A history of flooding in the Red River Basin

Ryberg, Karen R.; Macek-Rowland, Kathleen M.; Banse, Tara A.; Wiche, Gregg J.

doi:10.3133/gip55

Cited by 4 publications

(3 citation statements)

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“…The northward flowing Red River in North Dakota is naturally prone to seasonal flooding primarily due to its low gradient. In the last two centuries, more frequent major floods, where annual peak discharges were equal or great than 1,246 m 3 /s, have been recorded (or estimated for pre‐1870 events) at Grand Forks, such as floods in 1826, 1852, 1861, 1882, 1897, 1950, 1965, 1966, 1969, 1978, 1979, 1996, 1997, 1998, 1999, 2001, 2006, 2009, 2010, 2011, and 2013 (Thorleifson et al, 1998; George and Nielsen, 2003; Ryberg et al, 2007; Appendix 3). To better understand the drivers responsible for the increasing floods and the spatial scale of the flood‐related variable changes, the northward flowing Little Missouri River located in the western part of North Dakota was included in a pair‐basin approach (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Impacts of climatic variability on northward flowing rivers in North America, using a paired basin approach

Schenk

2020

River Research & Apps

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Northward flowing rivers are the most vulnerable system to a general early warming trend in terms of flood risk. In a changing climate, how well we understand the variability of precipitation and streamflow and the correspondence between them determines the appropriateness and efficiency of river engineering activities, flood control structures, and water resource management policies. Using both time and frequency domain approaches, this study investigated variations and periodicities in precipitation and discharge of two neighboring northward flowing river basins, the Red River of the North (referred to hereafter as the Red River) basin and the Little Missouri River basin in North America. Additionally, this study also characterized whether the most dominant quasiperiodic climate variation El Niño Southern Oscillation (ENSO) affected regional precipitation and streamflow. Results indicated that the southern and central Red River basin experienced significant increases in precipitation and discharge, particularly in cold season, while the Little Missouri River basin had no statistically significant change in precipitation or discharge. The global atmospheric oscillation ENSO had little effects on the regional precipitation and streamflow increases in the Red River basin. Furthermore, strong spectral coherences and prominent annual/semi‐annual periodicities in precipitation and discharge were revealed, confirming how precipitation determines frequency peaks and primary oscillation cycles of discharges in both basins. By removing broad‐scale climatic driver, this study indicated that some local forcing is most likely responsible for the excessive water abundances in the Red River basin, among which agriculture land usages stand out to be the most promising driver.

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Section: Methodsmentioning

confidence: 99%

Impacts of climatic variability on northward flowing rivers in North America, using a paired basin approach

Schenk

2020

River Research & Apps

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“…Due to a long and severe winter for snow accumulation, warmer temperatures in the spring, and flat topography with weak permeability soil, the mid-latitude regions of North America are highly vulnerable to spring-melt floods [2][3][4]. Spring-melt floods are frequent in the Red River as it heads north [5,6]. During the spring thaw, the southern part of the Red River basin melts first, and the river becomes hydrologically active; meanwhile, the northern part of the basin is often frozen.…”

Section: History Of Flood In Red River Of the Northmentioning

confidence: 99%

“…During the spring thaw, the southern part of the Red River basin melts first, and the river becomes hydrologically active; meanwhile, the northern part of the basin is often frozen. Along with the flat and homogenous topography, the river activity forms a slow, meandering river, which causes an overflow in the Red River of the North on the northern side, resulting in floods [5,7]. Surface runoff from snowmelt during significant floods leads the Red River to overflow its shallow banks, flooding the whole valley and causing immense damage.…”

Section: History Of Flood In Red River Of the Northmentioning

confidence: 99%

Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North

Atashi

Gorji

Shahabi

et al. 2022

Water

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The Red River of the North is vulnerable to floods, which have caused significant damage and economic loss to inhabitants. A better capability in flood-event prediction is essential to decision-makers for planning flood-loss-reduction strategies. Over the last decades, classical statistical methods and Machine Learning (ML) algorithms have greatly contributed to the growth of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. To make improvements to flood prediction for the Red River of the North, this paper presents effective approaches that make use of a classical statistical method, a classical ML algorithm, and a state-of-the-art Deep Learning method. Respectively, the methods are seasonal autoregressive integrated moving average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). We used hourly level records from three U.S. Geological Survey (USGS), at Pembina, Drayton, and Grand Forks stations with twelve years of data (2007–2019), to evaluate the water level at six hours, twelve hours, one day, three days, and one week in advance. Pembina, at the downstream location, has a water level gauge but not a flow-gauging station, unlike the others. The floodwater-level-prediction results show that the LSTM method outperforms the SARIMA and RF methods. For the one-week-ahead prediction, the RMSE values for Pembina, Drayton, and Grand Forks are 0.190, 0.151, and 0.107, respectively. These results demonstrate the high precision of the Deep Learning algorithm as a reliable choice for flood-water-level prediction.

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Development of a distributed nonlinear Muskingum model by considering snowmelt effects for flood routing in the Red River

Atashi,

Barati,

Lim

2023

Sci Rep

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This research paper presents the development of a nonlinear Muskingum model which achieves precise flood routing through river reaches while considering lateral inflow conditions. Fourteen pairs of flood hydrograph found at two specific United States Geological Survey (USGS) stations located along the Red River of the North, namely Grand Forks and Drayton, are used for the calibrations and validations of the Muskingum model. To enhance the accuracy of the procedure, a reach is divided into multiple sub-reaches, and the Muskingum model calculations are performed individually for each interval using the distributed Muskingum method. Notably, the model development process incorporates the use of the Salp Swarm algorithm. The obtained results demonstrate the effectiveness of the developed nonlinear Muskingum model in accurately routing floods through the very gentle river with a bed slope of (0.0002–0.0003). The events were categorized into three groups based on their dominant drivers: Group A (Snowmelt-driven floods), Group B (Rain-on-snow-induced floods), and Group C (Mixed floods influenced by both snowmelt and rainfall). For the sub-reaches in Group A, single sub-reach (NR = 1), the Performance Evaluation Criteria (PEC) yielded the highest value for SSE, amounting to 404.9 × 106. In Group B, when NR = 2, PEC results the highest value were SSE = 730.2 × 106. The number of sub-reaches in a model has a significant influence on parameter estimates and model performance, as demonstrated by the analysis of hydrologic parameters and performance evaluation criteria. Optimal performance varied across case studies, emphasizing the importance of selecting the appropriate number of sub-reaches for peak discharge predictions.

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A history of flooding in the Red River Basin

Cited by 4 publications

References 0 publications

Impacts of climatic variability on northward flowing rivers in North America, using a paired basin approach

Impacts of climatic variability on northward flowing rivers in North America, using a paired basin approach

Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North

Development of a distributed nonlinear Muskingum model by considering snowmelt effects for flood routing in the Red River

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