Reconciling the water balance of large lake systems

Gronewold, Andrew D.; Smith, Joeseph P.; Read, Laura; Crooks, James

doi:10.1016/j.advwatres.2020.103505

Cited by 17 publications

(20 citation statements)

References 70 publications

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“…The following section will describe our approach to parameterizing the Great Lakes water balance using the L2SWBM. It is informative to note that the following sections share some similarity to the recent publication on the L2SWBM 48 . However, we also included more details on specific modifications (e.g., data used to derive the L2SWBM parameters) in our application to derive a seventy-year long record for the Great Lakes water balance.…”

Section: The Large Lakes Statistical Water Balance Model (L2swbm) Thmentioning

confidence: 84%

“…The sign of D depends on whether water is diverted to (positive values) or from (negative values) a specific lake. In addition, this study used a rolling window of w = 12, which generally leads to better results regarding water balance closure 48,49 .…”

Section: The Large Lakes Statistical Water Balance Model (L2swbm) Thmentioning

confidence: 99%

“…3. This modification allowed a broader range of feasible values to account for a potential shift of evaporation in a warming climate 48 .…”

Section: The Large Lakes Statistical Water Balance Model (L2swbm) Thmentioning

confidence: 99%

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Seventy-year long record of monthly water balance estimates for Earth’s largest lake system

et al. 2020

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We develop new estimates of monthly water balance components from 1950 to 2019 for the Laurentian Great Lakes, the largest surface freshwater system on Earth. For each of the Great Lakes, lake storage changes and water balance components were estimated using the Large Lakes Statistical Water Balance Model (L2SWBM). Multiple independent data sources, contributed by a binational community of research scientists and practitioners, were assimilated into the L2SWBM to infer feasible values of water balance components through a Bayesian framework. A conventional water balance model was used to constrain the new estimates, ensuring that the water balance can be reconciled over multiple time periods. The new estimates are useful for investigating changes in water availability, or benchmarking new hydrological models and data products developed for the Laurentian Great Lakes Region. The source code and inputs of the L2SWBM model are also made available, and can be adapted to include new data sources for the Great Lakes, or to address water balance problems on other large lake systems.

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Section: The Large Lakes Statistical Water Balance Model (L2swbm) Thmentioning

confidence: 84%

Section: The Large Lakes Statistical Water Balance Model (L2swbm) Thmentioning

confidence: 99%

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Seventy-year long record of monthly water balance estimates for Earth’s largest lake system

et al. 2020

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“…Models are frequently used as the basis for prediction, but development of land surface models (Noilhan and Planton, 1989;Krinner et al, 2005;Balsamo et al, 2009) and river routing models intended for large scale applications (Vörösmarty et al, 1989;Hunger and Döll, 2008) have been generally focused on overland flow, groundwater representation and river routing with less attention on lateral fluxes (Davison et al, 2016). Among these, there was a lack of consideration of lake water mass dynamics (Gronewold et al, 2020) because of both the coarse resolution of global models and the associated increased computational costs. Global Climate Models (GCMs) usually consider lake energy budget without giving much importance on the river-lake connectivity, even if key regions in climate studies such as Scandinavia and Northern America are mainly dependent on this.…”

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confidence: 99%

“…General Hydrological Models (GHMs) usually represent lakes as large rivers with modified characteristics in order to retrieve the correct downstream river discharge. To address a comprehensive outcomes resulting from long-term water cycle evolution, GHMs need to characterise every key component interacting with each other (Gronewold et al, 2020). In the recent years, many studies have focused on anthropogenic open waters (Hanasaki et al, 2006;Haddeland et al, 2006;Gao et al, 2012) with less attention devoted to the understanding of natural lake global influence on the global water cycle.…”

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confidence: 99%

Parametrization of lakes water dynamics in the ISBA-CTRIP land surface system (SURFEX v8.1)

Guinaldo¹,

Munier²,

Moigne³

et al. 2020

Preprint

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Abstract. Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services such as freshwater supply. They also modify the local hydro-meteorological continuum as a lower boundary of the atmosphere. Sentinels of climate change and anthropization, these open water bodies are facing disruptions of their equilibrium generally leading to a notable reduction of their levels worldwide. Stream-flow variability and temporal evolution are impacted by the presence of lakes in the river network, therefore any change in the lake state can induce a modification of the regional hydrological regime. Despite the importance of the impact of lakes on hydrological fluxes and the water balance, a representation of the mass budget is generally not included in climate models and global scale hydrological modeling platforms. The goal of this study is to introduce a new lake mass module, MLake (Mass-Lake model), into the river routing model CTRIP to resolve the specific mass-balance of open water bodies. Based on the inherent CTRIP parameters, the development of the non-calibrated MLake model was introduced to examine the influence of such hydrological buffer areas on the global scale river routing performances. In the current study, an off-line evaluation was performed for four river networks using a set of state-of-the-art quality atmospheric forcings and a combination of in situ and satellite measurements for river discharge and lake level observations. The results reveal a general improvement in CTRIP simulated discharge and its variability, while also generating realistic lake level variations. MLake produces more realistic streamflows both in terms of daily and seasonal correlation. Excluding the specific case of Lake Victoria having low performances, the mean skill score of Kling-Gupta Efficiency (KGE) is 0.41 while the Normalized Information Contribution (NIC) shows a mean improvement of 0.56 (ranging from 0.15 to 0.94). Streamflow results are spatially scale-dependent, with better scores associated with larger lakes, and increased sensitivity to the width of the lake outlet. Regarding lake levels variations, results indicate a good agreement between observations and simulations with a mean correlation of 0.56 (ranging from 0.07 to 0.92) which is linked to the capability of the model to retrieve seasonal variations. Discrepancies in the results are mainly explained by the anthropization of the selected lakes which introduces high-frequency variations in both streamflows and lake levels that degraded the scores. Anthropization effects are prevalent in most of the lakes studied, but they are predominant for Lake Victoria and are the main cause for relatively low statistical scores for this river. However, results on the Angara and the Neva rivers also depend on the inherent gap of ISBA-CTRIP processes representation which relies on further development such as the partitioned energy budget between the snow and the canopy over a Boreal zone. The study is a first step towards a global coupled land system that will help to qualitatively assess the evolution of future global water resources leading to improvements in flood risk and drought forecasting.

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Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

Khayatnezhad

Youssefi

2022

Concurrency and Computation

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This article investigates the negative effect of land use and climate changes on water resources by the SWAT and SWAT-DEEP\LMSFO model. Due to the importance of runoff impact on water resources in this article, the hybrid hydrological-deep neural networks optimized by the improved SFO based on logistic map (LMSFO) algorithm have been used to provide more accurate results for runoff estimation. This method improves runoff simulation. Firstly, runoff under the influence of land use and climate change is estimated by the SWAT model. Once again, runoff is estimated by the SWAT-DEEP/LMSFO model. In the DEEP/LMSFO model, the primary runoff has been estimated by an un-calibrated SWAT model. Then the Primary runoff simulated is entered as an input into the DEEP model. Finally, the runoff is estimated by a test-training method. The results of the SWAT model and the DEEP/LMSFO model show that there is an inverse correlation between land use so that reducing land cover increases runoff. The results show that climate change and land-use change can affect annual runoff changes in the coming years.

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Reconciling the water balance of large lake systems

Cited by 17 publications

References 70 publications

Seventy-year long record of monthly water balance estimates for Earth’s largest lake system

Seventy-year long record of monthly water balance estimates for Earth’s largest lake system

Parametrization of lakes water dynamics in the ISBA-CTRIP land surface system (SURFEX v8.1)

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

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