Abstract
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.
Highlights
Streamflow is an important hydroclimatic variable, which is influenced both by change in climate condition and by human activities, including land-use changes, and water use by the agricultural and industrial sectors
Prior to the correlation analysis, the normality of the streamflow data was checked by using the Shapiro-Wilk test, and the p-values for most of the watersheds streamflow are less than the 5% significance value indicating the non-normality of original streamflow data (Table 1)
The overall goal of this manuscript was to explore the association among the precipitation, soil moisture, and streamflow and to evaluate the potential of satellite-based soil moisture products in streamflow forecasting models over a wide range of watersheds in the United States using Google Earth Engine (GEE)
Summary
Streamflow is an important hydroclimatic variable, which is influenced both by change in climate (e.g., precipitation, temperature) condition and by human activities, including land-use changes, and water use by the agricultural and industrial sectors. Wet soil moisture conditions result in overland flow and possible flooding during an extreme precipitation event [1]. Dry soil moisture conditions amplify the occurrence of temperature extremes [2]. Changes in climate result in changes in hydrologic process which lead to changes in the magnitude and frequency of extreme. Water 2020, 12, 1371 hydrologic events [3,4] It is of great scientific and practical importance to analyze and quantify the effect of climate-related drivers on streamflow for improved implementation of sustainable and efficient management of water-related systems. Zhao et al (2009) evaluated the relationship between precipitation and streamflow using sensitivity and simulation-based methods over the yellow river basin in China, where they found that the changes of streamflow are more sensitive to precipitation than evapotranspiration [5]
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