Streamflow response to climate variability and human activities in the upper catchment of the Yellow River Basin

Impacts of Climate Change on Hydroclimatic Conditions of U.S. National Forests and Grasslands

The conterminous United States includes national forests and grasslands that provide ecological, social, economic, recreational, and aesthetic services. Future climate change can alter long-term hydroclimatic conditions of national forests and grasslands and lead to negative consequences. This study characterizes shifts in hydroclimatology and basin characteristics of US National Forests (NFs) and National Grasslands (NGs) in response to climate change over the 21st century under the DRY, MIDDLE, and WET climate models with the representative concentration pathway (RCP) 8.5 emission scenario. Climatic projections for three climate models ranging from the driest to wettest conditions were obtained from the Multivariate Adaptive Constructed Analogs (MACA) dataset. Then, the variable infiltration capacity (VIC) model was used to model hydrological responses of the selected future climates. Changes in regional hydroclimatic conditions of NFs and NGs were assessed by the magnitude and direction of movements in the Budyko space. The Fu’s equation was applied to estimate changes in basin characteristics. The results indicate that NFs and NGs are likely to experience larger changes in basin characteristics compared to the average of the United States. In general, across the conterminous US, the NFs in mountainous regions are likely to have larger changes in hydroclimatic variables than NFs with lower elevation and NGs. Comparing Forest Service regions, Pacific Northwest, Intermountain, and Northern regions may have a less arid climate with lower freshwater availability. The Southwestern, Northern, Intermountain, and Rocky Mountain regions are likely to experience higher shifts in their basin characteristics. This study can help environmental scientists, and land and water managers improve future land management plans.

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

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.

SWAT-Based Runoff Simulation and Runoff Responses to Climate Change in the Headwaters of the Yellow River, China

Runoff in snowy alpine regions is sensitive to climate change in the context of global warming. Exploring the impact of climate change on the runoff in these regions is critical to understand the dynamics of the water cycle and for the improvement of water resources management. In this study, we analyzed the long-term variations in annual runoff in the headwaters region of the Yellow River (HRYR) (a typical snowy mountain region) during the period of 1956–2012. The Soil and Water Assessment Tool (SWAT) with different elevation bands was employed to assess the performance of monthly runoff simulations, and then to evaluate the impacts of climate change on runoff. The results show that the observed runoff for the hydrological stations at lower relative elevations (i.e., Maqu and Tangnaihai stations) had a downward trend, with rates of 1.91 and 1.55 mm/10 years, while a slight upward trend with a rate of 0.26 mm/10 years was observed for the hydrological station at higher elevation (i.e., Huangheyan station). We also found that the inclusion of five elevation bands could lead to more accurate runoff estimates as compared to simulation without elevation bands at monthly time steps. In addition, the dominant cause of the runoff decline across the whole HRYR was precipitation (which explained 64.2% of the decrease), rather than temperature (25.93%).

Effect partition of climate and catchment changes on runoff variation at the headwater region of the Yellow River based on the Budyko complementary relationship

The headwater region of the Yellow River (HRYR) is one of the most important water supply areas of the whole river basin, which has suffered a serious water shortage problem for recent years. A better understanding of impacts of climate and catchment changes on runoff variation will help to determine efficient measures to deal with the runoff reduction in the Yellow River. The Budyko complementary relationship between the partial elasticity of runoff (R) with respect to precipitation (P) and that with potential evapotranspiration (E₀) was used in this study to partition the effects of climate and catchment changes on runoff variation at the HRYR. The upper and lower bounds of the contributions of climate and catchment changes to runoff variation were determined for every five years during 1961-2010. Results show that the complementary relation method based on the Budyko hypothesis can partition the contributions of climate and catchment changes to runoff variation effectively. And the climate changes are the main reasons (account for 60%-70% of the runoff variation) for runoff reduction at the HRYR. The sensitivity coefficient of R with respect to P has a significant decreasing trend at the 0.05 level with arid ratio (E₀/P) and that with respect to E₀ has a significant increasing trend at the 0.05 level with E₀/P, indicating that with drying climate, R becomes more insensitive to climate changes. More precipitation is consumed by evapotranspiration returning to the atmosphere, leading to the runoff reduction at the HRYR.