Application of remote sensing to forest hydrology

The paper reviews the application of remote sensing to forest hydrology. After discussing the general advantages and disadvantages of satellite remote sensing, the estimation of precipitation, changes in soil moisture, runoff, radiation components, sensible heat flux, latent heat flux, soil heat flux, changes in energy storage in biomass, primary production and monitoring the extent, type and density of forests are reviewed. Finally, the paper looks forward to future developments and concludes that these are likely to come from the use of multitemporal data, combined analysis of different types of remotely sensed data and of remotely sensed and ground data, improved image analysis techniques and combining satellite data with models.     

II. Snow hydrology processes and remote sensing

In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. Snow distribution in a drainage basin can be adequately assessed by visible sensors. Although there are still some interpretation problems, the NOAA-AVHRR sensor can provide frequent views of the areal snow cover in a basin, and snow cover maps are produced operationally by the National Weather Service on about 3000 drainage basins in North America. Measurement of snow accumulation or snow water equivalent with microwave remote sensing has great potential because of the capabilities for depth penetration, all-weather observation and night-time viewing. Several critical areas of research remain, namely, the acquisition of snow grain size information for input to microwave models and improvement in passive microwave resolution from space. Methods that combine both airborne gamma ray and visible satellite remote sensing of the snowpack with field measurements also hold promise for determining areal snow water equivalent. Some remote sensing techniques can also be used to detect different stages of snow metamorphism. Various aspects of snowpack ripening can be detected using microwave and thermal infra-red capabilities. The capabilities for measurement of snow albedo and surface temperature have direct application in both snow metamorphism and snowpack energy balance studies. The potentially most profitable research area here is the study of the bidirectional reflectance distribution function to improve snow albedo measurements. Most of the remote sensing capabilities in snow hydrology have been developed for improving snowmelt-run-off forecasting. Most applications have used the input of snow cover extent to deterministic models, both of the degree day and energy balance types. Snowmelt-run-off forecasts using satellite derived snow cover depletion curves and the models have been successfully made. As the extraction of additional snow cover characteristics becomes possible, remote sensing will have an even greater impact on snow hydrology. Important remote sensing capabilities will become available in the next 20 years through space platform observing systems that will improve our capability to observe the snowpack on an operational basis.     

Remote sensing applications to hydrology: future impact

Abstract

The case is made that the hydrological sciences are data limited and that future progress in understanding hydrological processes is hampered by the lack of adequate data. The future applications of remote sensing to hydrology should provide new types of data that enable hydrologists to address the previously unsolved questions.     

Integrating remote sensing observations of flood hydrology and hydraulic modelling

The further development of two-dimensional finite element models of river flood flow is currently constrained by a lack of data for rigorous parameterization and validation. Remote sensing techniques have the potential to overcome a number of these constraints thereby allowing a research design for model development. This is illustrated with reference to a case study of a two-dimensional finite element model applied to the Missouri River, Nebraska and compared with a synchronous Landsat TM image of flood inundation extent. The case study allows research needs for the integration of hydraulic modelling and remote sensing to be defined. © 1997 John Wiley & Sons, Ltd.     

我国湖泊水文学研究进展与展望

湖泊是地球表层水体的重要组成部分,在区域社会经济发展和生物多样性保护等方面发挥着不可替代的作用.气候变化和高强度的水资源开发利用等,导致湖泊物理、化学特性在时空格局上发生显著的变化,引起一系列的社会、环境、气候等响应.湖泊水文学研究湖泊水文要素及其时空变化特征、平衡关系与变化规律,在水文过程演变与归因解析、湖泊洪旱发生机理与调控、湖泊资源评估与可持续利用等方面,解决了众多理论和实践问题,为区域发展提供了强大支撑.本文评述了近50年来我国湖泊水文学的发展与研究进展,重点阐述湖泊水量平衡与水量变化、湖泊水动力与水文过程调蓄、湖泊极端水文事件成因、湖泊水文遥感反演等方面的研究进展,展望了湖泊水文学的未来发展趋势.     

Remote sensing in hydrology

Remote sensing provides a means of observing hydrological state variables over large areas. The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar. Methods for estimating the hydrometeorlogical fluxes, evapotranspiration and snowmelt runoff, using these state variables are also described.     

湖泊水质遥感的几个关键问题

我国目前约有面积大于1km2的湖泊有3000个,绝大部分属高叶绿素和高悬浮物浓度水体,属于典型的Ⅱ类水体,物质组成多样,水体的光学辐射传输复杂,且有大范围的光学浅水.我国的湖泊水质/水色遥感虽然取得了一定进展,但借鉴海洋水色遥感的相关理论和经验,还需要解决以下四个关键问题:1)兼顾海洋沿海水质遥感,发展专用的静止卫星湖泊水质遥感器;2)在当前多光谱遥感资料基础上研发高光谱湖泊水质因子提取的遥感定量化模型,提高反演精度;3)深化湖底底质对湖泊水质/水质遥感影响研究,发展湖底水质遥感反射率精确计算模型;4)发展适用于湖泊水体区域性Ⅱ类水体大气校正方法,并集成反演、遥感产品制作、分发等技术,构建湖泊水体水质/水色业务化运行体系.     

Recent remote sensing applications for hydro and morphodynamic monitoring and modelling

It is not new to recognize that data from remote sensing platforms is transforming the way we characterize and analyse our environment. The ability to collect continuous data spanning spatial scales now allows geomorphological research in a data rich environment and this special issue [coming just eight years after the 2010 special issue of Earth Surface Processes and Landforms (ESPL) associated with the remote sensing of rivers] highlights the considerable research effort being made to exploit this information, for studies of geomorphic form and process. The 2010 special issue on the remote sensing of rivers noted that fluvial remote sensing articles made up some 14% of the total river related articles in ESPL. A similar review of articles up to 2017 reveals that this figure has increased to around 25% with a recent proliferation of articles utilizing satellite‐based data and structure from motion photogrammetry derived data. It is interesting to note, however that many studies published to date are proof of concept, concentrating on confirming the accuracy of the remotely sensed data at the expense of generating new insights and ideas on fluvial form and function. Data is becoming ever more precise and researchers should now be concentrating on analysing these early data sets to develop increased geomorphic insight, to challenge existing paradigms and to advance geomorphic science. The prospect of this occurring is increased by the fact that many of the new remote sensed platforms allow accurate spatial data to be collected cheaply and efficiently, reducing the need for substantial research funding to advance river science. Fluvial geomorphologists have never before been in such a liberated position. As techniques and analytical skills continue to improve it is inevitable that the prediction that remotely sensed data will revolutionize our understanding of geomorphological form and process will prove true, altering our ideas on the very nature of system functioning in the process. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

The hydrology of sand rivers in Zimbabwe and the use of remote sensing to assess their level of saturation

Sand rivers are ephemeral watercourses containing sand that are occasionally flooded with rainwater runoff during the rainy season. Although the riverbed appears dry for most of the year, there is perennial groundwater flow within the sand. This water flowing beneath the surface is a valuable resource for local communities; nonetheless our understanding of such river systems is limited. Hence, this paper aims to improve our understanding of the hydrology of sand rivers and to examine the potential use of remote sensing to detect the presence of water in the sand. The relationship between rainfall events and changes in the water level of two sand rivers in the Matabeleland South Province of Zimbabwe was investigated. A lagged relationship was observed for the Manzamnyama River but for the Shashani River the relationship was seen only when considering cumulative rainfall events. The comparison of the modelled flow as simulated by a water balance model with observations revealed the important influence of the effective sediment depth on the recharge and recession of the alluvial channels in addition to the length of the channel. The possibility of detecting water in the alluvial sands was investigated using remote sensing. During the wet season, optical images showed that the presence of water on the riverbed was associated with a smooth signal, as it tends to reflect the incident radiation. A chronological analysis of radar images for different months of the year demonstrates that it is possible to detect the presence of water in the sand rivers. These results are a first step towards the development of a methodology that would aim to use remote sensing to help reducing survey costs by guiding exploratory activities to areas showing signs of water abstraction potential.     

Hydro-economic models: Concepts, design, applications, and future prospects

Future water management will shift from building new water supply systems to better operating existing ones. The variation of water values in time and space will increasingly motivate efforts to address water scarcity and reduce water conflicts. Hydro-economic models represent spatially distributed water resource systems, infrastructure, management options and economic values in an integrated manner. In these tools water allocations and management are either driven by the economic value of water or economically evaluated to provide policy insights and reveal opportunities for better management. A central concept is that water demands are not fixed requirements but rather functions where quantities of water use at different times have varying total and marginal economic values. This paper reviews techniques to characterize the economic value of water use and include such values in mathematical models. We identify the key steps in model design and diverse problems, formulations, levels of integration, spatial and temporal scales, and solution techniques addressed and used by over 80 hydro-economic modeling efforts dating back 45-years from 23 countries. We list current limitations of the approach, suggest directions for future work, and recommend ways to improve policy relevance.     

Improving evapotranspiration estimation models through quantitative watershed parameter analysis and remote sensing applications

Numerous models had been developed to predict the annual evapotranspiration (ET) in vegetated lands across various spatial scales. Fu's (Scientia Atmospherica Sinica, 5, 23–31) and Zhang's (Water Resources Research, 37, 701–708) ET simulation models have emerged as highly effective and have been widely used. However, both formulas have the non-quantitative parameters (m in Fu's model and w in Zhang's model). Based on the collected 1789 samples from global long-term hydrological studies, this study discovered significant relations between m (or w) and vegetation coverage or greenness in collected catchments. Then, we used these relations to qualify the parameters in both Zhang's and Fu's models. Results show that the ET estimation accuracies of Fu's (or Zhang's) model are significantly improved by about 13.49 mm (or 6.74 mm) for grassland and cropland, 38.52 mm (or 29.84 mm) for forest and shrub land (coverage<40%), 19.74 mm (or 16.17 mm) for mixed land (coverage<40%), respectively. However, Zhang's model shows higher errors compared with Fu's model, especially in regions with high m (or w) values, such as those with dense vegetations or P/E₀ (annual precipitation to annual potential ET) smaller than 1.0. Additionally, this study also reveals that for regions with vegetation cover less than 40%, the annual ET is not only determined by vegetation types, but also relates to the sizes of vegetation-covered areas. Conversely, for regions with vegetation cover more than 40%, the annual ET is mainly determined by the vegetation density rather than vegetation types or vegetation coverage. Thus, linking m (or w) parameters with vegetation greenness allows leveraging remote sensing for forest management in data-scarce areas, safeguarding regional water resources. This study pioneers integrating vegetation-related indices with basin parameters, advocating for their crucial role in more effective hydrological modelling.     

Subsurface disposal of liquid wastes: Achievements, problems, and prospects

The hydrogeological fundamentals of subsurface disposal of toxic liquid wastes are considered along with the experience in its application in the chemical and nuclear industry of the USSR and Russia, the institutional problems, resulting from the imperfection of legislation, and the potential extension and liberalization of the hydrogeological conditions of its application.     

The remote sensing observation in experiments of rock failure and the beginning of remote sensing rock mechanics

The remote sensing observational study for infrared radiation of rocks was proceeded during the loading on rocks until failure. The major instruments used in experiments were transient spectrum apparatus, intelligent spectrum apparatus, infrared radiation thermometer, infrared spectrum radiometer, and infrared thermal imaging system. The experiments for 26 kinds of rocks were made. The studies show that infrared radiation temperature of rocks increases along with increasing of stress. The amplitude of infrared radiation spectrum of rocks also increases along with increasing of stress. The observational results of infrared thermal imaging of rocks are consistent with infrared radiation temperature. Before formation of major faults for some rocks, the belt-shape thermal imaging of temperature anomaly displaies in position of future major faults. This study has led the new technology of remote sensing into rock mechanics and tend to establish a new field in rock mechanics — remote sensing rock mechanics (or remote sensing rock physics). The application of remote sensing rock mechanics in prediction of earthquake and rock burst, and in measurement of stress field in rock mass is expected. The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,14, Supp., 645–652, 1992. Jin-Shen HAO, Ji-Han LI, Xiao-Hong LIU, Yi-Qiao ZHI, Jin-Kai ZHANG, Yong-Hong Lü, Yi LIU, Yun-Shen YU, He ZHANG, Quan-Quan JI, Xiao-Fan ZHU and Ning CHEN took part in this work. This subject is supported by the Chinese Joint Seismological Science Foundation (91006). Work of Institute of Geophysics, SSB (93A0009).     

Renormalization group methods in subsurface hydrology: overview and applications in hydraulic conductivity upscaling

The renormalization group (RG) approach is a powerful theoretical framework, more suitable for upscaling strong heterogeneity than low-order perturbation expansions. Applications of RG methods in subsurface hydrology include the calculation of (1) macroscopic transport parameters such as effective and equivalent hydraulic conductivity and dispersion coefficients, and (2) anomalous exponents characterizing the dispersion of contaminants due to long-range conductivity correlations or broad (heavy-tailed) distributions of the groundwater velocity. First, we review the main ideas of RG methods and their hydrological applications. Then, we focus on the hydraulic conductivity in saturated porous media with isotropic lognormal heterogeneity, and we present an RG calculation based on the replica method. The RG analysis gives rigorous support to the exponential conjecture for the effective hydraulic conductivity [Water Resour. Res. 19 (1) (1983) 161]. Using numerical simulations in two dimensions with a bimodal conductivity distribution, we demonstrate that the exponential expression is not suitable for all types of heterogeneity. We also introduce an RG coarse-grained conductivity and investigate its applications in estimating the conductivity of blocks or flow domains with finite size. Finally, we define the fractional effective dimension, and we show that it justifies fractal exponents in the range 1−2/dα<1 (where d is the actual medium dimension) in the geostatistical power average.     

Satellite remote sensing of river inundation area,stage, and discharge: a review

The growing availability of multi-temporal satellite data has increased opportunities for monitoring large rivers from space. A variety of passive and active sensors operating in the visible and microwave range are currently operating, or planned, which can estimate inundation area and delineate flood boundaries. Radar altimeters show great promise for directly measuring stage variation in large rivers. It also appears to be possible to obtain estimates of river discharge from space, using ground measurements and satellite data to construct empirical curves that relate water surface area to discharge. Extrapolation of these curves to ungauged sites may be possible for the special case of braided rivers. Where clouds, trees and floating vegetation do not obscure the water surface, high-resolution visible/infrared sensors provide good delineation of inundated areas. Synthetic aperture radar (SAR) sensors can penetrate clouds and can also detect standing water through emergent aquatic plants and forest canopies. However, multiple frequencies and polarizations are required for optimal discrimination of various inundated vegetation cover types. Existing single-polarization, fixed-frequency SARs are not sufficient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-parameter SAR, a synergistic approach using single-frequency, fixed-polarization SAR and visible/infrared data will provide the best results over densely vegetated river floodplains. © 1997 John Wiley & Sons, Ltd.     

Two kinds of moment ratio diagrams and their applications in hydrology

We refocus attention on moment ratio diagrams and their uses in hydrology with four major objectives: (1) to summarize the information available in the literature about possible uses of the traditional moment ratio diagram introduced by Karl Pearson, which uses the coefficient of skewness and of kurtosis to compare the shapes of various distributions commonly used in hydrology; (2) to complete this traditional MRD by integrating into it the regions occupied by the log-Pearson Type III and generalized gamma distributions which are more and more used in hydrology; (3) to present another MRD which uses ratios of moments of orders –1 (harmonic mean), quasi zero (geometric mean) and 1 (arithmetic mean); (4) to stress the need to consider the different MRD's (along with the more recently introduced L-moment ratio diagrams) as complementary tools for choosing between distributions fitted to hydrologic data. Finally, using Monte Carlo simulation we compare the two types of diagrams as tools to identify and discriminate between different distributions.     

Kalman filtering in groundwater flow modelling: problems and prospects

The popularity of applying filtering theory in the environmental and hydrological sciences passed its first climax in the 1970s. Like so many other new mathematical methods it was simply the fashion at the time. The study of groundwater systems was not immune to this fashion, but neither was it by any means a prominent area of application. The spatial-temporal characteristics of groundwater flow are customarily described by analytical or, more frequently, numerical, physics-based models. Consequently, the state-space representations associated with filtering must be of a high order, with an immediately apparent computational over-burden. And therein lies part of the reason for the but modest interest there has been in applying Kalman filtering to groundwater systems, as reviewed critically in this paper. Filtering theory may be used to address a variety of problems, such as: state estimation and reconstruction, parameter estimation (including the study of uncertainty and its propagation), combined state-parameter estimation, input estimation, estimation of the variance-covariance properties of stochastic disturbances, the design of observation networks, and the analysis of parameter identifiability. A large proportion of previous studies has dealt with the problem of parameter estimation in one form or another. This may well not remain the focus of attention in the future. Instead, filtering theory may find wider application in the context of data assimilation, that is, in reconstructing fields of flow and the migration of sub-surface contaminant plumes from relatively sparse observations. Received: October 27, 1997     

Kalman filtering in groundwater flow modelling: problems and prospects

The popularity of applying filtering theory in the environmental and hydrological sciences passed its first climax in the 1970s. Like so many other new mathematical methods it was simply the fashion at the time. The study of groundwater systems was not immune to this fashion, but neither was it by any means a prominent area of application. The spatial-temporal characteristics of groundwater flow are customarily described by analytical or, more frequently, numerical, physics-based models. Consequently, the state-space representations associated with filtering must be of a high order, with an immediately apparent computational over-burden. And therein lies part of the reason for the but modest interest there has been in applying Kalman filtering to groundwater systems, as reviewed critically in this paper. Filtering theory may be used to address a variety of problems, such as: state estimation and reconstruction, parameter estimation (including the study of uncertainty and its propagation), combined state-parameter estimation, input estimation, estimation of the variance-covariance properties of stochastic disturbances, the design of observation networks, and the analysis of parameter identifiability. A large proportion of previous studies has dealt with the problem of parameter estimation in one form or another. This may well not remain the focus of attention in the future. Instead, filtering theory may find wider application in the context of data assimilation, that is, in reconstructing fields of flow and the migration of sub-surface contaminant plumes from relatively sparse observations. Received: October 27, 1997