SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors

© 2019 China University of Geosciences (Beijing) and Peking University The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources. Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner. Hence, this study aimed at developing a user-friendly geographic information system (GIS) tool, Sub-Watershed Prioritization Tool (SWPT), using the Python programming language to decrease any possible uncertainty. It used geospatial–statistical techniques for analyzing morphometric and topo-hydrological factors and automatically identifying critical and priority sub-watersheds. In order to assess the capability and reliability of the SWPT tool, it was successfully applied in a watershed in the Golestan Province, Northern Iran. Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds. It provided a cost-effective approach for prioritization of sub-watersheds. Therefore, the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p

Statistical Downscaling of Precipitation and Temperature for the Upper Tiber Basin in Central Italy

Precipitation and temperature data are the most frequently used forcing terms in hydrological models. However, the available General Circulation Models (GCMs), which are widely used nowadays to simulate future climate scenarios, do not provide those variables to the need of the models. The purpose of this study is therefore, to apply a statistical downscaling method and assess its strength in reproducing current climate. Two statistical downscaling techniques, namely regression based downscaling and the stochastic weather generator, were used to downscale the HadCM3 GCM predictions of the A2 and B2 scenarios for the Upper Tiber River basin located in central Italy. Four scenario periods, including the current climate (1961-1990), the 2020s, the 2050s and the 2080s, were considered. The Statistical Downscaling Model (SDSM) based downscaling shows an increasing trend in both minimum and maximum temperature as well as precipitation in the study area until the end of the 2080s. Long Ashton Research Station Weather Generator (LARS-WG) shows an agreement with SDSM for temperature, however, the precipitation shows a decreasing trend with a pronounced decrease of summer season that goes up to -60% in the time window of the 2080s as compared to the current (1961-1990) climate. Even though the two downscaling models do not provide the same result, both methods reveal that there will be an impact of climate on the selected basin as observed through the time series analysis of precipitation and temperature. The overall result also shows that the performance of the LARSWG resembled the results of previous studies and the IPCC’s AR4 projections

Hydrological analysis of the Upper Tiber River Basin, Central Italy: a watershed modelling approach

"Quantification of the various components of hydrological processes in a watershed remains a challenging topic as the hydrological system is altered by internal and external drivers. Watershed models have become essential tools to understand the behavior of a catchment under dynamic processes. In this study, a physically-based watershed model called Soil Water Assessment Tool (SWAT) was used to understand the hydrologic behavior of the Upper Tiber River Basin, central Italy. The model was successfully calibrated and validated using observed weather and flow data for the period of 1963–1970 and 1971–1978 respectively. Eighteen parameters were evaluated and the model showed high relative sensitivity to groundwater flow parameters than the surface flow parameters. Analysis of annual hydrological water balance was performed for the entire upper Tiber watershed and selected sub-basins. The overall behavior of the watershed was represented by three categories of parameters governing surface flow, sub-surface flow and the whole basin response. The base flow contribution has shown that 60% of the stream flow is from shallow aquifer in the sub-basins. The model evaluation statistics that evaluate the agreement between the simulated and observed streamflow at the outlet of a watershed and other three different sub-basins has shown coefficient of determination (R2) from 0.68 to 0.81 and Nash Sutcliffe Efficiency (ENS) between 0.51 and 0.8 for the validation period. The components of the hydrologic cycle showed variation for dry and wet period within the watershed for the same parameter sets. Based on the calibrated parameters the model can be used for prediction of the impact of climate and land use changes and water resources planning and management

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes

Landslide susceptibility mapping using different GIS-Based bivariate models

© 2019 by the authors. Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area

Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

© 2018 Elsevier B.V. Sustainable water resources management in arid and semi-arid areas needs robust models, which allow accurate and reliable predictive modeling. This issue has motivated the researchers to develop hybrid models that offer solutions on modelling problems and accurate predictions of groundwater potential zonation. For this purpose, this research aims to investigate the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT) and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs. A spring location dataset consisting of 359 springs was provided by field surveys and national reports and from which three different sample data sets (S1–S3) were randomly prepared (70% for training and 30% for validation). Additionally, 16 spring-related factors were analyzed using regression logistic analysis to find which factors play a significant role in spring occurrence. Twelve significant geo-environmental and morphometric factors were identified and applied in all models. The accuracy of models was evaluated by three different threshold-dependent and –Independent methods including efficiency (E), true skill statistic (TSS), and area under the receiver operating characteristics curve (AUC-ROC) methods. Results showed that the LMT model had the highest accuracy performance for all three validation datasets (Emean = 0.860, TSSmean = 0.718, AUC-ROCmean = 0.904); although a slight sensitivity to change in input data was sometimes observed for this model. Furthermore, the findings showed that relative slope position (RSP) was the most important factor followed by distance from faults and lithology

Artificial intelligence models for suspended river sediment prediction : state-of-the art, modeling framework appraisal, and proposed future research directions

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Une méthode pour améliorer les performances de la sélection de rasters basée sur une condition définie par l'utilisateur: exemple d'application pour les données agroenvironnementales

Conference of ICT for Adapting Agriculture to Climate Change (AACC'18), Cali, COL, 21-/11/2018 - 23/11/2018International audienceMore and more environmental and agricultural data are now acquired with a high precision and temporal frequency. These data are often represented in the form of rasters and are useful for agricultural activities or climate change analyses. In this paper, we propose a new method to process very large raster. We present a new technique to improve the execution time of the selection and calculation of data summaries (e.g., the average temperature for a region) on a temporal sequence of rasters. We illustrate the use of our approach on the case of temperature data, which is important information both for agriculture and for climate change analyses. We have generated several data sets in order to ana-lyze the influence of the different value properties on the process performance. One of our final goals is to provide information about the value conditions in which the proposed processing should be used