[1] Changes in regional groundwater storage in North China were estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites data and ground-based measurements collected from 2003 to 2010. The study area (∼370,000 km2) included the Beijing and Tianjin municipality, the Hebei and Shanxi province, which is one of the largest irrigation areas in the world and is subjected to intensive groundwater-based irrigation. Groundwater depletion in North China was estimated by removing the simulated soil moisture changes from the GRACE-derived terrestrial water storage changes. The rate of groundwater depletion in North China based on GRACE was 2.2 ± 0.3 cm/yr from 2003 to 2010, which is equivalent to a volume of 8.3 ± 1.1 km3/yr. The groundwater depletion rate estimated from monitoring well stations during the same time period was between 2.0 and 2.8 cm/yr, which is consistent with the GRACE-based result. However, the estimated groundwater depletion rate in shallow plain aquifers according to the Groundwater Bulletin of China Northern Plains (GBCNP) for the same time period was only approximately 2.5 km3/yr. The difference in groundwater depletion rates estimated from GRACE and GBCNP data indicates the important contribution of groundwater depletion from deep aquifers in the plain and piedmont regions of North China.

Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements (EI)

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Applications of Remote Sensing in Precision Agriculture: A Review

The emerging popularity of the nexus discussion reflects the ongoing transition from a sectoral or silo approach to an integrative approach to address the global challenges pertinent to the three essential resources: food, energy, and water (FEW). Cities are critically important for advancing regional sustainable development and are thus placed at the center of the FEW nexus. This paper provides a comprehensive literature review to debate the current concepts and methods of the FEW nexus at different scales, with the aim of developing a conceptual knowledgebase framework for scientific analysis and policy making associated with the urban FEW nexus. Although the concept of nexus thinking has been widely accepted, a consistent and explicit cognition of the FEW nexus is still lacking, and a sophisticated methodological modeling framework is urgently required at various scales. As such, we proposed a three-dimensional conceptual framework of the urban FEW nexus from the perspective of resource interdependency, resource provision and system integration. This framework is useful in steering the systematic modeling and integrative management of the complex nexus issues of urban systems with different perspectives. Finally, the future directions of urban nexus research are identified from four aspects, including systematic characterization, cross-region tele-connection mechanisms, co-decision model development, and governance transition.

Food-energy-water (FEW) nexus for urban sustainability: A comprehensive review

Sustainable energy, water and food nexus systems: A focused review of decision-making tools for efficient resource management and governance

Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management

Water-energy-food nexus: Concepts, questions and methodologies

High accuracy models are required for informed decision making in urban flood management. This paper develops a new holistic framework for using information collected from multiple sources for setting parameters of a 2D flood model. This illustrates the importance of identifying key urban features from the terrain data for capturing high resolution flood processes. A Cellular Automata based model CADDIES was used to simulate surface water flood inundation. Existing reports and flood photos obtained via social media were used to set model parameters and investigate different approaches for representing infiltration and drainage system capacity in urban flood modelling. The results of different approaches to processing terrain datasets indicate that the representation of urban micro-features is critical to the accuracy of modelling results. The constant infiltration approach is better than the rainfall reduction approach in representing soil infiltration and drainage capacity, as it describes the flood recession process better. This study provides an in-depth insight into high resolution flood modelling.

An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features

Developing an adaptation option is challenging for long-term engineering decisions due to uncertain future climatic conditions; this is especially true for urban flood risk management. This study develops a real options approach to assess adaptation options in urban surface water flood risk management under climate change. This approach is demonstrated using a case study of Waterloo in London, UK, in which three Sustainable Drainage System (SuDS) measures for surface water flood management, i.e., green roof, bio-retention and permeable pavement, are assessed. A trinomial tree model is used to represent the change in rainfall intensity over future horizons (2050 s and 2080 s) with the climate change data from UK Climate Projections 2009. A two-dimensional Cellular Automata-based model CADDIES is used to simulate surface water flooding. The results from the case study indicate that the real options approach is more cost-effective than the fixed adaptation approach. The benefit of real options adaptations is found to be higher with an increasing cost of SuDS measures compared to fixed adaptation. This study provides new evidence on the benefits of real options analysis in urban surface water flood risk management given the uncertainty associated with climate change.

/pdf/assessing-real-options-in-urban-surface-water-flood-risk-sv5y0npjhp.pdf

Assessing real options in urban surface water flood risk management under climate change

The valuation of ecosystem services is critical to understand the current status of ecosystems and to develop an effective planning strategy for ecosystem protection. This study aims to analyse the spatio-temporal changes in ecosystem services driven by land use changes from 1984 to 2013 in Dalian, China. The land use changes are characterized using remote sensing data and then ecosystem service values (ESVs) are assessed using the equivalent factor method, i.e., assigning value coefficients to different land use categories. The total ESV of Dalian reduced significantly by 44.3% from 1984 to 2013, primarily due to the reduction of forests, water and wetlands. Water and climate regulations are the two largest service functions, contributing about 43.6% of the total ESV on average. In addition, ESVs show a spatial variation in different administrative regions, with the central city area having the maximum decreasing rate. Further, ESV changes and distributions are found to have a strong link with city development policies. This study provides an enhanced understanding of the implications of urban policies on ecosystem services, which is essential for sustaining the provision of ecosystem services and achieving sustainable development goals.

https://www.mdpi.com/2071-1050/10/4/1247/pdf

Evaluation of Temporal and Spatial Ecosystem Services in Dalian, China: Implications for Urban Planning

This paper presents a comparative analysis of three partitioning methods, including Fast Greedy, Random Walk, and Metis, which are commonly used to establish the district metered areas (DMAs) in water distribution systems. The performance of the partitioning methods is compared using a spectrum of evaluation indicators, including modularity, conductance, density, expansion, cuts, and communication volume, which measure different topological characteristics of the complex network. A complex water distribution network EXNET is used for comparison considering two cases, i.e., unweighted and weighted edges, where the weights are represented by the demands. The results obtained from the case study network show that the Fast Greedy has a good overall performance. Random Walk can obtain the relative small cut edges, but severely sacrifice the balance of the partitions, in particular when the number of partitions is small. The Metis method has good performance on balancing the size of the clusters. The Fast Greedy method is more effective in the weighted graph partitioning. This study provides an insight for the application of the topology-based partitioning methods to establish district metered areas in a water distribution network.

Chi Zhang

Papers

Water-energy-food nexus: Concepts, questions and methodologies

An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features

Assessing real options in urban surface water flood risk management under climate change

Evaluation of Temporal and Spatial Ecosystem Services in Dalian, China: Implications for Urban Planning

Comparing Topological Partitioning Methods for District Metered Areas in the Water Distribution Network