The Iowa Watersheds Project: Iowa's prototype for engaging communities and professionals in watershed hazard mitigation

Weber, Larry; Muste, Marian; Bradley, A. Allen; Amado, Antonio Arenas; Demir, İbrahim; Drake, Chad W.; Krajewski, Witold F.; Loeser, Tony J.; Politano, Marcela; Shea, Breanna R.; Thomas, Nicholas W.

doi:10.1080/15715124.2017.1387127

Cited by 38 publications

(29 citation statements)

References 40 publications

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“…It has a narrow shape with a much longer time of concentration than has the Clear Creek Watershed. Both watersheds are vulnerable to flooding and part of the Iowa Watershed Approach project (Demir et al, 2015;Weber et al, 2018) for flood risk reduction. Available data for the case study includes runoff data from the U.S. Geological Survey (2016), 15-min Radar Stage IV precipitation data (Lin, 2011), and monthly ET data from the Iowa Flood Center (Krajewski et al, 2017).…”

Section: Case Studymentioning

confidence: 99%

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Xiang

Yan²,

Demir

2020

Water Resources Research

Self Cite

380

169

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Rainfall-runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short-term memory (LSTM) model and sequence-to-sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long-term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall-runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24-hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash-Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root-mean-square error. The results show that the LSTM-seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM-seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short-term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology. Key Points:• An hourly runoff model was developed using the LSTM sequence-to-sequence learning method for 24-hr predictions on USGS stations • The proposed model shows better performance than traditional data-driven models and is applicable to different watersheds • The advantages and limitations of seq2seq models and how this model structure could work on the rainfall-runoff modeling is presented Supporting Information:• Supporting Information S1 Correspondence to: Z. Xiang, zhongrun-xiang@uiowa.edu Citation: Xiang, Z., Yan, J., & Demir, I. (2020). A rainfall-runoff model with LSTM-based sequence-to-sequence learning. Water Resources Research, 56, e2019WR025326. https://doi.

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Section: Case Studymentioning

confidence: 99%

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Xiang

Yan²,

Demir

2020

Water Resources Research

Self Cite

380

169

View full text Add to dashboard Cite

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“…According to Wikipedia, "in scientific usage, cyberinfrastructure is a technological and sociological solution to the problem, which efficiently connects laboratories, data, computers, and people with the goal of enabling derivation of novel scientific theories and knowledge". Contemporary cyberinfrastructure has become increasingly available, and sufficiently mature, so as to facilitate the development of digital platforms for supporting both scientific investigations and the management of various problems at the watershed scale Weber et al, 2018). These platforms have the potential to transform our capabilities of understanding how to address ecosystem changes, protect the environment, and predict and prevent natural and human disasters through knowledge-based adaptive management.…”

Section: Overviewmentioning

confidence: 99%

Data-driven framework for forecasting sedimentation at culverts

Xu¹

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“…In response, Iowa took action with the formation of the Iowa Flood Center in 2009 and the passage of legislation in 2 2010 authorizing the creation of Watershed Management Authorities (WMAs). WMAs serve the purpose of helping governmental organizations and local stakeholders cooperate in creating watershed plans and management efforts to address flooding and water-quality concerns (Weber et al 2017). At the same time, Iowa received $8.8 million from the U.S. Department of Housing and Urban Development Disaster Recovery Enhancement Fund (DREF) grants for the Iowa Watersheds Project (IWP) with the main objective of reducing and preventing future flood impacts (Weber et al 2017).…”

Section: Public Abstractmentioning

confidence: 99%

“…The IWA is composed of two primary components flood resiliency and flood mitigation. Flood resiliency is the ability of a watershed community to collectively plan and take action to mitigate, prepare, respond and recover from a flood using the available resources (Weber et al 2017…”

Section: Iowa Watershed Approachmentioning

confidence: 99%

Identification of potential conservation practices and hydrologic modeling of the upper Iowa watershed

Rundhaug¹

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The Iowa Watersheds Project: Iowa's prototype for engaging communities and professionals in watershed hazard mitigation

Cited by 38 publications

References 40 publications

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Data-driven framework for forecasting sedimentation at culverts

Identification of potential conservation practices and hydrologic modeling of the upper Iowa watershed

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