Reconstructed monthly river flows for Irish catchments 1766–2016

O’Connor, Paul; Murphy, Conor; Matthews, Tom; Wilby, Robert L.

doi:10.1002/gdj3.107

Cited by 14 publications

(19 citation statements)

References 45 publications

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“…Rintis and Setyoasri 25 found that the GR2M model gave a comparable performance to Mock and NRECA methods. The GR2M model and Artificial Neural Network were utilized to reconstruct monthly river flow for Irish catchments 26 . The regionalized GR2M model’s parameters were developed to predict monthly runoff in the ungauged basins for northern Algeria 27 .…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

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Section: Introductionmentioning

confidence: 99%

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Ditthakit

Pinthong

Salaeh

et al. 2021

Sci Rep

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“…O'Connor et al . (2021) highlight key assumptions regarding the reconstruction of river flows including land‐use change, changes in channel geometry (Slater et al ., 2019), and reductions in precipitation and temperature station density in early records (Ryan et al ., 2021), among others. Moreover, Murphy et al .…”

Section: Discussionmentioning

confidence: 99%

“…We evaluate historical droughts for 51 catchments across Ireland, employing reconstructed monthly precipitation and discharge estimates for each catchment for the period 1767–2016, derived by O'Connor et al . (2021). The choice of end year was determined by the availability of concurrent hydrological and meteorological data.…”

Section: Methodsmentioning

confidence: 99%

Historical droughts in Irish catchments 1767–2016

O’Connor

Murphy

Matthews

et al. 2022

Intl Journal of Climatology

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Recent prolonged dry periods in summer 2018 and spring 2020 have reawakened interest in drought in Ireland, prompting questions regarding historical drought occurrence and potential long‐term risks. Employing 250 years of monthly precipitation and flow reconstructions, we investigate historical drought in Irish catchments evaluating the characteristics (number of events, duration, and deficits) of moderate, severe, and extreme droughts as well as the propagation of meteorological to hydrological drought. Using standardized indices, we identify three distinct catchment types. Cluster 1 catchments, located in the wetter northwest are characterized by small areas, low groundwater storage, and the highest frequency of hydrological drought relative to other catchments. Cluster 3 catchments, located in the drier east and southeast have larger areas, greater groundwater storage, the highest frequency of meteorological drought but the least hydrological droughts. However, once established, droughts in Cluster 3 tend to be more persistent with large accumulated deficits. Cluster 2 catchments, located in the southwest and west, are intermediate to Clusters 1 and 3, with hydrological droughts typically of shorter durations, reduced accumulated deficits but greater mean deficits. The most extreme droughts based on accumulated deficits across all catchments occurred in 1803–1806, 1854–1859, 1933–1935, 1944–1945, 1953–1954, and 1975–1977. Although not as severe, droughts in 1887–1888, 1891–1894, and 1971–1974 also appear as significant extremes. Changes in drought characteristics reveal a complex picture with the direction, magnitude, and significance of trends dependent on the accumulation period used to define drought, the period of record analysed, and the reference period used to standardize indices. Of particular note is a tendency towards shorter, more intense meteorological and hydrological droughts. Our findings offer important insight for drought and water management in Ireland given the paucity of extreme droughts in short observed river flow records.

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“…The challenges posed by the lack of available long-term observations have prompted some to leverage advances in data rescue and historical climatology to extend discharge series back in time (e.g. O'Connor et al, 2020;Smith et al, 2017;Bonnet et al, 2020). Palaeo-hydroclimatic reconstructions are also employed to extend data back in time and provide greater insight into current conditions.…”

Section: Record Length and Completenessmentioning

confidence: 99%

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Slater

Anderson

Buechel

et al. 2021

Hydrol. Earth Syst. Sci.

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154

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Abstract. Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or storms have devastating effects each year. One of the key challenges for society is understanding how these extremes are evolving and likely to unfold beyond their historical distributions under the influence of multiple drivers such as changes in climate, land cover, and other human factors. Methods for analysing hydroclimatic extremes have advanced considerably in recent decades. Here we provide a review of the drivers, metrics, and methods for the detection, attribution, management, and projection of nonstationary hydroclimatic extremes. We discuss issues and uncertainty associated with these approaches (e.g. arising from insufficient record length, spurious nonstationarities, or incomplete representation of nonstationary sources in modelling frameworks), examine empirical and simulation-based frameworks for analysis of nonstationary extremes, and identify gaps for future research.

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Reconstructed monthly river flows for Irish catchments 1766–2016

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 14 publications

References 45 publications

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

Historical droughts in Irish catchments 1767–2016

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

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