The Use of Artificial Neural Networks to Estimate Reference Evapotranspiration

Evapotranspiration is defined as the loss of water from soil and vegetation to the atmosphere, driven by weather conditions. It reduces the availability of water for agricultural purposes, which affects the amount of irrigation water, particularly during the dry season. The objective of this paper is to present a comparative analysis of the estimated reference evapotranspiration value based on artificial neural networks (ANN) with backpropagation bias 1 (BP-1) and backpropagation bias 0 (BP-0) architectures. The model was fed with data of air temperature, relative humidity, and solar radiation. The model is utilized to calculate the evapotranspiration using the Hargreaves method as the training data. The performance of ANN model was evaluated using the mean square error (MSE), root mean square error (RMSE), and coefficient determination (R2). Our results showed that both ANN models performed well as indicated by low error (MSE < 0.01) and high R2 (>0.99). Also, we found that air temperature and relative humidity determine the optimal prediction. Further, this proposed model can serve as a reference for other models seeking to determine the most appropriate computational model for evapotranspiration value estimation

Land Management of Tidal Swamp Type B with Surjan System as Climate Change Anticipation

Agriculture is one of the most vulnerable sectors to climate change, which can significantly impact national food security. In addition to climate change, agricultural development faces challenges, including the conversion of agricultural land for non-agricultural purposes. As a result, agricultural extensification has expanded into marginal lands, such as tidal swamplands. This paper presents a literature review on the characteristics of tidal swamplands, the principles of the surjan system, and its relevance in addressing climate change, particularly in the context of food security and ecosystem sustainability. Various literature sources were analyzed to assess the advantages, challenges, and sustainable management strategies of tidal swamplands. The review highlights the importance of effective land management to create suitable soil conditions for optimal plant growth and increased productivity. The surjan system, a land management approach practiced by tidal swampland farmers, demonstrates high adaptability in mitigating the impacts of climate change. This system integrates cultural, ecological, and economic perspectives by combining local knowledge with technological advancements. Key components of the surjan system include a one-way water management system with flap-gates and stoplogs, as well as the use of climate-adaptive crop varieties on tidal swamplands

Bias Correction of CMIP6 Models for Assessment of Wet and Dry Conditions Over Sumatra

The performance of CMIP6 models in capturing local and regional precipitation patterns often requires refinement due to inherent biases. This study evaluates eleven CMIP6 models for their applicability over Sumatra Island and applies two bias correction methods namely Linear Scaling (LS) and Quantile Delta Mapping (QDM).  We used ERA5 precipitation datasets as a reference bias correction during 1981-2014. The performance was assessed using MAE, correlation, and PBIAS. Results reveals that raw model of CMIP6 generally underestimate precipitation, particularly during the DJF and SON seasons, with the largest errors over the mountainous western Sumatra. LS tends to overcorrect and shift precipitation estimates toward a wetter bias, while QDM significantly improves the accuracy and seasonal consistency of the simulations.  The multi-model ensemble mean (CMIP6-avg) outperforms individual models, and its performance is further enhanced with QDM, yielding higher correlation and lower error metrics. Spatial and seasonal analyses demonstrate that QDM more effectively reduces both dry and wet biases, especially during peak rainfall seasons. These findings underscore the importance of robust bias correction techniques to improve climate projections for hydrological and climate impact studies in Sumatra and other tropical regions with complex terrain

Contribution of Small Rainwater Reservoirs to Performance Off-season Vegetable Farming

Climate Smart Agriculture (CSA) has become essential in ensuring sustainable agricultural production amidst ongoing climate change. This study aims to analyze the impact of small rainwater reservoirs (SRRs) on the performance of off-season vegetable farming in Netpala Village, East Nusa Tenggara, Indonesia. The SRRs, constructed using plastic tarpaulin with storage capacities of 3.6 m³ and 4.3 m³, were applied to support the cultivation of mustard greens, Chinese cabbage, cabbage, carrots, and eggplant during the December 2017 to April 2018 growing season. An on-farm research (OFR) approach was used to assess the effects of SRR implementation compared to traditional water management practices. Key performance indicators include cultivated land area, planting index, crop diversification, and farmer income. Results revealed that SRRs expanded the cultivated area by 21.07%, increased the planting index by 0.52 for mustard greens and 0.64 for cabbage, and boosted farmer income by 29.38%. Income levels were also influenced by factors such as market absorption, commodity prices, and land availability. These findings demonstrate that SRRs can enhance the resilience and productivity of smallholder vegetable farming systems by improving water availability during the rainy or off-season. SRRs offer a practical and scalable solution to address water scarcity and promote sustainable intensification in vulnerable agricultural regions

Assessment of Rice Crop Water Requirements for Planting Season in Moderate Agroclimatic Area of West Sumatra

In changing climate, uncertainty in rice production becomes more frequent leading to threat of food security. However, research on rice cultivation in the rainfed agricultural areas of West Sumatra remains limited. The objectives of the study are to analyze the crop water requirements of rainfed rice and to determine rice planting patterns. The study was conducted in a moderate agro-climate area of West Sumatra based on oldeman agroclimate zone that experienced changes in planting patterns. We used climate data for 1991 – 2020 obtained from TerraClimate, which were utilized for monthly water balance computation based on the Thornwhite and Matter approach. The analysis focused on four major rice production centers, namely: Panti in Pasaman, Lima Kaum in Tanah Datar, Luak in Lima Puluh Kota and Sijunjung. The results showed change in water deficit periods across the study sites have changed planting season. Based on our analysis site in Lima Kaum, Tanah Datar experienced the longest deficit period, which lasted 5 months from May to September. This situation may not suitable to plant rice throughout the year without additional irrigation. Further, adjusting to the secondary crop may be considered to optimize agricultural productivity. These findings can serve as a reference for determining planting seasons and improving water use and distribution strategies in rainfed agricultural systems

Identification of Peatland Burned Area based on Multiple Spectral Indices and Adaptive Thresholding in Central Kalimantan

Nowadays, spectral index has become popular as a tool to identify fire-burned areas. However, the use of a single index may not be universally applicable to region with diverse landscape and vegetation as peatlands. Here, we propose to develop a procedure that integrates multiple spectral indices with an adaptive thresholding method to enhance the performance of burned area detection. We combined the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) using MODIS imagery from 2002 to 2022 to calculate  (Confirmed Burned Pixel) by filtering dNDVI and dNBR. The mean and standard deviation of  serve as inputs for image thresholding. We tested our approach in Sebangau peatland, Central Kalimantan, where fires occur annually. The results showed that the model performed well with overall accuracy > of 91%, indicating that the model is effective and reliable for identifying burned areas. The findings also revealed that the frequency of fire is below 2 times/year, with the southeastern is the most fire prone regions. Further, our findings provide an alternative approach for identifying burned areas in locations with diverse vegetation cover and different geographical regions. &nbsp

ENSO and IOD Influence on Extreme Rainfall in Indonesia: Historical and Future Analysis

Indonesia, as a maritime continent, is vulnerable to environmental disasters such as floods and landslides due to extreme rainfall. This study aims to identify changes in the influence of ENSO and IOD on extreme rainfall across Indonesia, specifically during the September-October-November period. We used rainfall and sea surface temperature data from the CMIP6 climate model for the historical period (1985-2014), near-future (2031-2060), and far-future (2061-2090) projections under SSP2-4.5 and SSP 5-8.5 climate scenarios. The relation between rainfall dan ENSO/IOD was simply defined by linear regression approach. We analyzed the change of influence by comparing the historical and the future condition. The results indicated that the changes in the teleconnection of ENSO and IOD to extreme rainfall in future is consistently negative, except for Java (near-future) and Kalimantan and southern Sumatra (far-future). Our finding revealed that significant changes in the teleconnection varied throughout maritime continent. The maximum change was found in Northern Kalimantan, which reached values of -80 mm/°C due to ENSO and -180 mm/°C due to IOD for near future. These findings highlight the spatial variability in teleconnection changes across Indonesia, underscoring the need for region-specific climate adaptation measures in response to evolving extreme rainfall patterns

Extreme Rainfall Analysis in the Bengawan Solo Watershed, Java

As the largest watershed in Java Island, the Bengawan Solo watershed has experienced recurrent hydrometeorological hazards, leading to infra-structure damage, casualties, and environmental degradation. Research on extreme rainfall causing the hazards in the Bengawan Solo watershed is still limited. This study examines extreme rainfall events by analyzing daily rainfall data (1991-2020) at three observation stations namely Musuk, Tinap, and Lowayu, which represent the upstream, middle, and downstream of the Bengawan Solo watershed. The Extreme Value Theory (EVT) using the Block Maxima approach with a Generalized Extreme Value (GEV) method was used to determine the rainfall return period of 5, 10, 20, 30, and 50-year. We applied the Mann-Kendall test to assess the annual trends of extreme rainfall indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results found that the highest estimated annual maximum of daily rainfall was in Musuk station (226.7 mm), followed by Tinap station (159.3 mm) and Lowayu station (149.4 mm). While no significant trend was observed for Musuk, other stations showed a significant trend for the decrease of the daily rainfall intensity, the increase of the number of annual rainy days, the decrease of the annually maximum amount of five consecutive precipitation days, and the increase of the annually number of consecutive wet days. There is also an increase in the maximum amount of annual rainfall for one day (Rx1day) at Lowayu station, which indicates a higher risk of disaster due to high rainfall. Additionally, an increasing trend in the total annual rainfall (PRCPTOT) at Musuk, Tinap, and Lowayu stations suggests a greater potential for water storage to meet water needs in these areas

A Preliminary Study on the Parameter Configuration of Weather Research Forecasting in Tropical Peatland, Central Kalimantan

Hydrometeorological variables are sensitively regulated by atmospheric dynamics and variability. Weather research and forecasting (WRF) model is the cutting-edge tool for studying and investigating the dynamics of physical atmospheric conditions, but the configuration scheme of WRF parameters remains a research challenge for topical peatland situated in the maritime continent. Here, we evaluated WRF parametrization based on three kalibration configuration schemes, which influence rainfall, temperature, and soil moisture dynamics. We tested the WRF evaluation for Sebangau-Kahayan peatland for a wet-dry season in August 2020. The best configuration was determined based on three statistical metrics namely mean absolute error, percent bias, and coefficient of correlation. Our results showed that WRF forecasts were greatly depend on a bias correction to improve the model performance, in which it was consistently found in all configurations. Rainfall was barely predicted in station level with a low performance in term of weekly spatial distribution. Other findings revealed that all configurations showed a good performance for temperature and soil moisture forecasts. Further, our findings emphasize the important physical parameter of WRF that control rainfall formation and dynamics. Last, we highlight an urgent need of more ground stations in term of spatial distribution to validate the weather forecast

Rainfall and Temperature Change Analysis and their Correlation on Maize Productionin Karawang, West Java

Maize is an important food commodity and its yields can be threatened by changes in climate variables, such as increasing air temperature and decreasing rainfall. The research identifies and detect the change in climate variables and analyze their correlation with maize production. Quantitative and descriptive methods were used namely trend analysis, correlation, and regression. We utilized climate data (temperature and rainfall) and maize production for 1991-2022, with tested study area in Karawang, West Java. We divided the climate data into two periods to analyze any change in climate variables. The results indicated a change in temperature (+0.56 °C) and rainfall (-47.34 mm) per year, but there is no change in the agroclimatic zone. Our findings showed a moderate correlation between rainfall and maize production and productivity, with the mean correlation coefficients of 0.31 and 0.35, respectively. Similarly, air temperature showed a moderate correlation with maize production and productivity, with the mean correlation coefficients of 0.30 and 0.32, respectively. Appropriate anticipatory and adaptation efforts are needed to maintain maize production in rainfed agriculture such as in Karawang Regency