Context

The Mekong Delta is widely known as the "rice bowl" of Vietnam, playing a vital role in ensuring national food security and supporting global rice exports. However, agricultural production in this region is heavily impacted by climate change and extreme weather events.

This dataset aims to provide a comprehensive view of the correlation between climate conditions (temperature, rainfall, sunshine hours, humidity) and agricultural output (planted area, rice yield) across different years and crop seasons in the Mekong Delta.

Content

The dataset consists of 2 CSV files (one raw and one processed) containing provincial-level agricultural statistics and corresponding weather data: 1. Agricultural Data: Includes planted area, total production, and rice yield by crop season (Winter-Spring, Summer-Autumn & Autumn-Winter). 2. Weather Data: Average or total climate indicators mapped to the corresponding season for each province.

Files & Data Dictionary

1. raw_mekong_delta_rice.csv (Raw Data)

This is the original data file. It is highly readable and perfect for basic Exploratory Data Analysis (EDA). * tinh_thanh: Province/city name in the Mekong Delta (e.g., An Giang, Dong Thap, etc.). * nam: Year of the record. * mua_vu: Name of the crop season (e.g., dong_xuan for Winter-Spring, he_thu-thu_dong for Summer-Autumn and Autumn-Winter). * dien_tich: Planted area (Thousand hectares). * san_luong: Harvested rice production (Thousand tons). * tổng số giờ nắng: Total sunshine hours during the crop season. * tổng lượng mưa: Total accumulated rainfall during the crop season (mm). * độ ẩm trung bình: Average air humidity (%). * nhiệt độ trung bình: Average temperature during the crop season (°C).

2. processed_mekong_delta_rice.csv (Processed Data)

This file has been cleaned, encoded, and features have been engineered (including lag features) to directly serve the training of Machine Learning and Time Series models. * lat, lon: Latitude and longitude coordinates of the province. * mua_vu_encoded: Categorical season variable encoded into numeric values (0, 1). * nam_offset: Year normalized into an index starting from 0. * tổng số giờ nắng, tổng lượng mưa, độ ẩm trung bình, nhiệt độ trung bình: The weather features. * dien_tich: Planted area. * nang_suat: Rice yield (Production / Area) - Target Variable commonly used for forecasting. * Time-series / Lagged features: * dien_tich_lag1_ss, dien_tich_lag2_ss: Lags of the planted area for the 1st and 2nd previous periods (normalized using StandardScaler). * nang_suat_lag1_ss, nang_suat_lag2_ss: Lags of the yield for the 1st and 2nd previous periods. * dien_tich_roll2_ss, nang_suat_roll2_ss: Rolling mean with a window size of 2 periods. * dien_tich_cross, nang_suat_cross: Cross features combining area and yield metrics.

Inspiration

You can use this dataset to explore several interesting data science tasks: 1. Crop Yield Prediction: Use the weather features and historical yield data (lag features) to forecast the yield for the upcoming season using Machine Learning models (e.g., XGBoost, Random Forest, LSTM). 2. Climate Change Impact Assessment: Analyze how rising temperatures or abnormal rainfall patterns correlate with production declines in specific provinces. 3. Time Series Analysis: Visualize the shifting trends in agricultural land use and planted areas over the years in the Mekong Delta.

Acknowledgements

• A huge thanks to the General Statistics Office of Vietnam (GSO) for providing transparent agricultural statistics.
• Special thanks to Open-Meteo for their high-quality open weather data.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

A case study about climate change on the Mekong delta, with a focus on livelihoods and food security in Vietnam. The author discusses the agricultural importance of the Mekong delta, the impact of climate change on the livelihoods of Mekong delta residents and potential for mass migration and conflict.

The State of Land in the Mekong Region is based on key data and information, largely available in the public domain, to identify and describe the key issues and processes revolving around land in the Mekong Region. It aims to be a basis for constructive dialogue and collaborative decision-making.

The State of Land in the Mekong Region is structured around five domains:

(1) the land-dependent people of the Mekong (2) the land resource base (3) the distribution of land (4) the land tenure security (5) the land governance.

One of the conclusions concerns the increase in the inequality in land distribution, with a reduction of small holders land holdings size on one side, and with a large proportion of agricultural land given in concessions, resulting in a boom in commodity/ export crops. Another striking feature is that contrary to common wisdom, farmers remain the majority of population in all countries except Thailand. The agrarian transition is on its way, but far from complete. Rural to rural migrations are higher than rural to urban ones. The creation of employment in secondary and tertiary sector is not sufficient to absorb the work force coming out of agriculture. This also results in growing cross country migrations. This means the demand for agricultural land is increasing, and explains some of the drive of farmers towards upland areas.

This dataset contains supporting shapefiles used in the article "Challenges in Estimating Long-Term InSAR-Derived Land Subsidence in the Mekong Delta, Vietnam". It includes point datasets derived from PS InSAR and SBAS InSAR analyses for the Mekong Delta, prepared for spatial comparison of point distribution and coherence.

The repository contains five shapefiles:

• PS_Mekong_Delta.shp: PS InSAR points for the full Mekong Delta study area, corresponding to the spatial extent shown in Figure 1 of the article;

• PS_Mekong_Delta_selected.shp: PS InSAR points selected for comparative analyses within the five coherence areas shown in Figure 1;

• SBAS_Mekong_Delta.shp: SBAS InSAR points for the full Mekong Delta study area;

• SBAS_Mekong_Delta_selected.shp: SBAS InSAR points selected for comparative analyses within the five coherence areas;

• Coherence_areas.shp: shapefile showing the location of the five selected comparison areas used in the article.

The InSAR attribute tables contain only four fields:
ID: unique identifier,
Lon: longitude,
Lat: latitude,
coh: coherence.

The Coherence_areas.shp attribute table contains only the field: ID.

These files are provided to document the spatial distribution of PS and SBAS points and to support the interpretation of the comparison presented in the article. The dataset does not include displacement velocity values or any additional processed deformation products.

This article assesses the effectiveness of the Mekong River Commission, its impact on the policies of its members, Thailand, Lao PDR, Cambodia and Viet Nam, and their engagement with the Commission. It also seeks to account for China’s influence on their cooperation, as China, the strongest upstream riparian state, is not a member of this cooperation scheme for the Mekong River basin. This is achieved using a model for explaining regime effectiveness which rests on the two main variables of problem malignancy and problem-solving capacity. Furthermore, the level of engagement of the riparians is accounted for by mainly two geographical variables: position on the river (upstream/downstream), and size of fraction of territory within the basin. (Manuscript received February 10, 2007; accepted for publication July 4, 2007)

[This dataset is embargoed until January 4, 2027]. This dataset provides a calculation of the habitat risk index of mangrove forest in Soc Trang, Ca Mau, Kien Giang, in the Mekong Delta, Vietnam, in 2017. The percentage of area for no, low, medium, and high risk are provided for each stressor across the 3 provinces. Stressors across the three provinces are categorised as erosion, fishing near shore, mangrove exploitation, drought and pollution. For Ca Mau, additional stressors include fishing near shore, erosion and pollution, for Kien Giang, stressors include erosion, and for Soc Trang, stressors include deforestation, fishing near shore, drought and erosion. The risk and intensity of each stressor is weighted by the frequency of the stressor's disturbance, the rate of land area change, change in structure, management effectiveness. Full details about this dataset can be found at https://doi.org/10.5285/75179e35-5f42-4335-ac71-883258e8285a

The report State of Land in the Mekong Region brings together key data and information on the current status of, and changes in, land resources, their social distribution, and the conditions of governance that shape them. The report stresses the need for urgent action towards transformational change. It was co-produced by the Centre for Development and Environment (CDE) of the University of Bern and the Mekong Region Land Governance Project (MRLG). Funding was provided by Switzerland, Germany, and Luxembourg. The Mekong region €“ Cambodia, Lao PDR, Myanmar, Thailand, and Vietnam €“ is in the midst of profound social and environmental change. Despite rapid urbanization, the region remains predominantly rural. More than 60 per cent of its population live in rural areas, and the vast majority of these people are engaged in agriculture. Due to rapid growth of its agricultural sector, the Mekong region has become a global centre of production and trade for commodities such as rubber, rice, cassava, wood, sugar cane, and palm oil. Between 1996 and 2015, overall agricultural land in the Mekong region grew by 20 per cent, or around 9 million hectares. Most of this expansion has occurred at the expense of the region’s natural capital especially forests and other vegetation

Operational, under construction, and planned dams along the Mekong River and its tributaries. Data provided by CGIAR.

WLE (2018), Dataset on the Dams of the Irrawaddy, Mekong, Red and Salween River Basins. Vientiane, Lao PDR: CGIAR Research Program on Water, Land and Ecosystems - Greater Mekong. https://wle-mekong.cgiar.org/maps/

No description is available. Visit https://dataone.org/datasets/sha256%3A8018623dba3a89a2ede47afcf9d48148cdb4cc822a546498f3972571e9533ab3 for complete metadata about this dataset.

Weight and length measurements for 37 Mekong River fish species of the ‘Dai’ fishery

https://doi.org/10.5061/dryad.dr7sqvb5h

Description of the data and file structure

The data package contains 6 .csv files and 4 folders containing plotted output of regressions.

Dai_2020_2021_TL.csv

This .csv file contains 32,274 observations of total length (tl) and weight measurements collected from November 2020 to March 2021 from two "dai" nets on the Tonle Sap River, Cambodia. Further details can be found in the methods.

The table contains three rows: Column "Genus_species" is the genus species name of each fish identified and measured; Column "tl.cm" is the total length measured to the nearest 0.1 centimeters (cm) and reported in centimeters; Column "W.g" is the weight of each fish measured to the nearest 0.1 grams (g) and reported in grams.

Dai_2020_2021_SL.csv

This .csv file contains 32,205 observations of standard length (sl) and weight measurements collected from November 2020 t...

The Lower Mekong Basin (LMB) is biologically diverse, economically important, and home to about 65 million people. The region has undergone extensive environmental changes since the 1990s due to such factors as agricultural expansion and intensification, deforestation, more river damming, increased urbanization, growing human populations, expansion of industrial forest plantations, plus frequent natural disasters from flooding and drought. The Mekong river is also heavily used for human transportation, fishing, drinking water, and irrigation. This paper discusses use of pre-existing LULC maps from 1997 and 2010 to derive a LMB regional LULC change map for 9 classes per date using GIS overlay techniques. The change map was derived to aid SWAT hydrologic modeling applications in the LMB, given the 2010 map is currently used in multiple LMB SWAT models, whereas the 1997 map was previously used. The 2010 LULC map was constructed from Landsat and MODIS satellite data, while the 1997 map was from before the MODIS era and therefore based on available Landsat data. The 1997–2010 LULC change map showed multiple trends. Permanent agriculture had expanded in certain sub-basins into previously forested areas. Some agricultural areas were converted to industrial forest plantations. Extensive forest changes also occurred in some locations, such as areas changed to shifting cultivation or permanent crops. Also, the 1997 map under classified some urban areas, whereas the 2010 LULC map showed improved identification of such areas. LULC map accuracy were assessed for 213 randomly sampled locations. The 1997 and 2010 LULC maps showed high overall agreements with reference data exceeding 87%. The LULC change map yielded a moderately high level of overall agreement (78%) that improved to ∼83% once LULC classification scheme specificity was reduced (forests and agriculture were each mapped as singular classes). The change map regionally showed a 4% decrease in agriculture and a 4% increase in deciduous and evergreen forests combined, though deforestation hot spot areas also were evident. The project yielded LULC map data sets that are now available for aiding additional studies that assess LMB LULC change and the impacts such change may pose to water, agriculture, forestry, and disaster management efforts. More work is needed to map, quantify and assess LULC change since 2010 and to further update the 2010 LULC map currently used in the LMB SWAT models.

Abstract: This dataset contains vertical displacement times series of the Earth’s surface in the Vietnamese Mekong Delta for the time period between April 2017 and April 2022, which were estimated with the Persistent Scatterer Interferometry (PSI) method described in Dörr et al. (2024, accepted for publication). The method was applied to descending and ascending SAR stacks acquired by ESA’s Sentinel-1 constellation, which contained 277 and 115 VV-polarized SAR scenes, respectively, and were acquired in the interferometric wide swath mode. The applied PSI method features (i) a full integration of Temporary Persistent Scatterers, enabling the monitoring of areas with infrastructural land cover changes during the considered time series; (ii) a method to optimally integrated reference points with known displacement to suppress spatially correlated noise. For the latter, we here made use of a combination of solid rock outcrops and an infrastructural reference network consisting of large bridges with deep pile foundations of about 70 m, which were selected in statistical cross-checks. Referencing to these bridges results in the fact that only subsidence originating from soil layers above their foundation depth of the used bridges could be measured in large parts of the study area. However, we discussed in Dörr et al. (2024, accepted for publication) that the largest part of the total subsidence originates from shallower layers. The provided dataset can make a crucial contribution to in-depth studies of the causes and future projections of land subsidence in the Mekong Delta. They can, for example, serve as input for hydro-geological modeling, or can be examined with regard to correlations with environmental and anthropogenic parameters, such as groundwater extraction rates, land cover and land use, as well as climatological data. Reference: Dörr, N., Schenk, A., Hinz, S. (2024, accepted for publication). Land subsidence in the Mekong Delta Derived from Advanced Persistent Scatterer Interferometry with an Infrastructural Reference Network, accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. TechnicalRemarks: The dataset contains two netCDF-4 files and a readme.txt file. The netcdf-4 files contain vertical displacement time series -between April 2017 and April 2022- and related information of identified (temporary) persistent scatterers in the analyzed descending and ascending Sentinel-1 stacks, respectively. The specific contents of the netCDF-4 files, as well as information on its recommended use are given in readme.txt.

This is the data for mass media, governmental websites and companies information about agritourism in Mekong Delta, Vietnam

The Greater Mekong Subregion (GMS: Myanmar, Thailand, Cambodia, Laos, Vietnam, and Yunnan and Guangxi in China) is undergoing unprecedented changes. Many of these are positive, reflecting political stabilization and economic growth following decades of poverty and conflict. But the rate and type of development is also threatening critical natural resources, particularly native forests, the Mekong River and its tributaries, and many wild plant and animal species. The GMS faces a critical choice: it can either continue with unsustainable development and see many of its unique natural resources disappear forever or switch policies and choose a more sustainable path into the future. This report gives an overview of what is happening, and provides key recommendations for how natural resource management can be made more sustainable. The core of the report is a series of maps, developed by WWF, describing the historical trends, current status and future projections of forests in the GMS excluding China. Future projections for the period 2009 to 2030 contrast two scenarios; an unsustainable growth scenario, which assumes deforestation rates between 2002 and 2009 continue, and a green economy scenario, which assumes a 50 per cent reduction in the annual deforestation rate relative to the unsustainable growth scenario, and no further losses in key biodiversity areas.

This rubber suitability raster dataset, with a pixel resolution of 1 x 1 km, covers the Lower Mekong Basin. The suitability of pixels in the region was assessed for cassava based on a comparison of land and hydrological conditions to the requirements of the different crops. This was achieved via the application of a modified version of the Land Use Suitability Evaluation Tool (LUSET) (developed by the International Rice Research Institute (IRRI)) and run for both current conditions and for predicted 2050 conditions. USAID Mekong ARCC worked with IRRI to modify the LUSET tool and apply it to produce these datasets.Download dataset : http://58.137.55.84/owncloud/index.php/s/NYMdMqh54vRLSDl

This report presents the findings from a research project that looked at the interconnections between water and climate change in the lower basin of the Mekong River in Southeast Asia. The project focused on the hydrological impacts of climate change and adaptation strategies in the Tonle Sap area of Cambodia and the Mekong Delta of Vietnam. The report focuses on climate change scenarios, hydrological impacts, livelihoods and local adaptation strategies and adaptation policies, and identifies possible future research and action.

No description is available. Visit https://dataone.org/datasets/sha256%3A1abdc0d6a021399a17e4b11bcdd67521f473375786589ec5935fd0471304cb78 for complete metadata about this dataset.

This dataset provides surface water quality monitoring records from the Tien River and the Hau River

The Southeast Asian region hosts a wealth of diverse forest resources, playing a critical role in pivotal ecological functions including watershed conservation, soil retention, carbon sequestration, and the regulation of regional climate dynamics. Spanning the territories traversed by the Lancang-Mekong River, the largest river system in Southeast Asia, the Greater Mekong Subregion (GMS) encompasses an array of nations, including China (Yunnan and Guangxi Zhuang Autonomous Region), Cambodia, Laos, Myanmar, Thailand, and Vietnam. In order to delineate the spatial coverage and distribution of forest resources within the GMS, this dataset is the result of an extensive international collaboration involving the collection of substantial ground-truth data and Landsat TM/ETM+ imagery. Employing an object-oriented classification approach and machine learning classification algorithms, spatial datasets of forest cover for the years 2005 and 2010 at a 30 m resolution were generated for the GMS and Malaysia. The accuracy of the dataset's validation exceeded 85%. Comprising 16 files, this dataset encompasses raster data (*.tif files) for the years 2005 and 2010 of eight economies (Cambodia, Laos, Thailand, Myanmar, Vietnam, Malaysia, China's Yunnan, and China's Guangxi). The data is projected in the WGS-84 coordinate system with a latitude and longitude projection, and each economy's data includes a buffer zone extending 5 km beyond its respective borders. This dataset not only facilitates an enhanced understanding of alterations in forest coverage within this region but also serves as a scientific foundation for Southeast Asian forest policies, sustainable forest resource management, as well as investigations pertaining to ecological environments and global climate change. The data product is supported by the "Forest Cover and Carbon Mapping in the Greater Mekong Subregion and Malaysia" project, initiated by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet). This collaborative effort includes governmental bodies, research institutions, and academic professionals from the economies within the Greater Mekong Subregion and Malaysia. The overarching objective of the project is to assess the impact of forest cover, forest dynamics, and carbon storage on both the regional environment and global climate change.