Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite‐Based Products and Streamflow Observations

Toure, Ally M.; Luojus, Kari; Rodell, Matthew; Beaudoing, H. K.; Getirana, Augusto

doi:10.1029/2018ms001389

Cited by 20 publications

(20 citation statements)

References 90 publications

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“…CLSM simulates the vertical water and energy transfers between the atmosphere, vegetation and soil, and HyMAP the surface water dynamics, including rivers and floodplains. CLSM has been widely used in the GRACE-DA context (e.g., Girotto et al, 2016;Girotto et al, 2017;Kumar et al, 2016;Li et al, 2012;Zaitchik et al, 2008), and HyMAP has been successfully used to simulate surface water dynamics in numerous applications worldwide (e.g., Getirana & Peters-Lidard, 2013;Getirana, Kumar et al, 2017;McNally et al, 2019) and to quantify the impacts of land surface parameterization and DA on streamflow (e.g., Getirana, Dutra, et al, 2014;Jung et al, 2017;Kumar et al, 2015;Toure et al, 2018). In this study, HyMAP and CLSM are one-way coupled.…”

Section: Modeling Framework and Evaluationmentioning

confidence: 99%

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

Getirana

Jung

Arsenault

et al. 2020

Water Resources Research

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West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite‐based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6‐month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE‐DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE‐DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water‐related disaster warning systems for Africa.

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Section: Modeling Framework and Evaluationmentioning

confidence: 99%

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

Getirana

Jung

Arsenault

et al. 2020

Water Resources Research

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“…Despite the enormous effort associated with data collection, the ability to capture representative snow depth patterns that accurately reflect grid‐cell averages and subgrid variability in forested environments has suffered from the poor extent and limited support of these measurements (Bloeschl, ; Pomeroy et al, ; Winkler & Moore, ). In contrast, validation efforts that use satellite products to evaluate large‐scale land surface models have strong limitations in forested environments (Toure et al, ). Consequently, identifying grid‐cell‐scale canopy structure metrics that can act as predictors of snow depth averages and variability remains a major challenge (Friesen et al, ).…”

Section: Introductionmentioning

confidence: 99%

Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data

Mazzotti

Currier

Deems

et al. 2019

Water Resources Research

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The retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.

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“…Poor performance in basins such as Pechora, Lena, and Yenisey basins with NSE less than 0.3 are mainly caused by remarkable underestimation of streamflow, which is consistent with (Li et al, ) that simulation using the QIAN forcing generally has lower runoff and streamflow compared to simulations using other forcing data sets (e.g., GPCP, GPCC, and HYBAM) due to the larger bias in downward shortwave radiation in the QIAN forcing data set. The poor performance may also be partly related to human activities, which are not represented in the model, and poor representation of snowmelt timing by the model (Toure et al, ). Despite the lower runoff and streamflow magnitude, the simulated seasonal cycle is quite consistent with observations, so the simulation provides useful water balance information for analysis of flood generation mechanisms.…”

Section: Resultsmentioning

confidence: 99%

Flood Inundation Generation Mechanisms and Their Changes in 1953–2004 in Global Major River Basins

et al. 2019

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Despite the serious threats posed by floods, the driving mechanisms of floods are still not well understood. Here we apply a physically based inundation model coupled with a river routing model (Model for Scale Adaptive River Transport (MOSART)) within the Energy Exascale Earth System Model (E3SM) framework to investigate flood inundation dynamics. After calibration using observed streamflow and satellite-derived flood extent, the model is used to simulate global flood inundation from 1953 to 2004. The mean date and seasonality of annual maximum flood, defined based on flood extent, exhibit significant regional differences across 16 major basins. Generally, soil moisture and monthly maximum daily rainfall (MMR) are the dominant drivers of flood in tropical basins while monthly maximum daily snowmelt (MMS) is the dominant driver in high-latitude basins. From 1953From -1982From to 1975From -2004 changes in flood generation mechanisms are found in some basins such as Amazon, Lena, Yenisey, and Kolyma. Analysis of the rainfall seasonality and water balance at grid scale reveals a stronger rainfall seasonality in the Amazon during the later period that increases the synchrony between extreme rainfall and wet soil. With high antecedent soil moisture coinciding with rainfall, MMR contributes more to flood in the later period. Fewer extreme rainfall events and increasing soil moisture reduced the contribution of MMR and increased the role of MMS in floods in the Lena and Yenisey basins, respectively. Lastly, increased soil moisture and frequency of large MMS reduced the contribution of the latter to floods in the Kolyma basin. Key Points: • Global flood inundation dynamics are simulated with a physically based inundation model coupled with a river routing model • Flood date, seasonality, and generation mechanisms show large spatial variability across 16 major river basins around the world • Rainfall importance to flood decreased in most rainfall-driven basins and snowmelt importance decreased at high latitudes in

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Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite‐Based Products and Streamflow Observations

Cited by 20 publications

References 90 publications

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data

Flood Inundation Generation Mechanisms and Their Changes in 1953–2004 in Global Major River Basins

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