Observation and Measurement of Ecohydrological Processes

The observation and measurement of ecohydrological processes have been witnessed a huge progress in terms of novel ideas, methodologies, and techniques. Many cutting-edge observing techniques, e.g., stable isotope, wireless sensor network, cosmic ray probe, multi-source remote sensing, are continuously introduced and widely applied. As the first chapter of this book, this chapter introduces the progresses, challenges, and perspectives of observing ecohydrological processes. We first introduced the key states and fluxes that control the ecohydrological processes and novel techniques that allow those controlling factors to be quantified. However, we found that knowledge gap remains, including: (1) improving the observation ability to understand and quantify the ecohydrological processes, (2) integrating multisource observations into a dynamics model to accurately estimate the state and flux variables of ecohydrological processes, (3) developing upscaling approaches through system observations to understand the scaling issue, and (4) estimating representativeness error to quantify the uncertainties. To this end, we pointed out the potential directions for filling these gaps, including: (1) to better translate remotely sensed data into information that helps us better understand ecohydrological processes and better inform land-surface models, (2) to better quantify the roles of subsurface processes in ecohydrological processes, (3) to develop observational systems that allow ecohydrological processes to be captured across different scales and across compartments, (4) to use well-instrumented watersheds as test beds of new concept for ecohydrological observations, (5) to combine monitoring and controllable and synthetic observation experiments, (6) to utilize technical advancements in new models, and (7) to integrate observation systems with integrated models, data services, and decision making. Overall, this chapter provides an insight into the-state-of-art of observing ecohydrological processes.

Soil water content is a key variable for understanding and modeling ecohydrological processes. In this chapter, we review the state of the art of ground-based methods to characterize the spatiotemporal dynamic of soil water content, from point to field scale. First, point measurements methods are briefly discussed. Then, field-scale hydrogeophysical approaches such as ground-penetrating radar, ground-based L-band radiometry, electromagnetic induction, electrical resistivity tomography, cosmic-ray neutron probes, global navigation satellite system reflectometry, and nuclear magnetic resonance are described in more details. The basic principles of the different techniques, the spatial and temporal characteristics of their measurements, their advantages and limitations, as well as the recent developments in the data processing are presented.

Soil moisture is the key variable that controls the surface water movements including infiltration, evapotranspiration, and groundwater recharge. It is one of the most important surface conditions of the exchange processes between land and atmosphere. Microwave remote sensing provides an efficient way to map surface soil moisture at a large scale from space and has achieved rapid development especially in large aperture systems at L-band. This chapter describes the basic theory and methodology for retrieving surface soil moisture including the soil roughness and vegetation effects. Special cases of soil moisture estimates from airborne and spaceborne measurements are presented. Results demonstrated that multichannel (multi-angle or multifrequency) microwave observations can be combined to enhance the retrieval accuracy and spatial resolution of remote sensed soil moisture.

Precipitation is one of the most important water cycle components. The chapter reviews modern instruments and techniques for global precipitation retrieval, including weather radars and satellites. Some of the most popular global multi-satellite precipitation products are introduced, including PERSIANN-CCS, TMPA, and IMERG. In addition, we extend to the typical regional and global studies about the assessment of various products and their application in flood detection and prediction.

Analyses of the long-term (1991–2010) intercomparison data quantify the consistency in winter precipitation observations by six identical Tretyakov gauges at the Valdai research station in Russia. Relative to the standard Tretyakov gauge, the mean catch ratios vary from 97% to 106% for dry snow, 94–104% for wet snow, 87–109% for blowing snow, 96–103% for mixed precipitation, to 98–101% for winter rain. The differences between the highest and lowest mean catches are about 10–11% for snow, 7% for mixed precipitation, and 3% for rain. On average, this difference is about 0.2 mm over the 12-h observation period. The catch difference for blowing snow is much higher, up to 22%, or average of 0.6 mm per observation. Comparisons of 12-h observations show a better consistency in gauge performance for the low snowfall events, and a large variation in gauge catch for the high snowfall cases. The differences in 12-h snow catches are mostly less than 2 mm among the 6 gauges. The difference in the 12-h observations is less than 1% for rain and 4% for mixed precipitation. Close linear relationships exist between the 12-h gauge observations for all precipitation types. The maximum differences in gauge snow catches increase very weakly with the wind speed, and higher differences are associated with the warmer temperatures from –5 °C to 0 °C. There is, however, no significant relationship between the max catch difference and mean wind speed or temperature over the 12-h period.

Land surface evapotranspiration (E) is a key component of the water and energy balance over terrestrial ecosystems, quantification of which has long been an important topic in hydrological, meteorological, agricultural, and ecological studies. This chapter focuses on the quantification of E using remote sensing-based approaches, which provide a promising opportunity for spatially consistent and temporally continuous E mapping. An introduction of surface energy balance is firstly presented, followed by three typical methods of estimating E from remote sensing imageries (i.e., surface energy balance-based models, vegetation index-land surface temperature space models, and Penman-Monteith-based models) and the ETWatch model that combines the energy balance and Penman-Monteith models. A working example of comparing three remote sensing E models is then provided to better inform the model physics, as well as advantages and drawbacks. Additionally, temporal and spatial scaling methods are presented. Finally, existing terrestrial E products that have a global coverage and are publically accessible are introduced.

Evapotranspiration (ET) is a combination of two distinct processes, soil or water evaporation (E) and plant transpiration (T), that occur between plants and the atmosphere, soil and the atmosphere, and water and the atmosphere. ET is also an important link between the terrestrial ecosystem and hydrological processes. In this chapter, we focus primarily on ET measurements using micrometeorological methods. Three typical ET measurement techniques, namely, the Bowen ratio-energy balance, eddy covariance, and scintillometer methods, which have a long history and are used widely throughout the world, are introduced. A brief review of their theoretical background, installation and maintenance, data processing and quality control, and footprint is presented, in addition to a brief summary of the advantages and disadvantages of each method. Additionally, ET measurements at observational networks and intensive experiments are presented. The ET measurement methods differ in observational theory, temporal–spatial scales, and precision. Researchers can select a suitable method according to their research objectives.

Surface runoff, or overland flow, is a fundamental process of interest in hydrology. Surface runoff generation can occur at multiple scales, ranging from small pools of excess water that propagate downhill to stream networks that drain large catchments. Accurate quantification of runoff is vital to clarify the mechanisms and effects of overland flow and also indispensable to understand fundamental hydrological processes. In this chapter, four kinds of measurement techniques, including runoff plot method, curve number method, isotopic tracer method, and salt solution method, are introduced. Runoff plot experiments are often conducted to evaluate the rainfall–runoff processes and widely used to study runoff and/or sediment losses. The curve number method is used to estimate watershed direct-runoff volume by a curve number value which is developed based on measured watershed runoff and rainfall data. The isotopic tracer method is used to measure the surface runoff by separating its contribution from multicomponent based on the mass balance of stable isotopes. The salt solution method is usually used to measure the shallow water flow by detecting the movement of salt. Besides, models of surface runoff are also summarized in this chapter. The models can be classified into conceptual models and process-based models. The conceptual models are simple transfer functions describing a linear relationship between rainfall and surface runoff. While the process-based models take into account of the spatial variability of climate, soil, vegetation, and terrain, which are able to make a series of hydrological processes interconnected. Despite their complexities, the process-based models are very helpful to study the changes in hydrological processes caused by human activities. Furthermore, the vegetation has important impact on surface runoff. For instance, with the increase of vegetation coverage, surface runoff can be reduced effectively. And root induces macropores, which are of importance for runoff mitigation due to their large diameters and high connectivity, enhancing rapid rainfall infiltration and percolation to deeper soil layers. Finally, we put forward some challenges about the measurement and simulation of the surface runoff including the establishment of surface runoff observation network in different ecological system, the combination of land surface model and distributed hydrological model, and the coupling between ecological processes and the runoff process on different scales.

Subsurface flow refers to any flow below the surface of the ground which includes low flow (base flow) and quick flow (subsurface stormflow). Subsurface flow has been attracting attention as an important topic of research in recent years because of its crucial role in water cycle calculation, flood prediction, slope stability, nutrient recycling, and soil–water–vegetation exchange processes. In early subsurface flow research, trenches (or pits) combined with hydrometric approaches are the main observation techniques. In recent decades, great progress has been made on the source, pathway, and residence time of subsurface flow due to the application of tracer and geophysical techniques. However, there is yet no broad consensus on the responsible mechanisms that explain runoff processes in the ground. This chapter seeks to provide an overview of current research on subsurface flow. We first give a brief description of the basic concept of subsurface flow and base flow and then focus on the flow regimes, controlling factors, and monitor and model of subsurface stormflow. In the end of this chapter, future research directions are proposed.

Carbon exchange between terrestrial ecosystems and environment is paid great attention in recent decades, because it can regulate the atmospheric carbon dioxide concentration. Photosynthesis is the key process in the carbon cycle. GPP, NPP, and NEP are key variables in carbon cycle study. Thus, accurate estimation of these carbon fluxes is important for understanding the interactions between terrestrial ecosystems and atmosphere. In this chapter, we introduce measuring methods of these carbon fluxes at field scale and estimating models of these carbon fluxes based on remote sensing data at regional or global scale. The processes and key questions in these methods or models are specifically analyzed.

Leaf area index (LAI) is a primary parameter for vegetation structure and is one of the important products from remote sensing data. Ground-based LAI estimation of LAI is the important activity for global satellite product validation. In this chapter, the main methods on LAI measurement were reviewed, and the emphasis was put on the new advanced method on LAI measurement. We present two new instruments (LAINet and LAISmart) designed by Beijing Normal University which use either modern communication network or mobile computing platform to obtain LAI with high efficiency and low cost. LAINet is an instrument constructed on the base of wireless sensor network, and the principle of LAINet is capturing sunlight transmittance using a series of wireless sensors in different sun zenith angles. And LAI is estimated from the sensed transmittances. Borrowing the wireless communication technique, the measured data can be transferred to remote computer server, thus, LAINet can reduce the cost of field data collection. LAISmart is a mobile application deployed on the smartphone, and the LAI is calculated by the classification of captured image. By integration of capturing images and real time computing of smartphone, LAISmart provide automatic measurement method compared with the traditional digital hemispherical photography method. In the end of this chapter, the prospect of the methods on the LAI ground-based measurement is summarized, and it is pointed out that the integration of passive and active optical signal to produce low cost and light weight and thus affordable and portable device may be a promising tendency.

This chapter describes some of the advantages and limitations of radar remote sensing in the context of land cover and agriculture. In the Introduction section, a short overview is given about the interest and techniques for remote sensing in this context. The motivation to focus on radar and SAR and a brief review of the main techniques are described in order to give a synopsis of the type of information that may be extracted from these sensors. Sections “SAR Remote Sensing for Land Cover” and “SAR Remote Sensing of Vegetation” depict some of the main or more relevant applications of SAR within the context of land cover and vegetation. Due to the broad scope of this topic, the focus has been put on four big topics: soil moisture retrieval and land cover classification as examples of SAR remote sensing applications over land and forest and agriculture monitoring as examples over vegetation. Finally, an outlook is given with a summary of the current state of the art, the limitations of remote sensing sensors, and the desired requirements for the different described applications.

Soil moisture patterns arise from the combined processes induced by vegetation, soil properties, climate, topography, parent material, and time. In this chapter, we focus on how vegetation induces soil moisture patterns, particularly how plant root processes affect the soil moisture distribution. Four different mechanisms were identified as potential drivers of soil moisture variability: root growth, root water uptake and transpiration, plant competitions, and rhizosphere properties. High transpiration, root growth, and root water uptake generally increase the soil moisture variability for drying conditions. On the other hand, other mechanisms reduce the soil moisture variation under drying condition including (1) compensation, which plants extract water in the wettest part of the soil; (2) hydrotropism, which roots tend to grow toward wetter zone of the soil; and (3) plant competition, which different plants try to segregate the depths at which they take up water. In addition, rhizosphere-specific properties tend to increase the variability when the soil is wetted from dry condition or to decrease it under wet conditions. We used a plant architecture model to illustrate how soil and root properties combine to generate or destroy soil moisture relations.

Ecohydrological processes are strongly controlled by complex interactions between the subsurface or vadose zone, the vegetation, and the atmosphere. Upscaling of ecohydrological processes requires an understanding of the fundamental processes and states controlling water-related fluxes in vegetation and soils as well as the characterization of the inherent spatial variability occurring in these systems from the local to the catchment scale and beyond. In this chapter we address upscaling of soil water processes and hydraulic properties in the vadose zone, the upscaling of soil water-plant processes, the use of data assimilation techniques to estimate ecohydrologically relevant parameters, and the use of novel sensing techniques and observational platforms. The integration of novel upscaling approaches and novel sensing techniques will provide a unique opportunity to improve our understanding of ecohydrological processes.

A watershed, regarded as the ideal unit for practicing earth system science, possesses all of the complexities of the land surface system. Thus, building a watershed observing system and conducting a field experiment for the observation of the ecohydrological processes at watershed scale is the best way to understand the complexities of the land surface system. This chapter presents an overview on the designing and conducting of a field experiment and establishing a watershed observing system. Several key scientific problems are addressed: (1) why we need a watershed observing system, (2) what are the characteristics of a watershed observing system, and (3) how can we design and establish a watershed observing system. We believe that a watershed observing system is the prerequisite of the watershed science development and helping to improve the understanding of the ecohydrological processes. The watershed observing system possesses the multidiscipline and multiscale characteristics. It must be able to capture the spatiotemporal heterogeneity and to quantify the uncertainty of the key ecohydrological variables. Finally, by taking two sequential watershed-scale observation experiments as example, we furthermore illustrate how to design and establish a watershed observing system.