Although streamflow data is important for water resource planning, it’s long-term availability for Indonesian rivers is limited. One factor could be identified for example lack of observation. Here, we presented observation-based modeling to predict long-term discharge data for Cimanuk watershed in Indonesia. The watershed is categorized as one of the critical watersheds, meanwhile it supports to more than one million people. A well-known hydrological model called Soil and Water Assessment Tools (SWAT) was used to predict monthly discharge. The model was fed with monthly climate data, topography, land use and soil characteristics. We calibrated the model with the observed data from 1974 to 1994 (20 years). Our results showed that the model was a good performance in estimating monthly discharge as indicated by three statistical metrics used. Based on statistical evaluation, the calibration resulted a low percent bias (3.20%), strong correlation (0.73), and high Kling-Gupta Efficiency (0.78). Further, we did a sensitivity analysis for the model, and we found that hydrological response unit was the most influential parameters for the Cimanuk watershed. A long-term discharge data indicated a monsoonal pattern for this watershed.
Peatland is a unique ecosystem that is key in regulating global carbon cycle, climate, hydrology, and biodiversity. Peat moisture content is a key variable in ecohydrological and biogeochemical cycles known to control peatland's greenhouse gas emissions and fire vulnerability. Peat moisture is also an indicator of the success of peat restoration projects. Here we present datasets of peat moisture dynamic and retention capacity of degraded tropical peatlands. The data were collected from automatic daily monitoring and field campaigns. The peat moisture content data consists of daily data from 21 stations across three peatland provinces in Sumatra Island, Indonesia, from 2018 to 2019. In addition, peat water retention data were collected from field campaigns in Riau province. This dataset represents human modified peatlands which can be used as a benchmark for hydrological and biogeochemical models.
Soil moisture, an essential parameter for hydroclimatic studies, is characterized by spatial and temporal variability, which poses a challenge for scientists to map it at fine spatiotemporal resolution. Although current remote sensing products may provide global soil moisture at fine temporal resolution, they are mostly at a coarse spatial resolution. In recent years, deep learning (DL) has been applied to generate high-resolution maps of various soil properties, but DL requires a large amount of training data. This study aimed to map daily soil moisture across Tasmania, Australia at 80 meters resolution based on a limited set of training data. We assessed three modelling strategies: DL models calibrated using an Australian dataset (51,411 observation points), models calibrated using the Tasmanian dataset (9,825 observation points), and a transfer learning technique that transferred information from Australia models to Tasmania. We also evaluated two DL approaches, i.e. Multilayer perceptron (MLP) and Long Short Term Memory (LSTM). Our models included data of Soil Moisture Active Passive (SMAP) dataset, weather data, elevation map, land cover and multilevel soil properties maps as inputs to generate soil moisture at the surface (0-30 cm) and subsurface (30-60 cm) layers. Results showed that (1) models calibrated on Australia dataset performed worse than Tasmanian models regardless of the type of DL approaches; (2) Tasmanian models, calibrated solely using Tasmanian data, resulted in shortcomings in predicting soil moisture; and (3) Transfer learning exhibited remarkable performance improvements (error reductions of up to 45% and a 50% increase in correlation) and resolved the drawbacks of the Tasmanian models. The LSTM models with transfer learning had the highest overall performance with average mean absolute error (MAE) of 0.07 m3m-3 and correlation coefficient (r) of 0.77 across stations for surface layer and MAE = 0.07 m3m-3, and r = 0.69 for subsurface layer. The fine resolution soil moisture maps captured the detailed landscape variation as well as temporal variation according to four distinct seasons in Tasmania. The best performance of soil moisture models from this study were made available live to predict near real-time soil moisture in Tasmania.
Abstract. Peatlands, which only cover 3 to 5 percent of the global land area, can store up to twice the amount of carbon as the world’s forests. Although recognised for their significant role in the global carbon cycle, discovering the global extent of peatlands and their carbon stock remains challenging. Referring to the UNEP's global peatland map, here we present PEATGRIDS, a data product containing global maps of peat thickness and carbon stock created created using the digital soil mapping approach. We compiled over 25,000 observations of peatland thickness, bulk density (BD) and carbon content (CC), globally. Using the Random Forest (RF) algorithm, we estimated peat thickness and peat BD and CC at ~1 km resolution at multiple depths (0–2 m) globally. The estimates were generated using 19 land surface covariates from digital maps and remote sensing images of land use, soil characteristics, topographical features, and climate parameters. The RF models for peat thickness were trained on 25,200 points grouped into six geographic regions. Validation of the peat thickness estimates showed a good performance, with the coefficient of determination (R2) ranging from 0.15 to 0.72. The prediction for peat BD and CC followed the same model architecture and were trained on 17,000 and 7,000 points, respectively. Overall, BD and CC models performed well and consistently across soil layers with average R2 values of 0.61 for BD and 0.48 for CC. Based on the estimated peat thickness, BD and CC, the carbon stock of global peatland was estimated to be 1,029 Pg C for peat dominated area of 6.57 million km2. PEATGRIDS is made available at https://doi.org/10.5281/zenodo.12559239 (Widyastuti et al., 2024) to support further analyses and modelling of peatlands across the globe.
Soil moisture has various critical roles in agricultural and environmental processes, yet monitoring its real-time spatial variability across a large area is challenging. Here, we assimilated soil moisture models and near-real-time datasets to generate daily soil moisture content prediction across Tasmania at an 80-m resolution. The maps have been embedded in a website for land monitoring since September 2023, with a fully automated mapping procedure to update the published maps within a day. A testing period in September-November 2023 showed that the predicted moisture maps performed well (daily correlation coefficient values varied between 0.60 to 0.75) against 50 observation stations across Tasmania.
Physical properties of peat are widely applied to detect the quality of peatland ecosystem. A comprehensive dataset on the peat properties is the foundation for the development tool and model of peat ecosystem, especially in region with frequent wildfire. Here we established a tabular dataset for physical properties of lowland tropical peatland in Indonesia. The data were obtained in dry season 2019 and 2023, respectively, at Jambi and Central Kalimantan peatlands. The dataset comprises of 66 peat samples from two land-uses namely secondary forest and ex-burned lowly vegetation. The physical properties are bulk density, porosity, water retention at four pressures (-1, -10, -25, and -1500 kPa), and water holding capacity. In addition, a set parameter of van Genuchten for water retention curve is available. The field-observed dataset provides a solid base for a better understanding of physical peat properties and can be used as a first step to develop peat water retention database in lowland tropical peatlands.
<p>Solar radiation greatly affects the development of plant biomass. The process of plant development is complex. Here, we simplified this complexity through modeling experiment by integrating climate variables. This study aims to determine the dynamics of canopy intercepted solar radiation under soybean (<em>Glycine Max (L.) Merrill</em>). We employed the shierary-rice model to calculate plant biomass. The results showed that intercepted radiation continuosly increased during vegetative phase, whereas the radiation remains constant during generative phase. Our observation confirmed that the pattern of intercepted radiation followed the angular pattern of sunlight. The intercepted radiation was optimum at 10:00 to 14:00 pm, and it was used to form the plant dry matter. We found that the intercepted radiation contributed until 12%. Based on this contribution, we built our crop model of soybean biomass. Our model performed well in simulating dry biomass with high R<sup>2</sup> (0.9), and as indicated by the plot 1:1 between dry matter of model and field observations. Further, the result of t test between model and observed data confirm this strong corelation (<em>p-value</em> 0.07).</p>
Rewetting peatland is an ongoing effort in Indonesia to restore the hydrological cycle and carbon balance of the ecosystem. However, quantifying the impact of rewetting on mitigating fire remains a challenge. Here, we assess the impact of large-scale rewetting on fire risks and occurrences (duration, coverage area, and the number of events) in 2015–2021. The weather research and forecasting (WRF) model was integrated with a drought–fire model to spatially quantify fire hazards in Riau, Sumatra. The results show that rewetting has resulted in decreasing the frequency of extreme events in the study area (pre- and post-rewetting, respectively, were seven and four events). Although the area influenced by extreme events reduced following rewetting by 5%, the mean duration of extreme events increased. Our findings reveal that widespread prolonged extreme fire hazards only occurred during drying El Niño events in 2015 and 2019. The findings obtained in this case study provide quantitative evidence of the reduced fire hazard resulting from peat restoration in Indonesia. Further, the findings assist in assessing the success of peatland restoration programs and improve our knowledge of the ability to monitor and forecast fire risks in tropical peatlands.
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