Flood Distribution Research Articles

Spatial Modeling of Flood Affected Areas of Krukut River in Pela-Mampang Segment, South Jakarta, Indonesia

Flooding is one of the most common natural disasters in Indonesia, and the capital city of DKI Jakarta is no exception. Every year, there are always areas that experience flooding, mainly triggered by the overflow of flowing rivers. Jakarta itself is located in a downstream area consisting of swampy areas that are floodplains. One of the river basins that most often experiences flooding is the Krukut River. This is mainly triggered by the high value of rainfall which can reach more than 200 mm in a day. Massive development along the riverbanks has exacerbated the flooding, especially in the Pela Mampang segment which includes Pela Mampang Village, Mampang Prapatan Subdistrict and Petogogan Village, Kebayoran Baru Subdistrict. This research models two-dimensional flood inundation using the HEC-RAS program to describe the distribution of potential floods for return periods of 2, 5, 10, 25, 50 and 100 years. In addition, a spatial analysis of the affected infrastructure up to the RT level was also conducted to provide an overview of the extent of inundation using 4 types of classifications (low, medium, high and extreme). The results showed that 19 out of 150 RTs in Pela Mampang urban village and 33 out of 79 RTs in Petogogan urban village were flooded with a total inundation area of 100-year flood reaching 39.58 ha. The affected infrastructure reached 1,903 units with the area of inundated roads reaching 3.27 ha. It is hoped that this research can provide input for future infrastructure planning and river management in order to anticipate and mitigate disasters better.

A GIS-Based Assessment of Flood Hazard through Track Records over the 1886–2022 Period in Greece

This paper addresses the riverine flood events that have occurred in Greece over the last 136 years (i.e., during the 1886–2022 period), focusing, amongst others, on the case of urban floods. The flood record of various sites of the country has been collected and analyzed to determine their spatial and temporal distribution. Greece is a country where flood data and records are very scarce. Therefore, as there is not an integrated catalog of Greek floods spanning from the 19th century to recently, this is the first attempt to create an integrated catalog for Greece. The sources used include published papers, local and regional newspapers and public bodies (mainly the Ministry of Environment and Energy and the official websites of Greek municipalities). Additionally, the main factors responsible for their occurrence have been issued, regarding the country’s climatic, geological and geomorphological setting, as well as human interventions. In addition, the atmospheric circulation driving factors of floods are assessed via an unsupervised neural network approach (i.e., Self-Organizing Maps). Based on the results of this research, an online GIS-based database has been created, depicting the areas that have been struck by riverine floods in Greece. By clicking a flood event in the online database, one can view several characteristics, depending on data availability, such as duration and height of the rainfall that caused them and number of fatalities. Long-term trends of mean and extremes seasonal precipitation also linked to the spatial distribution of floods. Our analysis shows that urban floods are a very large portion of the overall flood record, and they mainly occur in the two large urban centers, Athens and Thessaloniki, as well as near large rivers such as Pineios. Autumn months and mainly November are the periods with higher flood hazards, based on past records and cyclonic atmospheric circulation constitutes the principal driving factor. Our results indicate that a flood catalog at national level is of fundamental importance, as it can provide valuable statistical insights regarding seasonality, spatial distribution of floods, etc., while it can also be used by stakeholders and researchers for flood management and flood risk analysis and modelling.

Changes in streamflow drought and flood distribution over Poland using trend decomposition

Changes in streamflow drought and flood distribution over Poland using trend decomposition

Investigating the Storm Surge and Flooding in Shenzhen City, China

Tropical cyclones affecting Shenzhen city have shown a remarkable tendency to increase in both intensity and quantity, highlighting the urgency of accurate forecasts of storm surges and flooding for effective planning and mitigation. Utilizing satellite and field observations together with the advanced high-resolution baroclinic wave–current model (SCHISM), a comprehensive investigation aimed at storm surge and flooding in Shenzhen was conducted. Statistical work of historical tropical cyclones revealed that Shenzhen was most vulnerable to cyclones propagating from the southeast toward the northwest and passing Shenzhen down the Pearl River Estuary. Thus, a representative, i.e., super typhoon Hato (2017), was selected for further study. Validations of numerical results suggested satisfactory model performance in mapping the wave, tide, and surge processes. Remarkable differences in spatiotemporal distribution and intensity of storm surge and flooding were found along the Shenzhen coast, which was dominated by the propagation of far-field surge and tidal waves, cooperation between wind direction and coastline orientation, estuary morphology, and the land terrain. Intervention of wave–current interaction improved the simulation of the surge and flooding and triggered an earlier occurrence time of the maximum surge in specific areas. The Pearl River discharge significantly elevated the sea level height inside the estuary and contributed to a more severe surge. Given the extremely complicated river networks and huge freshwater flux of Pearl River and the increasing trend of concurrent heavy precipitation of tropical cyclones, future investigations on compound flooding were suggested.

The influence of permeability and heterogeneity on chemical flooding efficiency and remaining oil distribution—based on NMR displacement imaging

To investigate the impact of permeability and heterogeneity on oil displacement efficiency and remaining oil distribution of chemical flooding, three groups of high and ultrahigh permeability core samples from an ultrahigh water-cut oilfield in western China were selected as the research objects in this study. High-pressure mercury injection, scanning electron microscopy, wettability test, and other methods were used to characterize the reservoir properties of core samples. Six groups of experiments were performed using the nuclear magnetic resonance (NMR) displacement imaging technology to simulate the oilfield development process considering the economic benefits. The displacement stage with the best oil displacement effect in the process of waterflooding, chemical flooding and then waterflooding was defined, and the control effect of permeability and heterogeneity on the improvement of oil displacement efficiency by polymer-surfactant binary flooding was discussed. The distribution position of remaining oil in different displacement stages was quantitatively and visually displayed, and its control factors were revealed. The research shows that during the simulation process of first waterflooding followed by chemical flooding and then waterflooding in the oilfield, the T2 spectrum signal amplitude increases the most in the two stages, one is from saturated oil flooding to 50% water cut and the other one is from 95% water cut to the end of 1 PV polymer flooding. The oil displacement efficiency increases the most, and the oil is primarily discharged from pore throats larger than 90 ms (or with pore throat radius of 8.37 μm). Compared with heterogeneity, permeability plays a more obvious controlling role in improving the oil displacement efficiency of polymer-surfactant binary flooding. The influence of fingering phenomenon on the distribution of remaining oil is most obvious in the second waterflooding, and the distribution of remaining oil with polymer slug is more obviously affected by the fingering phenomenon than that with polymer-surfactant slug. The study results provide theoretical guidance for tapping the remaining oil potential of old oilfields with high to ultrahigh permeabilities.

Evaluation of urban flooding and potential exposure risk in central and southern Liaoning urban agglomeration, China

Urban flooding is an important natural hazard that affects urban security in the context of global climate change and urbanization. The simulation of stormwater inundation and risk assessment in urban environments is critical for the sustainable development of urban agglomerations. However, few studies have focused on rainstorm flooding at the urban agglomeration scale. In this study, the Central and Southern Liaoning Urban Agglomeration (CSLN) was selected a case study. A method was proposed to assess the urban flooding and potential exposure risk. The urban flooding of CSLN was characterized by the average flooding depth (AFD) of each sub-catchment, and exposure risk was indicated by potential regional and population exposure risk. The results illustrate that the AFD increased with an increase in the rainfall return period, especially in the urban core. The AFD of each city in the CSLN ranged from 10 to 80 mm, and there were strong differences among cities. The spatial distribution of urban flooding in the nine cities presented aggregation characteristics, and hotspots were mainly concentrated in the old urban districts. The potential regional exposure risk tended to increase as the rainfall return period increased from 10 to 100 years; however, there were significant differences among cities. Among them, the area of potential regional exposure risk in Shenyang was the largest, increasing from 365 km2 under a 10-year rainfall return period to 463 km2 under a 100-year rainfall return period. The largest proportion of potential regional exposure risk was in Anshan, which increased from 3% under 10-year to 13.8% under 100-year rainfall return periods. Most cities had some potential population exposure risk. More residents in populous cities, such as Shenyang and Dalian, were subject to higher exposure risks. However, Benxi, Anshan, and Tieling had a high proportion of severely affected areas, although the size of their cities is much smaller than that of Shenyang and Dalian. Urban flooding and potential exposure risk were apparently higher in the business and government zones than in the industrial and greenspace zones. These methods and results may provide references for rainstorm flooding mitigation through urban planning in urban agglomeration areas.

Evaluation of CMIP6 HighResMIP for Hydrologic Modeling of Annual Maximum Discharge in Iowa

AbstractThe High‐Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25‐ and 50‐km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope‐Link Model (HLM) at ∼1,000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff‐based simulations can capture the empirical distribution of flood peaks in five of the 10 models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community‐level assessment, we use nine statistical approaches to bias‐correct and downscale HighResMIP precipitation to a 4‐km resolution. The bias‐correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.

Mapping of Mount Semeru Volcanic Mudflow Susceptibility Along the Rejali River using the GIS-based AHP-TOPSIS Ensemble Approach

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.

Beyond Simple Trend Tests: Detecting Significant Changes in Design‐Flood Quantiles

AbstractChanges in annual maximum flood (AMF), which are usually detected using simple trend tests (e.g., Mann‐Kendall test (MKT)), are expected to change design‐flood estimates. We propose an alternate framework to detect significant changes in design‐flood between two periods and evaluate it for synthetically generated AMF from the Log‐Pearson Type‐3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design‐flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design‐flood quantiles were observed even without a significant trend in AMF and basins with statistically significant trend did not necessarily exhibit statistically significant changes in design‐flood. We recommend application of the framework for evaluating changes in design‐flood estimates considering changes in all the moments as opposed to simple trend tests.

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

The exploding popularity of social networks provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to floods in 2020. The spatial distribution of floods was analyzed using Kernel Density Estimation. Public emotion was analyzed using Natural Language Processing tools. The association between floods and public emotion was explored through correlation analysis methods. The results indicated that: (1) Weibo texts could be utilized as effective data to identify urban waterlogging risk in Guangdong and Guangxi. (2) The waterlogging was mainly distributed in the southern part of Guangdong and Guangxi, especially in the provincial capitals and coastal cities. (3) Public emotion was predominantly negative, especially during periods of heavy precipitation. (4) There was a strong correlation between public emotion and floods in spatial–temporal variation. The degree of negative public emotion was significantly influenced by the number of waterlogging points. The presented results serve as the preliminary data for future planning and designing of emergency management.

Disaster loss calculation method of urban flood bimodal data fusion based on remote sensing and text

Study regionMajor urban areas in Henan Province of central China. Study focusdata fusion technology is also a key and difficult point in the field of flood research. Remote sensing and text data have different modalities and scales, making fusion difficult. This study proposed a remote sensing and text bimodal data fusion model based on UFCLI, and we validated the spatiotemporal distribution of floods and the calculation results of disaster losses. The results show that through the coupling analysis of remote sensing and text bimodal data, rainstorm and flood events can be fully reproduced in space and time. The proposed UFCLI effectively improves the accuracy of remote sensing single-data inversion for urban flood disaster losses. The flood loss in Henan is 121.98 billion yuan, and the accuracy improvement result is R² increased by 0.08 and MAPE decreased by 0.88. New hydrological insights for the regionIn the case of sudden urban storm flooding with complex spatial and temporal evolution, the traditional hydrological-hydraulic model has many pending parameters, which makes it difficult to accurately calculate large-scale disaster losses. By establishing a theoretical model of bimodal data fusion, we effectively use the complementary spatiotemporal information using remote sensing and text to solve the differences in spatiotemporal scales existing between remote sensing and text data. The timeliness and accuracy of urban flood damage estimation have further improved. Data Availability StatementNot applicable.

Tidal Flood Susceptibility Mapping and Community Adaptation Assesment in Semarang Utara District

North Semarang District is one of the areas that is often affected by tidal floods. This study will discuss flood distribution through tidal flood susceptibility mapping and identify adaptations. The first step is to carry out flood susceptibility mapping using DEMNAS data and sea tides. The second stage is to determine the key areas based on the tidal flood susceptibility map made. Key areas are useful for identifying forms of community adaptation to tidal floods through data collection in the form of observations and interviews. The mapping results show that the flood susceptibility area reaches 582.89 Ha or around 50% of the area of North Semarang District. The key areas with high susceptibility are settlements on Jalan Tambakrejo, Tanjungmas Village and it is known that the community responds by adapting patterns to changes in physical infrastructure, economic activity, and social development.

Flood mitigation of Bila River in Sidrap Regency Indonesia based on eco-drainage retention pond

Abstract This study aims to analyze the distribution of floods in the Bila River and its countermeasures in reducing the impact that occurred in Sidrap Regency. The study performs hydrological analysis using the Bila watershed rainfall data, calculates the planned flood discharge using the HSS Soil Conservation Service (SCS) model, and simulates the flood flow profile using the HEC-RAS 2D numerical model. It is found that the Q20 flood discharge of the Bila and the Bulucenrana Rivers entered the Bila River downstream calculated from the HSS SCS analysis are 738.60 and 779.50 m3/s, respectively. The overflow of the Bila River flood affects nine villages, namely Kalola Village (0.06 km2), Sogi Village (0.01 km2), Kalosi Alau Village (0.32 km2), Kampale Village (0.11 km2), Salomalori Village (0.42 km2), Tanru Tedong Village (2.12 km2), Kalosi Village (0.91 km2), Salobukkang Village (1.70 km2), and Taccimpo Village (4.01 km2). It is proposed that the best solution to deal with the issue is by introducing an eco-drainage system, namely by constructing a retention pond with a maximum storage volume of 3.81 million m3 or with a normal storage of 2.4 million m3. The existence of a retention pond can reduce the inundated area around 8.28 km2 or 85.71%.

Flood sensitivity assessment of super cities

In the context of global urbanization, more and more people are attracted to these cities with superior geographical conditions and strategic positions, resulting in the emergence of world super cities. However, with the increasing of urban development, the underlying surface of the city has changed, the soil originally covered with vegetation has been substituted by hardened pavement such as asphalt and cement roads. Therefore, the infiltration capacity of urban rainwater is greatly limited, and waterlogging is becoming more and more serious. In addition, the suburbs of the main urban areas of super cities are usually villages and mountains, and frequent flash floods seriously threaten the life and property safety of people in there. Flood sensitivity assessment is an effective method to predict and mitigate flood disasters. Accordingly, this study aimed at identifying the areas vulnerable to flood by using Geographic Information System (GIS) and Remote Sensing (RS) and apply Logistic Regression (LR) model to create a flood sensitivity map of Beijing. 260 flood points in history and 12 predictors [elevation, slope, aspect, distance to rivers, Topographic Wetness Index (TWI), Stream Power Index (SPI), Sediment Transport Index (STI), curvature, plan curvature, Land Use/Land Cover (LULC), soil, and rainfall] were used in this study. Even more noteworthy is that most of the previous studies discussed flash flood and waterlogging separately. However, flash flood points and waterlogging points were included together in this study. We evaluated the sensitivity of flash flood and waterlogging as a whole and obtained different results from previous studies. In addition, most of the previous studies focused on a certain river basin or small towns as the study area. Beijing is the world's ninth largest super cities, which was unusual in previous studies and has important reference significance for the flood sensitivity analysis of other super cities. The flood inventory data were randomly subdivided into training (70%) and test (30%) sets for model construction and testing using the Area Under Curve (AUC), respectively. The results turn out that: (1) elevation, slope, rainfall, LULC, soil and TWI were highly important among these elements, and were the most influential variables in the assessment of flood sensitivity. (2) The AUC of the test dataset revealed a prediction rate of 81.0%. The AUC was greater than 0.8, indicating that the model assessment accuracy was high. (3) The proportion of high risk and extremely high risk areas was 27.44%, including 69.26% of the flood events in this study, indicating that the flood distribution in these areas was relatively dense and the susceptibility was high. Super cities have a high population density, and once flood disasters occur, the losses brought by them are immeasurable. Thus, flood sensitivity map can provide meaningful information for policy makers to enact appropriate policies to reduce future damage.

Mapping inundation extents in Poyang Lake area using Sentinel-1 data and transformer-based change detection method

Accurate and timely mapping of inundation extents during flood periods is essential for disaster evaluation and development of rescue strategies. With unique advantages over the optical sensors (e.g., little effect of clouds, and observations at day and night), Synthetic aperture radar (SAR) sensors provide an important data source for mapping inundation, particularly during flood periods. Freely available SAR images from Sentinel-1 have been increasingly used for many applications. This study applied an efficient transformer-based change detection method, bitemporal image transformer (BiT) with bitemporal Sentniel-1 images, to map inundation extents and evolution in Poyang Lake area in 2020. The transformer-based change detection method firstly adopted ResNet for high-level semantic features extraction, and applied a transformer mechanism to refine these features pixel-wise, followed by employing a FCN as the prediction head for generating the results of change detection. Besides, we constructed a water change detection dataset with spatial-and-temporal generalization from bitemporal Sentinel-1 images; this dataset consists of the seasonal variation water samples of Poyang Lake for years. We compared the results from the BiT method with other convolutional neural network (CNN) based methods (STANets and SNUNet). Mapped inundation extents were evaluated with the ground truth visually derived from high spatial resolution images. The evaluation showed the BiT method generated high accurate mapped inundation extents with the F1-score of 95.5%. The BiT model has proven its superior performance in detecting increased water. Based on the results of the BiT method, the variation of inundation extents in Poyang Lake during May-November 2020 was further analyzed. It was found that the water surface coverage of Poyang Lake is the smallest in late May; it gradually increased to the maximum on 14th July, and then began to stabilize and show a significant downward trend before November. The flood distribution map shows that cultivated land has been inundated with the largest area of approximately 600 km2.

Spatiotemporal analysis of 2020 monsoon floods and its impacts along selected reaches of the Padma River, Bangladesh

ABSTRACT The 2020 Monsoon Floods in Bangladesh had the longest duration since 1998 and affected 37% of the country. This study explored the impacts of flooding along selected reaches of the Padma River by analysing flood dynamics and bank erosion using Sentinel SAR-C and MSI image datasets, hydrologic, and field data. It involved mapping and validating the spatiotemporal distribution of flooding, erosion, and water turbulence. There were three phases of onset and recession of flooding between 1st July and 4th October, 2020. Image analysis showed that the highest floodwater extents during these phases were 14%, 20%, and 15% of the study area on 1st July, 6th August, and 23rd September, respectively. It was also found that a total of 92.8 km2 of riverbank area was eroded between 6th April and 7th November. The distribution of water turbulence and bank erosion showed concurrence in most parts while exceptions were also observed. From June to October 2020, Pearson’s correlation coefficients for monthly hydrologic data and erosion estimates were calculated to be (a) 0.791 between average river discharge and total eroded area, (b) 0.956 between maximum river discharge and total eroded area, (c) 0.741 between average water level and total eroded area, (d) 0.991 between average river discharge and average water level, (e) 0.519 between average water level and total rainfall, and (f) 0.484 between average water level and total rainfall. GIS analysis showed a total of 297 educational and healthcare facilities were flooded while 15 facilities were lost to bank erosion. A total of 489.75 km of road and 60.03 km2 of settlements were inundated.

Climate control on the relationship between erosion rate and fluvial topography

Abstract Conceptual and theoretical models for landscape evolution suggest that fluvial topography is sensitive to climate. However, it remains challenging to demonstrate a compelling link between fluvial topography and climate in natural landscapes. One possible reason is that many studies compare erosion rates to climate data, although theoretical studies show that, at steady state, climate is encoded in the relationship between erosion rate and topography rather than erosion rate alone. We use an existing global compilation of 10Be basin-averaged erosion rates to isolate the climate signal in topography as a function of erosion rate for morphologically steady-state, fluvially dominated basins underlain by crystalline bedrock. Our results show that the relationship between erosion rate (a proxy for rock uplift rate) and the normalized river channel steepness index (a proxy for fluvial relief) becomes increasingly nonlinear with increasing mean annual precipitation and decreasing aridity. This result indicates that erosional efficiency increases in wetter and more humid climates, lowering fluvial relief for a given erosion rate. When interpreted in the context of detachment-limited bedrock incision models that account for incision thresholds and stochastic flood distributions, this systematic pattern can be explained by a decrease in discharge variability in wetter and more humid landscapes, assuming incision thresholds are important on a global scale.

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

AbstractIn low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

UTILIZATION OF IMAGE SENTINEL-1 SAR FOR IDENTIFICATION OF FLOOD DISTRIBUTION AREA In PANGKALAN KOTO BARU SUMATERA DISTRICT

This research was conducted to determine the flood distribution area in Pangkalan Koto Baru District. Using the Normalized Difference Sigma Index (NDSI) method. By using this remote sensing method, it is possible to identify the flood distribution areas in Pangkalan Koto Baru District on March 15 2017.
 In this study, the identification of flood distribution areas using Sentinel-1 satellite imagery data. The sentinel-1 image data needed is before the flood (20 February 2017) and at the time of the flood (15 March 2017). Furthermore, Sentinel-1 Image processing begins with a subset, some radiometric corrections and geometric corrections. The Normalized Difference Sigma Index (NDSI) method is used to identify the flood distribution which is then vectorized.
 The results of the study have taken that based on the results of flood analysis using the GIS technique the area identified as flooding in this study is 41561.172 Ha. In Nagari Tanjuang Pauah it is ± 2454.301 Ha, Nagari Tanjuang Balik is ± 2076.138 Ha, Nagari Pangkalan is ± 14765.141 Ha, Nagari Mangilang is ± 917.724 Ha, Nagari Koto Alam is ± 8361.579 Ha, and Nagari Gunuang Manggilang is ± 917.724 Ha.

Study on the Impact of Future Climate Change on Extreme Meteorological and Hydrological Elements in the Upper Reaches of the Minjiang River

Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.