Smart roofs featuring predictive control: An upgrade for mitigating precipitation extreme-induced pluvial floods.

In the face of escalating urban pluvial floods exacerbated by climate change, conventional roof systems fall short of effectively managing precipitation extremes. This paper introduces a smart predictive solution: the Smart Internal Drainage Roof (SIDR) system, which leverages forecasted data to enhance the mitigation of pluvial floods in Central Business District (CBD) areas. Unlike traditional approaches, SIDRs utilize a synergistic combination of Rule-based Control (RBC) and Model Predictive Control (MPC) algorithms, tailored to optimize the operational efficiency of both grey and green roofs. Within the examined 1.3 km2 area in Beijing, China, SIDRs, covering 11% of the site, decreased total flooded areas by 30%-50% and eliminated 60%-100% of high-risk zones during three actual events. Moreover, SIDRs streamlined outflow processes without extending discharge time and reduced flood duration at a high-risk underpass by more than half. The SIDR's distinct features, including a high control resolution of 5 min, integration with existing waterproofs, and advanced 2D dynamic runoff visualization, position it as a scalable and cost-efficient upgrade in urban flood resilience strategies.

Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator.

Drought is a complex natural hazard. It occurs due to a prolonged period of deficient in rainfall amount in a certain region. Unlike other natural hazards, drought hazard has a recurrent occurrence. Therefore, comprehensive drought monitoring is essential for regional climate control and water management authorities. In this paper, we have proposed a new drought indicator: the Seasonally Combinative Regional Drought Indicator (SCRDI). The SCRDI integrates Bayesian networking theory with Standardized Precipitation Temperature Index (SPTI) at varying gauge stations in various month/seasons. Application of SCRDI is based on five gauging stations of Northern Area of Pakistan. We have found that the proposed indicator accounts the effect of climate variation within a specified territory, accurately characterizes drought by capturing seasonal dependencies in geospatial variation scenario, and reduces the large/complex data for future drought monitoring. In summary, the proposed indicator can be used for comprehensive characterization and assessment of drought at a certain region.

GLAMOUR: GLobAl building MOrphology dataset for URban hydroclimate modelling.

Understanding building morphology is crucial for accurately simulating interactions between urban structures and hydroclimate dynamics. Despite significant efforts to generate detailed global building morphology datasets, there is a lack of practical solutions using publicly accessible resources. In this work, we present GLAMOUR, a dataset derived from open-source Sentinel imagery that captures the average building height and footprint at a resolution of 0.0009° across urbanized areas worldwide. Validated in 18 cities, GLAMOUR exhibits superior accuracy with median root mean square errors of 7.5 m and 0.14 for building height and footprint estimations, indicating better overall performance against existing published datasets. The GLAMOUR dataset provides essential morphological information of 3D building structures and can be integrated with other datasets and tools for a wide range of applications including 3D building model generation and urban morphometric parameter derivation. These extended applications enable refined hydroclimate simulation and hazard assessment on a broader scale and offer valuable insights for researchers and policymakers in building sustainable and resilient urban environments prepared for future climate adaptation.

Accumulation of high concentration fluoride in the Ulungur Lake water through weathering of fluoride containing rocks in Xinjiang, China.

Fluoride is a potential contaminant at high concentrations when used for drinking due to its adverse human health effects. The Ulungur Lake in Xinjiang, China has a long history of high fluoride concentration in lake water, but the mechanism leading to such high concentrations of fluoride is still unclear. In this study we evaluate the fluoride concentration in different water bodies and upstream rock formations in the Ulungur watershed. The result show that fluoride concentration in the Ulungur Lake water fluctuates around 3.0 mg L-1, although the fluoride concentrations in the feeding rivers and groundwater are all lower than 0.5 mg L-1. A mass balance model is developed for water, fluoride, and total dissolved solid in the lake, and the model explains why the concentration of fluoride in the lake water is higher than those in river and ground water. Bedrock compositions are measured from nearby formations which confirm the potential of these rocks to release fluoride into water bodies through water-rock interactions. The whole-rock concentrations of fluoride are in the range of 0.4-2.4 g kg-1 and the water-soluble concentrations of fluoride in the upstream rocks are 0.26-3.13 mg L-1. Biotite and hornblende are identified as the fluorine containing minerals in the Ulungur watershed. The concentration of fluoride in the Ulungur has been declining slowly in recent years due to increased water inflow fluxes, and our mass balance model predicts that the fluoride concentration will eventually reach 1.70 mg L-1 under a new steady state, but it requires about 25-50 years to reach the new steady state. The yearly fluctuation of fluoride concentration in the Ulungur Lake is likely due to changes in water-sediment interactions reflected in changes in lake water pH.

Non-monotonic changes in Asian Water Towers' streamflow at increasing warming levels.

Previous projections show consistent increases in river flows of Asian Water Towers under future climate change. Here we find non-monotonic changes in river flows for seven major rivers originating from the Tibetan Plateau at the warming levels of 1.5 °C, 2.0 °C, and 3.0 °C based on an observation-constrained hydrological model. The annual mean streamflow for seven rivers at 1.5 °C warming level decreases by 0.1-3.2% relative to the present-day climate condition, and increases by 1.5-12% at 3.0 °C warming level. The shifting river flows for the Yellow, Yangtze, Brahmaputra, and Ganges are mostly influenced by projected increases in rainfall, but those for the Mekong, Salween, and Indus are dictated by the relative changes in rainfall, snowmelt and glacier melt. Reduced river flows in a moderately warmed climate threaten water security in riparian countries, while elevated flood risks are expected with further temperature increases over the Tibetan Plateau.

Exploring synergies in the water-food-energy nexus by using an integrated hydro-economic optimization model for the Lancang-Mekong River basin.

The Water-Food-Energy nexus study identifies developmental challenges and trade-offs present along the transboundary river basins. Intensive reservoir development for hydropower production impacts existing actors in the Lancang-Mekong River Basin. Concerns from these stakeholders highlight three major trade-offs that occur between hydropower and irrigation (HP-AG), hydropower and fisheries (HP-F), and irrigation and fisheries (AG-F). Dam construction has an impact not only on power production but also on ecosystems and ultimately the livelihood of people. In this study, we quantify the effects of reservoir operation on hydropower generation, irrigated crop production and fisheries yield in the Tonle Sap lake through a novel hydro-economic model at the whole basin scale. Our main finding is that trade-offs can be turned into synergetic opportunities. First, the dam operation can increase water availability for irrigation without severely harming hydropower production, raising irrigated crop revenue by 49% and reducing crop losses during droughts by 30%. Second, eco-friendly management increases fisheries yield by up to 75%, but decreases both irrigated crop production (-48%) and power production (-17%). Reservoirs can, therefore, benefit the whole basin by releasing more water in months with high irrigation demand (April and December) and by minimizing the adverse effects of flow fluctuations on the livelihood of farmers and fishers living downstream. Our results also reveal the overlooked trade-off between irrigated agriculture and fisheries. Cross-sectoral and transboundary partnerships should strengthen stakeholder participation in decision-making. Local solutions such as enhanced reservoir operation can respond to the broader global issue of natural resource trade-offs and sharing. Our alternative narrative enhances the dialogue about fair and efficient water use among Mekong riparian countries.

ThSSim: A novel tool for simulation of reservoir thermal stratification.

This study presents a novel tool, ThSSim, for simulation of thermal stratification (ThS) in reservoirs. ThSSim is a simple and flexible reduced-order model-based the basis function (RMBF) that combines CE-QUAL-W2 (W2) and proper orthogonal decomposition (POD). In a case study, it was used to simulate water temperature in the Karkheh Reservoir (KR), Iran, for the period 2019-2035. ThSSim consists of two space- and time-dependent components that add predictive ability to the RMBF, a major refinement that extends its practical applications. Water temperature simulations by the W2 model at three-hour time intervals for the KR were used as input data to the POD model to develop ThSSim. To add predictive ability to ThSSim and considering that space-dependent components are not a function of time, we extrapolated the first three time-dependent components by September 30, 2035. We checked the predictive ability of ThSSim against water temperature profiles measured during eight sampling campaigns. We then applied ThSSim to simulate water temperature in the KR for 2019-2035. Simulated water temperature values matched well those measured and obtained by W2. ThSSim results showed an increasing trend for surface water temperature during the simulation period, with a reverse trend observed for water temperature in the bottom layers for three seasons (spring, summer and autumn). The results also indicated decreasing and increasing trends in onset and breakdown of thermal stability, respectively, so that the duration of ThS increased from 278 days in 2019 to 293 days in 2035. ThSSim is thus useful for reservoir temperature simulations. Moreover, the approach used to develop ThSSim is widely applicable to other fields of science and engineering.