Abstract: Floods are one of the most common natural disasters, resulting in the extensive destruction of infrastructure, property, and human life. The destructive potential of a flood depends on numerous factors, including the size of the flood, the rate of flooding, the time it takes for the water to move through a given area, the river’s planform and cross-section geometry, and other similar factors. The present study is a unique analysis of flood mapping that was accomplished with the help of the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and hydrogeomorphic response to floods by integrating geospatial analysis and unit stream power modeling. The Indus catchment region of Pakistan is where the subject topic is put into practice. According to the hydrologic analysis of the yearly peak discharge, the hydro-station in Gilgit-Baltistan can move boulders measuring up to 0.5 m in height during significant flooding. On the other hand, there will be no change to the geometry of the cross-section throughout 1980–2020 in Gilgit-Baltistan. The flood susceptibility map is constructed using data from twelve influencing parameters, including elevation, proximity to the drainage network, slope, drainage density, geomorphology, rainfall, the curvature of the topography, flow accumulation, geology, land use, Topographic Wetness Index (TWI), and Stream Power Index (SPI). The area under the curve (AUC) approach, which demonstrates a substantial degree of accuracy (85% and 83%), is utilized to evaluate the effectiveness of the AHP and FR. The current study fills the gaps between the geospatial approach and the hydrogeomorphic assessment of flood to determine flood susceptibility.

Abstract: Combined large-scale rainwater harvesting (RWH) and flood mitigation systems are promising as a sustainable water management strategy in urban areas. These are multi-purpose infrastructure that not only provide a secondary, localised water resource, but can also reduce discharge and hence loads on any downstream wastewater networks if these are integrated into the wider water network. However, the performance of these systems is dependent on the specific design used for its local catchment which can vary significantly between different implementations. A multitude of design strategies exist, however there is no universally accepted standard framework. To tackle these issues, this paper presents a two-player optimisation framework which utilises a stochastic design optimisation model and a competing, high-intensity rainfall design model to optimise passively-operated RWH systems. A customisable tool set is provided, under which optimisation models specific to a given catchment can be built quickly. This reduces the barriers to implementing computationally complex sizing strategies and encouraging more resource-efficient systems to be built. The framework was applied to a densely populated high-rise residential estate, eliminating overflow events from historical rainfall. The optimised configuration resulted in a 32% increase in harvested water yield, but its ability to meet irrigation demands was limited by the operational levels of the treatment pump. Hence, with the inclusion of operational levels in the optimisation model, the framework can provide an efficient large-scale RWH system that is capable of simultaneously meeting water demands and reducing stresses within and beyond its local catchment.

Abstract: Quantifying evapotranspiration and drainage losses is essential for improving irrigation efficiency. The FAO-56 is the most popular method for computing crop evapotranspiration. There is, however, a need for locally derived crop coefficients (Kc) with a high temporal resolution to reduce errors in the water balance. The aim of this paper is to introduce a dynamic Kc approach, based on Leaf Area Index (LAI) observations, for improving water balance computations. Soil moisture and meteorological data were collected in a terraced nectarine (Prunus persica var. nucipersica) orchard in Cyprus, from 22 March 2019 to 18 November 2021. The Kc was derived as a function of the canopy cover fraction (c), from biweekly in situ LAI measurements. The use of a dynamic Kc resulted in Kc estimates with a bias of 17 mm and a mean absolute error of 0.8 mm. Evapotranspiration (ET) ranged from 41% of the rainfall (P) and irrigation (I) in the wet year (2019) to 57% of P + I in the dry year (2021). Drainage losses from irrigation (DR_I) were 44% of the total irrigation. The irrigation efficiency in the nectarine field could be improved by reducing irrigation amounts and increasing the irrigation frequency. Future studies should focus on improving the dynamic Kc approach by linking LAI field observations with remote sensing observations and by adding ground cover observations.