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Photographic scales providing resource data for decision making processes of land use and a legend system for barren lands, water resources, natural vegetation, agricultural, urban, and industrial lands in hierarchical framework are applied to various remote sensing techniques. Two natural vegetation resource and land use maps for a major portion of Maricopa County, Arizona are also produced.
The author has identified the following significant results. ERTS-1 data and aerial photography are proving to be a useful tool for the inventory and management of inland wetlands. Two examples of the application of remotely-sensed data to specific wetland management needs or requirements are discussed. Studies of the Great Dismal Swamp are utilizing ERTS-1 imagery and color IR photography in: (1) study area selection; (2) field inspection; (3) vegetation mapping; (4) identification of drainage characteristics and moisture regime; (5) location of intensive study areas; and (6) detection of change. Thematic extractions of ERTS-1 data made using the United States Geological Survey's Autographic Theme Extraction System are aiding analyses of swamp hydrologic regime and providing information pertinent to quick recognition and inventory of wetlands from ERTS-1. DCP'S in south Florida wetlands provide near-real time data for water resources managers. Data relayed by satellite can be entered into models to provide predictive data and water storage information for long-term and short-term decision making.
Low resolution meteorological satellite and high resolution earth resources satellite data were used to map snowcovered area over the upper Indus River and the Wind River Mountains of Wyoming, respectively. For the Indus River, early Spring snowcovered area was extracted and related to April through June streamflow from 1967-1971 using a regression equation. Composited results from two years of data over seven Wind River Mountain watersheds indicated that LANDSAT-1 snowcover observations, separated on the basis of watershed elevation, could also be related to runoff in significant regression equations. It appears that earth resources satellite data will be useful in assisting in the prediction of seasonal streamflow for various water resources applications, nonhazardous collection of snow data from restricted-access areas, and in hydrologic modeling of snowmelt runoff.
Ames Research Center has partnered with the California Department of Water Resources to develop satellite-based technologies to mitigate drought conditions. One project aims to help water managers adjust their irrigation to match the biological needs of each crop, and another involves monitoring areas where land is fallow so emergency relief can more quickly aid affected communities.
Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve substantially the climate, crop, and economic simulation tools that are used to characterize the agricultural sector, to assess future world food security under changing climate conditions, and to enhance adaptation capacity both globally and regionally. To understand better and improve the modeled crop responses, AgMIP has conducted detailed crop model intercomparisons at closely observed field sites for wheat (Asseng et al., 2013), rice (Li et al., in review), maize (Bassu et al., 2014), and sugarcane (Singels et al., 2013). A coordinated modeling exercise was one of the original motivations for AgMIP, and C3MP provides rapid estimation of crop responses to CO2, water, and temperature (CTW) changes, adding dimension and insight into the crop model intercomparisons, while facilitating interactions within the global community of modelers. C3MP also contributes a fast-track, multi-model climate sensitivity assessment for the AgMIP climate and crop modeling teams on Research Track 2 (Fig. 1), which seeks to understand the impact of projected climatic changes on crop production and food security (Rosenzweig et al., 2013; Ruane et al., 2014).
Space travel and planetary missions for long-endurance human presence require Environmental Control and Life Support (ECLS) loop-closure. The air and water resource loops are generally closed with their processes having been established through extensive development, testing, and long-term use on International Space Station. Closure of the air and water loops have enabled oxygen and hydrogen sustainability. Carbon sustainability has yet to be addressed. Waste and trash, which also contains hydrogen and oxygen and abundant carbon have not been developed to the extent air and water have. Waste and trash have generally been dealt with in terms of simple safening, storage, and disposal. In externally disposing of waste, which includes C1 and C2 hydrocarbon gases, lost resources not only increase mission cost they can result in space and planetary pollution that can lead to unfavorable public opinion. With all this in mind, we explore the idea of carbon sustainability within closed-cycle waste, trash, and C1/C2 reprocessing. Starting from the current ECLS System roadmap, we identify resources that are not yet captured in closed-cycle processes but are lost when crossing the thermodynamic boundaries of an open system. Such pseudo-resources are trash, waste, and the gaseous carbon products of carbon dioxide reduction. We then consider different means and processes for minimizing pseudo-resource loss so as to “close the carbon loop.” There are cases in which pseudo-resources need to be externally discarded, such as reducing spacecraft acceleration mass. In this case we examine various processes that can be beneficial to missions even when these pseudo-resources are lost. An example would be the conversion of waste into propellant, which can produce thrust while at the same time reducing mass for transit acceleration (and ultimately propellant mass at launch), in which case we propose a means for evaluating the value of converting waste into propellant for different propulsion systems.
Reverse osmosis desalination is one of the most important and increasingly popular technologies to augment available water resources. Central to the technology is a thin-film-composite polyamide membrane capable of separating pure water from seawater or brine. Since its conception and initiation, the membrane industry has followed a linear life-cycle scheme. However, increasing production costs of fossil-based materials and more stringent environmental regulations drive the initiatives to adapt to a circular economy of membrane materials. In this perspective, we briefly summarize the pressing issues in the state-of-the-art membrane industry, then discuss the opportunities in future technology innovations with a focus on sustainable membrane manufacturing and recycling, and lastly provided an outlook for future membrane design and fabrication towards a circular economy.
The preliminary study of the ERTS-1 imagery coverage of Iran, commenced on October 26, 1972. All of the images were carefully examined, and a photomosaic covering approximately ninety-five per cent of the country was prepared. A number of images of selected areas were studied in detail. In the field of geology, a number of large scale faults were identified, which do not figure on geological maps. Furthermore, a preliminary study was carried out on the recent sediments, their possible sources, and origin. A limited number of geological work maps were prepared as well. In the fields of agriculture and forestry, studies based on color composite prints of certain areas were undertaken, with a purpose of identifying potential arable areas. Investigations in the field of water resources resulted in the discovery of a number of small lakes, and streams. Furthermore, fluctuations of the water level in some lakes were observed.
Global warming driven by rising greenhouse-gas concentrations is expected to cause wet regions of the tropics and mid to high latitudes to get wetter and subtropical dry regions to get drier and expand polewards. Over southwest North America, models project a steady drop in precipitation minus evapotranspiration, P -- E, the net flux of water at the land surface, leading to, for example, a decline in Colorado River flow. This would cause widespread and important social and ecological consequences. Here, using new simulations from the Coupled Model Intercomparison Project Five, to be assessed in Intergovernmental Panel on Climate Change Assessment Report Five, we extend previous work by examining changes in P, E, runoff and soil moisture by season and for three different water resource regions. Focusing on the near future, 2021-2040, the new simulations project declines in surface-water availability across the southwest that translate into reduced soil moisture and runoff in California and Nevada, the Colorado River headwaters and Texas.
Growing population and water demand have increased pressure on water resources in various parts of the globe, including many transboundary river basins. While the impacts of upstream water use on downstream water availability have been analyzed in many of these international river basins, this has not been systematically done at the global scale using coherent and comparable datasets. In this study, we aim to assess the change in downstream water stress due to upstream water use in the world's transboundary river basins. Water stress was first calculated considering only local water use of each sub-basin based on country-basin mesh, then compared with the situation when upstream water use was subtracted from downstream water availability. Wefound that water stress was generally already high when considering only local water use, affecting 0.95-1.44 billion people or 33%-51% of the population in transboundary river basins. After accounting for upstream water use, stress level increased by at least 1 percentage-point for 30-65 sub-basins, affecting 0.29-1.13 billion people. Altogether 288 out of 298 middle-stream and downstream sub-basin areas experienced some change in stress level. Further, we assessed whether there is a link between increased water stress due to upstream water use and the number of conflictive and cooperative events in the transboundary river basins, as captured by two prominent databases. No direct relationship was found. This supports the argument that conflicts and cooperation events originate from a combination of different drivers, among which upstream-induced water stress may play a role. Our findings contribute to better understanding of upstream-downstream dynamics in water stress to help address water allocation problems.
Satellite observations of the time-variable gravity field revolutionized the monitoring of large-scale water storage changes, beginning with the 2002 launch of the Gravity Recovery and Climate Experiment (GRACE) mission. Most hydrologists were sceptical of the satellite gravimetry approach at first, but validation studies assuaged their concerns and high-profile, GRACE-based groundwater depletion studies caused an explosion of interest. The importance of GRACE observations for hydrologic and cryospheric science became so great that GRACE Follow-On (GRACE-FO) jumped the National Aeronautics and Space Administration’s Earth science mission queue and launched in 2018. A third mass change mission is currently under development. Here, we review key milestones in satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources studies and the sole source of observations of what is now an ‘essential climate variable’, terrestrial water storage.
What captivates me about the research field of satellite remote sensing of the earth system science is the opportunity to help human beings by addressing challenging questions related to the current and future availability of natural resources for domestic, agricultural, and industrial needs. For example, a continuously varying climate poses a threat to the water resource, not only in developing countries, but also in economically well-developed regions, creating the urgent need to characterize and predict its spatial and temporal availability. Specifically, my curiosity is driven by the challenge of merging cutting-edge space technology and earth observations (i.e., remote sensing) with state-of-the-art models for the purpose of improving our scientific knowledge about the variability and the change of the hydrologic cycle.The only practical way to observe the land surface processes (e.g., hydrologic cycle) on continental to global scales is via satellite remote sensing. Though remote sensing can make spatially comprehensive measurements of various components of the land surface system, it cannot provide direct information on the entire system, and the measurements represent only a snapshot in time. Physical based models may be used to continuously predict the temporal and spatial earth processes, but these predictions are often poor, due to model initialization, parameter and meteorological forcing errors, and inadequate model physics and/or resolution. Thus, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses of both) to provide a superior land surface state estimate. This is the goal of land surface data assimilation. Data assimilation provides a better estimate of the environmental states than either models or observations could individually do. The broad concept of data assimilation can be applied to various disciplines such as hydrology, ecology, environmental hazards, agriculture and economy.My research is targeted to integrating multiple satellite information having multi-sensor and multi-resolution assimilation within today's state-of-the-art hydrologic models. Multi-sensor and multi-resolution assimilation techniques represent a necessary milestone in future earth science applications because they offer the capability of comprehensively integrating disparate types of earth observations via deep-learning techniques. A multi-satellite assimilation and modeling platform will ultimately provide a robust and complete dataset to more fully understand earth's dynamics. In fact, the multi-satellite assimilation platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.My recent work has focused on recent multi-sensor data assimilation techniques targeted at improving snow, soil moisture, groundwater, and terrestrial water storage hydrological states.
Characterizing the impact of human actions on terrestrial water fluxes and storages at multi-basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human-water interactions in large-scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (~10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper-resolution” hydrological models (~0.1–1 km), multi-sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human-water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper-resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort.
Globally, riverine deltas comprise barely 1% of land area, but they support the livelihoods of more than 500 million people (Moorhouse et al., 2021). The Asian megadeltas (e.g., Indus, Bengal, Mekong, Red River, and Yangtze) and their riverine floodplains are the most dynamic economic and environmental systems in the world. These low-lying coastal landforms support surface water bodies and highly productive aquifer systems. However, water resources of the Asian megadeltas are extremely vulnerable to geogenic (e.g., arsenic) and anthropogenic (e.g., bacteria) contamination, increased water salinisation from rising sea levels (Figure 1), and chemicals (e.g., fertilizers, pesticides) from land-use practices (Mukherjee et al., 2024) – all of which are threatening the sustainability of water supply and food security in the region. This Research Topic collection features five original articles that explore water chemistry including arsenic and bacteriological contamination, water salinisation, water-energy-food nexus in irrigated agriculture, and groundwater depletion resulting from irrigation, rapid urbanization, and climate change. Although the Research Topic collection does not include case studies from all the Asian Megadeltas shown in Figure 1, the relevance of multi-hazard driven water risks to all deltaic and river floodplain environments around the world is well-illustrated.
The author has identified the following significant results. The most significant results were obtained in the water resources research, urban land use mapping, and soil association mapping projects. ERTS-1 data was used to classify water bodies to determine acreages and high agreement was obtained with USGS figures. Quantitative evaluation was achieved of urban land use classifications from ERTS-1 data and an overall test accuracy of 90.3% was observed. ERTS-1 data classifications of soil test sites were compared with soil association maps scaled to match the computer produced map and good agreement was observed. In some cases the ERTS-1 results proved to be more accurate than the soil association map.
Utilization of new sources of statewide remote sensing data, taken from high-altitude aircraft and from spacecraft is discussed along with incorporation of information extracted from these sources into on-going land and resources management programs in Arizona. Statewide cartographic applications of remote sensor data taken by NASA high-altitude aircraft include the development of a statewide semi-analytic control network, the production of nearly 1900 orthophotoquads (image maps) that are coincident in scale and area with the U.S. Geological Survey (USGS) 7. 5 minute topographic quadrangle map series, and satellite image maps of Arizona produced from LANDSAt multispectral scanner imagery. These cartographic products are utilized for a wide variety of experimental and operational earth resources applications. Applications of the imagery, image maps, and derived information discussed include: soils and geologic mapping projects, water resources investigations, land use inventories, environmental impact studies, highway route locations and mapping, vegetation cover mapping, wildlife habitat studies, power plant siting studies, statewide delineation of irrigation cropland, position determination of drilling sites, pictorial geographic bases for thematic mapping, and court exhibits.
As the worlds water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring.
As the world’s water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor (in this case, initially MODIS with a planned transition to VIIRS) rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. This presentation will focus on global applications for mapping water use and drought using data mining and data fusion across spatial scales extending from 5-km to 30-m “field-scale” estimates.