Applying resilience concepts to inland river system

As environmental uncertainty increases, incorporating resilience into project assessments, research recommendations, and future plans is becoming even more critical. This US Army Engineer Research and Development Center special report (SR) demonstrates how the concepts of resilience can be applied in a uniform framework and illustrates this framework through existing case studies on large inland river systems. This SR presents the concepts of resilience in inland river systems, the application of these concepts across disciplines, basic parameters of a resilience assessment, and the challenges and opportunities available for incorporating a more holistic approach to understanding resilience of the US Army Corps of Engineers mission areas on inland rivers. Finally, these concepts are demonstrated in several case studies in the United States to exemplify how these parameters have been applied to improve the overall performance of the system.

Hydrochemical Characteristics and Ion Sources of River Water in the Upstream of the Shiyang River, China

As the largest tributary of the Shiyang River, with the average annual inflow of total runoff accounting for 23%, the Xiying River has representative of mountain runoff of inland rivers in the Northwest of China. Using samples collected in this basin from September 2016 to October 2017, the water chemical composition and ion source characteristics of river were studied. The results show that the river is weakly alkaline, the average pH is 8.0 and the TDS is 179.29 mg·L-1. With the elevation decreasing along the river, the TDS of main stream tend to increase firstly and then decrease, but those of TDS of each tributary decrease, and latter is lower than the former. Affected significantly by the flow, the lowest value of ion concentration in river occurs in summer, and the highest occurs in autumn and winter. The hydrochemical type of river is CaMg-HCO3. In the river, the order of cation mass concentration is NH4+<K+<Na+<Mg2+<Ca2+, and that of anion is F-<NO3-<Cl-<SO42-<HCO3-. The sources of ions in river are mainly from the weathering of Silicates and Carbonates. With the elevation decreasing along the river, the influence of Silicates on the inflowing tributaries is gradually strengthened.

The Variation Characteristics and Influencing Factors of Base Flow of the Hexi Inland Rivers

The climate is becoming warmer and more humid in the inland area of northwestern China. In addition, human activities have changed the underlying surface of the river basin, and the instability of the runoff changes has intensified. As a component of river runoff, the base flow reflects the impacts of climate change and human activities. Therefore, it is necessary to carry out research on the change in the base flow and its influencing factors in the context of climate change and human activities. In this study, a base flow method suitable for the inland rivers in northwestern China was assessed, and the variation rules and influencing factors of the base flow were analyzed. The results reveal that since the 1980s, the base flow of the Hexi inland rivers has exhibited an increasing trend, and the growth rate has exhibited the following order: western > central > eastern. The Base Flow Index (the proportion of the base flow to the total runoff in a period) values are in the range of 0.45–0.65. Overall, the change in the base flow of the Hexi inland rivers is the result of the coupling of climate factors and land-use change. The influence of land-use change on the base flow of the Hexi inland rivers gradually weakens from east to west, except for the Xiying River, while the influence of climate change gradually increases. The contribution rates of land-use change to the base flow in the eastern, central, and western regions were 75%, 55%, and 27%. Temperature and precipitation are the main climate factors affecting the change in the base flow in the western and central regions, respectively.

Assessment of Flood Forecast Products for a Coupled Tributary-Coastal Model

Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users.

Remote Estimation of Water Quality Parameters of Medium- and Small-Sized Inland Rivers Using Sentinel-2 Imagery

In the application of quantitative remote sensing in water quality monitoring, the existence of mixed pixels greatly affects the accuracy of water quality parameter inversion, especially for narrow inland rivers. Improving the image spatial resolution and weakening the interference of mixed pixels in the image are some of the urgent problems to be solved in the study of water quality monitoring of medium- and small-sized inland rivers. We processed Sentinel-2 multispectral images using the super-resolution algorithm and generated a set of 10 m spatial resolution images with basically unchanged reflection characteristics. Both qualitative and quantitative evaluation results show that the super-resolution algorithm can weaken the influence of mixed pixels while maintaining spectral invariance. Before the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH3-N, the R2 was 0.61, the root mean squared error (RMSE) was 0.177 and the mean absolute percentage error (MAPE) was 29.33%; for Chemical Oxygen Demand (COD), the R2 was 0.26, the RMSE was 0.756 and the MAPE was 4.62%; for Total Phosphorus (TP), the R2 was 0.69, the RMSE was 0.032 and the MAPE was 30.58%. After the application of the super-resolution algorithm, the inversion accuracy of water quality parameters in this study were as follows: for NH3-N, the R2 was 0.67, the RMSE was 0.161 and the MAPE was 25.88%; for COD, the R2 was 0.53, the RMSE was 0.546 and the MAPE was 3.36%; for TP, the R2 was 0.60, the RMSE was 0.034 and the MAPE was 24.28%. Finally, the spatial distribution of NH3-N, COD and TP was obtained by using a machine learning model. The results showed that the application of the super-resolution algorithm can effectively improve the retrieval accuracy of NH3-N, COD and TP, which illustrates the application potential of the super-resolution algorithm in water quality remote sensing quantitative monitoring.

Dynamic Ship Domain Model Based on AIS Data for Inland Waterways

The existing ship domain models are mostly based on the navigation behavior of open water vessels, and they are not practicable to directly apply to inland rivers. Therefore, it is necessary to establish an inland ship safety domain model based on the ship traffic characteristic therein. Based on the AIS data in the Yangtze River, this paper establishes the functional relationship between these data through multiple regression analysis using data such as ship spacing, ship length, ship speed, and heading angle. Based on this, the safety distance between ships of different lengths in different situations and other ships is determined, so as to establish a dynamic ship domain model. At the same time, this paper explores the geographical relationship between ship and channel boundary and incorporates it into the ship domain model. Finally, a quantitative approach for ship collision risk is proposed, and the collision threat degree is calculated according to the relative heading of the ship and the position in the dynamic ship domain model. Two case studies, including crossing and overtaking situations, are performed to validate the proposed model.

Quantitative Determination of Some Parameters in the Tennant Method and Its Application to Sustainability: A Case Study of the Yarkand River, Xinjiang, China

Analysis of basic eco-environmental water requirements (BEEWRs) along inland rivers characterized by extreme aridity can provide a theoretical basis for sustaining riverine ecosystems stressed by increasingly dry conditions and human activity. In the past, analyzing the ecological base flow as determined by the Tennant method was the predominant method used to calculate the BEEWR of a river. However, some parameter values within this method are determined subjectively, increasing uncertainty in the estimated values. In this paper, quantitative methods for these subjectively determined parameters are proposed and used to analyze the BEEWR of the Yarkand River, Xinjiang, China. The results demonstrate that: (1) the flood and non-flood seasons of a river can be delineated by analyzing the increase rate of monthly runoff as compared to the monthly runoff of the previous month; (2) the ecological base flow standard in the Tennant method can be more quantitatively determined by comparing the BEEWR for each ecological base flow standard to the annual average river loss, where the BEEWR must exceed the annual average river loss; and (3) BEEWRs of other up- and downstream river reaches can be obtained using the formula “BEEWR in the next downstream section equals the BEEWR in the last section minus the river loss between these two sections”.