Flash Flood Characteristics of Ciberang River, Its Impact and Mitigation

Flash flood is a deadly natural disaster that develops at space and time scales caused by high rainfall intensities and frequently occurs. It occurs in mountainous regions with steep slope relief and often causes a loss of economy, society, and environment and threatens human lives. The Ciberang river is located at Lebak district, Banten Province, and has been experiencing a significant flash flood from December 31, 2019 to January 01, 2020, which contributed to various damages, including households nearby the river. In this study, the impact areas were identified with the HEC-RAS model and satellite image data. The impact area was damage on eight sub-districts, including agriculture (77.86 Ha), buildings (0.80 Ha), roads (2.5 km), and nine bridges. In addition, this study tried to build Sabo Dam upstream of Banjar Irigasi sub-districts to respond to the flash flood occurrence. The results have reduced the discharge to around 40 m3/s, and the travel time was delayed about 53 min. The results of this study help the community and decision-makers be ready for further flash flood disasters.

A Review of Case Studies on Climate Change Impact on Hydrologic Cycle: An Indian Perspective

In recent times, several studies focused on the global warming that may affect the hydrological cycle due to intensification of temporal and spatial variations in precipitation. Such climatic change is likely to impact significantly upon freshwater resources availability. In India, demand for water has already increased manifold over the years due to urbanization, agriculture expansion, increasing population, rapid industrialization and economic development. Numerous scientific studies also report increases in the intensity, duration, and spatial extents of floods, higher atmospheric temperatures, warmer sea, changes in precipitation patterns, and changing groundwater levels. This work briefly discusses about the present scenario regarding impact of climate change on water resources in India. Due to the insufficient resolution of climate models and their generally crude representation of sub-grid scale and convective processes, little confidence can be placed in any definite predictions of such effects, although a tendency for more heavy rainfall events seems likely, and a modest increase in frequency in floods. Thus to analyses this effect, this work considers real problems about the changing flood characteristics pattern in two river regions, and the effect of spatial and temporal pattern in rainfall. In addition to these, the work also examines the trend of groundwater level fluctuations in few blocks of Ganga–Yamuna and Sutlej-Yamuna Link interfluves region. As a whole, it examines the potential for sustainable development of surface water and groundwater resources within the constraints imposed by climate change.

A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use

Land use changes can significantly influence the water balance, and thus especially the development of flood-triggering runoff peaks. Hence, it is advisable to assess possible changes already at the level of municipal planning. Since many different actors are usually involved in spatial planning, it is useful to provide a shared platform where stakeholders can access the same information to analyze and evaluate flood hazards. Therefore, a GIS routine for the prediction of soil sealing induced runoff peaks and resulting potential flooding in the watercourse was developed, which is embedded in a GIS based decision support system (GIS-DSS). The so-called storm water routine (SWR) is founded on preprocessed flood characteristics, calculated by means of hydrological/hydraulic models (described in part 1). The potential impact of land use change is assessed purely in GIS as flow difference which is routed through the river system. To validate this simplified method, a process model was set up with an exemplary land use change and its results were compared with the GIS-based results. For 16 of the 18 rainfall scenarios tested, the SWR provided very good to good agreement with the detailed model. For short and highly dynamic rain events the SWR approach is less reliable. Several supplements like the integration of LID are conceivable.

Investigating the interaction of waves and river discharge during compound flooding at Breede Estuary, South Africa

Recent studies have drawn special attention to the significant dependencies between flood drivers and the occurrence of compound flood events in coastal areas. This study investigates compound flooding from tides, river discharge (Q) and specifically waves using a hydrodynamic model at Breede Estuary, South Africa. We quantify vertical and horizontal differences in flood characteristics caused by driver interaction, and assess the contribution of waves. Therefore, we compare flood characteristics resulting from compound flood scenarios to those in which single drivers are omitted. We find that flood characteristics are more sensitive to Q than to waves, particularly when the latter only coincide with high spring tides. When interacting with Q, however, the contribution of waves is high, causing 10–12 % larger flood extents and 45–85 cm higher water depths, as waves caused backwater effects and raised water levels inside the lower reaches of the estuary. With higher wave intensity, the first flooding began up to 12 hours earlier. Our findings provide insights on compound flooding in terms of flood magnitude and timing at a South African estuary and demonstrate the need to account for the effects of compound events, including waves, in future flood impact assessments of open South African estuaries.

Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin

Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.

Fuzzy-probabilistic modeling the flood characteristics using bivariate frequency analysis and α-cut decomposition

Water allocation in agricultural lands, optimal design of hydraulic structures and climatic phenomena are the events in water management science that face hydrological uncertainties. The purpose of this study is to estimate the characteristics of surface runoff based on probabilistic and fuzzy analysis. Separation and generation of basic hydrological information, probabilistic modeling, fuzzy analysis, and optimization to achieve the solution were the main steps of the decision-making problem. Long-term hydrological data of the study area were collected, analyzed and used as a basis for the simulation model. In this study, a copula-based stochastic method was developed to deal with uncertainties related to rainfall and runoff characteristics as well as to address the nonlinear dependence between multiple random variables. The relationship between rainfall variables and flood characteristics was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using the non-dominated sorting genetic algorithm to find the optimal extreme points. The obtained solutions were used as a fuzzy response to calculate the flood of the Baghmalek plain in Khuzestan province in southwestern Iran. The results showed that the maximum model error occurred in predicting rainfall depth and flood volume, and the maximum rainfall rate and runoff flow could be calculated more accurately. Moreover, the developed fuzzy-probabilistic model was able to predict more than 90% of flood events within the defined fuzzy range.