An Overview of Drought Monitoring and Prediction Systems at Regional and Global Scales

2017 ◽  
Vol 98 (9) ◽  
pp. 1879-1896 ◽  
Author(s):  
Zengchao Hao ◽  
Xing Yuan ◽  
Youlong Xia ◽  
Fanghua Hao ◽  
Vijay P. Singh
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Information Dissemination ◽  
Data Sources ◽  
Drought Monitoring ◽  
Severe Drought ◽  
Drought Prediction ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Prediction Systems

Abstract In past decades, severe drought events have struck different regions around the world, leading to huge losses to a wide array of environmental and societal sectors. Because of wide impacts of drought, it is of critical importance to monitor drought in near–real time and provide early warning. This article provides an overview of the development of drought monitoring and prediction systems (DMAPS) at regional and global scales. After introducing drought indicators, drought monitoring (based on different data sources and tools) is summarized, along with an introduction of statistical and dynamical drought prediction approaches. The current progress of the development and implementation of DMAPS with various indicators at different temporal and/or spatial resolutions, based on the land surface modeling, remote sensing, and seasonal climate forecast, at the regional and global scales is then reviewed. Advances in drought monitoring with multiple data sources and tools and prediction from multimodel ensembles are highlighted. Also highlighted are challenges and opportunities, including near-real-time and long-term data products, indicator linkage to impacts, prediction skill improvement, and information dissemination/communication. The review of different components of these systems will provide useful guidelines and insights for the future development of effective DMAPS to aid drought modeling and management.

scholarly journals Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta

2019 ◽  
Vol 11 (3) ◽  
pp. 216 ◽  
Author(s):  
Martha Anderson ◽  
George Diak ◽  
Feng Gao ◽  
Kyle Knipper ◽  
Christopher Hain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Water Resource Management ◽  
Radiant Energy ◽  
Data Sources ◽  
Delta Region ◽  
Spatiotemporal Resolution ◽  
Sky Conditions ◽  
Mean Square Errors

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento–San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts.

scholarly journals A PROBABILITY MODEL FOR DROUGHT PREDICTION USING FUSION OF MARKOV CHAIN AND SAX METHODS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 101-104 ◽  
Author(s):  
Y. Jouybari-Moghaddam ◽  
M. R. Saradjian ◽  
A. M. Forati
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Land Surface ◽  
Probability Model ◽  
Sensor Data ◽  
Experimental Result ◽  
Quality Data ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Prediction

Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015.<br><br> For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain.<br><br> The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data.

Combining Live Drilling Data Stream with a Cloud Data Analytics Pipeline to Perform Real-Time Automated Projections to the Bit

2021 ◽  
Author(s):  
Daniel Cardoso Braga ◽  
Mohammadreza Kamyab ◽  
Brian Harclerode ◽  
Deep Joshi
Keyword(s):  
Trend Analysis ◽  
Real Time ◽  
Data Stream ◽  
Data Analytics ◽  
Data Sources ◽  
Rotary Drilling ◽  
Cloud Data ◽  
West Texas ◽  
Multiple Data ◽  
Drilling Data

Abstract During drilling, surveys to determine the wellbore trajectory are performed at every drilling connection. However, due to the offset between the survey instrument and the bit (typically between 30-100 ft), this survey represents the sensor's position which is lagged compared to the bit. This paper describes a method to automatically calculate projections to the bit in real-time utilizing multiple data sources: WITSML stream, BHA components and rotary trend analysis while rotary drilling. The projection to the bit calculation routine is performed in real time every 30 seconds. This paper presents results of projections for four horizontal unconventional wells drilled in West Texas. Nearly 75,000 projections were generated on the four wells, validated with 839 survey stations, with median divergence of the projections from the nearest survey stations being less than one foot.

scholarly journals Monitoring droughts in Eswatini: A spatiotemporal variability analysis using the Standard Precipitation Index

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel H. Mlenga ◽  
Andries J. Jordaan
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Spatiotemporal Variability ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Severe Drought ◽  
Standard Precipitation Index ◽  
Ecological Zones

The spatiotemporal analysis of drought is of great importance to Eswatini as the country has been facing recurring droughts with negative impacts on agriculture, the environment and the economy. In 2016, the country experienced the most severe drought in over 35 years, resulting in food shortages, drying up of rivers as well as livestock deaths. The frequent occurrence of extreme drought events makes the use of drought indices essential for drought monitoring, early warning and planning. The aim of this study was to assess the applicability of the Standard Precipitation Index (SPI) for near real-time and retrospective drought monitoring in Eswatini. The 3-, 6- and 12-month SPI were computed to analyse the severity and onset of meteorological drought between 1986 and 2017. The results indicated that the climate of Eswatini exhibits geospatial and temporal variability. Droughts intensified in terms of frequency, severity and geospatial coverage, with the worst drought years being 1985–1986, 2005–2006 and 2015–2016 agricultural seasons. Moderate droughts were the most prevalent, while the frequency of severe and very severe droughts was low. Most parts of the country were vulnerable to mild and moderate agricultural droughts. Spatial analysis showed that the most severe and extreme droughts were mostly experienced in the Lowveld and Middleveld agro-ecological zones. The 3-, 6- and 12-month SPI computations conducted in January detected the onset of early season drought, thereby affirming the applicability of the index for monitoring near real-time and retrospective droughts in Eswatini. Drought monitoring using the SPI provides information for early warning, particularly in drought-prone areas, by depicting a drought before the effects are felt.

scholarly journals Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

2020 ◽  
Vol 21 (4) ◽  
pp. 611-623
Author(s):  
Manjunatha S ◽  
Annappa B
Keyword(s):  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Public Safety ◽  
Big Data Analytics ◽  
Data Sources ◽  
Data Driven ◽  
The Public ◽  
Multiple Data Sources ◽  
Multiple Data

Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification  performs better in generating emergency alerts.

scholarly journals Development of Integrated Land Simulator

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tomoko Nitta ◽  
Takashi Arakawa ◽  
Misako Hatono ◽  
Akira Takeshima ◽  
Kei Yoshimura
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
General Purpose ◽  
Climate Simulation ◽  
Surface Model ◽  
Drought Prediction ◽  
Multiple Data ◽  
Computational Speed ◽  
Macro Scale ◽  
Change Impact

Abstract Accurate simulations of land processes are crucial for many purposes, such as climate simulation, weather, flood, and drought prediction, and climate change impact assessment studies. In this paper, we present a new land simulator called the Integrated Land Simulator (ILS). The ILS consists of multiple models that represent processes related to land (hereafter, referred to as “land models”). They are coupled by a general-purpose coupler, Jcup, and executed using the Multiple Program Multiple Data approach. Currently, ILS includes a physical land surface model, the Minimal Advanced Treatments of Surface Interaction and Runoff model, and a hydrodynamic model, the Catchment-based Macro-scale Floodplain model, and the inclusion of additional land models is planned. We conducted several test simulations to evaluate the computational speed and scalability and the basic physical performance of the ILS. The results will become a benchmark for further development.

scholarly journals Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

2016 ◽  
Vol 18 (1) ◽  
pp. 5-23 ◽  
Author(s):  
Xuejun Zhang ◽  
Qiuhong Tang ◽  
Xingcai Liu ◽  
Guoyong Leng ◽  
Zhe Li
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Climate Model ◽  
Initial Conditions ◽  
Dry Season ◽  
Drought Monitoring ◽  
Southwestern China ◽  
Forecast Errors ◽  
Wet Season ◽  
Drought Prediction

Abstract In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONIT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. A quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead times. This study highlights a value of this framework in generating near-real-time ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning.

Sign in / Sign up

Export Citation Format

Share Document