Climate-Smart Agro-Hydrological Model for a Large Scale Rice Irrigation Scheme in Malaysia

Abstract

Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976–2005) and future (2010–2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

“…The use of satellite imagery as a data source in urban planning also helps identify climate-friendly solutions (Milojevic-Dupont et al, 2020). Web-based water management (Mourtzios et al, 2021) can be supported with trends identified from time-series data (Ise et al, 2020), but remotely sensed water flow data also complements the agricultural water management model (Ismail et al, 2020). And if we increase the resolution of the data (Jimenez et al, 2019), we can also understand the causal relationships related to consumption.…”

“…Agricultural satellite imagery applications (Majidi et al, 2021) can be transferred to air quality satellite monitoring (Semlali and El Amrani, 2021), or time-series data (Ise et al, 2020) can be used to plan better agricultural interventions. By implication, satellitebased support plays an important role in modeling agricultural water management (Ismail et al, 2020), but disaster news (Park et al, 2020) also helps provide a deeper understanding of social involvement. In assessing disaster resilience in different areas, (Sasaki et al, 2020) satellite imagery provides feedback on risks that can even be revealed over time (Joshi et al, 2019).…”

“…Identifying patterns in time-series data (Ise et al, 2020) helps with research in many other areas, whether it is agricultural water management (Ismail et al, 2020) or marine habitat protection (Coro et al, 2020). It allows (Kubo et al, 2020) forecasting and a better understanding of coastal traffic and increases the reliability of disaster resilience estimation (Sasaki et al, 2020).…”

“…The data of the Internet of Things sensors (Mabrouki et al, 2021) allow the conclusions drawn from the satellite images to be verified (Majidi et al, 2021), as a measuring station (Jimenez et al, 2019) increases the number of observations, thus better downscaling solutions (Wang Q. et al, 2020) can be made. It can be used for causal exploration of plant morphological damage (Fenu and Malloci, 2019) and supports agricultural irrigation water demand planning (Ismail et al, 2020), but can also be imported into flood models (Avand et al, 2021).…”

“…Based on the presented system of systems framework, it can be seen how the new results of Big Data applications related to climate change contribute to other areas. Remote sensing of water consumption (Mourtzios et al, 2021), analysis of cloud water content (He et al, 2020), and the agricultural water management model (Ismail et al, 2020) contribute to the goal of clean water and sanitation (SDG6). Planning based on the analysis of traffic data (Hu et al, 2020), studying population movements (Gurram et al, 2019) and flooding models (Avand et al, 2021) support the goal of industry, innovation and infrastructure (SDG9).…”