A wavelet based approach for combining the outputs of different rainfall–runoff models

The rainfall–runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall–runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall–runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall–runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance.     

Geophysical Investigation of Fresh‐Saline Water Interface: A Case Study from South Punjab,Pakistan

The importance of the study of fresh‐saline water incursion cannot be over‐emphasized. Borehole techniques have been widely used, but they are quite expensive, intrusive, and time consuming. The electrical resistivity method has proved very successful in groundwater assessment. This advanced technique uses the calculation of Dar‐Zarrouk (D‐Z) parameters, namely longitudinal unit conductance, transverse unit resistance, and longitudinal resistivity has been employed by using 50 vertical electrical sounding points to assess the groundwater and delineate the fresh‐saline water interface over 1045 km² area of Khanewal in Southern Punjab of Pakistan. The x‐y plots and maps of D‐Z parameters were produced to establish a decipherable vision for the occurrence and distribution of different water‐bearing formations of fresh‐saline water aquifers through a complicated situation of intermixing of different resistivity ranges for fresh‐saline water bodies. This technique is useful to reduce the ambiguity produced by the process of equivalence and suppression which cause intermixing in differentiating fresh, brackish, and saline aquifers during interpretation. The fresh‐saline water interface is correlated very well with the previous studies of water quality analysis carried out in Khanewal area. The results suggest that the D‐Z parameters are useful for demarcating different aquifer zones. The behavior and pattern of D‐Z parameters with respect to occurrence and distribution of different water‐bearing formations were effectively identified and delineated in the study area.     

Modeling the Impact of Stream Discharge Events on Riparian Solute Dynamics

The biogeochemical composition of stream water and the surrounding riparian water is mainly defined by the exchange of water and solutes between the stream and the riparian zone. Short-term fluctuations in near stream hydraulic head gradients (e.g., during stream flow events) can significantly influence the extent and rate of exchange processes. In this study, we simulate exchanges between streams and their riparian zone driven by stream stage fluctuations during single stream discharge events of varying peak height and duration. Simulated results show that strong stream flow events can trigger solute mobilization in riparian soils and subsequent export to the stream. The timing and amount of solute export is linked to the shape of the discharge event. Higher peaks and increased durations significantly enhance solute export, however, peak height is found to be the dominant control for overall mass export. Mobilized solutes are transported to the stream in two stages (1) by return flow of stream water that was stored in the riparian zone during the event and (2) by vertical movement to the groundwater under gravity drainage from the unsaturated parts of the riparian zone, which lasts for significantly longer time (> 400 days) resulting in long tailing of bank outflows and solute mass outfluxes. We conclude that strong stream discharge events can mobilize and transport solutes from near stream riparian soils into the stream. The impact of short-term stream discharge variations on solute exchange may last for long times after the flow event.     

Dambreak flood impact on mountain stream bedload transport after 13 years

Studies of the bedload transport regime of the Roaring River, Colorado, in 1984–88, following a dambreak flood in 1982, showed that bedload transport rates were an order of magnitude higher than under pre-flood conditions. A gorge eroded by the flood in glacial moraine acted as a major sediment supply source. Measurements in early June 1995 showed a continued potential for high sediment supply from the gorge and a bedload transport regime similar to that of 1984–88. A major snowmelt flood in mid-June flushed sediment supplies from the gorge and measurements in July showed a corresponding reduction in bedload transport. However, high sediment supply will continue until the gorge cliffs revegetate or erode to a stable slope. The measurements demonstrate both the control exercised by sediment supply on transport rates and the persistent long-term impact of major floods on mountain streams. © 1998 John Wiley & Sons, Ltd.     

Nexus between natural and technical disaster shocks,resource depletion and growth-specific factors: evidence from quantile regression

Natural Hazards - Debris flows are a hazardous natural calamity in mountainous regions of Nepal. Torrential rainfall within a very short period of the year is the main triggering factor for...     

Scenario-based seismic hazard analysis using spectral element method in northeastern Pakistan

Natural Hazards - Seismic hazard analysis is carried out in this study by estimating ground motion for hypothetical earthquakes in the area of Muzaffarabad, Pakistan, with the MT solution of the...     

Modeling the Potential Distribution of Pine Forests Susceptible to Sirex Noctilio Infestations in Mpumalanga,South Africa

Reducing the impact of the siricid wasp, Sirex noctilio is crucial for the future productivity and sustainability of commercial pine resources in South Africa. In this study we present a machine learning model that serves as a spatial guide and allows forest managers to focus their existing detection and monitoring efforts on key areas and proactively adopt the most appropriate course of intervention. We implemented the random forest model within a spatial framework to determine which pine forests in Mpumalanga are highly susceptible to S. noctilio infestations. Results indicate that a majority (63%) of pine forest plantations located in Mpumalanga have a high susceptibility (>70%) to S. noctilio infestation. A KHAT value of 0.84 and F measures above 0.87 indicate that the random forest model is a robust classifier that produces accurate results. Additionally, the use of the backward variable selection method enabled us to simplify the random forest modeling process and identify the minimum number of explanatory variables that offer the best discriminatory power and help in the empirical interpretation of the final random forest model. Overall, the results show that pine forests that experience stress caused by evapotranspiration and evaporation followed by rainfalls, especially during the summer months are more susceptible to S. noctilio infestations.     

Integration of geoelectric and hydrochemical approaches for delineation of groundwater potential zones in alluvial aquifer

Geoelectric and hydrochemical approaches are employed to delineate the ground-water potential zones in District Okara, a part of Bari Doab, Punjab, Pakistan. Sixty-seven VES surveys are conducted with the Electrical Resistivity Meter. The resultant resistivity verses depth model for each site is estimated using computer-based software IX1D. Aquifer thickness maps and interpreted resistivity maps were generated from interpreted VES results. Dar-Zarrouk parameters, transverse resistance (TR), longitudinal conductance (SL) and anisotropy (λ) were also calculated from resistivity data to delineate the potential zones of aquifer. 70% of SL value is ≤3S, 30% of SL value is > 3S. According to SL and TR values, the whole area is divided into three potential zones, high, medium and low potential zones. The spatial distribution maps show that north, south and central parts of study area are marked as good potential aquifer zones. Longitudinal conductance values are further utilized to determine aquifer protective capacity of area. The whole area is characterized by moderate to good and up to some extent very good aquifer protective area on the basis of SL values. The groundwater samples from sixty-seven installed tube wells are collected for hydro-chemical analysis. The electrical conductivity values are determined. Correlation is then developed between the EC (μS/cm) of groundwater samples vs. interpreted aquifer resistivity showing R2 value 0.90.     

First Record of the Belemnite Genus Hibolithes from Late Jurassic–Early Cretaceous Turbidites from Malaysia

Discovery of the remains of belemnites referred to the Hibolithes sp. from the Jurassic–Cretaceous Pedawan Formation in Sarawak, on the island of Borneo(Malaysia) comprises four fragments of belemnite rostra. The specimens are characterized by multiple fractures and vein filling. Two fragments measuring about 130 mm are relatively intact, with only part of the alveolar region missing; a third piece represents the middle part of a rostrum, and the fourth specimen is too fragmentary to be assigned to any specific part of the rostrum. Based on specimen characteristics, a Tithonian–Hauterivian age is plausible. The sedimentary succession that yielded the belemnite material comprises thick shale that reflects the Te division of Bouma sequence. The occurrence of a Hibolithes taxon in the uppermost Jurassic to lowermost Cretaceous Pedawan Formation sediments in Borneo reflects a near to global palaeobiogeographic distribution of this genus.     

Landslide inventory and susceptibility modelling using geospatial tools,in Hunza-Nagar valley,northern Pakistan

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.