Indonesia has a large number of primate diversity where a majority of the species are threatened. In addition, climate change is conservation issues that biodiversity may likely face in the future, particularly among primates. Thus, species-distribution modeling was useful for conservation planning. Herein, we present protected areas (PA) recommendations with high nature-conservation importance based on species-richness changes. We performed maximum entropy (Maxent) to retrieve species distribution of 51 primate species across Indonesia. We calculated species-richness change and range shifts to determine the priority of PA for primates under mitigation and worst-case scenarios by 2050. The results suggest that the models have an excellent performance based on seven different metrics. Current primate distributions occupied 65% of terrestrial landscape. However, our results indicate that 30 species of primates in Indonesia are likely to be extinct by 2050. Future primate species richness would be also expected to decline with the alpha diversity ranging from one to four species per 1 km2. Based on our results, we recommend 54 and 27 PA in Indonesia to be considered as the habitat-restoration priority and refugia, respectively. We conclude that species-distribution modeling approach along with the categorical species richness is effectively applicable for assessing primate biodiversity patterns.

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Oil palm plantation has a high potency to absorb carbon. Limited observed data and expensive instrumentations to measure the absorbed carbon have caused an inaccurate estimation of carbon storage from oil palm. The objectives of this research were to develop a CO2 absorption model, and to calculate the carbon cycle based on climate factors and plant age. CO2 absorption was derived from gross primary production (GPP) and net primary production (NPP), which were ​​based on solar radiation. From NPP we derived net ecosystem exchange (NEE) by calculating the difference between NPP and soil respiration. Our results showed that age of oil palm has influenced the CO2 absorption from 9.8 (1 year) to 117 tons ha-1 year-1 (19 years), with average of 86.5 tons ha-1 year-1 (over 25-year life cycle). We validated our NPP model with biomass that indicated a very good performance of the model with R2 0.95 and RMSE 1.81. Meanwhile, the performance of NEE model was slightly lower (R2 0.71 and 0.72, for wet and dry conditions), but the model had a similar pattern with the measured NEE. Based on the model performance, the findings imply that the model is useful to estimate CO2 absorption, where there is no eddy covariance measurement. This research suggests that carbon modeling will contribute to global terrestrial carbon modeling.

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Analyze Surface Ocean Currents (SOCs) with one year of HF Radar data (2018-2019) for each season to determine the characteristics of the SOC direction and speed of the crossing route and its control factors carried out in the Bali Strait and the Flores Sea. Method of data analysis by computing the SOC speed and direction of the zonal and meridional components. The results showed that the SOC pattern in the Bali Strait affects the season where its speed in the DJF season is lower than the JJA season. Moreover, the SOC direction in the Bali Strait is dominant towards the south due to the influence of bathymetry. Meanwhile, the SOC pattern in the Flores Sea has a random pattern every season for the influence of topography in the form of small islands that influence the SOC dominant pattern. Furthermore, the SOC characteristics on the Bali Strait crossing route throughout the month are divided into two patterns: random on the eastern side of East Java Island and dominant towards the south on the west side of Bali Island with a maximum speed of 83 cm/s. Meanwhile, the crossing route in the Flores Sea is random, with a maximum speed of 32 cm/s. Whereas, based on the normal cross-correlation method, the SOC control factors in the Bali Strait tend to be influenced by tides, while the factors in the Flores Sea are less influential based on the distribution of zonal and meridional currents of HF Radar.

Sustainable water use in agriculture faces several challenges due to future climate change, increasing population, and higher living standards. Adapting to possible future changes in climate and sustaining the use of water are some of the challenges that face future agricultural water management. In this research, the sustainability of irrigation water use was assessed by performance criteria that consider the effects of climate change and adaption management on irrigation. The model, built using the Water Evaluation and Planning (WEAP) system, is calibrated using the stream flow and the requirement of water for irrigation. The model was used to examine two future climate projections (A2 and B2), for time periods until 2099, and for four scenarios: (1) an increase in the irrigated area, (2) an increase in crop intensity, (3) a change in the crop pattern, and (4) a combination of increased irrigation area and increased crop intensity. Results show water supply is projected to increase by about 85 and 60% (relative to the historical period) in A2 and B2 climate scenarios, respectively, by the end of the century. The requirement for irrigation water will decrease in the future, relative to the historical period. The sustainability index will also decrease in the future, relative to the historical period. Relative to the baseline scenario, increasing the irrigated area is more sustainable than increasing the crop intensity or combining increased crop intensity with increased area under irrigation. Increasing the irrigated area is more amenable to adaption to possible future climate changes. (Résumé d'auteur

Rainfall dynamics play a vital role in tropical peatland by providing sufficient water to keep peat moist throughout the year. Therefore, information of rainfall data either historical or forecasting data has risen in recent decades especially for an alert system of fire. Here the Weather and Research Forecasting (WRF) model may act as a tool to provide forecasting weather data. This study aims to do parameterization on WRF parameters for peatland in Sumatra, and to perform bias correction on the WRF’s rainfall output with observed data. We performed stepwise calibration to choose the best five physical schemes of WRF for use in the study area. The output WRF’s rainfall was bias corrected by spatially observed rainfall data for 2019 at day resolution. Our results showed the following schemes namely (i) Eta scheme for cloud microphysical parameters; (ii) GD scheme for cumulus cloud parameters, (iii) MYJ scheme for planetary boundary layer parameters; (iv) RRTM for longwave radiation; and (v) New Goddard schemes for shortwave radiation are best combination for being used to predict rainfall in maritime continent. The spatially interpolated observed rainfall with the Inverse Distance Weighting (IDW) was outperformed for calibration process of WRF’s rainfall as shown by statistical indicators used in this study. Further, the findings have contributed to advance knowledge of rainfall forecasting in maritime continent, particularly in providing data to support the development of fire danger rating system for Indonesian peatland.

<p>The use of economic approach on water allocation are inclusively becoming integrated on water resource management. Competing among water users is expected to escalate due to increasing water demand despite of limited water availability. This research used economic approach aiming to optimize water allocation in Ambang-Brantas subbasin, Malang, and to calculate the total benefit for different sectors of allocated water. We distinguished two scenarios (2012–2015 and 2016–2035) to reflect the existing and the future water allocation. We modelled the water allocation with the Aquarious application. In this subbasin, three main sectors of water users were identified i.e. domestic, agriculture, and industries. The results showed that the agricultural sector was the highest water demand compared to other sectors. This finding was consistent both monthly and annually. Our findings revealed that industries sector show the maximum benefit per unit water used. Based on the scenario, either a decreasing water availability by 10% or an increasing water demand by 10% will decline the total benefit by 44%. If we increase the scenario to 20% it will reduce the total benefit until 71%. This modelling exercise using Aquarius application shows that the model is a promising tool for water resource management with integration of economic approach.</p>