Improving monthly streamflow forecasts through assimilation of observed streamflow for rainfall-dominated basins across the CONUS

Mazrooei, Amirhossein; Sankarasubramanian, A.

doi:10.1016/j.jhydrol.2019.05.071

Cited by 21 publications

(14 citation statements)

References 61 publications

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“…The streamflow-hydroclimatic variables relationship discussed above is also associated with streamflow forecasting, which provides vital information for environmental impact assessments, agriculture studies, climate change impacts, groundwater assessment, and reservoir operations [10,11]. A wide variety of physically-based and data-driven models exist, including autoregressive moving average (ARMA), linear regression (LR), wavelet transform (WT), artificial neural networks (ANNs), support vector machines (SVMs), and also their combinations are commonly used for hydrologic application [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Sazib

Bolten

Mladenova

2020

Water

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An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

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Section: Introductionmentioning

confidence: 99%

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Sazib

Bolten

Mladenova

2020

Water

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“…1). Further details of downscaling and disaggregation methods, the assessment of uncertainty propagation, and the seasonal skill of downscaled precipitation forecasts can be found in Mazrooei et al (2015).…”

Section: Echam45 Precipitation Forecastsmentioning

confidence: 99%

“…Likewise, accurate soil moisture (SM) forecasting can significantly assist the decision-making for agricultural systems. Most evaluation of climate forecasts has traditionally focused only on the skill in predicting seasonal precipitation, temperature, and the resultant terrestrial fluxes, primarily monthly-toseasonal (M2S) streamflow (Devineni et al, 2008;Armal et al, 2018;Mazrooei et al, 2015) Also, studies have fo-cused on the utility of climate forecasts for agriculture systems by evaluating the skill in predicting seasonal crop yield under rain-fed agriculture (Hansen et al, 2006). As rain-fed agriculture heavily depends on actual soil moisture conditions and the stress that crops face during the growing phase, long-range SM forecasts would be more advantageous to improve crop yield forecasts.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

Mazrooei

Sankarasubramanian

Lakshmi

2020

Hydrol. Earth Syst. Sci.

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Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly-to-seasonal (M2S) top layer SM forecasts by forcing 1- to 3-month-ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern US (SEUS), and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by the Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-month (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.

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“…To overcome this problem, hydrologists have developed and implemented a variety of data assimilation (DA) methods, which allow finding the best estimate of the actual states based on modeled variables and their associated observations, usually streamflow (Mazrooei & Sankarasubramanian, 2019; Vrugt et al, 2006) but also snow water equivalent (Huang et al, 2017; Magnusson et al, 2020; Smyth et al, 2019) and soil humidity (Reichle et al, 2008; Zhang et al, 2019). The DA methods ensure that the modeled states are reasonable and within the expected range according to the estimates of model and observation uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

Mai

Arsenault

Tolson

et al. 2020

Water Resources Research

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Streamflow forecasting is essential in many applications, including the management of hydropower reservoirs and of flood risks. To increase the performance of these hydrologic forecasts, a hydrological model is typically updated with optimal initial model states. These states are usually selected a priori and adjusted to obtain the optimal initial setup. The model states to adjust are generally selected by experts with deep knowledge of the model's behavior. These relationships are rarely documented and formalized, leading to difficulties in reproducibility and transferability to other models. The method here provides a fully automatized system to find the model states based on the parameter-free sensitivity method of efficient elementary effects (EEE), which identifies the most informative variables of the hydrologic model CEQUEAU. The analysis is applied to 1,826 model setups for four Canadian basins. The identified states are formalized using clustering techniques to obtain adjustment "recipes" conditioned on given meteorological conditions. Several recipes are tested and compared to expert-based recipes. The recipes are tested according to their ability to (a) obtain optimal initial states and (b) their forecast performance. The results show that (1) the EEE-based recipes perform similarly or better than the expert-based recipes in both objectives; (2) the recipes are similar across the four basins, highlighting their potential of spatial transferability; and (3) the adjustment of hydrologic model inputs significantly decreases forecast performance. The proposed approach to use EEE-based recipes to obtain optimal initial states for forecasting is reproducible, objective, and suitable to be applied to other models and basins.

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Improving monthly streamflow forecasts through assimilation of observed streamflow for rainfall-dominated basins across the CONUS

Cited by 21 publications

References 61 publications

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

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