to reduce the uncertainty in the reconstruction at each site and across sites.…”

Section: Previous Streamflow Reconstruction Efforts and The State Of mentioning

confidence: 99%

“…Streamflow reconstructions have traditionally been performed using multiple linear regression models, or modifications thereof, and the predictors used in these regression models are typically tree ring chronologies or their principal components. The regression models used in these studies include stepwise multiple linear regression (Barnett et al, 2010;Earle, 1993;Watson et al, 2009;Woodhouse, 2001;Woodhouse & Lukas, 2006); standard multiple linear regression or canonical regression, often in the form of principal components regression (PCR) (Barnett et al, 2010;Cook & Jacoby, 1983;Cook et al, 2013;Day & Sandifer, 2015;Gedalof et al, 2004;Ma et al, 2014;Maxwell et al, 2011;Meko & Graybill, 1995;Meko et al, 2001;Stockton & Jacoby, 1976;Timilsena et al, 2007;Watson et al, 2009); or hierarchical Bayesian regression (Bracken et al, 2016;Devineni et al, 2013;Rao et al, 2018). With the exception of Devineni et al (2013), Rao et al (2018), and Bracken et al (2016), the reconstructions in these studies are done using variations of ordinary least squares (OLS) regression for reconstructing flow at each single-site individually (point-by-point regression) rather than multiple sites simultaneously and accounting for spatial correlation across the sites by explicitly estimating the inherent correlation structure either physically or statistically.…”

Section: Previous Streamflow Reconstruction Efforts and The State Of mentioning

confidence: 99%

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Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

Ravindranath

Devineni

Lall

et al. 2019

Water Resources Research

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A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree ring chronologies for paleo‐streamflow reconstruction is presented. In any river basin, a convergent, dendritic network of tributaries come together to form the main stem of a river. Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin‐scale streamflow reconstruction model that uses the information in streamflow and tree ring chronology data to inform the reconstructed flows, while maintaining the space‐time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo‐streamflow at all gauges in the basin progressing upstream to downstream along the river. Our application to 18 streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted R2 for the basin is approximately 0.5 with good overall cross‐validated skill as measured by five different skill metrics. The spatial network structure produced a substantial reduction in the uncertainty associated with paleo‐streamflow as one proceeds downstream in the network aggregating information from upstream gauges and tree ring chronologies. Uncertainty was reduced by more than 50% at six gauges, between 6% and 50% at one gauge, and by less than 5% at the remaining 11 gauges when compared with the traditional principal component regression reconstruction model.

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Section: Water Resources Researchsupporting

confidence: 66%

Section: Resultsmentioning

confidence: 77%

Section: Previous Streamflow Reconstruction Efforts and The State Of mentioning

confidence: 99%

Section: Previous Streamflow Reconstruction Efforts and The State Of mentioning

confidence: 99%

See 2 more Smart Citations

Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

Ravindranath

Devineni

Lall

et al. 2019

Water Resources Research

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“…Gallant and Gergis, 2011;Ho et al, 2014;Allen et al, 2015aAllen et al, , 2015bHo et al, 2015aHo et al, , 2015bVance et al, 2015;Tozer et al, 2016Tozer et al, , 2018Dixon et al, 2017Dixon et al, , 2019Kiem et al, 2020);  Asia (e.g. Davi et al, 2013;Feng et al, 2013;Pederson et al, 2014;Nguyen and Galelli, 2018;Rao, 2018;Wang et al, 2019);…”

Section: Introductionmentioning

confidence: 99%

Comparison of published palaeoclimate records suitable for reconstructing annual to sub-decadal hydroclimatic variability in eastern Australia: implications for water resource management and planning

Flack¹,

Kiem²,

Vance³

et al. 2020

Preprint

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Abstract. Knowledge of past, current and future hydroclimatic risk is of great importance. However, like many other countries, Australia's observed hydroclimate records are at best only ~ 120 years long (i.e. from ~ 1900 to present) but are typically less than ~ 50 years long. Therefore, recent research has focused on developing longer hydroclimate records based on palaeoclimate information from a variety of different sources. Here we review and compare the insights emerging from 11 published palaeoclimate records that are relevant to annual to sub-decadal hydroclimatic variability in eastern Australia over the last ~ 1000 years. The sources of palaeoclimate information include ice cores, tree rings, cave deposits and lake sediment deposits. The published palaeoclimate information was then analysed to determine when (and where) there was agreement (or uncertainty) about the timing of wet and dry epochs in the pre-instrumental period (1000–1899). The occurrence, frequency, duration and spatial extent of pre-instrumental wet and dry epochs was then compared to wet and dry epochs since 1900. The results show that instrumental records (~ 1900–present) underestimate (or at least misrepresent) the full range of rainfall variability that has occurred, and is possible, in eastern Australia. Even more disturbing is the suggestion, based on insights from the published palaeoclimate data analysed, that 71 % of the pre-instrumental period appears to have no equivalent in the instrumental period. This implies that the majority of the past 1000 years was unlike anything encountered in the period that informs water infrastructure, planning and policy in Australia. A case study, using a typical water storage reservoir in eastern Australia, demonstrates that current water resource infrastructure and management strategies would not cope under the range of pre-instrumental conditions that this study suggests has occurred. When coupled with projected impacts of climate change and growing demands, these results highlight some major challenges for water resource management and infrastructure. Though our case study location is eastern Australia, these challenges, and the limitations associated with current methods that depend on instrumental records that are too short to realistically characterise interannual to multidecadal variability, also apply globally.

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Coherent streamflow variability in Monsoon Asia over the past eight centuries---links to oceanic drivers

Nguyen

Turner

Buckley³

et al. 2020

Preprint

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Six Centuries of Upper Indus Basin Streamflow Variability and Its Climatic Drivers

Cited by 47 publications

References 75 publications

Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

Comparison of published palaeoclimate records suitable for reconstructing annual to sub-decadal hydroclimatic variability in eastern Australia: implications for water resource management and planning

Coherent streamflow variability in Monsoon Asia over the past eight centuries---links to oceanic drivers

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