Assessing impacts of agriculture and dams on hydrological ES to people and dams in the Volta basin using the WaterWorld hydrological model

This guide focuses on how to run a baseline analysis using the WaterWorld hydrological model (Mulligan, 2013) with a particular focus on using two WaterWorld metrics to examine the impact of agriculture on hydrological ecosystem services to people and small dams and then the impact of small dams on hydrological ecosystem services provided to people. The former is assessed using the model results-based hydrological footprint - which calculates the hydrological footprint (downstream influence) of cropland based on modelled water balance and runoff. This assesses the volume of water potentially influenced by a land cover type in relation to the total volume of water in flow. The latter is assessed using the model results-based flow footprint (WWFF) - which calculates the flow footprint (downstream influence) of small dams based on modelled water balance and runoff. The flow footprint is a special case of the hydrological footprint for in-stream features such as dams rather than on-land features such as land uses, but is calculated in the same way. The number of people affected are calculated as the first (dis-)beneficiaries of these footprints according to the spatial distribution of population in relation to the footprints. This accounts for the footprints influence on these first beneficiaries (water users, irrigators, fishers) but not for the impact of these footprints on supply chain beneficiaries through for example increased prices as a result of reduced availability

GOODD, a global dataset of more than 38,000 georeferenced dams.

By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example land use and climate change) on the catchments of dams. This paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam

Identifying opportunity areas for cocoa agroforestry in Ghana to meet policy objectives

Ghana is one of the world’s leading cocoa producers. Between 1994 and 2018, the area under cocoa production has nearly ripled. This has increased income, but it has also imposed costs. As rainforests have been converted into land for cocoa farming, habitat for species has decreased and become increasingly fragmented in one of the world’s biodiversity hotspots. Rainforest loss also has huge implications for the ability of land to capture carbon and mitigate climate change globally. Expansion of cocoa farming is expected to aggravate these issues further. To increase income from cocoa, Ghana could expand cocoa plantations but increasing yields on the plantations that it already has would be better for both farmers and the environment. Cocoa yields in Ghana are low and the prices that the crop gets on the global market are poor. This is because most plantations in Ghana are small and run by farmers who often lack the right knowledge, resources and credit to apply management practices, like pruning, pest control and managing soil fertility, that would help them to increase the quality and size of their yields. Climate change is also expected to make lives harder and put the cocoa supply chain at risk by making yields lower than they already are. Agroforestry farming systems are increasingly being proposed as a solution to address these problems and a potential way for the small-holder cocoa farmers of Ghana to improve their livelihoods and for the cocoa sector to maintain a sustainable cocoa supply. Cocoa can be grown in direct sunlight or under shade provided by taller trees. Farmers in Ghana have been advised over the years that shade would harm their cocoa production, but evidence shows that well-managed shade can also benefit it. Shade trees suppress weed growth and provide habitats for predatory species that control insect pests. Growing cocoa under shade trees also helps to create a stable microclimate beneath the canopy. It can also enhance soil fertility and provide farmers with supplemental income when these other trees produce commercially valuable fruits and timber. Most importantly, well-shaded cocoa plantations will experience lower maximum temperatures than are expected from climate change, can store up to 2.5 times more carbon than those that are unshaded and support higher levels of biodiversity that help protect valuable ecosystem services. The types and magnitude of benefits from agroforestry systems for different beneficiaries depend highly on their design and the local context. Shade trees can harbour pests. They can also compete with cocoa for resources like water. This is particularly true in drier areas. High humidity levels under canopies created by other plant species can also foster fungal diseases. These challenges are not to be ignored but, when agroforestry systems are well designed, they are outweighed by the overall benefits in smallholder production systems. Indeed, Ghana is now promoting cocoa agroforestry through national level policies such as the Cocoa and Forests Implementation plan, the Ghana Cocoa Forest REDD+ Programme (GCFRP) and the National Climate-Smart Agriculture and Food Security Action Plan. It is not realistic to establish shaded plantations throughout the southwestern regions of Ghana all at once. The process will need to be staged as there are 2.3 million hectares of plantations and 1.9 million of them currently have little to no shade. Areas where benefits from increased shading will be highest need to be identified and prioritised. This new work looked at the locations of all cocoa plantations in the country and applied cocoa and forest national policy objectives as well as spatially explicit climate change adaptation strategies to implement a transition towards more shaded cocoa farming. Using modelling approaches, the work sought to understand the biodiversity, carbon sequestration and erosion control benefits granted by increased shading being implemented in different locations. Combined, this information generated a map that reveals the areas where the implementation of shading would be most beneficial for achieving a combination of benefits for people, nature and climate. The work shows that establishing appropriately shaded and well-managed plantations in the proposed areas has the potential to protect at least 4,000 tonnes of sediment from erosion each year and store an additional 52 million tonnes of carbon in trees. While shifting to this sort of farming will have some implementation costs and not yield the immediate financial gains that would be expected from more forests being converted into plantations, such a transition can yield significant long-term benefits as smallholder farmers face the challenges presented by a changing climate. When implemented appropriately, it will also enhance ecosystem services that benefit cocoa production, conserve biodiversity and support the livelihoods of farmers. Above and beyond all else, the carbon sequestration benefits granted by shaded plantations have the potential to play a pivotal part in combating climate change. For this to be fully realised, farmers need to be incentivised to adopt agroforestry practices by giving them ownership of the land that they are farming and the trees that grow there. Paying them for the ecosystem services that their land provides would further these incentives by strengthening and diversifying their income too. Beyond the specific situation faced by cocoa farmers in Ghana, this study demonstrates the potential for decision-makers to use spatial planning to understand where, and (partly) how, to implement cocoa agroforestry at scale to meet different objectives

Low-cost electronic sensors for environmental research: pitfalls and opportunities

Repeat observations underpin our understanding of environmental processes, but financial constraints often limit scientists’ ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IoT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on 6 years of developmental work with Arduino-based open-source hardware and software, extensive laboratory and field testing, and incor- poration of such technology into active research programmes, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap, and a sonic anemometer for monitoring sand transport. Schematics, code and purchasing guidance to reproduce our sensors are described in the paper, with detailed build instructions hosted on our King’s College London Geography Environmental Sensors Github repository and the FreeStation project website. We show in each case study that manual design and construction can produce research-grade scientific instrumentation (mean bias error for calibrated sensors –0.04 to 23%) for a fraction of the conventional cost, provided rigorous, sensor-specific calibration and field testing is conducted. In sharing our collective experiences with build-it- yourself environmental monitoring, we intend for this paper to act as a catalyst for physical geographers and the wider environmental science community to begin incorporating low-cost sensor development into their research activities. The capacity to deploy denser sensor networks should ultimately lead to superior envi- ronmental monitoring at the local to global scales