The utilization of Lido Lake will continue to grow, particularly in the tourism aspect, strenghtened by the 2016-2036 Bogor Regency Spatial Planning (RTRW) and the Government Regulation No. 69 of 2021 which have stipulated it to be a tourism area and a special economic tourism area, respectively.  However, at the same time increased garbage accumulation at its lakeside, wastes entering the waters and sedimentation have become prominent problems.  This study aimed to develop a management strategy so that the sustainability of Lido Lake can be maintained facing development changes. The SWOT method was used to identify and to analyze the existing internal and external factors in Lido lake including lake's biophysical conditions and preferences/participation of people around the lake. The evaluation of the internal and external factors placed Lido lake in quadrant V forming a strategy to sustain and to maintain. The SWOT matrix  recommend 6 management strategies i.e. (1) developing ecotourism as the basis for tourism, (2) implementing and managing environmentally friendly cage fish farming systems, (3) controlling sedimentation in lake waters, (4) Making building design directions on the lakeside, (5) managing the causes of degradation in lake water catchment areas, (6) providing facilities and infrastructure for waste control.

The Keetch–Byram Drought Index (KBDI) is a numerical value reflecting the dryness of the top layer of soils, deep forest litter, logs, and living vegetation. The KBDI is expressed as a scale from 0 to 200, where the number represents the amounts of rainfall (in millimeters) to return the soil to saturation. We proposed a method to integrate peatland groundwater as a key variable for the peatland forest fire hazard, and we called it mKBDI. The groundwater table was obtained from the SWAT-MODFLOW model simulation. The MODFLOW model uses a 200 m × 200 m grid. The SWAT-MODFLOW model was calibrated and validated using daily water level measurements in the river. The model failed to represent peak flow, but the model produced the average water level. Output from the simulation was read using the FloPy module, and then mKBDI was calculated. The daily calculations of the mKBDI for each grid for the catchment were saved in the NetCDF format using the x-array module. We applied this model in the Peatland Hydrological Unit (PHU) Merang-Kepahyang, South Sumatera, Indonesia, in 2015 (El-Niño year) and 2016 (La-Nina Year). The daily mKBDI index from all the grids in the catchment was classified into three classes: low (mKBDI < 100), moderate (mKBDI = 101–150), or high (mKBDI > 150). Then, the whole catchment was classified according to these classes. Therefore, every day the percentage of the area with low, moderate, or high class in the catchment dynamically changed. When these classes were verified with hotspot data, all hotspots only coincided with the high hazard classes, where more than 60% was area of the catchment. No hotspot data were reported on low/moderate levels throughout 2015/2016. In the larger area with high mKBDI classes, the frequency of hotspots substantially rose. Sixty-three hotspots occurred during August–October of 2015 when the area of high hazard classes was above 70%. Through this finding, we proposed to use a 60% area of the catchment with high mKBDI classes as a threshold value indicating that the area is prone to peatland fire. Therefore, the peatland restoration project in preventing the fire could be evaluated using this indicator. If the restoration projects could reduce the area with high mKBDI classes to less than 60% for the whole year, we could accept it as a successful project.

Kondisi geografis dan geologis, menjadikan Indonesia rawan terhadap bencana geologis seperti gempa bumi dan tsunami. Secara historis, tercatat sejak tahun 1900 hingga tahun 2018, terdapat 134 kejadian tsunami yang melanda wilayah pesisir Indonesia. Salah satu wilayah pesisir yang memiliki potensi terjadinya tsunami adalah wilayah pesisir Kabupaten Tasikmalaya. Berdasarkan hal tersebut, diperlukan adanya analisis tingkat kerawanan dan upaya mitigasi tsunami di Kabupaten Tasikmalaya. Adapun tujuan dari penelitian ini, yaitu memperkirakan luas wilayah yang terdampak tsunami dengan skenario tinggi gelombang 10 m, 15 m, dan 20 m, menganalisis tingkat kerawanan tsunami, memperkirakan jumlah penduduk yang terdampak, memetakan shelter, tempat evakuasi, dan jalur evakuasi tsunami, serta merencanakan upaya mitigasi struktural. Metode yang digunakan, yaitu dengan tool model builder, weighted overlay, InaSAFE, dan Network Analyst. Hasil pemodelan menunjukkan tsunami setinggi 10, 15, dan 20 meter dapat menggenangi wilayah seluas 12,25 km2, 18,47 km2, dan 24,69 km2. Wilayah Pesisir Kabupaten Tasikmalaya didominasi oleh tingkat kerawanan rendah sebesar 54.62%, kemudian tingkat kerawanan sangat rendah sebesar 26.25%, tingkat kerawanan sedang sebesar 12.61%, tingkat kerawanan tinggi sebesar 5.30%, dan tingkat kerawanan sangat tinggi sebesar 1.22%. Jumlah penduduk terdampak tsunami setinggi 10, 15, dan 20 meter adalah sebanyak 8280, 12320, dan 17770 orang. Terdapat 59 jalur evakuasi, dimana jalur yang termasuk kategori sedang sebanyak 27 jalur, kategori dekat 14 jalur, kategori sangat dekat dan jauh masing-masing sebanyak 9 jalur. Upaya mitigasi struktural yang dilakukan adalah dengan membuat green belt dan sea wall yang efektif mereduksi tsunami dengan tinggi di bawah 15 meter.

Indonesia has 673 peat hydrological units (PHUs) covering more than 26.5 million hectares, of which approximately 70% are located on the Kalimantan and Sumatra Islands. Merang-Kepahyang PHUs in South Sumatra cover a total area of approximately 1.094 km², comprising three watersheds, namely Merang (360.3 km²), Buring (458.5 km²), and Kepahyang (275.3 km²). This area is globally known as a carbon (C)-rich ecosystem. However, there is still a lack of understanding of the C cycle in this area, primarily associated with land use and cover changes. This study spatially estimates belowground carbon stocks and relates them to land elevation, land use, and soil unit. To reduce inaccurate estimates, the volume of the peat is discretized by a 200 m × 200 m grid as a grid based analysis. This assessment aimed to obtain the baseline data with particular attention to provide information on the peat carbon and its spatial distribution in each watershed. We conducted field surveys and image analysis based on SPOT 6 (1.5 m/pixel with raster format 200 m/pixel) to produce interpolated data and maps of land use, soil unit, land elevation, peat thickness, and peat carbon. We found that the land elevation ranged from 1.5 to 13.0 m-MSL in Merang, from 1.1 to 13.5 m-MSL in Buring, and from 0.2 to 11.6 m-MSL in Kepahyang. Peat thickness in ranged from 1.3 m to 12.9 $\mathrm{m}$ in Merang, from 0.8 m to 13.2 $\mathrm{m}$ in Buring, and from 0.4 m to 11.4 $\mathrm{m}$ in Kepahyang. Peat carbon was 220 $\mathrm{M}$$\mathrm{t}$ in Merang, 225.8 $\mathrm{M}$$\mathrm{t}$ in Buring, and 116.8 $\mathrm{M}$$\mathrm{t}$ in Kepahyang. On average, peat carbon density was 6.11 kt ha⁻¹ in Merang, 4.92 kt ha⁻¹ in Buring, and 4.24 kt ha⁻¹ in Kepahyang. The cumulative area covering the peat with a thickness greater than 3 $\mathrm{m}$ was 334.9 km² (93%) in in Merang, 379.4 km² (83%) in Buring, and 193.9 km² (70%) in Kepahyang. There is a relationship between carbon content and elevation, where most of the high carbon content is in the higher elevation. Furthermore, the trees in the secondary forest are primarily found at higher elevations, while the shrubs are located at lower elevations. This is due to water table conditions below the land surface at higher elevations, and close to land surface at lower elevations. In conclusion, these watersheds are carbon-rich areas which are worthy of conservation while a small portion (<30%) may be used for cultivation.

Roads are one of the land transportation infrastructures that have a vital role in supporting community activities. Road damage is often encountered in various places. If left unchecked, handling road damage will cost a lot. The purpose of this study is to identify the type and extent of damage from road pavement and drainage in the study area, as well as determine handling recommendations for road pavement using the Bina Marga method. The method used is the Bina Marga method and the main output of this method is the value of the Priority Order (UP) and the recommendation for handling the observed road. The results of the study showed that the most common type of damage encountered at the study site was elongated cracks with a total area of 582.44m2, and lateral cracks were damages with the smallest total area, which was 6.27 m2. The value of road condition obtained from summing the number of damage in each segment showed that the condition value of 7 was mostly owned by the road segments at the research site, namely 18 segments, while the condition values 3 and 6 were only owned by 1 segment. Recommendations for handling the east side drainage were obtained as a result that 27 segments require routine maintenance, 18 segments require periodic maintenance and 10 segments require addition to the drainage system. The drainage on the west side was obtained as a result of 35 road segments requiring routine maintenance, 14 road segments requiring periodic maintenance and 6 road segments requiring addition to the drainage system. The recommendations for handling on the road were obtained as a result that 10 road segments require routine maintenance, 26 road segments require periodic maintenance and 19 road segments require improvement with the required handling costs of Rp 39,857,105, Rp 302,529,219 and Rp 1,719,020,425 respectively.

Soil and Water Assessment Tools (SWAT) which is developed in America was used to analyze the effect of land management on discharge, sedimentation, and water quality in a river basin area. The hydrological conditions in the Americas are different from Indonesia, so a modified SWAT is done. This research aimed to implement a modified SWAT program to predict the results of a better discharge analysis based on hydrological analysis in the sub-Watershed area. About 13.90% of the area was paddy fields. The evaluation of the module had been done by looking at the coefficient of determination (R²) and Nash-Sutcliffe Efficiency (NSE) based on the comparison of simulated discharge results with an observation discharge. Research began with delineation DEM, HRU formation, simulation, calibration, and validation. The R² value for daily data on modified SWAT was 0,725. It was better than the original SWAT and SWAT with Pothole, which were 0,706 and 0,708 respectively. Likewise, the NSE value for daily data on modified SWAT was 0,721, on original SWAT and on SWAT with Pothole was 0,668 and 0,685 respectively. The analysis result showed that the modified SWAT provide good discharge prediction results, indicated by relatively high R² and NSE values. Keywords: hydrological analysis,modified SWAT, paddy field, pothole, SWAT