Urban hydrological model (UHM) developed for urban flash flood simulation and analysis of sensitivity of flood intensity to urbanization

A two-dimensional raster gridded urban hydrological model has been developed to simulate the hydrologic response to urban land surfaces with consideration of the hydraulic characteristics of urban areas, and to produce mappings of urban inundation associated with rainstorms. The model is forced using radar-observed QPEs, in conjunction with parameter sets of land use and land cover (LULC) derived from satellite multispectral images and high spatial resolution GIS datasets relating to urban hydrology and land surface hydrodynamic properties. Urban drainage flow capacity is derived from a GIS road-network dataset using a generalization method. Submodels deduce runoffs of both the impervious and the pervious. Methodologically, the D8 method (eight slope directions) is used to derive the channel paths for gravity-driven nondispersive streamflow, which its hydrodynamics can be described by the hydraulic model based on simplified 1D − 2D St. Venant equation. A case study was undertaken to reproduce the urban flash flooding that occurred in Beijing following thunderstorms on 21 July 2012. The model results were verified qualitatively using media reports of the flooding. Through manipulation of model parameters, the test on the sensitivity of flash flood intensity to urban LULC variability and drainage network settings revealed the following: 1) flood intensity is enhanced slightly if the current urban LULC is substituted with a pure impervious, 2) increasing the pervious surface area (PSA) attenuates flood intensity considerably, and 3) flash flood intensity will increase by 30–60% in the absence of an underground drainage system.

Pengelompokan Daerah Rawan Demam Berdarah Dengue di Provinsi DKI Jakarta

Indonesia is the country with the highest cases of dengue fever in Southeast Asia, according to The World Health Organization (WHO) from 1968 to 2009. DKI Jakarta with high population density and flood intensity being cautioned for being aware of cases of dengue fever, because it may make growth in dengue mosquitoes spreading. Data show that almost every year DKI Jakarta is in the top 10 provinces with the highest cases of dengue fever. By using the clustering method, it is possible to make groups of data with similar characteristics into specific classes. Data used is secondary data from 2016 which is collected from the website https://data.jakarta.go.id/. The result from clustering can be used to decide which zone needs special treatment based on numbers of dengue fever cases, prone to flood, areas wide, total population, temporary landfills, and green space numbers variables. From the analysis, we can conclude that DKI Jakarta has 19 sub-districts with high risk, 10 sub-districts with medium risk, and 15 sub-districts with low risk.Keyword: dengue fever, cluster analysis, DKI Jakarta AbstrakIndonesia merupakan negara dengan kasus demam berdarah dengue tertinggi di Asia Tenggara sejak tahun 1968 hingga tahun 2009 berdasarkan data World Health Organization (WHO). DKI Jakarta dengan kepadatan penduduknya yang tinggi dan intensitas banjir yang cukup tinggi menjadi perhatian agar dapat waspada terhadap kasus demam berdarah dengue, dikarenakan dapat memicu perkembangan nyamuk penyebab demam berdarah dengue. Data profil kesehatan Republik Indonesia menunjukan bahwa hampir setiap tahunnya provinsi DKI Jakarta memasuki 10 besar provinsi di Indonesia dengan kasus demam berdarah dengue terbanyak. Dengan menggunakan metode klaster, dapat dilakukan pengelompokan data dengan karakteristik yang memiliki kemiripan menjadi kelas-kelas tertentu. Data yang digunakan merupakan data sekunder pada tahun 2016 yang dikumpulkan melalui website https://data.jakarta.go.id/. Hasil dari analisis klaster dapat digunakan untuk menentukan daerah mana yang masuk ke dalam kategori yang perlu penanganan khusus berdasarkan variabel jumlah penderita demam berdarah dengue, jumlah rw rawan banjir, luas wilayah, jumlah penduduk, tempat pembuangan sementara dan jumlah ruang terbuka hijau. Dari analisis data yang dilakukan, dapat disimpulkan bahwa di DKI Jakarta terdapat kecamatan dengan tingkat kerawanan tinggi sebanyak 19 kecamatan, 10 kecamatan dengan tingkat kerawanan sedang dan 15 kecamatan dengan tingkat kerawanan rendah.Kata Kunci: demam berdarah, analisis klaster, DKI Jakarta

Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems

Floods after monsoon rains are frequent disasters that affect millions of lives in Pakistan. Human lives are lost, agriculture economies are destroyed, and livestock animals, houses, fruit farms, and crops are lost which are the major livelihoods of thousands of people in Punjab. Each year there are heavy rains in the monsoon season and, due to global warming, there is the rapid melting of snow in northern glaciers; these factors subsequently cause floods. There is also loss of life due to the spread of waterborne diseases and snake bites. Flood monitoring provides early detection of a flood and the calculation of its intensity, which results in reduced human life losses and economic losses. Most casualties are caused by the lack of timely real-time, authentic information about the high-risk areas, and flood intensity, speed, and direction. Therefore, the proposed approach is centered on formal modeling and verification of safety and liveness properties of flood monitoring perceivers. Each flood perceiver has several sensors. It requires the collection of information starting from the flood perceiver, observer, and environmental forecast. This information is processed to determine the flood intensity level. We have developed a CP-Nets’ formal model and model-checked it. We have verified the safety and liveness properties of correctness by exhaustive verification of the system using model-based proof obligations (Event-B method using Rodin). Our objective in this research is to propose a correct, reliable, and efficient flood warning, monitoring, and rescue (WMR) SoS based on formal methods. We have used formal modeling and model-checking based on state-of-the-art hierarchical CP-Nets supported by exhaustive formal proof obligations of Event-B.

Land Use Change and Its Impacts on Surface Runoff in Rural Areas of the Upper Citarum Watershed (Case Study: Cirasea Sub-watershed)

Land-use change in upstream regions is a recognized driver of the increase in surface runoff, resulting in increasing flood intensity and occurrence. The rapid urbanization of outlying districts surrounding large metropolitan areas is a known driver of land-use change. Therefore, we study land-use change patterns within the Cirasea Sub-watershed within the last 20 years and changes in the runoff coefficient within the same time frame. This paper examines how land-use change patterns on the outskirts of the Bandung Metropolitan Area influence runoff. Spatial analysis and surface runoff calculation using the curve number method were applied. The study found significant changes in land use, particularly in the watershed’s southern reaches, whereby forest and shrub land gave way to agriculture in a water recharge zone, resulting in an increased runoff coefficient upstream. Urbanization within the Cirasea Sub-watershed did not encroach into areas identified as recharge zones and had a minimal direct impact on increased runoff. Aggregate runoff coefficient (curve number) in the Cirasea Sub-watershed increased from 70.98 in 1999 to 72.04 in 2018. For a design 24-hour period rainfall of 120 mm, runoff increased from 48.49 mm (1999) to 51.8 mm (2018). We conclude that the changes above in land use have increased runoff in the Cirasea Sub-watershed. Furthermore, land-use policies laid down by the RTRW Bandung Regency for 2016-2036 provide provisions to reforest previously deforested areas, with deforested areas being zoned as protected forest. Therefore, we propose promoting agroforestry as part of land use policy in order to restore runoff to its 1999 level under existing land use planning policy.Abstrak. Perubahan guna lahan pada wilayah hulu diketahui sebagai salah satu penyebab peningkatan limpasan air permukaan, meningkatkan intensitas dan frekeuensi banjir. Urbanisasi pesat pada wilayah pinggiran kawasan metropolitan diketahui sebagai faktor pendorong terjadinya perubahan guna lahan. Berdasarkan kondisi tersebut, kami melakukan studi terhadap pola perubahan guna lahan pada Sub-DAS Cirasea, pada 20 tahun terakhir, serta perubahan limpasan air permukaan pada rentang waktu yang sama. Paper ini meneliti bagaimana perubahan guna lahan pada wilayah pinggiran Metropolitan Bandung Raya mempengaruhi limpasan air permukaan. Analisis spasial dan perhitungan limpasan air permukaan dilakukan, menggunakan metode bilangan kurva. Berdasarkan hasil studi, diketahui telah terjadi perubahan guna lahan yang signifikan pada wilayah hulu DAS. Hutan dan semak belukar berubah menjadi kawasan pertanian, pada wilayah resapan air tanah, sehingga terjadi peningkatan limpasan air permukaan di wilayah hulu. Urbanisasi pada wilayah Sub-DAS Cirasea diketahui tidak menjalar hingga wilayah resapan air tanah dan memiliki dampak minim terhadap kenaikan limpasan air permukaan. Angka koefisien limpasan air permukaan (bilangan kurva) di DAS Cirasea meningkat dari 70.98 (1999), menjadi 72.04 (2018). Pada curah hujan (asumsi) 24 jam sebesar 120mm, limpasan air permukaan meningkat dari 48.49mm (1999), menjadi 51.8mm (2018). Berdasarkan hasil tersebut, kami simpulkan bahwa perubahan guna lahan telah mengakibatkan peningkatan limpasan air permukaan di DAS Cirasea.  Kata kunci.Perubahan tata guna lahan, potensi aliran permukaan, DAS citarum hulu, pencegahan banjir.

Group Decision Support System for Business Place Establishment using Fuzzy SAW Borda

The economic development of a city is marked by the increasing number of established businesses. Inaccurate decision about business location can result in losses for the owner. To support proper decision making, various decision making methods have been developed. For example, by using Fuzzy, Analytical Hierarchy Process (AHP), Promethee, Simple Additive Weighting (SAW), Borda, and Hybrid (a combination of several methods). In this research, the author tries to solve the problem of decision making in determining business location, by designing a decision support system that uses a hybrid method approach, a combination of Fuzzy, Simple Additive Weighting (SAW), and Borda. There are 3 alternative locations (A1, A2, and A3), 5 decision makers, and 5 assessment criteria used, such as land eligibility (benefit), population density (benefit), traffic jam (benefit), completeness of files (benefit), and flood intensity (cost). The Decision Support System accepts input in the form of natural language/linguistic (very/quite/kinda/less) and converts it into a fuzzy membership value (range 0 to 1), processes it based on weight, cost, and benefit of existing criteria using Simple Additive Weighting (SAW), and combines the assessment results using Borda from 5 decision makers/business owners with their respective perceptions. So as to produce a final group decision. Where the final result obtained in the Borda method, that the alternative location A1 (A1 score is 6) gets the first priority to be business place establishment location, followed by A3 (A3 score is 5) in the second priority, and A2 (A2 score is 4) in the third priority/last.

Climate change impact on flood and extreme precipitation increases with water availability

The hydrological cycle is expected to intensify with global warming, which likely increases the intensity of extreme precipitation events and the risk of flooding. The changes, however, often differ from the theorized expectation of increases in water‐holding capacity of the atmosphere in the warmer conditions, especially when water availability is limited. Here, the relationships of changes in extreme precipitation and flood intensities for the end of the twenty-first century with spatial and seasonal water availability are quantified. Results show an intensification of extreme precipitation and flood events over all climate regions which increases as water availability increases from dry to wet regions. Similarly, there is an increase in the intensification of extreme precipitation and flood with the seasonal cycle of water availability. The connection between extreme precipitation and flood intensity changes and spatial and seasonal water availability becomes stronger as events become less extreme.

Using Disaster Outcomes to Validate Components of Social Vulnerability to Floods: Flood Deaths and Property Damage across the USA

Social vulnerability indicators seek to identify populations susceptible to hazards based on aggregated sociodemographic data. Vulnerability indices are rarely validated with disaster outcome data at broad spatial scales, making it difficult to develop effective national scale strategies to mitigate loss for vulnerable populations. This paper validates social vulnerability indicators using two flood outcomes: death and damage. Regression models identify sociodemographic factors associated with variation in outcomes from 11,629 non-coastal flood events in the USA (2008–2012), controlling for flood intensity using stream gauge data. We compare models with (i) socioeconomic variables, (ii) the composite social vulnerability index (SoVI), and (iii) flood intensity variables only. The SoVI explains a larger portion of the variance in death (AIC = 2829) and damage (R2 = 0.125) than flood intensity alone (death—AIC = 2894; damage—R2 = 0.089), and models with individual sociodemographic factors perform best (death—AIC = 2696; damage—R2 = 0.229). Socioeconomic variables correlated with death (rural counties with a high proportion of elderly and young) differ from those related to property damage (rural counties with high percentage of Black, Hispanic and Native American populations below the poverty line). Results confirm that social vulnerability influences death and damage from floods in the USA. Model results indicate that social vulnerability models related to specific hazards and outcomes perform better than generic social vulnerability indices (e.g., SoVI) in predicting non-coastal flood death and damage. Hazard- and outcome-specific indices could be used to better direct efforts to ameliorate flood death and damage towards the people and places that need it most. Future validation studies should examine other flood outcomes, such as evacuation, migration and health, across scales.