Geospatial Hydrological Analysis in GIS Environment for Selecting Potential Water Harvest Sites: The Case of Badrah –Wasit

في هذه الدراسة, تم دمج تقنية نظم المعلومات الجغرافية وبيانات الاستشعار عن بعد في هذه الدراسة لإنتاج خرائط للمواقع المحتملة لجمع المياه في بدرة -واسط، شرق العراق. وتستخدم هذه الطريقة لتحديد مواقع مناسبة لتجمع المياه وكذلك لتحسين إدارة الموارد المائية. تم استخدام خمسة معايير في هذا البحث لتحديد مواقع تجميع المياه وهي:الرتب النهرية, الانحدار, المسافة إلى الطرق, الهاطل المطري ومؤشر الغطاء النباتي. تم تقييم هذه الطبقات الموضوعية باستخدام طريقة تحليل القرار متعدد المعايير ثم دمجها ومعالجتها معا باستخدام طريقة التراكب المرجح، ثم تم تعيين أوزان مناسبة ومتكاملة في نظام المعلومات الجغرافية لتوليد خريطة ملائمة. ونتيجة لذلك، تم تصنيف المنطقة إلى ثالث مناطق: منطقة ذات ملائمة عالية بمساحة 2٪، ومنطقة ذات ملائمة متوسطة (27٪) ومنطقة ذات ملائمةمنخفضة (35٪) وفقا للمعايير المحددة التي استخدمت لهذا الغرض.In this study, GIS technique and remote sensing data have been integrated to createa suitability map for the probable sites of water harvesting in Badrah-Wasit, EasternIraq.Hydrological analysis used to find the potential water-harvesting sites, as well as to improve the water resource management. In this research, five criteria have been used, which is astream order, slope, distance to roads, rainfall and Normalized Difference Vegetation Index. These thematic layerswere evaluated with the multi-criteria analysis method, then combine and process together using weighted overlay method, then assigned suitable weights and integrated into a GIS to generate a suitability map.As a result, the region has been classified into three zones: high suitability zone (2%), moderate suitability zone (27%), and low suitability zone (35%) depending on the specific criteria used for this purpose and have high potential in terms of their suitability for water harvesting

Downscaling Gridded DEMs Using the Hopfield Neural Network

A new Hopfield neural network (HNN) model for downscaling a digital elevation model in grid form (gridded DEM) is proposed. The HNN downscaling model works by minimizing the local semivariance as a goal, and by matching the original coarse spatial resolution elevation value as a constraint. The HNN model is defined such that each pixel of the original coarse DEM is divided into f × f subpixels, represented as network neurons. The elevation of each subpixel is then derived iteratively (i.e., optimized) based on minimizing the local semivariance under the coarse elevation constraint. The proposed HNN model was tested against three commonly applied alternative benchmark methods (bilinear resampling, bicubic and Kriging resampling methods) via an experiment using both degraded and sampled datasets at 20-, 60-, and 90-m spatial resolutions. For this task, a simple linear activation function was used in the HNN model. Evaluation of the proposed model was accomplished comprehensively with visual and quantitative assessments against the benchmarks. Visual assessment was based on direct comparison of the same topographic features in different downscaled images, scatterplots, and DEM profiles. Quantitative assessment was based on commonly used parameters for DEM accuracy assessment such as the root mean square error, linear regression parameters m and b, and the correlation coefficient R. Both visual and quantitative assessments revealed the much greater accuracy of the HNN model for increasing the grid density of gridded DEMs

An enhanced technique in construction of the discrete drainage network from low-resolution spatial database

A digital elevation model (DEM) of a watershed can be used to acquire various parameters such as basin-wide information about overland flow direction, flow accumulation and area contributing flow to any point. The resolution and quality of a DEM are important to achieve a significant level of accuracy in derived parameters. Inadequate elevation information exacerbated by applied interpolation methods, reduce DEM accuracy, resulting in pits and flat areas and makes flow tracing a difficult task. These types of problems are more prominent in cases of residual undulating terrains. In the present paper, an attempt has been made to review and suggest an improved method for the generation of a DEM from raster contour data. Further, a criteria-based region growing method (CBRGM) is presented for the extraction of discrete drainage network (cell size: 20×20 m) of the watershed. Here, the flat area removal algorithm, with a variable increment, is used to generate the DEM. This induces a gradual slope even in the case of a large contour interval (20 m) extended over larger area, as is commonly available from a topographic map at a scale of 1:50,000. Further, in order to capture topographic information in flow tracing, the CBRGM is followed. The rasterised stream network from the same topographic sheet is used as ancillary data to make the concentrated flow lines to follow the channel. The methodology has been tested over Gandheshwari subwatershed under the lower part of Chhotanagpur Plateau in Eastern India. The DEM generated using this method gives a better representation of the terrain, which shows good agreement with the terrain information delineated by using the contour and channel information available in the topographic sheet. The drainage network derived shows additional extra-concentrated flow lines, many of which match the drainage network obtained from satellite imagery (cell size: 23.5×23.5 m). The algorithm thus shows superiority over other available methods for the extraction of drainage networks.© Elsevie

Groß-skalige 2D-hydraulische Modellierung: Verbesserung der Analyse der Flutdynamik mit remote sensing und freien geographischen Informationen

This work investigates the integration of hydro-geomorphic models, traditional data (static stage gages) and novel data sources, such as remotely sensed images and Crowdsourced data (Volunteering Geographic Information or VGI), for observation-driven improvements of hydro-modelling tools. The Tiber river basin, was selected as case study with a focus domain on the approximately 120 km channel upstream of Rome for its strategic importance in the protection of the historical city centre and the coastal urbanized zone. A parsimonious hydrological modelling algorithm was implemented, calibrated and validated for calculating the flow hydrographs of the ungauged small basins contributing to the study area. Furthermore, to delineate the boundaries computational domain of the hydraulic model for the Data Assimilation application, a DEM-based hydro-geomorphic floodplain delineation algorithm adapted from literature was tested with different DEMs and considering also its parametrization varying the stream orders. The adopted DA methodology is the Ensemble Kalman Filter (EnKF) that requires multiple simulations for representing the uncertainties related to the model and the observations errors. New approaches were proposed for integrating, as observations in the DA method, traditional static sensors, and simultaneously remotely sensed images and VGI data. Despite the static sensor have already been adopted in literature as observations in a DA framework, some new technical measures were necessary for integrating them in Quasi-2D hydraulic model. The assimilation of satellite images resulted to be effective, since the whole computational domain is interested by the water levels correction, although the improvement of the model performance persisted for only some hours of simulation. The usefulness of VGI have been investigated considering the uncertainties related to their reliability mostly in terms of accuracy and time allocation. Results show the potential of new data for improving the performance of the flood model, partially overcoming the limitations and the potential scarce availability of the traditional sensors. Finally, the simultaneous integration of all the three types of observations gave promising results, improving the performance of the model compared to the ones obtained assimilating only Satellite images or VGI observations.Diese Arbeit untersucht die Integration von hydro-geomorphen Modellen, traditionellen Daten (statische Stufenpegeln) und neuartigen Datenquellen wie Remote-Sensing-Bildern und Crowdsourced-Daten (volunteering Geographic Information oder VGI), um beobachtungsorientierte Verbesserungen von Hydromodellierungswerkzeugen zu erreichen. Das Tiber-Flusseinzugsgebiet wurde als Fallstudie mit einem Schwerpunkt auf dem etwa 120 km stromaufwärts von Rom gelegenen Kanal ausgewählt und zwar wegen seiner strategischen Bedeutung für den Schutz des historischen Stadtzentrums und der urbanisierten Küstenregion. Ein sparsamer hydrologischer Modellierungsalgorithmus wurde implementiert, kalibriert und validiert, um die Fluss-Hydrographen der durch Pegel nicht erfassten kleinen Becken zu berechnen, die zum Untersuchungsgebiet beitragen. Um die Grenzen des rechnerischen Bereichs des Hydraulikmodells für die Data-Assimilation-Anwendung abzugrenzen, wurde außerdem ein DEM-basierter, aus der Literatur angepasster Algorithmus zur Abgrenzung von hydrogeomorphen Flutebenen mit verschiedenen DEMs getestet, wobei auch die Parametrisierung der Stream-Reihenfolge berücksichtigt wurde. Die angenommene DA-Methode ist der Ensemble Kalman Filter (EnKF), der mehrere Simulationen zur Darstellung der mit dem Modell verbundenen Unsicherheiten und Beobachtungsfehler erfordert. Es wurden neue Ansätze für die Integration herkömmlicher statischer Sensoren, von Fernerkundungs-Bildern und von VGI-Daten als Beobachtungen für das DA-Verfahren vorgeschlagen. Obwohl die statischen Sensoren bereits in der Literatur als Beobachtungen in einem DA-Rahmen verwendet wurden, waren einige technische Maßnahmen erforderlich, um sie in das Quasi-2D-Hydraulikmodell zu integrieren. Die Assimilation von Satellitenbildern erwies sich als effektiv, da der gesamte rechnerische Bereich an der Korrektur des Wasserstandes interessiert ist; die Verbesserung der Modellleistung dauerte allerdings nur einige Stunden in der Simulation an. Die Nützlichkeit von VGI wurde unter Berücksichtigung der mit ihrer Zuverlässigkeit verbundenen Unsicherheiten hauptsächlich hinsichtlich Genauigkeit und Zeitzuordnung untersucht. Die Ergebnisse zeigen das Potenzial neuer Daten zur Verbesserung der Leistung des Flutmodells, wobei teilweise die Einschränkungen und die oftmals knappe Verfügbarkeit herkömmlicher Sensoren überwunden werden. Schließlich ergab die gleichzeitige Integration aller drei Arten von Beobachtungen vielversprechende Ergebnisse und verbesserte die Leistung des Modells im Vergleich zur Nutzung nur von Satellitenbilder oder VGI-Beobachtungen