Other articles

May 2022
DROP App, a climate information services with soil moisture module, is ready to be tested
February 2022
Lunchtime Talks on EXtreme Climate: Water & Climate Information Services for Society
January 2022
WATERAPPscale weather schools: weather and climate information services help smallholder farmers during cyclone Jawad in Bangladesh
December 2021
Developing Climate Information Services (CIS) with a soil moisture forecast module
November 2021
Ten new farmer field schools have been established
October 2021
WATERAPPS is implemented in Sylhet and Mymensingh Baba Mohammadu Jamaldeen is going to collect soil moisture information in Tamale Highlighted publication in Nature Climate Change
September 2021
Reinforcing Community Trust and Relationship through Climate Information Services in Bangladesh Delta
July 2021
Building Resilient River Deltas Through Innovations - UDW Regional Events
May 2021
WATERAPPS & WATERAPPscale participation in the UDW regional events call
March 2021
WATERAPPscale - upscaling WATERAPPS information services in Bangladesh
October 2020
Dogbey Richard Kwame in the field to test the Farmer Support App
August 2020
FarmerSupport app traingings have started
June 2020
FarmerSupport mobile App now online
May 2020
Super Cyclone Amphan - People ask for a strong and sustainable embankment
February 2020
Weather Club - A New Horizon for Smallholder Farmers in the Ganges Delta How a Bengali Female farmer experiences Climate Information services
December 2019
WaterApps team workshop and field visit in Khulna How a Bangladeshi farmer experiences the WaterApps Climate Service
September 2019
On a climate service training mission with Uthpal Kumar Apply for the WaterApps Ghana Business until September 15th
July 2019
What makes the best climate/weather app for famers? A Ghana business challenge PICSA monitoring and evaluation workshop
March 2019
Making weather forecasts part of the Bangladeshi farming practice
January 2019
An introduction to scientific seasonal forecasts
November 2018
Identification of success factors in a review of agricultural information services in peri-urban Khulna, Bangladesh
August 2018
Exploring how the flow of water-related information affects farming practices and decision-making in Ada East
May 2018
An innovation systems approach to examine the organization of ICT-based IPs for extension services in Ghana Tailoring weather and water information for sustainable crop production
December 2017
Workshop on Information Services for Farmers in Peri-Urban Khulna
January 1
New publication on the role of soil moisture information WATERAPPS & WATERAPPscale shared their findings in the UDW final conference: Breaking barriers-urbanising deltas of the world Seminar - Building bridges for delta interventions: Crossing scales, domains and engaging local stakeholders using the MOTA and WATERAPPstools Climate Coffee Chat: Agriculture, Africa and Women Policy Brief: WATERAPPS & EVOCA Climate Information Services for Food Security in Ghana

Developing Climate Information Services (CIS) with a soil moisture forecast module

Written by: Doctor (Dr) Samuel Sutanto
Published: Wednesday, December 15, 2021
Thumbnail Developing Climate Information Services (CIS) with a soil moisture forecast module

Although Climate Information Services (CIS) has been promoted in Ghana and also in Bangladesh (see Gbangou et al. 2020 and Kumar et al., 2021), this system only provides information on the recent and forecasted meteorological variables, primarily precipitation and temperature. Soil moisture that plays an important role in the soil-plant-atmosphere system is still missing. Understanding soil moisture condition is key in agriculture practice because the plant establishment and growth are directly impacted by the soil moisture stored in the soil layer. For small-holder farmers, having access to soil moisture information when practicing rainfed agriculture would help them in the decision-making process and managing the effects of climate change on agriculture.

Providing CIS with soil moisture module is challenging due to missing soil moisture observations. Then how the soil moisture forecast information can be developed in the existing CIS when the soil moisture data is not available in e.g., Ghana and Bangladesh? In the Waterapps, we will use a simple bucket/water balance model to estimate the soil moisture condition (Figure 1). Then the soil moisture condition for the coming days will be estimated using the water balance model in combination with weather forecast data. The idea is to create a simple estimation of SM forecasts that can be run on a low-cost server within seconds. Therefore, the water balance model has only 1 soil layer and water exchange within soil layers is neglected (vertical and horizontal to deep soil layer/groundwater). Soil moisture is calculated using the water balance equation as follow:

DSM = inputs of water - losses of water = (P+I+C)-(ET+D+RO)

Where DSM is change in soil moisture, P is the rainfall, I is irrigation, C is the water from the groundwater, ET is the evapotranspiration, D is the water loss to deep drainage, and RO is surface runoff. In our simple model, we neglect the input water from groundwater and irrigation, and we also neglect the water loss to deep drainage and to surface runoff. In the end, the change in soil moisture is estimated from the difference between precipitation as input and evapotranspiration as output (DSM=P-ET).

Figure 1. Schematization of a bucket/water balance model

The available soil moisture at time step i is calculated as follow:

SMi = SMi-1+DSM

Where SMi-1 is the soil moisture in the previous time step, which is estimated and inputted to the apps by the farmers or using the data obtained from remote sensing products (see Figure 2 for example). In doing so, we have trained the farmers on how to estimate the soil moisture condition at the field by feel and appearance using the method introduced by USDA. So far, we conducted training on how to measure the soil moisture condition by feel and appearance in three communities in Tolon and Savelugu Districts in northern Ghana. Another method to measure the soil moisture condition is by using a soil moisture sensor that has become cheap and cheaper nowadays.

Figure 2. Detailed soil moisture data for Belgium obtained from Vandersat.

The soil data can be obtained from the ISRIC database but for some locations in northern Ghana, we took soil samples and measured them in the laboratory. To forecast the soil moisture condition, we use the water balance model with the forecasted precipitation (P), forecasted Evapotranspiration (ET), and observed soil moisture condition as input. The ET will be calculated using the Thornthwaite method, which requires only temperature data.

While the training to measure soil moisture condition by feel and appearance in the northern region of Ghana is still ongoing, we are updating the Waterapps to be ready to provide soil moisture information for small-holder farmers in Ghana and elsewhere. We expect that next year the CIS embedded with soil moisture module will be ready for operational purposes. We are also discussing with our partners in Senegal to test and implement the soil moisture advisory module there within the WAGRINNOVA project. Let’s give two fingers crossed for the Waterapps team.