Dealing with water conflicts: a comprehensive review of mcdm approaches to manage freshwater ecosystem services

This paper presents a comprehensive review of the application of Multiple-Criteria Decision-Making (MCDM) approaches exclusively to water-related freshwater ecosystem services. MCDM analysis has been useful in solving conflicts and it works well in this framework, given the serious conflicts historically associated with water use and the protection of freshwater ecosystems around the world. In this study, we present a review of 150 papers that proposed the use of MCDM-based methods for the social, economic, or ecological planning and management of water ecosystem services over the period 2000–2020. The analysis accounts for six elements: ecosystem service type, method, participation, biogeographical realm, waterbody type, and problem to solve. A Chi-square test was used to identify dependence between these elements. Studies involving the participation of stakeholder groups adopted an integrated approach to analysing sustainable water management, considering provisioning, regulating, and cultural services. However, such studies have been in decline since 2015, in favour of non-participatory studies that were strictly focused on ecological and provisioning issues. Although this reflects greater concern for the health of freshwater ecosystems, it is a long way removed from the essence of ecosystem services, which entails an integrated approach to the interrelationships between hydrology, landscapes, ecology, and humans.The authors thanks the UNESCO UNED-URJC Chair in Water and Peace institutional coverage to the development of this study.info:eu-repo/semantics/publishedVersio

Stoichiometry and stable isotopes of plants and their response to environmental factors in boreal peatland, Northeast China

The alterations of plant composition and diversity pose a threat to the stability of the carbon pool in boreal peatland under climate change. We collected the samples of three plant functional types (deciduous shrubs, evergreen shrubs, and sedge) in seven permafrost peatlands of the Great Hing’an Mountains, China, and measured the properties of total carbon (TC), nitrogen (TN), and phosphorus (TP), their stoichiometric ratios (C:N, C:P, and N:P), and the stable isotope values (δ13C and δ15N) of six tissues (ranging from leaves to roots). For TC, TN, and TP, the contents had an average of 470.69 ± 1.56, 8.03 ± 0.23, and 1.71 ± 0.61 mg·g−1, respectively. TC contents of sedge were lower than those of shrubs for the whole plant. The allocations of N and P to shrub leaves were higher than to stems and roots. There was a similar trend of TN and TP contents, and stoichiometric ratios from leaves to roots between deciduous shrubs and evergreen shrubs. Shrubs and sedge have similar C: N in leaves and fine roots, while leaves of sedge C:P and N:P ratios were higher than shrubs, mainly showed that sedge is N and P co-limitation and shrubs are N limitation. The values of δ13C and δ15N were significantly higher in leaves and roots of sedge than those of shrubs, which means shrubs have higher nutrient acquisition strategies. These results support the shrubs are expanding in the boreal peatland under climate warming through nutrient competition. TC contents of all deciduous shrubs and sedge tissues were positively linear correlated to MAT and the values of δ13C and δ15N in sedge had significant relationships with MAT and MAP. Our results imply warming can increase plant photosynthesis in boreal peatland, and sedge was more sensitive to climate change. These findings would be helpful to understanding the responses of different plant tissues to climate changes in permafrost peatland

Celebrating 25 Years of World Wetlands Day

The purpose of this Special Issue is to celebrate 25 years of “World Wetlands Day”. There is no other ecosystem that has its very own Ramsar Convention or such a challenge impacting ecosystem sustainability. Papers for this Special Issue provide an overview of wetland status and function within different regions of the world. The papers in this Special Issue of Land consist of three review papers, ten research articles and one perspective paper. Edward Maltby’s review paper provides us with an overview of the paradigm shift of how we value and assess wetlands over time. Ballut-Dajud et al. provide us with a worldwide perspective on factors affecting wetland loss. Finally, Jan Vymazal provides us with a historical overview of the development of water quality treatment wetlands in Europe and North America. The research papers can be grouped into four groups: 1) use of remote sensing to analyze stability and dynamic factors affecting wetlands; 2) factors affecting the wetlands’ ability to store carbon; 3) assessment of wetlands effect on water quality; and 4) understanding historical use and value of wetlands, farmer’s attitudes about wetland management, and how we can value wetland ecosystem services. Finally, Bryzek et al. remind us that, as wetland researchers and managers, we should minimize damage to wetlands even through field monitoring work

Linking Biophysical and Economic Assessments of Ecosystem Services for a Social–Ecological Approach to Conservation Planning: Application in a Biosphere Reserve (Biscay, Spain)

The search for a balance between nature conservation and sustainable development remains a scientific and spatial planning challenge. In social-ecological systems based on traditional rural activities and associated with protected areas, this balance is particularly complex. Quantifying the economic impact of land use changes on ecosystem services can be useful to advise policy makers and improving social-ecological sustainability. In this study, we evaluated the land use changes in a time series and estimated the monetary value of the ecosystem services of the Urdaibai Biosphere Reserve (Biscay, Spain). In addition, we linked the monetary and biophysical values of land uses in each zoning units of the reserve, in order to identify the spatial adjustment between both assessments. Results showed that land use changes have clearly homogenized the landscape without substantially affecting its economic value. The methodological approach allowed detection that the reserve zoning was performed based more on its biophysical values than on economic ones. Thus, evident divergences between the biophysical and economic assessments were found. The core area was the one that had the highest coincidences (medium values) between both ecosystem services assessments, which highlights its importance not only in biophysical terms, is also economical. The procedure followed proved to be a useful tool to social-ecological planning and design of specific conservation strategies for the sustainable development of the area

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions