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At least 109 records · Page 6Linked to original sources
USGS2016

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi -season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea ( Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary ( kharif ) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second ( rabi ) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

International Journal of Digital Earth↗
USGS2017

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html

International Journal of Digital Earth↗
USGS2024

Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood within the United States (U.S.). The U.S. Department of Agriculture Cropland Data Layer does provide cropland fallow areas; however, at a significantly lower confidence than their cropland classes. This study developed a methodology to map cropland fallows within the Northern Great Plains region of the U.S. using an easily implementable decision tree algorithm leveraging training and validation data from wet (2019), normal (2015), and dry (2017) precipitation years to account for climatic variability. The decision trees automated cropland fallow algorithm (ACFA) was coded on a cloud platform utilizing remotely sensed, time-series data from the years 2010–2019 to separate cropland fallows from other land cover/land use classes. Overall accuracies varied between 96%-98%. Producer’s and user’s accuracies of cropland fallow class varied between 70-87%.

International Journal of Digital Earth↗
USGS2009

Review of FEWS NET biophysical monitoring requirements

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users; it focused upon operational requirements to determine additional useful remote sensing data and, subsequently, to assess whether such data would be beneficial as FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard; that high resolution remote sensing products continue to be in demand; and that rainfall and vegetation products are valued as data that provide actionable food security information.

Environmental Research Letters↗
USGS2026

Inland recreational fisheries harvest far exceeds reported inland harvest in the United States

Recreational fisheries are important global contributors to food security, socio-cultural practices, and local and regional economies. However, inland recreational fisheries are often overlooked by policymakers due to a limited understanding of the magnitude of participation, harvest, and economic impact. Here, we used the U.S. Inland Creel and Angler Survey Catalog and catch and effort model (CreelCatch) and several assumptions to provide an initial estimate of the magnitude of total inland recreational fisheries harvest in the conterminous USA. The CreelCatch model projected fishing harvest across lakes, ponds, and reservoirs based on fishing effort, water body area, and regional effects. We estimated that recreational lake fisheries in the conterminous USA likely harvest 236,000–671,000 tonnes of fish per year, 17–48 times greater than total inland fisheries harvest reported to the United Nations. Inland recreational fisheries may warrant greater consideration for their contribution to national scale socioeconomics and impacts on fish stocks and ecosystems.

conterminous United States↗
USGS2019

Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface

Managing pathogen spillover at the wildlife–livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host–pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife–livestock interface.

Philosophical Transactions of the Royal Society A:↗
USGS2018

Behavioural plasticity modulates temperature-related constraints on foraging time for a montane mammal

Contemporary climate change is altering temperature profiles across the globe. Increasing temperatures can reduce the amount of time during which conditions are suitable for animals to engage in essential activities, such as securing food. Behavioural plasticity, the ability to alter behaviour in response to the environment, may provide animals with a tool to adjust to changes in the availability of suitable thermal conditions. The extent to which individuals can alter fitness‐enhancing behaviours, such as food collection, to proximately buffer variation in temperature, however, remains unclear. Even less well understood are the potential performance advantages of flexible strategies among endotherms. We examined the degree to which individuals altered rates of food collection in response to temperature, and two potential benefits, using the American pika ( Ochotona princeps ), a temperature‐sensitive, food‐hoarding mammal, as a model. From July–September 2013–2015, we used motion‐activated cameras and in situ temperature loggers to examine pika food‐caching activity for 72 individuals across 10 sites in the central Rocky Mountains, USA. We quantified % nitrogen by cache volume as a metric of cache quality, and the number of events during which pikas were active in temperatures ≥25°C as a measure of potential thermoregulatory stress. We found a strong negative effect of temperature on the rate at which pikas cached food. Individual responses to temperature varied substantially in both the level of food‐collecting activity and in the degree to which individuals shifted activity with warming temperature. After accounting for available foraging time, individuals that exhibited greater plasticity collected a comparable amount of nitrogen, while simultaneously experiencing fewer occasions in which temperatures eclipsed estimated thermal tolerances. By varying food‐collection norms of reaction, individuals were able to plastically respond to temperature‐driven reductions in foraging time. Through this increased flexibility, individuals amassed food caches of comparable quality, while minimizing exposure to potentially stressful thermal conditions. Our results suggest that, given sufficient resource quality and availability, plasticity in foraging activity may help temperature‐limited endotherms adjust to climate‐related constraints on foraging time.

Wyoming↗
USGS2018

Determinants of Pseudogymnoascus destructans within bat hibernacula: Implications for surveillance and management of white-nose syndrome

Fungal diseases are an emerging global problem affecting human health, food security and biodiversity. Ability of many fungal pathogens to persist within environmental reservoirs can increase extinction risks for host species and presents challenges for disease control. Understanding factors that regulate pathogen spread and persistence in these reservoirs is critical for effective disease management. White-nose syndrome (WNS) is a disease of hibernating bats caused by Pseudogymnoascus destructans ( Pd ), a fungus that establishes persistent environmental reservoirs within bat hibernacula, which contribute to seasonal disease transmission dynamics in bats. However, host and environmental factors influencing distribution of Pd within these reservoirs are unknown. We used model selection on longitudinally collected field data to test multiple hypotheses describing presence–absence and abundance of Pd in environmental substrates and on bats within hibernacula at different stages of WNS. First detection of Pd in the environment lagged up to 1 year after first detection on bats within that hibernaculum. Once detected, the probability of detecting Pd within environmental samples from a hibernaculum increased over time and was higher in sediment compared to wall surfaces. Temperature had marginal effects on the distribution of Pd . For bats, prevalence and abundance of Pd were highest on Myotis lucifugus and on bats with visible signs of WNS. Synthesis and applications . Our results indicate that distribution of Pseudogymnoascus destructans ( Pd ) within a hibernaculum is driven primarily by bats with delayed establishment of environmental reservoirs. Thus, collection of samples from Myotis lucifugus , or from sediment if bats cannot be sampled, should be prioritized to improve detection probabilities for Pd surveillance. Long-term persistence of Pd in sediment suggests that disease management for white-nose syndrome should address risks of sustained transmission from environmental reservoirs.

Journal of Applied Ecology↗
USGS2022

Global groundwater solute composition and concentrations

Informed analysis of policies related to food security, global climate change, wetland ecology, environmental nutrient flux, element cycling, groundwater weathering, continental denudation, human health, etc. depends to a large extent on quantitative estimates of solute mass fluxes into and out of all global element pools including the enigmatic global aquifer systems. Herein for the first time, we proffer the mean global solute concentration of all major and selected minor and trace solutes in the active groundwater that represents 99% of liquid fresh water on Earth. Concentrations in this significant element pool have yielded to a geospatial machine learning kNN-nearest neighbors’ algorithm with numerous geospatial predictors utilizing a large new lithology/climate/aquifer age/elevation based solute database. The predicted concentrations are consistent with traditional solute ratios, concentrations, and thermodynamic saturation indices.

Groundwater↗
USGS2012

Detecting insect pollinator declines on regional and global scales

Recently there has been considerable concern about declines in bee communities in agricultural and natural habitats. The value of pollination to agriculture, provided primarily by bees, is >$200 billion/year worldwide, and in natural ecosystems it is thought to be even greater. However, no monitoring program exists to accurately detect declines in abundance of insect pollinators; thus, it is difficult to quantify the status of bee communities or estimate the extent of declines. We used data from 11 multiyear studies of bee communities to devise a program to monitor pollinators at regional, national, or international scales. In these studies, 7 different methods for sampling bees were used and bees were sampled on 3 different continents. We estimated that a monitoring program with 200-250 sampling locations each sampled twice over 5 years would provide sufficient power to detect small (2-5%) annual declines in the number of species and in total abundance and would cost U.S.$2,000,000. To detect declines as small as 1% annually over the same period would require >300 sampling locations. Given the role of pollinators in food security and ecosystem function, we recommend establishment of integrated regional and international monitoring programs to detect changes in pollinator communities.

Conservation Biology↗
USGS2024

Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways

The Central Valley of California (CVC) and Mid-Atlantic (MA) in the U.S. are both critical sites for nationwide food security, and many waterfowl species annually, especially during the winter, providing feeding and roosting locations for a variety of species. Mapping waterfowl distributions, using NEXRAD, may aid in the adaptive management of important waterfowl habitat and allow various government agencies to better understand the interface between wild and domestic birds and commercial agricultural practices. We used 9 years (2014–2023) of data from the US NEXRAD network to model winter waterfowl relative abundance in the CVC and MA as a function of weather, temporal period, environmental conditions, and landcover characteristics using boosted regression tree modelling. We were able to quantify the variability in effect size of 28 different covariates across space and time within two geographic regions which are critical to nationwide waterfowl management and host a high density of nationally important commercial agriculture. In general, weather, geographic (distance to features), and landcover condition (wetness index) predictors had the strongest relative effect on predicting wintering waterfowl relative abundance in both regions, while effects of land cover composition were more regionally and temporally specific. Increased daily mean temperature was a major predictor of increasing relative waterfowl abundance in both regions throughout the winter. Increasing precipitation had differing effects within regions, increasing relative waterfowl abundance in the MA, while decreasing in general within the CVC. Increasing relative waterfowl abundance in the CVC are strongly tied to the flooding of the landscape and rice availability, whereas waterfowl in the MA, where water is less limiting, are generally governed by waste grain availability and emergent wetland on the landscape. Waterfowl relative abundance in the MA was generally higher nearer to the Atlantic coast and lakes, while in the CVC they were higher nearer to lakes. Our findings promote a better understanding of spatial associations of waterfowl to landscape features and may aid in conservation and biosecurity management protocols.

California, Delaware, Maryland, New Jersey, North ↗
USGS2025

C4 photosynthesis, trait spectra, and the fast-efficient phenotype

It has been 60 years since the discovery of C 4 photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C 4 photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C 4 CO 2 -concentrating mechanism supports a general C 4 plant phenotype capable of fast growth and high resource-use efficiencies. The fast-efficient C 4 phenotype has the potential to operate at high productivity rates, while allowing for less biomass allocation to root production and nutrient acquisition, thereby providing opportunities for the evolution of novel trait covariances and the exploitation of new ecological niches. We propose the placement of the C 4 fast-efficient phenotype near the acquisitive pole of the world-wide leaf economic spectrum, but with a pathway-specific span of trait space, wherein selection shapes both acquisitive and conservative adaptive strategies. A trait-based perspective of C 4 photosynthesis will open new paths to crop improvement, global biogeochemical modeling, the management of invasive species, and the restoration of disturbed ecosystems, particularly in grasslands.

New Phytologist↗
USGS2016

Low-cost grass restoration using erosion barriers in a degraded African rangeland

Rangeland degradation, typified by extensive bare ground and soil erosion, is a serious problem around the world. In sub-Saharan Africa, rangeland degradation threatens the food security of millions of people who depend on livestock and the region's large mammalian wildlife diversity. We tested the ability of five simple, low-cost erosion barriers to promote grass and forb establishment in a bare ground-dominated rangeland in Kenya. These treatments were: (1) trenches with small berms; (2) bundles of branches; and bundles of branches with (3) elephant dung balls, (4) burlap sacking, or (5) nylon mesh sacking inside them. We also tested whether barrier performance depended on (1) supplemental seeding with the grass Cenchrus ciliaris and (2) whether a barrier was located next to existing vegetation patches versus in the open. Within months, the trench and nylon mesh barriers had accumulated 20–50% more sediment than other treatments and had greater grass and forb seedling establishment. Seeding with Cenchrus resulted in higher herbaceous cover but was not necessary for other grasses to establish. After 3 years, the trench and nylon mesh barriers had created patches of new vegetation averaging 18–63% larger than patches created by the other treatments. Barriers that were initially adjacent to existing vegetation had created new vegetation patches averaging 65% larger than those created by solitary barriers. Results suggest that all barrier types increase grass cover but that trenches—especially if placed next to existing vegetation patches—are a particularly cost-effective way to reduce bare ground and erosion in degraded rangelands.

Restoration Ecology↗
USGS2011

Mapping rice areas of South Asia using MODIS multitemporal data

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

South Asia↗
USGS2014

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

California↗
USGS2017

East African weathering dynamics controlled by vegetation-climate feedbacks

Tropical weathering has important linkages to global biogeochemistry and landscape evolution in the East African rift. We disentangle the influences of climate and terrestrial vegetation on chemical weathering intensity and erosion at Lake Malawi using a long sediment record. Fossil pollen, microcharcoal, particle size, and mineralogy data affirm that the detrital clays accumulating in deep water within the lake are controlled by feedbacks between climate and hinterland forest composition. Particle-size patterns are also best explained by vegetation, through feedbacks with lake levels, wildfires, and erosion. We develop a new source-to-sink framework that links lacustrine sedimentation to hinterland vegetation in tropical rifts. Our analysis suggests that climate-vegetation interactions and their coupling to weathering/erosion could threaten future food security and has implications for accurately predicting petroleum play elements in continental rift basins.

Geology↗
USGS2025

The geology of Canadian potash: A critical mineral for feeding the world

Potash, potassium-bearing water-soluble salt, is the primary global economic source of potassium. Potash is recognized as a critical mineral in Canada as it is the largest source of potassium used in fertilizers. It is essential for global agricultural productivity and food security. Canada is the world’s largest potash exporter with vast deposits in the widely mined Prairie Evaporite of Saskatchewan, which formed in the epicontinental Elk Point Basin during the Middle Devonian. Potash is also found in the Windsor Group of Atlantic Canada where it formed in a series of tectonically active basins during the Mississippian that have undergone substantial post-depositional subsurface alteration and deformation. Potash deposits were mined in New Brunswick up until 2016. Both deposits are salt giants, recording times in the geologic record of extensive and long-lasting evaporite genesis under arid conditions in restricted seas. This paper reviews the geological and economic significance of Canadian potash, including (1) the genesis of each deposit, (2) diagenetic, erosional, and tectonic modification, and (3) exploration and mining in each basin. Underdeveloped regions, possible undiscovered resources, environmental considerations, and the importance of sustainable practices in light of climate change and socioeconomic risks are also addressed.

Facets↗
USGS2012

Flood pulsing in the Sudd wetland: analysis of seasonal variations in 2 inundation and evapotranspiration in Southern Sudan

Located on the Bahr el Jebel in South Sudan, the Sudd is one of the largest floodplain wetlands in the world. Seasonal inundation drives the hydrologic, geomorphological, and ecological processes, and the annual flood pulse is essential to the functioning of the Sudd. Despite the importance of the flood pulse, various hydrological interventions are planned upstream of the Sudd to increase economic benefits and food security. These will not be without consequences, in particular for wetlands where the biological productivity, biodiversity, and human livelihoods are dependent on the flood pulse and both the costs and benefits need to be carefully evaluated. Many African countries still lack regional baseline information on the temporal extent, distribution, and characteristics of wetlands, making it hard to assess the consequences of development interventions. Because of political instability in Sudan and the inaccessible nature of the Sudd, recent measurements of flooding and seasonal dynamics are inadequate. Analyses of multitemporal and multisensor remote sensing datasets are presented in this paper, in order to investigate and characterize flood pulsing within the Sudd wetland over a 12-month period. Wetland area has been mapped along with dominant components of open water and flooded vegetation at five time periods over a single year. The total area of flooding (both rain and river fed) over the 12 months was 41 334 km2, with 9176 km2 of this constituting the permanent wetland. Mean annual total evaporation is shown to be higher and with narrower distribution of values from areas of open water (1718 mm) than from flooded vegetation (1641 mm). Although the exact figures require validation against ground-based measurements, the results highlight the relative differences in inundation patterns and evaporation across the Sudd.

Earth Interactions↗