bluuebunny/crossref_metadata_2025 huggingface dataset:
Perspectives for expanding sorghum production in Europe in the face of climate change 
Authors: Davoudkhani Mohsen , Guilpart Nicolas , Makowski David , Viovy Nicolas , Ciais Philippe , Lauerwald Ronny
Published: March 2024

Sorghum holds the fifth position worldwide in terms of both grain production and cultivation area. However, sorghum is still a minor crop in Europe where, on average, only 0.12% of the cropland area was used for sorghum production between 2017 and 2021. Nonetheless, its production is expanding in this region, with a 57% increase in total sorghum production during the last decade compared to the first decade of the 21st century. Indeed, sorghum is considered a crop of interest for climate change adaptation in Europe due to its high heat tolerance compared to other crops, especially maize. In this study, we aimed to investigate the feasibility of expanding sorghum cultivation in Europe under current and future (middle and end of the 21st century) climatic conditions. We also explored the possibility of replacing maize with locally-produced sorghum for feeding livestock in Europe.To this end, we developed a machine-learning model that predicts sorghum yields from high-resolution climate data using a random forest algorithm. The model was trained on historical sorghum yield data collected in France, Italy, Spain, and the USA, covering the period from 2000 to 2020. The historical sorghum yield dataset comprises 11,644 data points at subnational ‎administrative levels‎. The set of predictors included monthly climate variables such as solar radiation, minimum and maximum temperature, rainfall, and relative humidity calculated over the growing season (April-November) from the ERA5-Land dataset. The model's performance was evaluated based on cross-validation (R2=0.83, RMSE=0.94 t ha-1) for the 2000 to 2020 period.In total, we ran the model for 30 future scenarios using bias-corrected climate data produced by five Global Climate Models of the Coupled Model Intercomparison Project phase 6 (CMIP6), following three Representative Concentration Pathways scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5), and focusing on two periods (2041-2060 and 2081-2100). In almost all scenarios, sorghum yields decreased up to - 1.5 t ha-1 in the southern part of Europe (e.g., center of Spain, south of France, and Italy) but increased substantially up to + 3 t ha-1 in the northern part (e.g., north of Germany, Poland, and Lithuania) compared to historical yields. In all scenarios, at least 39% of European croplands were projected to support sorghum yields higher than 4.6 t ha-1 (the average sorghum actual yield in Europe in the last decade). Our results showed that sorghum production could increase significantly in Europe under future climates. Regardless of the scenario, if sorghum was grown in one out of three years (respectively, one out of six years), at least 90% (respectively, 45%) of maize used as livestock feed could be replaced by sorghum in Europe. These results could provide valuable information for improving feed security in Europe in the face of climate change.
What do people think about 3D geosite models?
Authors: Hajdú Edina , Pál Márton
Published: March 2024

Geoheritage and geotourism are dynamically evolving fields, and much scientific research tries to establish their scientific communication and economic potential at the international level. Geotourism aims to present geoscience to the public through inanimate natural formations of geoscientific importance. Virtual and GIS-based solutions have been applied for years in evaluation, identification, and presentation too. In our case, a web-based platform where visitors can explore geosites through 3D models was designed to expand the scientific toolbox. For this purpose, 12 geosites were selected from the Bakony–Balaton UNESCO Global Geopark. With the web environment, geotourists can get a virtual view of sites when planning their hikes. It also provides information to those who cannot visit the geosites because of physical limitations. It can also be used as a new, interactive element in education. But is it enough to create a platform without asking people what they need? After the creation of the basic interface, a questionnaire was created to assess user opinions. Before filling it, it is recommended to look at least at one model, as the questions are related to it. The questionnaire contains several answer possibilities: multiple choice, rating scale, and open-ended questions are included. The first inquiry is about the structure of the website and its accessibility. This is followed by the quality of the models and their texture. One of the most important parts is where we ask about the usability of the models. At the end of the questionnaire, there was an opportunity to give personal views and opinions.We have received 128 answers in the 3-month time of investigation. It was important to get responses from non-expert tourists and professionals too. We also tried to work with a wide age range. Only half of the respondents were satisfied with the quality of the models, the other half thought that the quality could be improved in terms of detailedness. Most votes were for "interesting", followed by "education research" on the question of model use possibilities. At the end of the questionnaire, many people chose to give their opinions and suggestions in text form. People missed pictures and annotations. The negative side of the models was sometimes confusing. Among the constructive suggestions was the idea of an overview map, where the user can spatially locate where the geosites are. The basic geological background knowledge about the sites was also missed.The models were shared in a Sketchfab platform, and we tried to implement the proposed changes in it. The annotation of the models was easily done, providing them with the names of the geological formations and images of the information boards on the field. The view angle was also restricted to hide the negative side of the models. The website was given an overview map with the models shown as red symbols. Furthermore, the website contains a short geological description of each location and a link to read more interesting information. 
Acid sulfate soil mapping in western Finland: How to work with imbalanced datasets and machine learning
Authors: Estévez Virginia , Mattbäck Stefan , Boman Anton , Liwata-Kenttälä Pauliina , Björk Kaj-Mikael , Österholm Peter
Published: March 2024

One of the main challenges in digital soil mapping is the imbalanced datasets for soils classification. For these datasets, machine learning techniques use to overestimate the majority classes and underestimate the minority ones. In general, this generates maps with poor precision and unrealistic results. Considering these maps for land use decision-making can have dire consequences. This is the case of acid sulfate (AS) soils, a type of harmful soil that can generate serious environmental damage when drained in agricultural or forestry activities. In the study area, the probability of finding AS soils is very high. Furthermore, some of the most hazardous AS soils in Finland are located there [1]. Therefore, it is necessary to create high-precision maps to avoid environmental damage. Since the dataset for this region is highly imbalanced, the first step in creating accurate maps is to balance the dataset. Although most  soil class datasets in nature are imbalanced, this problem has been hardly studied. In this work, we analyze different techniques to address the problem of imbalanced datasets. The methods considered to balance the dataset are under- and oversampling techniques and the combination of both. For the oversampling of the minority class, we create a kind of artificial samples from the quaternary geological map. The method used for the modeling is Random Forest, one of the best methods for the classification of AS soils [2,3]. Balancing the dataset improves the performance of the model in all the studied cases, where the values of the metrics for both classes are above 80%. Furthermore, we create AS soil probability maps for the four balanced datasets and the imbalanced dataset. A detailed comparison between the maps is made. In addition, the extent of the AS soils obtained in all the cases is compared with the extent of the AS soils in the conventionally produced occurrence map [1]. The modeled probability maps created from the balanced datasets have a high precision. The results of this study confirm the importance of balancing the dataset to improve the prediction and classification of AS soils.[1] Geological Survey of Finland. Acid Sulfate Soils–map services http://gtkdata.gtk.fi/hasu/index.html [2] V. Estévez et al. 2022.  “Machine learning techniques for acid sulfate soil mapping in southeastern Finland”. Geoderma 406, 115446.[3] V. Estévez et al. 2023. “Improving prediction accuracy for acid sulfate soil mapping by means of variable selection”. Front. Environ. Sci. 11:1213069.  doi: 10.3389/fenvs.2023.1213069  
Deep Learning based Automation for Rooftop Solar Potential Estimation using high-resolution UAV data 
Authors: Bhattacharya Apratim , Khatua Aniruddha , Aithal Bharath H.
Published: March 2024

The acquisition of data pertaining to built-up areas and the accessibility of their suitable vector format are crucial in a multitude of remote sensing applications. The adoption of a renewable energy generation method is inevitable in response to the escalating energy demand and to fulfill Sustainable Development Goals. Rooftop solar photovoltaic energy generating optimizes panel installation area efficiently and minimizes transmission losses. The process of identifying a suitable rooftop area for solar photovoltaic (PV) systems using a conventional approach to digitizing buildings through vectorization is not only laborious but also consumes a significant amount of time. The accurate and efficient creation of building rooftop extraction has posed a substantial and intricate challenge within the field of remote sensing. Though many strategies have been developed and these demonstrate a high level of efficacy in feature recognition and extraction but lack the integrity of geospatial information. This research introduces a model pipeline that aims to facilitate the transfer of geospatial information from the input image. Additionally, the proposed process has the capability to generate vector shapefiles representing the building rooftops and the capability of estimating the solar rooftop potential utilizing the extracted rooftop in a suitable format. This is developed as an open-source GUI to help decision makers and planners to easily utilize the developed pipeline. Keywords: Remote sensing, GIS, Deep Learning, Automation, UAV data processing, Rooftop solar potential
The spatiotemporal distribution of water level extreme events in global lakes from 2000 to 2022
Authors: Bai Bingxin , Mu Lixa , Ma Chunyong , Chen Ge , Tan Yumin
Published: March 2024

Global extreme climate events are becoming more frequent or severe, and climate change may have a significant impact on lake systems. However, the understanding of extreme events in lake water levels worldwide is still relatively limited. In this study, we used the water level records of 245 lakes worldwide from altimetry satellite over the past thirty years to quantitatively analyze the extreme water level events of lakes. We labeled these lakes as reservoirs, endorheic lakes, permafrost-fed lakes, etc. Also, we analyzed the spatiotemporal differences of extreme water level events between different types of lakes. Then, we analyzed the relationship between the extreme events of lakes water level, weather variables, and large-scale climate oscillations, so as to understood the factors driving the occurrence of extreme events of lake water level. We have found out that in the past two decades, under the definition of extreme events in this study, the percentage of lakes experienced extreme events of water level exhibits a vibration of approximately ten years, but has not increased as imagined. The spatiotemporal distribution of extreme water level events in different types of lakes varies significantly. It was observed that extreme events in lake water levels are closely related with evaporation and temperature. The implications of this study would be potentially profound, including the forecasting of lake ecology, understanding of the ramifications of climate change, and the facilitation of sustainable water resource management.
Time Lag and Cumulative Effects of Extreme Climate on Coastal Vegetation in China
Authors: Tong Dong , Liu Dahai
Published: March 2024

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable regions such as coastal areas of China. The response of vegetation growth to extreme climates and the time lag-accumulation relationship still require further exploration. We characterize the vegetation growth status by solar-induced chlorophyll fluorescence (SIF), analyzed the vegetation dynamic in coastal areas of China from 2000 to 2019, also explored the spatiotemporal pattern of vegetation, and assessed the response of vegetation to extreme climate in term of time lag-accumulation by combines gradual analysis and abrupt analysis. The results showed that (1) Coastal areas of China were sensitive to global climate change, with extreme high temperatures and extreme precipitation increasing from 2000 to 2019, and the warming in high latitudes was greater than in low latitudes, while the increase in precipitation was concentrated in the southern regions, which are already water-rich. (2) The vegetation in coastal areas of China improved significantly, with gradual analysis showed that the vegetation improvement area accounts for 94.12% of the study area, and the abrupt analysis showed that the majority (69.78%) of the vegetation change types were "monotonic increase", with 11.77% showing "increase with negative break" and 9.48% "increases to decreases." (3) Significant lag-accumulation relationships were observed between vegetation and extreme climate in coastal areas of China, and the time-accumulation effects was stronger than time-lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2-3 months. These findings contribute to filling gaps in understanding the time lag-accumulation effects of extreme climates on vegetation in sensitive coastal regions. It provides a foundational basis for predicting the growth trend of coastal vegetation, environmental changes and ecosystem evolution, which is essential for a comprehensive assessment of coastal ecological security.
Capturing geospatial data on farm management practices in vulnerable farmer-water systems: Lessons from the Sahel
Authors: Elagib Nadir Ahmed , Ahmed Bashir M. , Sulieman Hussein M. , Rahma Abbas E. , Ali Marwan M.A. , Schneider Karl
Published: March 2024

 Emphasis has been placed worldwide recently on the need to view social sensing and geospatial big data as an analogue of remote sensing data. The attempt to establishing a firm footing of this kind of data is essential, for example, to: 1) understand the complex coupling of human and natural systems and 2) make useful policy interventions related to sustainable land and water management. However, most vulnerable communities to natural disasters, whose livelihood and economies are dependent on farming, lack such data. Without suitable socio-economic and farm management data, agricultural governance becomes less responsive or even fails, particularly when the agricultural systems are affected by natural disasters. In this study, we highlight nine lessons learned from our first experience during extensive and comprehensive household surveys of farm management practices recently conducted in the arid and semi-arid zones of Sudan. The aim here is to offer guidelines for researchers and practitioners to carry out successful campaigns in similar settings. These campaigns were implemented as part of the DFG funded SHADRESS project, “Sociohydrological analysis of drought resilience in Sahelian Sudan farming systems”. The surveys were conducted by means of Information and Communication Technology (ICT) via smartphone app and traditional paper-based approach. Two hypotheses were assessed: First, the two survey methods can be integrated and utilized to generate direct and ongoing communication between farming stakeholders. Second, this stakeholder network and dialogue can help acquire big social datasets to fill the data gap within the agriculture sector and, subsequently, address water and food security. We categorize the lessons and guidelines as logistics, technology, culture and behavior related. More than 70 questions related to the socio-hydrological farming system were addressed. The surveys resulted in capturing responses from ~1640 households distributed over three farming systems, namely traditional rainfed, mechanized rainfed and irrigated systems. This dataset contains rich information to enable detailed spatial analyses of farm management strategies and understanding of generic concepts of farmer-water interactions in a drought-vulnerable region. 
Measures to reduce land subsidence and greenhouse gas emissions in peatlands: a Dutch case study
Authors: Wils Tommy , van den Akker Jan , Korff Mandy , Bakema Guido , Hegger Dries , Hessel Rudi , van den Ende Mandy , van Gils Martijn , Verstand Daan
Published: March 2024

Worldwide, peatlands suffer from land subsidence and greenhouse gas emissions due to artificial drainage inducing peat decomposition. Under anthropogenic climate change, these issues require measures to reduce the emission of greenhouse gases and protect low-lying areas from increasing flood risk. It is evident that tighter control of groundwater levels is required, both within existing agricultural systems and through the development of new agricultural systems suitable for farming under high groundwater levels or inundation. The complexity and value-laden nature of the issue warrants the development of a comprehensive overview of potential and side effects of measures. In this paper such an overview is synthesized based on a mixed-method approach for a special case, The Netherlands. The Dutch peatlands comprise extensive land areas in the low-lying west and north of The Netherlands. The case is exceptional as most of the these peatlands lie below sea level, sustain world-class intensive dairy farming and are subject to multiple other environmental, economic and societal challenges. Here, land subsidence increases flood risk, salt water intrusion and the costs of water management, particularly under global climate change. We review 27 technical measures and alternative land use options and synthesize evidence and insights for 15 effects. Technical measures allowing continuation of existing dairy farming provide relatively low-risk interventions, but will only reduce, not stop land subsidence and greenhouse gas emissions. Alternative land-use options, particularly paludiculture, are in a pioneering stage of development and can stop land subsidence. However, more research is required to reduce and control methane and nitrous oxide emissions during inundation required for crops such as (narrowleaf) cattail and azolla. Paludiculture can provide ecosystem services related to water management and nutrient status, as well as raw materials for a bio-based economy. Gradual transitions in space and time between farming and nature can be envisaged, providing incentives to diversify land use in the Dutch peatlands. This case study identifies key questions and provides valuable insights for peatland management worldwide. Reducing land subsidence and greenhouse gas emissions from peatlands is feasible, but requires thoughtful interventions that cautiously make and align trade-offs between various interests and uncertainties.
Ramp-Like PM2.5 Accumulation Process and Z-Less Similarity in the Stable Boundary Layer
Authors: Shi Yu , Hu Fei
Published: March 2024

Similar ramp-like structures exist in the time variations of surface PM2.5 accumulationduring the four different heavy haze pollution processes in Beijing, from 3 November 2017 to 15 January2018. Based on the ultrasonic anemometer observations at seven different altitudes on a 325 m tower, it isshown that turbulence momentum flux exchanges between different altitudes were very weak duringthe ramp period of PM2.5 concentration. Turbulence at the higher altitude associated with strong windshear, occasionally mixed downward toward the surface, showing upside-down transportation of turbulentkinetic energy (TKE), especially during the rapid removal stage of the pollution. This was different fromthe traditional upward TKE transportation above the surface. Vertical distribution of nondimensionalizedstandard deviation of the horizontal velocity, vertical velocity, and potential temperature in the stableboundary layer within the ramp-like structure show obvious “z-less” similarity, independent of z, andalmost equal to constants (𝜎u/u∗l ∼ 3.9, 𝜎w/u∗l ∼ 1.52, and 𝜎𝜃/T∗ ∼ 3.96).
Occurrence of drought in groundwater over the last 12 years
Authors: Slivova Valeria , Kurejova Stojkovova Michaela
Published: March 2024

Groundwater is a very important component of water circulation in nature, it is indispensable in every country’s wealth. Ensuring protection of its sustainable use is the most important requirement for preserving the quality of life, health of natural conditions and economic development of each sector. Groundwater is the main source of drinking water in Slovakia. This contribution assesses groundwater drought occurrence recorded at 207 objects in the Slovak groundwater monitoring network.  This comprises 141 groundwater level boreholes and 66 spring yield gauging stations. The Sandre method was used for this assessment. This method is based on a statistical comparison of the average monthly values of the hydrological year evaluated with the long-term monthly average over the reference period 1981-2010. For each month of the reporting period, five separate categories are established on the basis of the statistical treatment of the average monthly values of spring yields and groundwater levels. The period of the last 12 years (2011-2012) has been evaluated. The results show that 3 years (2012, 2019 and 2022) were assessed as the dry years, 3 years were assessed as wet (2011, 2013 and 2021) and 6 years were assessed as average (period 2014 - 2016, 2018 and 2020). Within each years, groundwater drought occurred most frequently in winter, spring and summer. The main source of groundwater is the spring melting of snow. In the last years we can see, there is earlier melting of the snow as a result of warm winters and has been a lack of snow cover in the lower positions in the Slovakia. These are the main causes of the occurrence of droughts in groundwater in the winter and spring period. During the summer period, groundwater drought is caused by high evapotranspiration and rainfall deficits. The occurrence of local storms does not have a significant impact on the replenishment of groundwater resources.    Keywords: groundwater drought, rainfall deficit, spring yield, groundwater level
Solar variability and delayed ionospheric response: Insights from complex 27-day solar EUV signatures
Authors: Dühnen Hanna , Vaishnav Rajesh , Schmölter Erik , Jacobi Christoph
Published: March 2024

The solar extreme ultraviolet (EUV) radiation drives the major ionization processes in the upper atmosphere. Its variability causes a related response in ionospheric observables. Especially of interest is the delayed response of electron density (Ne), integrated total electron content (TEC), and the density of major neutral and ionized species to the 27-d solar rotation period. But this solar signature is often influenced by underlying trends on shorter and longer time-scales. Therefore, this study examines the ionospheric response to a solar 27-d signature superposed with a long-term increase in solar EUV, showing that complex composition changes in the upper atmosphere influence the expected response significantly. Using high-resolution simulations of the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), we compare two different 27-d solar rotation periods from the year 2014 with enhanced solar activity. This allows us to compare an almost ideal solar activity input with one that is superposed with an increase in solar activity. The main results show that the accumulation of ionized species O+ and O2+ in the lower ionosphere, especially up to the maximum density of ionized oxygen (O+) at about 230 km, is significantly affected by the long-term increase in solar activity.  Nevertheless, the 27-d solar rotation period dominates the ionization in both, ideal and complex model run for altitudes above 230 km. Thus, our results are in good agreement with preceding studies and extend the study of the delayed ionospheric response to more complex cases.
A global efficiency map of ocean alkalinity enhancement (OAE) for CO2 removal
Authors: Zhou Mengyang , Tyka Michael D. , Bachman Scott , Yankovsky Elizabeth , Karspeck Alicia R. , Ho David T. , Long Matthew C.
Published: March 2024

To limit global warming to below 2°C by 2100, carbon dioxide removal (CDR) from the atmosphere will be necessary. Ocean alkalinity enhancement (OAE) is a promising approach to achieving CDR at a large scale. However, OAE deployments are subject to slow or incomplete air-sea CO2 exchange, reducing the efficiency of carbon removal, defined as the excess CO2 uptake per mol of alkalinity addition. We used a coupled ocean circulation-biogeochemistry model to generate the first global, time-resolved map of OAE efficiency across four different seasons and investigated its controlling factors. An ensemble of alkalinity pulse injections in the global ocean were simulated with the global 1-degree ocean component of the Community Earth System Model version 2 (CESM2). Alkalinity was added to the surface ocean for 1 month in a total of 690 patches and in 4 different seasons of a year. Each simulation was run for 15 years for each patch and season to compute OAE efficiency, residence time of excess alkalinity retained in the mixed layer, and CO2 re-equilibration timescales - all referenced to the geographic location of the induced perturbation. OAE efficiency showed large spatial and seasonal variations. The highest seasonal mean OAE efficiency achieved after 15 years, ranging from 0.7 to 0.9, were found in the subpolar oceans, the semi-closed regions, such as the Gulf of St. Lawrence and the North Sea, as well as the coastal zones along the Pacific and South Atlantic. The lowest seasonal mean, ranging from 0.3 to 0.5, was found in the high latitudes Atlantic and Southern Ocean where deep water forms. The intermediate values, ranging from 0.5 to 0.7, were found predominantly in the subtropical gyres, as well as western and eastern boundary currents. Seasonally, higher maximum OAE efficiency could generally be achieved when alkalinity is released in the summer rather than in winter. Accurate understanding of the CO2 response curves, as provided by our maps, is critical for choosing suitable OAE deployment sites and is central to the MRV (Measurement, Reporting & Verification) challenge faced by all marine CDR methods. 
Exploring the potential of horizontal wells for Aquifer Storage and Recovery
Authors: Kreipl Simon , van Breukelen Boris M. , Bakker Mark
Published: March 2024

Aquifer Storage and Recovery (ASR) is a groundwater management technology in which freshwater is infiltrated into the subsurface during periods of abundance and later extracted for use in periods of scarcity. An example application is balancing water supply and demand for agriculture in regions with strong seasonal fluctuations in precipitation or evaporation. Horizontal directionally drilled wells offer advantages over conventional vertical wells. They are suitable in thin aquifers and the drawdown during pumping is spatially distributed and therefore less pronounced. Well fields consisting of multiple vertical wells can be replaced by a single horizontal well, thereby reducing the required above-ground infrastructure. Furthermore, it is expected that horizontal wells are favorable in saline conditions, where the buoyancy effect and dispersion deform the injected freshwater bubble. Such deformations commonly reduce the recovery efficiency, which is the percentage of injected freshwater that can be recovered. There is currently a lack of knowledge on the behavior of a freshwater bubble in a saline aquifer during a horizontal well ASR cycle. An ASR cycle consists of an injection period, a storage period, and a recovery period.In this investigation, the application of horizontal well ASR is investigated by means of density-dependent, numerical groundwater modeling with SEAWAT. A horizontal well is compared to a system of multiple vertical wells arranged in a line. The concept of Multiple Partially Penetrating Wells is adopted for the vertical wells. The two systems are compared for a variation of hydrogeological conditions and design choices. The investigated hydrogeological conditions include hydraulic conductivity, dispersivity, aquifer thickness, and groundwater salinity. The investigated design choices include pumping rate and well depth in the aquifer. The systems are evaluated based on the recovery efficiency and the maximum pressure induced by pumping. It is demonstrated under what conditions a horizontal well has distinct advantages over a row of vertical wells.
Extreme atmospheric rivers in a warming climate
Authors: Wang Shuyu , Ma Xiaohui , Zhou Shenghui , Wu Lixin , Wang Hong , Tang Zhili , Xu Guangzhi , Jing Zhao , Chen Zhaohui , Gan Bolan
Published: March 2024

Extreme atmospheric rivers (EARs) are responsible for most of the severe precipitation and disastrous flooding along the coastal regions in midlatitudes. However, the current non-eddy-resolving climate models severely underestimate (~50%) EARs, casting significant uncertainties on their future projections. Here, using an unprecedented set of eddy-resolving high-resolution simulations from the Community Earth System Model simulations, we show that the models’ ability of simulating EARs is significantly improved (despite a slight overestimate of ~10%) and the EARs are projected to increase almost linearly with temperature warming. Under the Representative Concentration Pathway 8.5 warming scenario, there will be a global doubling or more of the occurrence, integrated water vapor transport and precipitation associated with EARs, and a more concentrated tripling for the landfalling EARs, by the end of the 21st century. We further demonstrate that the coupling relationship between EARs and storms will be reduced in a warming climate, potentially influencing the predictability of future EARs.
Urban green space: global assessment of potential energy demand reduction in buildings
Authors: Falchetta Giacomo , De Cian Enrica
Published: March 2024

Climate change impacts are increasingly felt, and a key hazard for human health is exposure to chronic and acute heat. Air conditioning is an effective indoor adaptation technology. However, it is widely regarded as a form of “maladaptation” due to its high energy intensity and the detrimental impact it has on urban outdoor temperatures and global greenhouse gas emissions. On the other hand, urban green space (UGS) is widely regarded as an effective green infrastructure with potential to mitigate the urban heat island effect. In this context, here we built on a global validated model based on street-level vegetation density, satellite imagery, and ancillary covariates to track UGS in a large sample of cities worldwide (Falchetta and Hammad, forthcoming) and derive a context-aware but generalized statistical linkage with buildings electricity consumption statistics. Based on the modelled relations, we derive future projection of the potential contribution of UGS expansions to energy demand reduction in buildings in different regions of the world. Our study advances the quantitative, globally relevant understanding of the intersection between climate change adaptation and mitigation, and the role of nature-based solutions to reduce the feedback impacts of adaptation while providing ecosystem service co-benefits.
The biomechanics of path of least resistance of roots in heterogeneous substrates
Authors: Yao Jiaojiao , Barès Jonathan , Kolb Evelyne , Dupuy Lionel
Published: March 2024

Rooting depth is critical for plants to acquire water and nutrient efficiently. However, when progressing deeper into the soil, a growing root must overcome physical obstacles such as stones and zones with different mechanical impedance (like hard pans and aggregates) which results in tortuous trajectories and a reduced ability to reach deeper soil horizons. We have developed different model systems which consists of roots growing in artificial substrates made of a customized arrays of stiff or deformable obstacles which the root can either bypass or penetrate based on the resistance of the obstacle. High-throughput imaging systems were used to capture time lapse data and image analysis techniques were used to track root responses to obstacles. In the presence of rigid obstacles, only a limited number of growth responses were observed with a transition from vertical to oblique trajectories observed as a function of size and distance between physical obstacles. When obstacles were deformable the likelihood of penetration could be predicted from factors such as the incidence angle, the length of the root that can bend freely, and the degree to which previous obstacles compress and anchor its base. Overall, our results showed that primary root growth in heterogeneous substrates is largely deterministic and can be predicted from the maximum curvature a root can bend, the spatial arrangements of obstacles and the mechanical stress anchoring the base of the root.Keywords: root, soil, mechanical impedance, heterogeneity, biomechanics
Overcoming Barriers to Sustainable Rice Production: A Remote Sensing-Enabled Approach
Authors: Kupfer Nick , Montzka Carsten , Quoc Vo Tuan
Published: March 2024

In Vietnam, conventional rice cultivation is under strong economic and ecological pressure. Against this backdrop, there is a rising demand for organic products both domestically and globally. In response, OrganoRice aims to facilitate the transition to organic farming in the model provinces of Vinh Long, Dong Thap, and An Giang in the Mekong Delta through a collaborative effort between German and Vietnamese partners. The initiative encompasses not only addressing physical challenges such as soil and water pollution reduction, optimal fertilization, and ecological plant protection but also delves into critical socio-economic dimensions, including enhancing the income of rice farmers and product marketing. The project acknowledges the intricate task of integrating cultural identity and individual farmers into the social fabric of the village community as a crucial factor for success in the conversion process. Direct communication with the rural population is prioritized, and key local stakeholders and scientific institutions, such as Can Tho University, play pivotal roles in ensuring the project's sustainable success.
The Mekong Delta's agricultural landscape is being explored through advanced tools such as remote sensing and hydrological simulations to map, predict, and optimize crop types, agricultural practices (both conventional and organic), and irrigation water pathways. Leveraging European Copernicus satellites Sentinel-1 and Sentinel-2, alongside PlanetScope equipped with radar and multispectral sensors, allows for monitoring plant growth conditions at a high spatial resolution. The analytical process involves examining remotely sensed data through phenological metrics, quantile mapping, and Fourier transform, complemented by conceptual simulations of irrigation flow paths. The initial phase comprises a comprehensive high-resolution time-series analysis of land use and land cover (LULC) dynamics to identify all potential LULCs influencing organic rice farming. Subsequently, irrigation flow path modeling is employed to estimate complex water dependencies. Ultimately, data fusion of LULC and irrigation analysis, combined with crop-specific pesticide data, results in an opportunity map highlighting suitable areas for organic rice farming. This interdisciplinary approach underscores the importance of integrating technological advancements with socio-economic considerations for a comprehensive and sustainable organic farming transition in the Mekong Delta.
Operational low-flow forecasting using Long Short-Term Memory networks
Authors: Deng Jing , Couasnon Anaïs , Dahm Ruben , Hrachowitz Markus , van Heeringen Klaas-Jan , Korving Hans , Weerts Albrecht , Taormina Riccardo
Published: March 2024

This study delves into the potential application of Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily basis, extending up to 46 days ahead. The study introduces an innovative LSTM-based model architecture which incorporates both historical observations and forecasted meteorological data for multi-step discharge time series forecasting. The feature and target selection experiment reveals that subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach, and using time-differenced discharge as the target variable boosts short-term forecast accuracy compared to using discharge. While incorporating historical discharge improves discharge forecasting, its impact on predicting time-differenced discharge is less pronounced for short lead times. The model is then trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast data. In the operational forecast, compared to the benchmark process-based model wflow_sbm, the DL model exhibits skill in forecasting low flows and better accuracy in forecasting drought events within short lead times. This study also explores the flexibility of the DL model through fine-tuning the pretrained model with limited seasonal forecast dataset. The fine-tuning approach reveals marginal short-term enhancements in forecasting low-flow events compared to a non-fine-tuned model. Overall, this study contributes to advancing the field of low-flow forecasting using deep learning approach.
Reclaimed wastewater or surface water use in irrigation: Delineating potential fate and impacts of pharmaceuticals 
Authors: Mukhopadhyay Anwesha , Banerjee Sonali , Jha Sonam , Ghosh Saibal , Bhattacharyya Pradip , Mukherjee Abhijit
Published: March 2024

The depletion of groundwater storage is being acknowledged as a progressively severe global issue. Around 69% of the total groundwater abstracted is used in the agricultural sector. Hence, it is imperative that we refrain from excessive abstraction of groundwater for irrigation and instead focus on utilizing surface water or reclaimed wastewater. However, these alternate sources can be contaminated with certain emerging contaminants (EOCs) that might raise concern in the future. Hence, this study aims to investigate the effect of surface water or reclaimed wastewater irrigation on rice (Oryza sativa), a water-intensive staple grain in the Asian region. A field experiment was carried out on rice, in which they were subjected to irrigation with water spiked with three commonly detected pharmaceuticals in the environment, namely sulfamethoxazole (SMX), carbamazepine (CBZ), and ibuprofen (IBP). The crops were irrigated at regular intervals for ten times throughout the growing period with three different pharmaceutical doses (0.5g), medium (1g), and high (5g). The control set was irrigated with uncontaminated water. Post-harvest agronomical analysis suggests that the grain yield remained unaffected by contaminant addition, whereas the straw yield was increased by up to 25%, 29% and 33% for SMX, CBZ, and IBP, respectively. The contaminant concentration in the rice grains was found to be greater than the limit of detection (LOD) for all the contaminants at different doses but was not > LOQ for some of them. The health quotient (HQ) for SMX and IBRU was
An elucidation on the distribution of arsenic safe aquifers: Introspection from the Gangetic basin
Authors: Bhowmik Tridip , Bose Oindrila , Kumar Kanhaiya , Dutta Ankit Dipta , Jha Maya , Bose Nupur , Ghosh Ashok , Singh Chander Kumar , Sengupta Probal , Mukherjee Abhijit
Published: March 2024

The obscurity in the distribution of arsenic safe aquifers poses critical challenges in devising mitigation strategies. This study attempts to bridge this knowledge gap by providing insights on the distribution of redox distinct sediments, hydrogeochemical characteristics and the plausible controls that govern As distribution in the safe and unsafe aquifers. In this study, a total of 75 drillings have been conducted in 3 study sites (2 in West Bengal and 1 in Bihar, India) across ~25 km2 of area in each of the sites. The results revealed that in the case of North 24 Parganas (NP) in West Bengal, a continuous layer of brown-colored sand was observed at a depth between 40 and 60 m overlain by a grey-colored sand layer, whereas in the case of Nadia (ND) in West Bengal and Bhagalpur (BHG) in Bihar, the grey-colored sand layer was prominent. The brown-colored/brownish-grey-colored sand layer was fragmentary in both ND and BHG, with small lenses found in some parts of the study area. In the case of NP, the brown sand layer was protected by an aquitard layer. Groundwater chemical analysis revealed that the majority of the grey sand aquifers in both NP and ND yielded water with a high As concentration, while >80% of the wells installed in the brown sand layers exhibited a low As concentration (
Utilizing AQUARIUS as Water Resource Management Software: A Decision Support Tool in the Operational Area of Jasa Tirta I State-Owned Enterprise, Indonesia
Authors: nugrahany astria , Amirul Muttaqin Rasyid Muhammad , hidayat fahmi
Published: March 2024

Perusahaan Umum Jasa Tirta I (PJT-I) is a State-Owned Enterprise in Indonesia that focuses on Water Resources Management. Currently, PJT-I manages and utilizes water resources in five river basins under the jurisdiction of the Public Works and Housing Ministry. In conducting its business processes, PJT-I is highly influenced by hydroclimatic conditions, water availability, river basin conditions, and water quality. Therefore, the operational activities of PJT-I heavily rely on accurate data and information related to water resources. It is crucial for PJT-I to have the capability to access, analyze, and manage accurate and timely information to enhance the efficiency of water resource management. Hence, the company has implemented the AQUARIUS software as a decision support tool. The four main modules of AQUARIUS—Connect, Time-Series, Forecast, and WebPortal—are integrated into PJT-I to provide timely and reliable data for decision-makers. The AQ Connect module is responsible for managing the automatic extraction of time-series data from external sources. Meanwhile, the AQ Time-Series system is the core of AQUARIUS, serving as the primary platform for managing water resource data, including quantity and quality data, as well as meteorological information and other sensors. This system includes components such as the Time-Series Server, Database, Springboard, and Tools that work together to handle time-series data corrections without affecting raw data, the development of rating curves, derivation, and automatic data computation, as well as the production workflow and data publication from various data sources. AQUARIUS Forecast, as a flexible modeling environment, is specifically designed for river system modeling, processing, and time-series simulation. This module can incorporate complex operational rules into the model, replicating specific operational requirements such as reservoir rules, environmental releases, and water allocation. Finally, AQUARIUS WebPortal is a browser-based information and data presentation system that integrates various aspects of data collection, data storage, reporting, data computation, and data management, providing an efficient real-time information display. With this implementation, PJT-I can be more effective and efficient in managing water resources, enhancing the overall performance of the company. The implementation of AQUARIUS currently provides real-time information on 16 parameters of water resource from a total of 324 sensors and observations managed by PJT-I. Graphical customization of all observation data and its derivatives serves as a decision support tool. Real-time alert notifications from forecasts and monitoring of parameter conditions prepare PJT-I to handle disaster actions. Thus, the implementation of AQUARIUS has improved the effectiveness and efficiency of water resource management by PJT-I overall.
Satellite Images Analysis for Sargassum Recognition
Authors: Montufar-Chaveznava Rodrigo , Vilchis-Ibarra Jesus Daniel , Caldelas-Sanchez Ivette
Published: March 2024

The sargassum has become a problem in the south of Mexico; it arrives massively, and it is necessary to study available data concerning these algae to plan and take actions that minimize its effects when arrives to the coast. In this work we propose a method based on basic image processing techniques to identify sargassum presence from satellite images. We analyze the bands 4, 5 and 6 of Landsat images to visually detect sargassum. We employ floating algae and normalized difference vegetation indexes and the estimation of the temperature in surface earth to establish some kind of relationship between their presence and the season of the year. In this work, the spectral analysis of images shows that frequency amplitudes are constant except in the ones corresponding to sargassum where some anomalies are detected. This observation was verified in the selected bands. We conclude the proposed approximation can be useful to remotely study some characteristics and detect the sargassum (Figure 1) when it is getting close to coasts of the south of Mexico. 

Figure 1. Original image and sargassum detection image.

Acknowledgements: This work was supported by UNAM-PAPIIT IT101521 
Local and nonlocal biophysical effects of historical land use and land cover changes in CMIP6 models and the intermodel uncertainty
Authors: Luo Xing , Ge Jun , Cao Yipeng , Liu Yu , Yang Limei , Wang Shiyao , Guo Weidong
Published: March 2024

Land use and land cover changes (LULCCs) can influence surface temperature through local and nonlocal biophysical processes. However, the local and nonlocal effects of historical LULCCs have rarely been explicitly investigated. In this study, we separate the local and nonlocal effects of historical (1985–2014) human land use activities based on a set of simulations with and without LULCCs from the Coupled Model Intercomparison Project Phase 6. We also attempt to explore the sources of the intermodel difference in the LULCC effects using a variance decomposition method. The results show that the nonlocal effects (-0.06 ℃ at the global scale) dominate the cooling effect of historical LULCCs mainly via decreases in downward longwave radiation and increases in upward shortwave radiation. The local effects are relatively small at the global scale (0.01 ℃) and manifest as warming at low latitudes (driven by weakened sensible and latent heat fluxes) and cooling in the boreal regions (driven by enhanced upward shortwave radiation). There remains a large intermodel uncertainty in the total effects of historical LULCCs, most of which is contributed by the intermodel difference in nonlocal effects. Such intermodel inconsistency in nonlocal effects is mainly attributed to the intermodel difference in changes in downward longwave radiation and surface sensible heat flux. This study highlights the importance of nonlocal effects of historical LULCCs in terms of the magnitude and the contribution to the intermodel uncertainty in the LULCC effects.