How do natural climate variability, anthropogenic climate and basin underlying surface change affect streamflows? A three-source attribution framework and application

Abstract

The streamflows in many rivers have been detected to be declining recently due to environmental changes, which are dominating water management strategies and affecting water security. The decline was usually ended up with attribution to climate change and underlying surface changes, without further exploring the effects of natural and anthropogenic components in climate. It might lead to overestimation for human-induced climate change impacts and inappropriate adaptation. The identification of the anthropogenic elements in streamflow change is very crucial and increasingly required by water management planning, climate mitigation and adaptation actions. A novel framework is proposed for streamflow change attribution in terms of three sources of natural climate variability (NCV), anthropogenic climate change (ACC) and the underlying surface changes in basins. The results suggest that the role of the underlying surface changes in basins overwhelms that of climate change in the recent historical period. However, the natural climate variability could not be neglected since it may play the dominant role in streamflow change, compared with that of the anthropogenic climate change. The results also highlight that we need to clarify the effects of human-induced climate change and the underlying surface changes, which are the goals of climate change adaptation and mitigation. The uncertainties of the attribution are analyzed in contrast with other attribution work in the same study case, and the sources are presented explicitly. The conclusion could facilitate a better understanding of the hydrological processes as a result of environmental changes and provide an efficient reference for administrators and decision makers.

Introduction

Projections of climate change impacts on water resources have been an increasing requirement for water management decisions, due to their critical role in the sustainability and security of societies all over the world (Dey and Mishra, 2017, Fu et al., 2007, Sunde et al., 2017, Lee et al., 2014, Peng et al., 2017, Vo et al., 2016), especially for China with its vast population and rapid economic development. China is eager to know what will happen in the future and how to respond. Thus, an adaptation and mitigation strategy is very important to decision makers. However, all actions shall aim at the human-induced change, while the internal climate variability (natural climate variability) shall be separated since it could not be changed. Assessments of the regional impacts of human-induced climate change on a wide range of social and environmental systems are fundamental for determining the appropriate policy responses to climate change (Parry et al., 1996, Wiglegy et al., 1996, IPCC, 1998). Delineating the relative role of anthropogenic forcing, natural forcing, and long-term natural variability in climate change presents a significant challenge (Stott et al., 2000, Broccoli et al., 2003, Nkomozepi and Chung, 2014). It is not only a scientific issue but a real and current demand. The internal-seasonal variability might overwhelm the climate change signals. For example, the summer monsoon rain band and the northward propagation of the East Asian summer monsoon (EASM) are most important for China, and their unusual behaviors often cause local flooding and drought (Ding, 2004). Quantifying whether there is a large role for long-term natural variability in the climate system is important, since such variability could exacerbate or ameliorate the impact of climate change in the near future (Swanson et al., 2009). Attributing such changes to the human forcing of the climate system, where possible, is important for the development of effective mitigation and adaptation (Rosenzweig and Neofotis, 2013). Without rigorous attribution, detection studies can be of limited use for planning and could even lead to mal-adaptation, thereby increasing risks to society and the environment, or wasting limited economic resources (Harrigan et al., 2014). But so far, the streamflow change has been still widely ascribed to two-source of climate change and human activity, without further revealing the natural and human forcing in climate variation.

Human activity leads to changes in the atmospheric composition either directly (via emissions of gases or particles) or indirectly (via atmospheric chemistry) (IPCC, 2013) and is called anthropogenic climate change. The precipitation pattern might change and cause streamflow variation under climate change. Moreover, human activities play a crucial role in the hydrological circulation change (Kuchment, 2004). Streamflow generation is also affected by human activities in terms of underlying surface as well as natural factors (Liu et al., 2010, Zhang et al., 2012a, Zhang et al., 2012b). The underlying surface changes, including urbanization, changes in agricultural practices and construction of hydraulic structures and water extraction, have affected the runoff generation and routing processes. Thus, the human activity brings human-induced climate change and underlying surface change, which both influence streamflow variation as anthropogenic elements. The attribution work shall focus on revealing the contribution of anthropogenic elements.

Historical attribution is of great importance in understanding the future projections for streamflow changes resulting from different drivers. The Haihe Basin is a crucial economic center of China, and this area experienced a long-term drought from the 1980s to 1990s. In the past 30 years, the runoff from the mountain region has decreased sharply (Yang and Tian, 2009, Cong et al., 2010, Bao et al., 2012a), and the groundwater level in the plain area has severely declined (Liu and Xia, 2004, Liu et al., 2001, Bluemling et al., 2010, Zhang et al., 2012a, Zhang et al., 2012b). The annual runoff has a decreasing trend in most rivers, such as in the Luan River (Wang et al., 2013, Xu et al., 2013), Chaobai River (Wang et al., 2009, Ma et al., 2010, Bao et al., 2012b), and Zhang River (Wang et al., 2013). The water shortage has been a barrier to economic development. Thus, the attribution of multiple drivers is necessary for decision makers to plan effective mitigation and adaptation strategies. There have been a great number of studies that have analyzed the reasons for the decrease in runoff in Haihe Basin and have attributed it to the impacts of climate change and human activities. Land use/cover change was estimated as the main and most likely factor for the runoff decline (Yang and Tian, 2009, Wang et al., 2009), and water use or extraction might also play an important role (Ren et al., 2002). Tributaries with different geographical locations in the Haihe River were demonstrated. For example, climate variability was the major driving factor in the Luan River, and human activities were the main driving factor in the northern and southern parts of Haihe Basin (Bao et al., 2012b). The local human activities accounted for 79.5% of the estimated decrease in the annual inflow for the Panjiakou Reservoir in the Luan River (Xu et al., 2013). In general, although there are different conclusions presented for different proportions of runoff, human activities were the dominant driver for runoff or streamflow decrease. However, decision makers need to know how the anthropogenic drivers affect streamflow variation, which is valuable for sustainable management. It is taken for granted that the climate change and underlying surface change are caused by human activities when doing streamflow change attribution, without considering the natural climate variability. When talking about climate change adaptation or mitigation, we actually mean anthropogenic climate change, rather than the natural climate variability. Thus, the role of natural climate variability should be better understood before any effective and efficient decisions are made.

This study focuses on the new view and further understanding of the attribution of streamflow variation, in terms of three sources: natural climate variability (NCV), anthropogenic climate change (ACC) and a basin’s underlying change. A three-source attribution and decomposition framework is proposed incorporating current popular methods. The case study is the Zhanghe catchment in Haihe Basin, China. TOPMODEL is used to deduce rainfall-runoff processing, which is also used to discriminate the impacts of climate change (including NCV and ACC) and the basin’s underlying changes on hydrological systems as a hydrological model method. Then it makes use of a seasonal resampling method to simulate the climate natural variability and separates the roles of natural climate variability and anthropogenic climate change, to achieve a three-source decomposition.