A Citizen Observatory at schools to train a rainfall retrieval algorithm based on Earth Observation

2020 ◽  
Author(s):  
Sandra de Vries ◽  
Monica Estebanez Camarena
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
West Africa ◽  
Rain Gauge ◽  
Earth Observation ◽  
Rainfall Data ◽  
Northern Ghana ◽  
Retrieval Algorithm ◽  
Educational Module ◽  
Rainfall Retrieval

<p>West Africa’s economy is mainly sustained on agriculture and over 70% of crops are rain-fed. Economic growth and food security in this region is therefore highly dependent on the knowledge of rainfall patterns. According to the IPCC, the Global South will seriously suffer from climate change. As traditional rainfall patterns shift, accurate rainfall information becomes crucial for farmers to optimize food production.</p><p>The scarce rain gauge distribution and data transmission challenges make rainfall analysis difficult in these regions. Satellites could offer a solution to this problem, but present satellite products do not account for local characteristics and perform poorly in West Africa.</p><p>A rainfall retrieval algorithm, developed within the Schools and Satellites (SaS) project, could overcome the lack of ground data and good rainfall satellite products through earth observation and advanced machine learning. However, to validate such an algorithm requires a high amount of rainfall data from ground stations. Since rain gauges are scarce in West Africa, a (temporary) high density observation network is necessary to strengthen the training and validation dataset provided by TAHMO and GMet ground measurements. SaS therefore engages with schools in Northern Ghana to build a Citizen Observatory. </p><p>SaS is being funded by the European Space Agency as one of the pilot projects of CSEOL (Citizen Science and Earth Observation Lab). It is being developed in a cooperation between TU Delft, PULSAQUA, TAHMO Ghana, Smartphones4Water (S4W) and GMet. The Proof-of-Concept Algorithm will be fed with data collected in the Citizen Observatory during the rainy season of 2020.</p><p>This Citizen Observatory will be built around the already existing infrastructure of a classroom where Climate Change is amongst the topics in the Ghanaian teaching curriculum. We aim to provide a Climate Change educational module that can be used directly by the teachers. The educational module incorporates the building of their own low-cost rain gauge to be used for manual rainfall data collection. This rainfall collection method has already been highly tested by S4W in Nepal. Students will design their own research around the daily rainfall measurements, which they will submit via a web application called Open Data Kit (ODK). The data is being validated by including a picture of the rainfall measurement that is checked with the number passed on by the citizen scientist.</p><p>The Citizen Observatory will be placed under the existing TAHMO and S4W infrastructures to respectively continue the interaction with schools and to continue data collection, -validation and -visualization. If the algorithm proofs to indeed perform better than current satellite products for the pilot area in Northern Ghana, the Citizen Observatory could in the future help to validate and improve the product for the whole of West-Africa.</p><p>To enable the use of this Citizen Observatory for management of water resources and in this case more and better rainfall data, much effort is needed. We will demonstrate which measures we have taken to ensure that the Citizen Observatory performs with enough quality, and how (if done well) it has the potential to increase the impact of this study.</p>

scholarly journals A measurement campaign to assess sources of error in microwave link rainfall estimation

2018 ◽  
Vol 11 (8) ◽  
pp. 4645-4669 ◽  
Author(s):  
Thomas C. van Leth ◽  
Aart Overeem ◽  
Hidde Leijnse ◽  
Remko Uijlenhoet
Keyword(s):  
Communication Networks ◽  
Global Analysis ◽  
Time Lapse ◽  
Rain Gauge ◽  
Operating Conditions ◽  
Retrieval Algorithm ◽  
Measurement Campaign ◽  
Temporal Sampling ◽  
Microwave Link ◽  
Rainfall Retrieval

Abstract. We present a measurement campaign to address several error sources associated with rainfall estimates from microwave links in cellular communication networks. The core of the experiment is provided by three co-located microwave links installed between two major buildings on opposite sides of the small town of Wageningen, approximately 2 km apart: a 38 GHz formerly commercial microwave link, as well as 26 and 38 GHz (dual-polarization) research microwave links. Transmitting and receiving antennas have been attached to masts installed on the roofs of the two buildings, about 30 m above the ground. This setup was complemented with an infrared large-aperture scintillometer, installed over the same path, as well as five laser disdrometers positioned at several locations along the path and an automated rain gauge. Temporal sampling of the received signals was performed at a rate of 20 Hz. The setup was monitored by time-lapse cameras to assess the state of the antennas as well as the atmosphere. The experiment was active between August 2014 and December 2015. Data from an existing automated weather station situated just outside Wageningen was further used to compare and to interpret the findings. In addition to presenting the experiment, we also conduct a preliminary global analysis and show several cases highlighting the different phenomena affecting received signal levels: rainfall, solid precipitation, temperature, fog, antenna wetting due to rain or dew, and clutter. We also briefly explore cases where several phenomena play a role. A rainfall intensity (R) – specific attenuation (k) relationship was derived from the disdrometer data. We find that a basic rainfall retrieval algorithm without corrections already provides a reasonable correlation to rainfall as measured by the disdrometers. However, there are strong systematic overestimations (factors of 1.2–2.1) which cannot be attributed to the R–k relationship. We observe attenuations in the order of 3 dB due to antenna wetting under fog or dew conditions. We also observe fluctuations of a similar magnitude related to changes in temperature. The response of different makes of microwave antennas to many of these phenomena is significantly different even under the exact same operating conditions and configurations.

RainRunner - Machine Learning and Earth observation for reliable rainfall information in West Africa

2020 ◽  
Author(s):  
Monica Estebanez Camarena ◽  
Nick van de Giesen ◽  
Marie-Claire ten Veldhuis ◽  
Sandra de Vries
Keyword(s):  
Machine Learning ◽  
Economic Growth ◽  
Food Security ◽  
West Africa ◽  
Land Surface Temperature ◽  
Atmospheric Aerosols ◽  
Land Surface ◽  
Earth Observation ◽  
Local Characteristics ◽  
Rainfall Retrieval

<p>West Africa’s economy is mainly sustained on agriculture and over 70% of crops are rain-fed. Economic growth and food security in this region is therefore highly dependent on the knowledge of rainfall patterns. According to the IPCC, the Global South will seriously suffer from climate change. As traditional rainfall patterns shift, accurate rainfall information becomes crucial for farmers to optimize food production.</p><p>The scarce rain gauge distribution and data transmission challenges make rainfall analysis difficult in these regions. Satellites could offer a solution to this problem, but present satellite products do not account for local characteristics and perform poorly in West Africa. For example, comparing the widely used TAMSAT and CHIRPS satellite rainfall products with ground data in our pilot area in the Northern Region of Ghana, we found a very poor correlation with TAMSAT and CHIRPS grossly overestimating the number of rainy days, while underestimating the amount of rainfall per event.</p><p>The RainRunner rainfall retrieval algorithm, developed within the Schools and Satellites (SaS) project, aims to overcome the lack of ground data and good rainfall satellite products through Earth Observation and advanced Machine Learning (ML). SaS is being funded by the European Space Agency as one of the pilot projects of CSEOL (Citizen Science and Earth Observation Lab). It is being developed in a cooperation between TU Delft, PULSAQUA, TAHMO Ghana, Smartphones4Water and the Ghana Meteorological Agency (GMet).</p><p>Research suggests that local characteristics are the reason for traditional rainfall retrieval algorithms to perform poorly in West Africa, where the land surface temperature and the concentration of atmospheric aerosols are higher than in other regions in the world. Hence, RainRunner will utilize information relevant to the rain process other than the traditionally used cloud top temperature, namely, cloud amount, atmospheric aerosols, soil moisture and land surface temperature. These data are derived from diverse sensors onboard ESA’s Sentinel satellites (S1, S2, S3 and S5P), as well as MSG’s Aviris. The satellite products, together with a Digital Elevation Model, will be pre-processed into datacubes to be fed to a Convolutional Neural Network (CNN) to estimate precipitation for a certain geographic point.</p><p>CNNs have shown to achieve better results when modelling complex natural processes than other ML algorithms, when provided with big amounts of data and well-designed architectures that represent the physical process knowledge. Furthermore, they have the main advantages of computing efficiency and the ability to represent processes beyond numerical simulations. The latter is essential for understanding the complex interactions between variables, therefore resulting in not only improving rainfall estimates but also in increasing our understanding of processes in poorly measured regions.</p><p>The Proof-of-Concept algorithm will be trained and validated with TAHMO and GMet ground measurements. Eventually, the training and validation dataset will incorporate data acquired by a rainfall observation network combining low-cost sensors and Citizen Science data collected by schoolchildren in Ghana.</p><p>Once operative, the RainRunner will guide agricultural extension agents, support crop insurance and ultimately contribute to economic growth and food security in the Global South.</p>

scholarly journals Local climate change projections and impact on the surface hydrology in the Vea catchment, West Africa

2021 ◽  
Author(s):  
Isaac Larbi ◽  
Fabien C. C. Hountondji ◽  
Sam-Quarcoo Dotse ◽  
Daouda Mama ◽  
Clement Nyamekye ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
West Africa ◽  
Surface Runoff ◽  
Swat Model ◽  
Northern Ghana ◽  
Water Yield ◽  
Mean Annual Temperature ◽  
Ensemble Mean ◽  
The Impact

Abstract Water security has been a major challenge in the semi-arid area of West Africa including Northern Ghana, where climate change is projected to increase if appropriate measures are not taken. This study assessed rainfall and temperature projections and its impact on the water resources in the Vea catchment using an ensemble mean of four bias-corrected Regional Climate Models and Statistical Downscaling Model-Decision Centric (SDSM-DC) simulations. The ensemble mean of the bias-corrected climate simulations was used as input to an already calibrated and validated Soil and Water Assessment Tool (SWAT) model, to assess the impact of climate change on actual evapotranspiration (ET), surface runoff and water yield, relative to the baseline (1990–2017) period. The results showed that the mean annual temperature and actual ET would increase by 1.3 °C and 8.3%, respectively, for the period 2020–2049 under the medium CO2 emission (RCP4.5) scenario, indicating a trend towards a dryer climate. The surface runoff and water yield are projected to decrease by 42.7 and 38.7%, respectively. The projected decrease in water yield requires better planning and management of the water resources in the catchment.

DISSEMINATION AND USE OF INFORMATION ON CLIMATE CHANGE AND VARIABILITY: A CASE STUDY OF FARMERS IN MALUGA AND CHIBELA VILLAGES IN SEMI-ARID CENTRAL TANZANIA

Mousaion ◽  
2016 ◽  
Vol 33 (3) ◽  
pp. 1-24
Author(s):  
Emmanuel Elia ◽  
Stephen Mutula ◽  
Christine Stilwell
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
Diffusion Of Innovations ◽  
Agricultural Development ◽  
Agricultural Sector ◽  
Policy Framework ◽  
Accurate Information ◽  
Adaptation To Climate Change ◽  
Climate Change And Variability ◽  
Semi Arid

This study was part of broader PhD research which investigated how access to, and use of, information enhances adaptation to climate change and variability in the agricultural sector in semi-arid Central Tanzania. The research was carried out in two villages using Rogers’ Diffusion of Innovations theory and model to assess the dissemination of this information and its use by farmers in their adaptation of their farming practices to climate change and variability. This predominantly qualitative study employed a post-positivist paradigm. Some elements of a quantitative approach were also deployed in the data collection and analysis. The principal data collection methods were interviews and focus group discussions. The study population comprised farmers, agricultural extension officers and the Climate Change Adaptation in Africa project manager. Qualitative data were subjected to content analysis whereas quantitative data were analysed to generate mostly descriptive statistics using SPSS.  Key findings of the study show that farmers perceive a problem in the dissemination and use of climate information for agricultural development. They found access to agricultural inputs to be expensive, unreliable and untimely. To mitigate the adverse effects of climate change and variability on farming effectively, the study recommends the repackaging of current and accurate information on climate change and variability, farmer education and training, and collaboration between researchers, meteorology experts, and extension officers and farmers. Moreover, a clear policy framework for disseminating information related to climate change and variability is required.

Climate change and food security in tropical West Africa – a dynamic-statistical modelling approach

Erdkunde ◽  
2008 ◽  
Vol 62 (2) ◽  
pp. 101-115 ◽  
Author(s):  
Heiko Paeth ◽  
Arcade Capo-Chichi ◽  
Wilfried Endlicher
Keyword(s):  
Climate Change ◽  
Food Security ◽  
West Africa ◽  
Statistical Modelling ◽  
Modelling Approach

scholarly journals A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Matieu Henry ◽  
Zaheer Iqbal ◽  
Kristofer Johnson ◽  
Mariam Akhter ◽  
Liam Costello ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Data Collection ◽  
Land Cover ◽  
Forest Inventory ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
Forest Resources ◽  
National Forest ◽  
Land Cover Map

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

scholarly journals Climate Variability Since 1970 and Farmers’ Observations in Northern Ghana

2016 ◽  
Vol 5 (2) ◽  
pp. 41 ◽  
Author(s):  
Emmanuel Nyadzi
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Water Conservation ◽  
Crop Production ◽  
Meteorological Data ◽  
Northern Region ◽  
Northern Ghana ◽  
Climate Variability And Change ◽  
Urban Centers ◽  
Access To Credit

<p>The study examines how farmers’ observations of climate variability and change correspond with 42 years (1970-2011) meteorological data of temperature and rainfall. It shows how farmers in the Northern Region of Ghana adjust to the changing climate and explore the various obstacles that hinder the implementation of their adaptation strategies. With the help of an extension officer, 200 farmers from 20 communities were randomly selected based on their farming records. Temperatures over the last four decades (1970-2009) increased at a rate of 0.04 (± 0.41) ˚C and 0.3(± 0.13)˚C from 2010-2011 which is consistent to the farmers (82.5%) observations. Rainfall within the districts are characterised by inter-annual and monthly variability. It experienced an increased rate of 0.66 (± 8.30) mm from 1970-2009, which was inconsistent with the farmers (81.5%) observation. It however decreased from 2010-2011 at a huge rate of -22.49 (±15.90) mm which probably was the reason majority of the respondents claim rainfall was decreasing. Only 64.5% of the respondents had adjusted their farming activities because of climate variability and change. They apply fertilizers and pesticides, practice soil and water conservation, and irrigation for communities close to dams. Respondents desire to continue their current adaptation methods but may in the future consider changing crop variety, water-harvesting techniques, change crop production to livestock keeping, and possibly migrate to urban centers. Lack of climate change education, low access to credit and agricultural inputs are some militating factors crippling the farmers’ effort to adapt to climate change.</p>

scholarly journals Climate change impact on streamflow in a tropical basin of Ghana, West Africa

2021 ◽  
Vol 34 ◽  
pp. 100805
Author(s):  
Alfred Awotwi ◽  
Thompson Annor ◽  
Geophrey K. Anornu ◽  
Jonathan Arthur Quaye-Ballard ◽  
Jacob Agyekum ◽  
...  
Keyword(s):  
Climate Change ◽  
West Africa ◽  
Climate Change Impact ◽  
Change Impact

scholarly journals Climate Change in the Arctic—The Need for a Broader Gender Perspective in Data Collection

2021 ◽  
Vol 18 (2) ◽  
pp. 628
Author(s):  
Arja Rautio ◽  
Natalia Kukarenko ◽  
Lena Maria Nilsson ◽  
Birgitta Evengard
Keyword(s):  
Climate Change ◽  
Data Collection ◽  
Human Health ◽  
Economic Benefits ◽  
Theoretical Background ◽  
The Arctic ◽  
Gender Analysis ◽  
Roles And Responsibilities ◽  
Disaggregated Data

Climate change in the Arctic affects both environmental, animal, and human health, as well as human wellbeing and societal development. Women and men, and girls and boys are affected differently. Sex-disaggregated data collection is increasingly carried out as a routine in human health research and in healthcare analysis. This study involved a literature review and used a case study design to analyze gender differences in the roles and responsibilities of men and women residing in the Arctic. The theoretical background for gender-analysis is here described together with examples from the Russian Arctic and a literature search. We conclude that a broader gender-analysis of sex-disaggregated data followed by actions is a question of human rights and also of economic benefits for societies at large and of the quality of services as in the health care.

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