A global-scale applicable framework of landslide dam formation susceptibility

The formation and failure of landslide dams is an important and understudied, multi-hazard topic. A framework of landslide dam formation susceptibility evaluation was designed for large-scale studies to avoid the traditional dependence on landslide volume calculations based on empirical relationships, which requires comprehensive local inventories of landslides and landslide dams. The framework combines logistic regression landslide susceptibility models and global fluvial datasets and was tested in Italy and Japan based on landslide and landslide dam inventories collected globally. The final landslide dam formation susceptibility index identifies which river reach is most prone to landslide dam formation, based on the river width and the landslide susceptibility in the adjacent delineated slope drainage areas. The logistic regression models showed good performances with area under the receiver operating characteristics curve values of 0.89 in Italy and 0.74 in Japan. The index effectively identifies the probability of landslide dam formation for specific river reaches, as demonstrated by the higher index values for river reaches with past landslide dam records. The framework is designed to be applied globally or for other large-scale study regions, especially for less studied data-scarce regions. It also provides a preliminary evaluation result for smaller catchments and has the potential to be applied at a more detailed scale with local datasets

FOREWARNS: development and multifaceted verification of enhanced regional-scale surface water flood forecasts

Surface water flooding (SWF) is a severe hazard associated with extreme convective rainfall, whose spatial and temporal sparsity belie the significant impacts it has on populations and infrastructure. Forecasting the intense convective rainfall that causes most SWF on the temporal and spatial scales required for effective flood forecasting remains extremely challenging. National-scale flood forecasts are currently issued for the UK and are well regarded amongst flood responders, but there is a need for complementary enhanced regional information. Here we present a novel SWF-forecasting method, FOREWARNS (Flood fOREcasts for Surface WAter at a RegioNal Scale), that aims to fill this gap in forecast provision. FOREWARNS compares reasonable worst-case rainfall from a neighbourhood-processed, convection-permitting ensemble forecast system against pre-simulated flood scenarios, issuing a categorical forecast of SWF severity. We report findings from a workshop structured around three historical flood events in Northern England, in which forecast users indicated they found the forecasts helpful and would use FOREWARNS to complement national guidance for action planning in advance of anticipated events. We also present results from objective verification of forecasts for 82 recorded flood events in Northern England from 2013–2022, as well as 725 daily forecasts spanning 2019–2022, using a combination of flood records and precipitation proxies. We demonstrate that FOREWARNS offers good skill in forecasting SWF risk, with high spatial hit rates and low temporal false alarm rates, confirming that user confidence is justified and that FOREWARNS would be suitable for meeting the user requirements of an enhanced operational forecast

The credibility challenge for global fluvial flood risk analysis

Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30-40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections

Uncertainty and Sensitivity Analysis in Reservoir Modeling: a Monte Carlo Simulation Approach

Water resource modelling plays a crucial role in water resources management, but it involves many inherent uncertainties. This research investigates how epistemic uncertainties affect reservoir water budgets, projecting forward over a 30 year period using Monte Carlo simulation. It encompasses long-term variations in water demand, reservoir volume, precipitation, evaporation and inflow, while also considering siltation processes, reservoir dredging, population growth, reduced water consumption, and the hydrological impacts of climate change. The research focuses on fifty reservoirs in a semi-arid region of Brazil. The findings demonstrate that some reservoirs consistently met their demands with high level reliability, even within a wide range of uncertainty. Conversely, reservoirs with morphohydric indices indicating a tendency toward water scarcity are significantly affected by input variability introduced through uncertainty analysis. An empirical model is proposed to estimate the probability of these reservoirs achieving the reference volume reliability of 90%, while considering the uncertainties of: annual average inflow, reservoir maximum volume and annual demand. Sensitivity analysis reveals that reservoir inflow and demand are the two most influential variables affecting a reservoirs’ ability to meet its demand. For over exploited reservoirs, variations in these variables strongly impact the volume reliability. This research provides a valuable tool for estimating the likelihood of reaching a 90% volume reliability, while taking into account the inherent uncertainties in the modeling process. Additionally, it identifies key variables that have the most influence on the reservoirs’ ability to meet its demand. Notably, this study conducts uncertainty and sensitivity analyses in the context of physical and hydrological reservoir features for a large number of reservoirs, adding novelty to the research field

Assessing uncertainties of a remote sensing-based discharge reflectance model for applications to large rivers of the Congo Basin

Adequate monitoring of river discharge is crucial for effective water resource management. However, this objective remains difficult to achieve in the context of large and ungauged river basins. This study assesses the performance of remote sensing applications for discharge monitoring in the lower reach of the Congo River, where daily discharge information is required to support many water resource operations. The approach is based on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing imagery to produce a daily time series of a ratio of reflectance values (C/M) for discharge monitoring. The validation of the approach is performed based on three-year water level data collected at the outlet gauging site and limited in situ Acoustic Doppler Current Profiler (ADCP) cross-section measurements for high and low flow seasons. The simulated discharge closely matches the observed values and falls within acceptable ranges, with errors below 10% and Nash-Sutcliffe coefficients ranging from 0.65 to 0.76 for ADCP and gauging station, respectively

Dose-response studies for pituitary and testicular function in male dogs treated with the GnRH superagonist, deslorelin

We tested the effect of dose of GnRH superagonist on pituitary and testicular function in a study with four groups of four male dogs. The Controls received blank implants and the other three groups received implants containing 3, 6 or 12 mg deslorelin (d-Trp 6-Pro 9-des-Gly 10-GnRH ethylamide). In all deslorelin-treated groups, there was initially an acute increase in plasma concentrations of LH and testosterone, followed by declines such that both hormones became undetectable after approximately 12 days. There was a dose-response in some of these early aspects of the hormone profiles. With respect to long-term effects of treatment, the 12-mg dose had significantly greater effects than the smaller doses for the duration of minimum testicular volume [366 ± 77, mean ± SEM (3 mg), 472 ± 74 (6 mg), and 634 ± 59 (12 mg) days], absence of ejaculate [416 ± 88 (3 mg), 476 ± 83 (6 mg), and 644 ± 67 (12 mg) days], undetectable plasma concentrations of LH and testosterone [367 ± 64 (3 mg), 419 ± 72 (6 mg), and 607 ± 69 (12 mg) days], the delay until complete recovery of LH and testosterone secretion [394 ± 65 (3 mg), 484 ± 72 (6 mg) and 668 ± 47 (12 mg) days], and the delay until testes had regrown to normal volume [408 ± 77 (3 mg), 514 ± 74 (6 mg), 676 ± 59 (12 mg) days]. The time taken to restore full ejaculates was also longest for the 12-mg dose: 716 ± 67 (12 mg) days vs 440 ± 66 (3 mg) and 538 ± 83 (6 mg) days after implantation. There was no correlation between delay to recovery of normal ejaculate quality and body mass. We conclude that the dose-response relationship with deslorelin implants is not expressed with respect to the degree of suppression of reproduction, but on the maximum duration of suppression and thus to delay until recovery

Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa

Introduction: Cities located in lower income countries are global flood risk hotspots. Assessment and management of these risks forms a key part of global climate adaptation efforts. City scale flood risk assessments necessitate flood hazard information, which is challenging to obtain in these localities because of data quality/scarcity issues, and the complex multi-source nature of urban flood dynamics. A growing array of global datasets provide an attractive means of closing these data gaps, but their suitability for this context remains relatively unknown. Methods: Here, we test the use of relevant global terrain, rainfall, and flood hazard data products in a flood hazard and exposure assessment framework covering Addis Ababa, Ethiopia. To conduct the tests, we first developed a city scale rain-on-grid hydrodynamic flood model based on local data and used the model results to identify buildings exposed to flooding. We then observed how the results of this flood exposure assessment changed when each of the global datasets are used in turn to drive the hydrodynamic model in place of its local counterpart. Results and discussion: Results are evaluated in terms of both the total number of exposed buildings, and the spatial distribution of exposure across Addis Ababa. Our results show that of the datasets tested, the FABDEM global terrain and the PXR global rainfall data products provide the most promise for use at the city scale in lower income countries

Pituitary and testicular endocrine responses to exogenous gonadotrophin-releasing hormone (GnRH) and luteinising hormone in male dogs treated with GnRH agonist implants

The present study tested whether exogenous gonadotrophin-releasing hormone (GnRH) and luteinising hormone (LH) can stimulate LH and testosterone secretion in dogs chronically treated with a GnRH superagonist. Twenty male adult dogs were assigned to a completely randomised design comprising five groups of four animals. Each dog in the control group received a blank implant (placebo) and each dog in the other four groups received a 6-mg implant containing a slow-release formulation of deslorelin (d-Trp6-Pro 9-des-Gly10?LH-releasing hormone ethylamide). The same four control dogs were used for all hormonal challenges, whereas a different deslorelin-implanted group was used for each challenge. Native GnRH (5 μg kg-1 bodyweight, i.v.) was injected on Days 15, 25, 40 and 100 after implantation, whereas bovine LH (0.5 μg kg-1 bodyweight, i.v.) was injected on Days 16, 26, 41 and 101. On all occasions after Day 25-26 postimplantation, exogenous GnRH and LH elicited higher plasma concentrations of LH and testosterone in control than deslorelin-treated animals (P < 0.05). It was concluded that, in male dogs, implantation of a GnRH superagonist desensitised the pituitary gonadotrophs to GnRH and also led to a desensitisation of the Leydig cells to LH. This explains, at least in part, the profound reduction in the production of androgen and spermatozoa in deslorelin-treated male dogs

Bathymetry and discharge estimation in large and data-scarce rivers using an entropy-based approach

This study implements an entropy theory-based approach to infer bathymetry for 29 selected cross-sections along a 1740 km reach of the Congo River. A genetic algorithm optimization approach is used based on an analysis of near-surface velocity measurements to generate a random sample of 1000 bathymetry profiles from which the analysis is carried out. The resulting simulated bathymetry shows good agreement compared to the measurements obtained via Accoustic Doppler Current Profiler (ADCP), with a correlation that varies from 0.49 to 0.88. The bathymetry results are subsequently used to estimate the two-dimensional cross-sectional flow velocity distribution and, consequently, to calculate the river discharge. The mean errors observed for flow area, discharge, and mean velocity are found to be 2.7%, 1.3%, and 1%, respectively. This study demonstrates, for the first time, the successful application of an entropy-based approach to estimate bathymetry and discharge in large rivers and has significant implications for remote sensing applications