Search

The urban development increase in the built-up areas leads to more impervious areas with the consequence of larger runoff. Undeniably, this excess water has many benefits. Low-impact development (LID) is one of the innovations to conserve wasted runoff water. The two LID scenarios (water storage – WS; infiltration – I) under different rainfall depths (20, 25, 30, 35 mm) are assessed using Storm Water Management Model (SWMM) and analyzed based on their benefit–cost. This study aims to evaluate the hydrological performance and the benefit–cost ratio to identify the optimal LID design. The benefit calculation is not only projected by runoff reduction aspects, but also the other opportunities aspects. Based on the hydrological performance, scenario I shows a higher runoff reduction performance than scenario WS. Based on the benefits aspects studied, scenario I provides greater benefits with more cost than the WS scenario. Rainfall depth influenced the life cycle cost with 20-mm WS scenario experiencing faster payback period than other scenarios. HIGHLIGHTS Scenario infiltration excels in reducing runoff, particularly in low rainfall conditions.; Scenario water storage with 20-mm rainfall depth offers the best cost-effectiveness, but sensitivity to costs and discount rate emphasized.;

In December 2021, Mount Semeru experienced an eruption accompanied by extreme rainfall, which resulted in lava floods, known as lahars or debris flows. The lava flood destroyed infrastructure, resulting in loss of life. Various rivers surrounding Mount Semeru, including the Rejali River, experienced the effects of this phenomenon. To address this, a study is needed to analyze the occurrence and frequency of lava floods over specific time intervals through the creation of a hazard map. This study aims to map the hazard of lava floods for various return periods using a coupled HEC-HMS and HEC-RAS software alongside a lava flood discharge approach. The HEC-HMS software is used to simulate hydrological processes, to obtain the lava flood discharge, while the HEC-RAS is used to model a two-dimensional (2D) lava flood hazard map. The input parameters of the modeling in this study are rainfall intensity, soil type, land cover, river distance, slope, and elevation. The results show that the flood area covers 9.55% of the total study area by 2 year return period (Q2), 11.80% by Q10, 14.10% by Q50, and 15.72% by Q200 with an overall validation Root Mean Square Error (RMSE) of 0.16. These changes are determined by the discharge volume from each return phase and the river's shallow depth, which causes overflow beyond the river's ability to accommodate the flow. Thus, this study suggests that the models successfully generated a reliable model for mapping the risk of lava floods on the Rejali River. These findings can help the government reduce disaster losses through adequate adaptation and mitigation initiatives.

Low impact development (LID) is a sustainable land use and planning strategy that aims to minimize the environmental impacts of development. A community can enhance their water resources and create sustainable and resilient neighbourhoods. This approach has demonstrated success in managing stormwater and promoting water reuse globally, however, its suitability in developing countries like Indonesia remains uncertain and requires further investigation. The implementation of LID in developing countries may face several challenges including high density and complex drainage networks, combined sewer usage, clay soil type, irregular housing layouts, community socio-economic characteristics, affordability, cost, and the availability of regulations and policies. With proper planning and site-specific strategies, LID can be implemented effectively in Indonesia. Clear regulations, secured funding source and community-based LID are all essential for successful LID deployment. This paper can be used as a starting point for considering LID implementation in Indonesia and other countries with similar characteristics. HIGHLIGHTS The applicability of LID in developing countries like Indonesia remains uncertain and requires further investigation.; The implementation of LID in Indonesia faces several challenges including high-density and complex drainage networks, combined sewer usage, clayey soil type, irregular housing layouts, community socio-economic, affordability, and cost, coupled with the availability of regulations and policies.; With proper planning and site-specific strategies that address these challenges, LID can be implemented effectively in Indonesia.;

Landslides are one of the natural disasters that often occur in the Upper Tanggul Watershed. This study aims to produce a landslide susceptibility map based on the Geographic Information System (GIS). Seven landslides conditioning factors used, namely land cover, river density, slope, aspect, elevation, curvature and Normalized Difference Vegetation Index (NDVI). Landslide susceptibility areas were mapped using the frequency ratio and weights-of-evidence methods. The accuracy of the landslide susceptibility maps generated from both methods is validated using the Area Under Curve (AUC). The estimation results of the AUC plot show that the susceptibility map using the frequency ratio model has an accuracy of 92,6% and the weights-of-evidence method has an accuracy of 90,1%.

The Sub-watershed of Sampean Hulu, located on the slopes of Mount Raung is susceptible to erosion hazards. Erosion indicators have been observed in the irrigation channels, mainly due to frequent sedimentation, which compromises the irrigation infrastructure's optimization and water distribution. To address this, erosion monitoring using the Universal Soil Loss Erosion (USLE) model, along with GIS and remote sensing techniques, is essential. It was found that the irrigation networks associated with Clangap and Tamanan rain gauges are at higher risk due to intense rainfall. Maesan and Wonosari II rain gauges cover a larger irrigation network area, highlighting the need for erosion prevention measures. The dominant soil type, Tv, with a high clay content, is highly susceptible to erosion. Flat and gently sloping slopes have a lower risk compared to steeper slopes, while very steep and steep slopes pose higher erosion risks. Paddy fields and well-managed forest plantations have lower erosion hazards, while bare land and certain agricultural practices contribute to increased erosion risks. The analysis classified the erosion hazard into five classes, with the sub-watershed being predominantly low and very low susceptible to soil erosion. Implementing conservation practices, sustainable land management, and land use regulations are crucial for erosion prevention.

DAS Bedadung merupakan salah satu DAS terbesar di Kabupaten Jember yang didominasi oleh sawah irigasi dan sering mengalami bencana banjir. Salah satu cara untuk mengurangi banjir dengan melakukan peningkatan resapan air menggunakan teknologi rainwater harvesting. Tujuan penelitian ini adalah mengidentifikasi lokasi yang berpotensi untuk diterapkannya 4 tipe rainwater harvesting di DAS Bedadung. Tahapan penelitian ini dibagi menjadi 4 tahap utama, yaitu: analisis dan reklasifikasi kriteria lokasi rainwater harvesting, uji multikolinearitas untuk menemukan korelasi antar kriteria, analisis pembobotan menggunakan metode AHP, dan pemetaan potensi lokasi rainwater harvesting. Berdasarkan hasil uji multikolinearitas, delapan kriteria dapat digunakan untuk analisis lokasi. Bobot terbesar ada pada kriteria elevasi dan bobot terkecil ada pada tutupan lahan. Lokasi yang berpotensi untuk penerapan guludan kontur, pemanenan air hujan dari atap dan kolam pertanian adalah Kecamatan Kaliwates, sedangkan tipe embung berpotensi di Kecamatan Ambulu.

Floods are the most recurring natural disasters and impact human life, causing a severe economic downturn. The level of flood risk is a function of hazard and vulnerability. Flood risk-prone areas need to be identified to reduce the flood risk level. This study aims to assess flood risk based on spatial analysis results with Geographic Information Systems (GIS). The flood hazard map is built from remote sensing-based data in the form of a digital elevation model (DEM) applied using the frequency ratio weighting (FR) technique. Vulnerability maps are generated from social, economic, physical, and environmental data applied through techniques overlays weighted using the analytical hierarchy process (AHP). The resulting risk map shows that 21.93% and 6.40% of the total area are at a very high and high level of flood risk. This map is very useful for planning the flood management.

In January 2006, the flash flood in Panti Sub-district was a national disaster, causing damage to building infrastructure and fatalities. From this incident, it is necessary to have flood mitigation by providing a map of the distribution of flood inundation using a 2D hydraulic model to provide information regarding the extent of flood inundation in the study area. Due to the limited DEM data for 2D modeling, it is necessary to use UAV images to provide a DSM with good and higher resolution. This study aims to assess the performance of 2D flood modeling results using HEC-RAS equipped with RAS Mapper through UAV processing as input. There are 21 GCP in the study area as an increase in accuracy, the RMSE value in the horizontal direction is 0.3853m, and the vertical direction is 0.1836m. From the CE90 accuracy test results for a horizontal accuracy of 0.58m and LE90 for a vertical accuracy of 0.30m, it can be concluded that the map accuracy test meets the 1:2500 scale. Terrain maps are input to HEC-RAS; selected meshes are 5x5m and 2x2m. The modeling results can show the inundation depth in each GCP from the min-max depth. The model calibration shows an RMSE value of 0.183, while the flood depth validation shows an RMSE value of 0.13. In other words, modeling can represent the distribution of flood inundation in the study area and provide benefits for the community to be more alert in the event of a flood in the coming year.

Volcanic mudflow floods occur when rainfall runoff combines with volcanic material and flows downstream. These devastating events cause significant damage to infrastructure, disrupt economies, and result in injuries and casualties. One area where the flow of volcanic material greatly affects the situation is the Rejali River, which receives a substantial amount of volcanic debris from Mount Semeru. To address this issue and begin mitigating the associated risks, it is crucial to start by mapping the potential distribution of volcanic mudflow floods. Therefore, this study aimed to assess factors impacting volcanic mudflow flood susceptibility and to create a corresponding susceptibility map. The study employed the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the influence of various factors and classify the areas, respectively. These methods were integrated with the Geographic Information System (GIS) to enhance the analysis. The weighted analysis results showed that the most impactful factors conditioning volcanic mudflow floods, in descending order, were rainfall (42.40%), land cover (13.89%), elevation (13.39%), slope (12.51%), distance from the river (7.09%), soil type (6.58%), and rock distribution (4.13%). The TOPSIS calculation further highlighted that rainfall intensity between 104.03 and 109.65 mm day-1 had the greatest influence on susceptibility. The successful integration of AHP and TOPSIS methods with GIS helped develop a volcanic mudflow flood susceptibility model with an outstanding accuracy of 0.969. The model showed that approximately 46.40% of the areas along the Rejali River exhibited very high susceptibility to volcanic mudflow floods, while an additional 16.21% indicated high susceptibility and substantial risk in most regions. Therefore, the generated susceptibility map offered important insights for shaping future mitigation strategies and influencing policy decisions.

The southern region of East Java is located on the Indo-Australian plate. The shifting of these plates allows for earthquakes that trigger tsunamis to occur. The earthquake leads to the impact of a tsunami in several areas, one of which is Puger Sub-District, Jember Regency. The main economic activity of the Puger Sub-District is the fishery; hence significant local coastal communities are vulnerable to tsunami threats. This study aims to map the tsunami risk using a Geographic Information System. The tsunami hazard map used the analysis of the tsunami inundation map from the BMKG. Vulnerability and capacity maps were prepared based on BNPB Chief Regulations. Vulnerability is evaluated based on social, physical, environmental, and economic data. Capacity considers components of the institution, early warning and risk analysis or evaluation, disaster awareness education, reduction of basic risk, and preparedness. The hazard, vulnerability, and capacity maps are classified into five classes. The tsunami risk map is then derived by overlaying the three input maps. Results showed that the villages with very high risk are Puger Kulon and Mojosari at the coastal front areas, covering an area of 13.01% of the total regional area of the Puger Sub-District.

The existence of a flood inundation rapid map is needed in flood mitigation. This research is intended to map flood inundation quickly by utilizing satellite image technology. Sentinel 2 Level 1C technology uses before and after the flood. The extraction process uses the methods of NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index). The result is treated with a threshold value by dividing the pixel value into flooded and unflooded areas. The performance model shows that the MNDWI method is more accurate than the NDWI method in producing flood inundation maps. Thus, the results of the MNDWI method are practical and able to map flood inundation quickly.

In Indonesia, ground-based rainfall monitoring is uneven and sometimes lacks continuity especially in small watersheds, which makes hydrological modeling difficult. This paper aims to the performance evaluation of the HBV Light model from the manual measurement of rainfall (MMR), Global Precipitation Measurement (GPM-3IMERGDF), and Tropical Rainfall Measuring Mission (TRMM-3B42) as input for the hydrological model. The Hydrologiska Byrans Vattenbalansavdelning (HBV) Light hydrological model is applied to three small watersheds, namely Sampean Baru, Bedadung, and Mayang. The model's performance evaluation is assessed based on the correlation between the average rainfall data for the satellite product area and the MMR product, the stationarity of the rainfall and discharge data, and the model accuracy. The model simulation results show that the MMR rainfall in all watersheds provides a better discharge response than the other two products. Meanwhile, the simulation model of the GPM-3IMERGDF satellite product is slightly better than TRMM-3B42. The stationarity test of rainfall and discharge data needs to be enforced before modeling.

Flood mapping is an essential component of planning flood mitigation. The availability of remote sensing data makes rapid flood mapping possible. This article develops an accurate method for rapid flood mapping using satellite imagery. Sentinel-2 imagery was tested by acquiring data before and after a flood event in a lowland area. Flooding extraction was performed using the newly developed Flood Inundation Extraction Index (FIEI) and compared to the Modified Normalized Difference Water Index (MNDWI), the most commonly used index. Based on the choice of threshold, the results are divided into flooded and non-flooded areas. Evaluation of the performance accuracy based on the total and kappa coefficients showed that the FIEI approach is more accurate than the MNDWI approach.

Banjir merupakan faktor yang mengakibatkan kerusakan infrastruktur jaringan irigasi pada daerah irigasi di aliran Sungai Jompo. Kerusakan tersebut berdampak pada kurang optimalnya kinerja jaringan irigasi. Oleh karena itu, dengan terbatasnya anggaran biaya rehabilitasi maka perlu dilakukan analisis prioritas rehabilitasi jaringan irigasi. Studi ini mengambil lokasi pada Daerah Irigasi (DI) yang berada di aliran Sungai Jompo, Kabupaten Jember, Jawa Timur. Penentuan prioritas rehabilitasi jaringan irigasi dalam penelitian ini menggunakan metode Simple Additive Weighting (SAW) dengan cara menentukan bobot masing-masing kriteria rehabilitasi yang meliputi kondisi prasarana, ketersediaan air, luas areal fungsional, dan produktivitas tanam. Selanjutnya melakukan normalisasi dan perangkingan setiap alternatif dari nilai prioritas. Hasil analisis prioritas rehabilitasi jaringan irigasi menggunakan metode SAW menyatakan bahwa daerah irigasi yang menjadi prioritas utama adalah DI. Bedus. Untuk prioritas kedua hingga kesembilan secara berurutan adalah DI. Arah III, DI. Sekar, DI. Pengarengan, DI. Polo, DI. Langon Patrang, DI. Sembah Kurung, DI. Jaki, dan DI. Sembah.

Banjir bandang di Kabupaten Jember pada pergantian tahun 2006 merupakan salah satu bencana nasional yang membawa banyak korban. Kejadian banjir bandang di Kaliputih terus berulang, untuk itu keberadaan peta zonasi banjir sangat diperlukan untuk antisipasi sebelum kejadian. Tujuan dari penelitian ini adalah untuk melakukan zonasi tingkat kerawanan banjir bandang di sub-DAS Kaliputih dengan bantuan Sistem Informasi Geografis. Metode yang digunakan dalam penelitian ini adalah metode Weights of Evidence. Lima faktor diantaranya kemiringan lereng, tata guna lahan, kerapatan jaringan sungai, jenis tanah dan Normalized Difference Vegetation Index (NDVI) digunakan untuk membuat peta zonasi. Dari yang terkuat, parameter pengkondisi kerawanan banjir adalah jenis tanah latosol coklat dan regosol abu-abu, penggunaan lahan sawah, kemiringan lereng, penggunaan lahan rumah, NDVI pada nilai rendah, dan kerapatan jaringan sungai pada nilai rendah. Hasil yang didapatkan dari analisis menunjukkan sebesar 12,25% dari total wilayah di sub-DAS Kaliputih memiliki tingkat kerawanan banjir bandang yang sangat tinggi, 14,58% tinggi, 12,81% sedang sampai tinggi, 14,89% sedang, 16,98% rendah, dan 28,49% sangat rendah dengan nilai Area Under Curve (AUC) sebesar 80,0%.

Floods in coastal areas occur yearly in Indonesia, resulting in socio-economic losses. The availability of flood susceptibility maps is essential for flood mitigation. This study aimed to explore four different types of models, namely, frequency ratio (FR), weight of evidence (WofE), random forest (RF), and multi-layer perceptron (MLP), for coastal flood susceptibility assessment in Pasuruan and Probolinggo in the East Java region. Factors were selected based on multi-collinearity and the information gain ratio to build flood susceptibility maps in small watersheds. The comprehensive exploration result showed that seven of the eleven factors, namely, elevation, geology, soil type, land use, rainfall, RD, and TWI, influenced the coastal flood susceptibility. The MLP outperformed the other three models, with an accuracy of 0.977. Assessing flood susceptibility with those four methods can guide flood mitigation management.