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Floods are among the major natural disasters that cause loss of life and economic damage worldwide. Floods damage homes, crops, roads, and basic infrastructure, forcing people to migrate from high flood-risk areas. However, due to a lack of information about the effective variables in forecasting, the development of an accurate flood forecasting system remains difficult. The flooding process is quite complex as it has a nonlinear relationship with various meteorological and topographic parameters. Therefore, there is always a need to develop regional models that could be used effectively for water resource management in a particular locality. This study aims to establish and evaluate various data-driven flood forecasting models in the Jhelum River, Punjab, Pakistan. The performance of Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR), Two Layer Back Propagation (TLBP), Conjugate Gradient (CG), and Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based ANN models were evaluated using R2, variance, bias, RMSE and MSE. The R2, bias, and RMSE values of the best-performing LLR model were 0.908, 0.009205, and 1.018017 for training and 0.831, −0.05344, and 0.919695 for testing. Overall, the LLR model performed best for both the training and validation periods and can be used for the prediction of floods in the Jhelum River. Moreover, the model provides a baseline to develop an early warning system for floods in the study area. [ABSTRACT FROM AUTHOR]

Surface irrigation networks in Indonesia are damaged by several factors, and sedimentation is among the most severe challenges. Sand traps play a substantial role in improving irrigation system efficiency by reducing sedimentation. There are two periods in sand trap operation: the operational and maintenance periods. Pengasih is one of the irrigation schemes implemented in the Progo Opak Serang (POS) River Basin, which has a high level of erosion. This study aimed to propose an appropriate management strategy for the Pengasih sand trap as the first barrier in irrigation network sedimentation based on mathematical modeling. The HEC-RAS simulation software was used to simulate the sand trap hydraulic behaviour. The results show that the validated Manning's coefficient was 0.025. The optimal transport parameters were Laursen for the potential function, Exner 5 for the sorting method, and Rubey for the fall velocity method. The recommended flushing timeframe is 315 min, with a discharge of 2 m3/s. We suggest that the sand trap flushing frequency be performed twice a year, and it can be performed at the end of March and October. This coincides with the end of the first and third planting seasons of the irrigation scheme. [ABSTRACT FROM AUTHOR]

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system's accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations. [ABSTRACT FROM AUTHOR]

The concept of cultural ecosystem services has been increasingly influential in both environmental research and policy decision making, such as for urban green spaces. However, its popular definitions tend to conflate "services" with "benefits", making it challenging for planners to employ them directly to manage urban green spaces. Thus, attempts have been made to redefine cultural ecosystem services as the function of cultural activities in environmental spaces which result in people's enjoyment of cultural ecosystem benefits. The operability of such a redefinition needs to be evaluated, which this study seeks to achieve with Bishan-Ang Mo Kio Park in Singapore presenting itself as a prime case study research area. Transdisciplinary mixed methods of a public participation geographic information system, which leverages on spatial data from public park users, and social media text mining analysis via Google reviews were used. A wealth of cultural ecosystem services and benefits were reported in the park, especially the recreational and aesthetic services and experiential benefits. Policy and methodological implications for future research and urban park developments were considered. Overall, this paper would recommend the employment of the redefined cultural ecosystem services approach to generate relational, data-driven and actionable insights to better support future urban green space management. [ABSTRACT FROM AUTHOR]

Deltas are among the most productive and diverse global ecosystems. However, these regions are highly vulnerable to natural disasters and climate change. Nature-based solutions (Nbs) have been increasingly adopted in many deltas to improve their resilience. Among decision support tools, assessment of ecosystem services (ES) through spatially explicit modelling plays an important role in advocating for Nbs. This study explores the use of the Land Utilisation and Capability Indicator (LUCI) model, a high-resolution model originally developed in temperate hill country regions, to map changes in multiple ecosystem services (ES), along with their synergies and trade-offs, between 2010 and 2018 in the Vietnamese Mekong Delta (VMD). In so doing, this study contributes to the current knowledge in at least two aspects: high-resolution ES modelling in the VMD, and the combination of ES biophysical and economic values within the VMD to support Nbs implementation. To date, this is the highest resolution (5 by 5 m) ES modelling study ever conducted in the VMD, with ~1500 million elements generated per ES. In the process of trialling implementations of LUCI within the VMD's unique environmental conditions and data contexts, we identify and suggest potential model enhancements to make the LUCI model more applicable to the VMD as well as other tropical deltaic regions. LUCI generated informative results in much of the VMD for the selected ES (flood mitigation, agriculture/aquaculture productivity, and climate regulation), but challenges arose around its application to a new agro-hydrological regime. To address these challenges, parameterising LUCI and reconceptualising some of the model's mechanisms to specifically account for the productivity and flood mitigation capability of water-tolerant crops as well as flooding processes of deltaic regions will improve future ES modelling in tropical deltaic areas. The ES maps showed the spatial heterogeneity of ES across the VMD. Next, to at least somewhat account for the economic drivers which need to be considered alongside biophysical valuations for practical implementations of ES maps for nature-based solutions (Nbs) in the upstream VMD, economic values were assigned to different parcels using a benefit transfer approach. The spatially explicit ES economic value maps can inform the design of financing incentives for Nbs. The results and related work can be used to support the establishment of Nbs that ultimately contribute to the security of local farmers' livelihoods and the sustainability of the VMD. [ABSTRACT FROM AUTHOR]

Seismically triggered landslides are a major hazard and have caused severe secondary losses. This problem is especially important in the seismic prone Mailuu-Suu catchment in Kyrgyzstan, as it hosts disproportionately sensitive active or legacy uranium sites with deposited radioactive extractive wastes. These sites show a quasi-continuous release of radioactive contamination into surface waters, and especially after natural hazards, a sudden and massive input of pollutants into the surface waters is expected. However, landslides of contaminated sediments into surface waters represent a substantial exposure pathway that has not been properly addressed in the existing river basin management to date. To fill this gap, satellite imagery was massively employed to extract topography and geometric information, and the seismic Scoops3D and the one-dimensional numerical model, Hydrologic Engineering Centre, River Analysis System (HEC-RAS), were chosen to simulate the landslide-induced mass transport of total suspended solids (TSS) and natural radionuclides (Pb-210 as a proxy for modeling purposes) within the Mailuu-Suu river networks under two earthquake and two hydrological scenarios. The results show that the seismically vulnerable areas dominated in the upstream areas, and the mass of landslides increased dramatically with the increase of earthquake levels. After the landslides, the concentrations of radionuclides increased suddenly and dramatically. The peak values decreased along the longitudinal gradient of river networks, with the concentration curves becoming flat and wide in the downstream sections, and the transport speed of radionuclides decreased along the river networks. The conclusions of this study are that landslides commonly release a significant amount of pollutants with a relatively fast transport along river networks. Improved quantitative understanding of waterborne pollution dispersion across national borders will contribute to better co-ordination between governments and regulatory authorities of riparian states and, consequently, to future prevention of transnational political conflicts that have flared up in the last two decades over alleged pollution of transboundary water bodies. [ABSTRACT FROM AUTHOR]

River bank (RB) erosion is a global issue affecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world's third largest delta, compared to salinity intrusion and flooding. There have been several studies examining RB and coastal erosion in the VMD using remotely sensed satellite data, but the applied methodology was not adequately validated. Therefore, we developed a novel SRBED (Spectral RB Erosion Detection) method, in which the M-AMERL (Modified Automated Method for Extracting Rivers and Lakes) is proposed, and a new RB change detection algorithm using Landsat data. The results show that NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) using the M-AMERL algorithm (i.e., NDWIM-AMERL, MNDWIM-AMERL) perform better than other indices. Furthermore, the NDWIM-AMERL; SMA (i.e., NDWIM-AMERL using the SMA (Spectral Mixture Analysis) algorithm) is the best RB extraction method in the VMD. The NDWIM-AMERL; SMA performs better than the MNDWI, NDVI (Normalized Difference Vegetation Index), and WNDWI (Weighted Normalized Difference Water Index) indices by 35–41%, 70% and 30%, respectively. Moreover, the NDVI index is not recommended for assessing RB changes in the delta. Applying the developed SRBED method and RB change detection algorithm, we estimated a net erosion area of the RB of –1.5 km2 from 2008 to 2014 in the Tien River from Tan Chau to My Thuan, with a mean erosion width of –2.64 m and maximum erosion widths exceeding 60 m in places. Our advanced method can be applied in other river deltas having similar characteristics, and the results from our study are helpful in future studies in the VMD. [ABSTRACT FROM AUTHOR]

Saltwater intrusion, a consequence of climate change and decreased water levels, has been increasingly severe in the Mekong Delta region. Thanh Phu District, Ben Tre Province, Vietnam, is a coastal region where agricultural production and local livelihood have been impaired by saltwater intrusion, resulting in the adoption of multiple coping strategies, including rotations and intercropping. This study aims to measure and evaluate soil and water quality indicators of multiple farming systems in Thanh Phu district and contributes to developing suitable cropping patterns. Soil indicators were pH, electrical conductivity, and exchangeable Na+. Water quality characteristics include pH, salinity, dissolved N and P, alkalinity, H2S, and chemical oxygen demand (COD). The results indicated that water pH and salinity were at suitable levels to support the growth of prawn but were below the critical level required to grow black tiger shrimp and white-legged shrimp. Water alkalinity, dissolved N, P, and COD were not constraining for the growth of shrimps. However, a significant concentration of H2S may cause disadvantages for shrimp growth. [ABSTRACT FROM AUTHOR]

Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed "Re-Obs" and the proposed approach as "GCM-Obs". Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin. [ABSTRACT FROM AUTHOR]