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نتیجه جستجو - Drought

تعداد مقالات یافته شده: 24
ردیف عنوان نوع
1 Cultural consensus knowledge of rice farmers for climate risk management in the Philippines
دانش اجماع فرهنگی کشاورزان برنج برای مدیریت ریسک آب و هوایی در فیلیپین-2021
Despite efforts and investments to integrate weather and climate knowledges, often dichotomized into the scientific and the local, a top-down practice of science communication that tends to ignore cultural consensus knowledge still prevails. This paper presents an empirical application of cultural consensus analysis for climate risk management. It uses mixed methods such as focus groups, freelisting, pilesorting, and rapid ethnographic assessment to understand farmers’ knowledge of weather and climate conditions in Barangay Biga, Oriental Mindoro, Philippines. Multi-dimensional scaling and aggregate proximity matrix of items are generated to assess the similarity among the different locally perceived weather and climate conditions. Farmers’ knowledge is then qualitatively compared with the technical classification from the government’s weather bureau. There is cultural agreement among farmers that the weather and climate con- ditions can be generally grouped into wet, dry, and unpredictable weather (Maria Loka). Damaging hazards belong into two subgroups on the opposite ends of the wet and dry scale, that is, tropical cyclone is grouped together with La Ni˜na, rainy season, and flooding season, while farmers perceive no significant difference between El Ni˜no, drought, and dry spells. Ethnographic information reveals that compared to the technocrats’ reductive knowledge, farmers imagine weather and climate conditions (panahon) as an event or a phenomenon they are actively experiencing by observing bioindicators, making sense of the interactions between the sky and the landscape, and the agroecology of pest and diseases, while being subjected to agricultural regulations on irrigation, price volatility, and control of power on subsidies and technologies. This situated local knowledge is also being informed by forecasts and advisories from the weather bureau illustrating a hybrid of technical science, both from the technocrats and the farmers, and personal experiences amidst agricultural precarities. Speaking about the hybridity of knowledge rather than localizing the scientific obliges technocrats and scientists to productively engage with different ways of knowing and the tensions that mediate farmers’ knowledge as a societal experience.
keywords: دانش اجماع | پیش بینی آب و هوا | کشاورزی | خطر ابتلا به آب و هوا | Consensus knowledge | Weather forecasting | Agriculture | Climate risk
مقاله انگلیسی
2 Capacity building in participatory approaches for hydro-climatic Disaster Risk Management in the Caribbean
ایجاد ظرفیت در رویکردهای مشارکتی برای مدیریت ریسک بلایای آبی-اقلیمی در کارائیب-2021
The participatory approach to Disaster Risk Management (DRM) considers socio-economic factors and facilitates the incorporation of local and indigenous knowledge into management plans while offering an opportunity to all resource users to have an input. Caribbean WaterNet/Cap-Net UNDP, Global Water Partnership-Caribbean (GWP-C), and the Faculty of Food and Agriculture, The University of the West Indies (FFA, UWI) conducted a series of regional training of trainers’ workshops in Integrated Urban Flood Risk Management and Drought Risk Management to build regional capacity in this approach. The trainings took place over two years in six (6) Caribbean Small Island Developing States (SIDS). Over 150 persons from a range of sectors relevant to water resource management participated and contributed. The workshop gathered information on sectoral impacts, potential mitigation measures and challenges of hydro-climatic hazards. Capacity building and knowledge transfer was evaluated at two stages; at the end of the last day of training and 6 months after, as part of a monitoring and evaluation assessment. Both the initial and 6-month evaluations revealed significant knowledge transfer and subsequent institutional and policy impacts. Initial evaluation indicated 99% participant satisfaction with both training content and structure. In the six-month evaluation, 85% of participants indicated that the knowledge gained was used to improve their work performance and, in some cases, contributed to changes in institutional policy and frameworks.
keywords: کاهش خطر بلایا | خشکسالی و سیل | مشاوره با ذینفعان | کشورهای جزیره ای کوچک در حال توسعه | Disaster risk reduction | Drought and floods | Stakeholder consultations | Small island developing states
مقاله انگلیسی
3 Future trade-offs and synergies among ecosystem services in Mediterranean forests under global change scenarios
معاملات آتی و هم افزایی بین خدمات اکوسیستم در جنگل های مدیترانه تحت سناریوهای تغییر جهانی-2020
Mediterranean forests play a key role in providing services and goods to society, and are currently threatened by global change. We assessed the future provision of ecosystem services by Mediterranean pine forests under a set of management and climate change scenarios, built by combining different regional policies and climate change assumptions. We used the process-based model SORTIE-ND to simulate forest dynamics under each scenario. We coupled the outputs of SORTIE-ND with empirical and process-based models to estimate changes in harvested timber, carbon storage, mushroom yield, water provision, soil erosion mitigation and habitat for biodiversity by 2100, and assessed the trade-offs and synergies between services. Our results suggest that future provision of ecosystem services by Mediterranean forests will be more strongly determined by management policies than by climate. However, no management policy maximized the provision of all services. The continuation of the business-as-usual management would benefit some services to the detriment of water provision, but leads to higher vulnerability to extreme drought-events or wildfires. Managing for reducing forest vulnerability will balance the provision of services while reducing the risk of damage to forest functioning. We also found multiple spatial synergies between ecosystem services provision, likely driven by differences in site productivity.
Keywords: Ecosystem services | SORTIE-ND | Forest dynamics | EU-forest policies | Climate change | Multi-functional forest | Climate-smart forestry
مقاله انگلیسی
4 Managing underground transfer of floods for irrigation: A case study from the Ramganga basin, India
مدیریت انتقال زیرزمینی سیلاب برای آبیاری : یک مطالعه موردی از حوضه رامگانگا ، هند-2020
Protecting flood prone locations through floodwater recharge of the depleted aquifers and using it for protecting dry season irrigated agriculture is the rationale for a form of intervention termed as ‘underground transfer of floods for irrigation’ (UTFI). This helps reduce the intensity of seasonal floods by tapping and storing excess floodwater in aquifers for productive agricultural use. This paper presents a case study of managing the recharge interventions in the context of the Ramganga basin, India. Using a case study approach, this study determines the socio-economic and institutional context of the study area, proposes three potential routes to institutionalize UTFI, and provides insights for scaling up the interventions in the Ganges and other river basins that face seasonal floods and dry season water shortages. Managing the interventions involves community participation in regular operations and maintenance tasks. Given the limited scale of the pilot UTFI intervention implemented to date, and the socio-economic and institutional context of the case study region, the benefits are not conspicuous, though the piloting helped in identifying potential ways forward for the long-term management of the pilot site, and for scaling up the interventions. Initially pilot site management was handled by the project team working closely with the community leaders and villagers. As the intervention was demonstrated to perform effectively, management was handed over to the district authorities after providing appropriate training to the government personnel to manage the system and liaise with the local community to ensure the site is operated and managed appropriately. The district administration is willing to support UTFI by pooling money from different sources and routing them through the sub-district administration. While this is working in the short term, the paper outlines a programmatic longer term approach for wider replication.
Keywords: Floods | Droughts | Groundwater | Management | Institution | Ganges basin | India
مقاله انگلیسی
5 An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
یک مدل ماشین پیشرفته افراطی برای پیش بینی جریان رودخانه: پیشرفته ترین برنامه ها ، کاربردهای عملی در منطقه مهندسی منابع آب و جهت گیری تحقیقات آینده-2019
Despite the massive diversity in the modeling requirements for practical hydrological applications, there remains a need to develop more reliable and intelligent expert systems used for real-time prediction purposes. The challenge in meeting the standards of an expert system is primarily due to the influence and behavior of hydrological processes that is driven by natural fluctuations over the physical scale, and the resulting variance in the underlying model input datasets. River flow forecasting is an imperative task for water resources operation and management, water demand assessments, irrigation and agriculture, early flood warning and hydropower generations. This paper aims to investigate the viability of the enhanced version of extreme learning machine (EELM) model in river flow forecasting applied in a tropical environment. Herein, we apply the complete orthogonal decomposition (COD) learning tool to tune the output-hidden layer of the ELM model’s internal neuronal system, instead of the conventional multi-resolution tool (e.g., singular value decomposition). ToA-ELM, AdaBoost.RT-extreme learning machine; AI, artificial intelligence; ANFIS, adaptive neuro-fuzzy inference system; ANN, artificial neural network; ARIMA, autoregressive integrated moving average; AtmP, atmospheric pressure; B-ANN, bootstrap-artificial neural network; BCSO, binary-coded swarm optimization; B-ELM, bootstrap-extreme learning machine; C-ELM, complex-extreme learning machine; Cl−1, chloride; COD, complete orthogonal decomposition (COD); CRO-ELM, coral reefs optimization-extreme learning machine; DE-ELM, deferential evolution-extreme learning machine; DID, department of Irrigation and Drainage; DO, dissolved oxygen concentration; EC-SVR, evolutionary computation-based support vector machine; EDI, effective drought index; ELM, extreme learning machine; EELM, enhanced extreme learning machine; EEMD, ensemble empirical mode decomposition; EL-ANFIS, extreme learning adaptive neuro-fuzzy inference system; EMD, empirical mode decomposition; Ens, Nash-Sutcliffe coefficient; Ensemble-ELM, ensemble-extreme learning machine; EPR, evolutionary polynomial regression; ESNs, echo state networks; ETo, evapotranspiration; Fe, iron; Fr, Froude number; FS, factor of safety; GA-ELM, genetic algorithm-extreme learning machine; GCM, general circulation model; G-ELM, geomorphology extreme learning machine; GP, genetic programming; GRNN, generalized regression neural network; HCO3 -1, bicarbonate; HDSR, diffuse solar radiation; HRT, hydraulic retention time; I-ELM, integrated extreme learning machine; KELM, Kernelextreme learning machine; LST, land surface temperature; LASSO, least absolute shrinkage and selection operator; LSTM, long short-term memory network; LSSVM, least square support vector machine; MAE, mean absolute error; MARS, multivariate adaptive regression spline; MBFIPS, Multi-objective binary-coded fully informed particle swarm optimization; MC-OS-ELM, meta cognitive-online sequential-extreme learning machine; MLPNN, multi-linear perceptron neural network; MLR, multiple linear regression; MME, multi-model ensemble; NEMR, northeast monsoon rainfall; NO2 -1, nitrite; NO3 -1, nitrate; NO2, nitrogen dioxide; NT, total nitrogen; O3, ozone; OP-ELM, optimally pruned-extreme learning machine; OSELM, online sequential extreme learning machine; PCA, principal component analysis; pH, power of hydrogen; PM10, air pollution “suspended particulate matters”; PO4 -3, phosphorus; R-ELM, radial basis-extreme learning machine; r, determination coefficient; RE, relative error; RF, rainfall; RH, relative humidity; RHmax, maximum relative humidity; RHmean, mean relative humidity; RHmin, minimum relative humidity; RMSE, root mean square error; RVM, relevance vector machine; SaE-ELM, self-adaptive evolutionary-extreme learning machine; SC, specific conductance; S-ELM, sigmoid-extreme learning machine; SHr, sunshine hour; SR, solar radiation; SO4 -2, sulfate; SiO2, Silicon; SO2,
مقاله انگلیسی
6 Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing
ارزیابی تناسب اراضی برای پیش بینی عملکرد از این گونه گیاهان با استفاده از سیستم های خبره فازی جغرافیایی و سنجش از دور-2019
Cassava has the potential to be a promising crop that can adapt to changing climatic conditions in Indonesia due to its low water requirement and drought tolerance. However, inappropriate land selection decisions limit cassava yields and increase production-related costs to farmers. As a root crop, yield prediction using vegetation indices and biophysical properties is essential to maximize the yield of cassava before harvesting. Therefore, the purpose of this research was to develop a yield prediction model based on suitable areas that assess with land suitability analysis (LSA). For LSA, the priority indicators were identified using a fuzzy expert system combined with a multicriteria decision method including ecological categories. Furthermore, the yield prediction method was developed using satellite remote sensing datasets. In this analysis, Sentinel-2 datasets were collected and analyzed in SNAP® and ArcGIS® environments. The multisource database of ecological criteria for cassava production was built using the fuzzy membership function. The results showed that 42.17% of the land area was highly suitable for cassava production. Then, in the highly suitable area, the yield prediction model was developed using the vegetation indices based on Sentinel-2 datasets with 10m resolution for the accuracy assessment. The vegetation indices were used to predict cassava growth, biophysical condition, and phenology over the growing seasons. The NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the model for predicting cassava growth. The generated models were validated using regression analysis between observed and predicted yield. As the vegetation indices, NDVI showed higher accuracy in the yield prediction model (R2=0.62) compared to SAVI and IRECI. Meanwhile, LAI had a higher prediction accuracy (R2=0.70) than other biophysical properties, fAPAR. The combined model using NDVI, SAVI, IRECI, LAI, and fAPAR reported the highest accuracy (R2=0.77). The ground truth data were used for the evaluation of satellite remote sensing data in the comparison between the observed and predicted yields. This developed integrated model could be implemented for the management of land allocation and yield assessment in cassava production to ensure regional food security in Indonesia.
Keywords: Land suitability | Cassava | Yield prediction | Fuzzy expert systems | Remote sensing
مقاله انگلیسی
7 Construction of a drought monitoring model using deep learning based on multi-source remote sensing data
ساخت مدل مانیتورینگ خشکسالی با استفاده از یادگیری عمیق بر اساس داده های سنجش از دور چند منبعی-2019
Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought.
Keywords: Drought | Remote sensing | Deep learning
مقاله انگلیسی
8 Managing climate risks on the ranch with limited drought information
مدیریت خطرات آب و هوایی برای دامداری با اطلاعات محدود از خشکسالی-2018
Ranching involves complex decision-making and risk management in the face of uncertainty about climate conditions. The profitability and sustainability of ranching depend heavily on sufficient and timely rainfall for rangeland forage production. As a result, ranchers may either adopt conservative long-term stocking strategies as a hedge against drought or practice a more dynamic approach in which they vary stocking rates and supplemental feed in response to drought. Yet, some strategies require more information about climate risks than is often available to ranchers. We review the literature to draw out the drought management options as well as the tools and products for drought monitoring and early warning that are available to ranchers. We find that a large gap remains between the information needs of ranchers seeking to adapt dynamically to drought and the information that is available. Moreover, even when actionable information is available, it is unclear whether ranchers are optimally incorporating that information into their risk management decisions. Further research is needed to understand how to package existing information into risk management decision tools in a way that addresses cognitive and operational barriers to support timely decisions that will reduce the impact of drought on profits and the long-term sustainability of rangelands. Due to the multi-faceted nature of climate risk management in ranching, further study of ranching behavior and decisions has the potential to bring new insights into climate risk management and decision and risk theory far beyond the field of ranching and agriculture.
keywords: Drought adaptation |Climate |Cattle |Ranching |Risk management |Information |Decision-making
مقاله انگلیسی
9 Anatomy of an interrupted irrigation season: Micro-drought at the Wind River Indian Reservation
کالبدشناسی یک فصل آبیاری منقطع: خشکسالی ریز در ذخیره سازی رودخانه بادی هند-2018
Drought is a complex phenomenon manifested through interactions between biophysical and social factors. At the Wind River Indian Reservation (WRIR) in west-central Wyoming, water shortages have become increasingly common since the turn of the 21st century. Here we discuss the 2015 water year as an exemplar year, which was characterized by wetter-than-normal conditions across the reservation and, according to the U.S. Drought Monitor, remained drought-free throughout the year. Yet parts of the reservation experienced harmful water shortages, or “micro-drought” conditions, during the growing season in 2015. In this assessment of the 2015 water year at the WRIR we: (1) describe the hydroclimatic and social processes under way that contributed to the 2015 water year micro-drought in the Little Wind Basin; (2) compare water availability conditions within and between other basins at the WRIR to illustrate how micro-droughts can result from social and environmental features unique to local systems; and (3) describe how a collaborative project is supporting drought preparedness at the WRIR. We combine a social science assessment with an analysis of the hydroclimate to deconstruct how shortages manifest at the WRIR. We provide insights from this study to help guide drought assessments at local scales.
keywords: Drought |Climate vulnerability |Drought preparedness |Indigenous adaptation |Co-production
مقاله انگلیسی
10 Adaptation opportunities and maladaptive outcomes in climate vulnerability hotspots of northern Ghana
فرصت های سازگاری و خروجی های ناسازگاری در نقاط حساس آسیب پذیری های آب و هوایی شمال غنا-2018
How climate change adaptation practices can constrain development and deliver maladaptive outcomes in vulnerability hotspots is yet to be explored in-depth using case study analyses. This paper explores the effects of climate change coping and adaptation responses in three case study villages across the Central Gonja district of northern Ghana. The study addresses the following research questions: i) What are the key climatic and non-climatic stressors confronting households in northern Ghanaian communities? ii) How are households adapting to climatic and non-climatic stressors? and iii) What are the outcomes of these coping and adaptation responses on development? The study employs a mixed-method approach including key informant interviews, focus group discussions and household questionnaire surveys. Data identified socioeconomic stressors including a lack of access to (and high cost of) farm inputs, labour shortages and population growth. Climatic stressors include erratic rainfall, high temperature, droughts and floods. Climatic and non-climatic stressors interact to affect agricultural practices and related livelihoods. The study identified various adaptation measures including extensification and intensification of agriculture, temporary migration, planting of drought resistant varieties, irrigation, and livelihood diversification. We show that many coping measures (e.g. livelihood diversifications activities such as selling of firewood and charcoal production) and adaptation responses (including intensification, extensification and irrigation) currently deliver maladaptive outcomes, resulting in lock-ins that could exacerbate future climate vulnerabilities. The paper contributes to the growing literature on adaptation and climate risk management by providing empirical evidence showing how coping and adaptations measures can deliver maladaptive outcomes in vulnerable communities.
keywords: Maladaptation |Climate change and variability |Livelihoods |Mixed methods |Africa
مقاله انگلیسی
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