Case Studies in Distributed Hydrology

Revisiting the forest transition theory with historical records and geospatial data: A case study from Mississippi (USA)

Recent advances in hydrologic models have depended on the use of radar rainfall input in physically based, fully distributed models. Previous research conducted for case studies near Houston, Texas, have focused on the use of radar rainfall for large storm events such as tropical storms and hurricanes. A fully distributed model, Vflo, was used to model streamflow during small storm events in the Cypress Creek watershed, near Houston, Texas. Two events were simulated both with rain gauge—corrected radar data and exclusively with rain gauges. Then a third event was modeled exclusively with rain gauge data to assess model performance with only rain gauge data. The modeled streamflow was then compared to the USGS observed streamflow, using peak streamflow, time to peak, and volume streamflow, to evaluate the model performance between radar and rain gauge input. A comparison of the models for the events shows that the radar input results better match the observed streamflow for the streamflow volume an...

The structures and parameters of physically-based distributed hydrological models (PBDHMs) can now be established and derived from remote-sensing data with relative ease. When engineers apply PBDHMs for flood forecasting in mesoscale catchments, they encounter varying rain gauge infrastructure conditions. Understanding model performance expectations under varying rain gauge density conditions is crucial for wide PDBHM construction. This study presents a case study of a PBDHM called the Liuxihe Model and examines six rain gauge density scenarios designed based on real-world data to assess the impact of rain gauge density on model flood forecasting performance. The study focuses on a mesoscale catchment in Jiangxi Province, China, covering an area of 2364 km2 with 62 rain gauges. The results indicate that models optimized under an adequate rain gauge density condition are less affected by gauge density changes, maintaining accuracy within a range of change. Compared to Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE), the indicators absolute peak time error (APTE) and peak relative error (PRE) are less sensitive to variation in rain gauge density. The study further discusses how rain gauge density changes related to the interpolated rainfall surfaces and parameter optimization, hoping to facilitate the broader application of PBDHMs and offer insights for future practices.

<p>Rainfall plays a critical role in the hydrological cycle, being the main downward forcing. It is well known that rainfall exhibits large variability in space and time due to the storm dynamics and its interaction with the topography. It is a difficult task to reconstruct the rainfall over an area accurately. Rainfall is usually collected through rain gauges, disdrometers, and weather radars. Rain gauges and disdrometers provide quite accurate measurements of rainfall on the ground, but at a single site, while weather radars provide an indication of rainfall field variability in space, even if their use is restricted to plain areas.</p><p>Recently, unconventional observations have been considered for the monitoring of rainfall. These consist in signal attenuation measurements induced by rain on a mesh of point-to-point commercial microwave links (CML). These data, integrated with the ones collected by a network of conventional rain gauges, can provide further information about rainfall dynamics leading to improvements in hydrological modelling, which requires accurate description of the rainfall field.</p><p>The work we are going to describe is part of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves), a scientific project funded by Fondazione Cariplo (see also the EGU abstract of Nebuloni et al., 2020). Here we use rainfall data, obtained both from a rain gauge network and from signal attenuation measurements, into a hydrological model in order to evaluate the improvement in the hydrological modelling due to a better description of the rainfall field. We consider a semi-distributed rainfall-runoff model and we apply it to the Mallero catchment (Western Rhaetian Alps, Northern Italy), with the outlet located in Sondrio. This catchment is equipped with 13 microwave links and a network of 13 rain gauges.</p><p>Firstly, we implement and test the Rain field Reconstruction Algorithm (RRA), which retrieves the 2D rainfall field from CML data through a tomographic inversion technique, developed by D’Amico et al., 2016. By RRA we generate synthetic rainfall maps from attenuation data measured by 13 links located in the Mallero basin, for a few historical events in the period 2016-2019. To improve the accuracy of rainfall field reconstruction, we also integrate the reconstructed maps with on ground data from 13 rain gauges. These maps are used as input to the hydrological rainfall-runoff model. Finally, we compare the observed discharge with the calculated one using the hydrological model and different rainfall inputs.</p>

A conceptual data model coupling with physically-based distributed hydrological models based on catchment discretization schemas

Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

The chapter titled “Distributed Geospatial Information Systems Challenges and Opportunities” delves into the comprehensive landscape of distributed geospatial technologies and their evolution. It begins with an introduction to Distributed Geospatial Information Systems (DGIS), outlining the fundamental concepts and features. It traces the evolution of DGIS, highlighting significant advancements, emphasising DGIS critical role in managing and analysing large-scale geospatial data efficiently. The architecture of DGIS is examined, elucidating the components and frameworks that enable their distributed nature. A comparative comparison between distributed and centralised geospatial information systems is provided, showcasing the advantages and limitations of each approach. The chapter explores geospatial data replication and synchronization, emphasizing methods to ensure consistency and integrity in distributed systems. The chapter explores DGIS’s role in SDI, enhancing geospatial data accessibility and usability. The chapter also covers parallel and distributed geospatial data processing and analysis techniques, which are pivotal for handling complex and voluminous geospatial datasets. To provide practical insights, case studies and applications of DGIS in various fields are presented, demonstrating their real-world utility and impact. Finally, the chapter concludes with an exploration of future trends and directions in DGIS, forecasting emerging technologies and potential developments that will shape the future of geospatial information systems.

Comparison of different hydrological and stability assumptions for physically-based modeling of shallow landslides

In this chapter, an overview of the geospatial techniques of remote sensing (RS) and Geographical Information System (GIS) and the integration of RS and GIS is briefly discussed. Different types of RS datasets, its extraction, integration of different spatial datasets and the challenges in the use of GIS with specific focus on distributed model application is also discussed. Further, application of integration of geospatial techniques are illustrated with the help of two hydrological models, SHETRAN and SWAT. The application of integration of geospatial techniques is demonstrated in a river basin belonging to Western Ghats region for streamflow and sediment load simulation. As demonstrated in the case study, the integration of GIS and RS datasets with hydrologic models has facilitated wide range application of distributed models for environment monitoring. This study will be useful for modellers to handle different geospatial datasets to setup distributed hydrologic models or environment monitoring systems.

Abstract. Rain gauge measurements are one of the primary techniques used to estimate a precipitation field, but they require careful quality control. This paper describes a modified RainGaugeQC system, which is applied to real-time quality control of rain gauge measurements made every 10 min. This system works operationally at the national meteorological and hydrological service in Poland. The RainGaugeQC algorithms, which have been significantly modified, are described in detail. The modifications were made primarily to control data from non-professional measurement networks, which may be of lower quality than professional data, especially in the case of personal stations. Accordingly, the modifications went in the direction of performing more sophisticated data control, applying weather radar data, and taking into account various aspects of data quality, such as consistency analysis of data time series and bias detection. The effectiveness of the modified system was verified based on independent measurement data from manual rain gauges, which are considered one of the most accurate measurement instruments, although they mostly provide daily totals. In addition, an analysis of two case studies is presented. This highlights various issues involved in using non-professional data to generate multi-source estimates of the precipitation field.

In urban areas, drainage networks are usually formed by small subnetworks, hence resulting in small-sized basins with fast times of response. For this reason, the prediction of pluvial flood hydrographs in urban basins is hampered by considerable uncertainty due to the spatial and temporal variability of rainfall intensity, which is difficult to characterize with the sparse observations from the limited amount of rain gauges that are typically available. To better understand and quantify this uncertainty, the drainage network of Turin is considered as a case study. The city of Turin has >800.000 residents and is located in Northwestern Italy on a relatively flat area bordering a hill on the East. The area is mostly urbanized, and it receives an average precipitation of around 800 mm per year. The drainage network was developed since the end of the 19th century as a separate network that receives only stormwater from a catchment area of around 100 km2, for a total network length of around 1200 km. At present, the network experiences some criticalities due to infrastructure ageing and urban development, and occasional flooding episodes are observed at some points. The sensitivity of flood hydrographs at the basin outlet to spatial and temporal patterns of rainfall intensity is analyzed using a SWMM hydraulic model of different parts of the drainage networks. Spatial and temporal variability of rainfall intensity over the area is quantified using the observations of a set of 20 rain gauges. Then, the analysis of the sensitivity of the flood hydrograph in a monitored subnetwork is performed based on two rain gauges (2 km apart) and one flow meter at the basin outlet. The results provide valuable insight into the response of urban drainage networks to heterogeneous spatial fields of precipitation.

Physically-based distributed hydrological models have always played an important role in watershed hydrology. Existing hydrological modeling applications focused more on the estimation of water balance and less on the simulation of water transportation in a catchment. Different from the prediction of flow production, the dynamic simulation of flow concentration depends largely on the field distribution of water-flow velocity. However, it is still difficult to determine the water-flow velocity with terrain analysis techniques, which had always hampered the application of hydrological models in surface water transportation simulation. This study, therefore, proposes a data-driven method for creating a field map of overland flow velocity based on the Manning’s equation. Case study on a gauged watershed is undertaken to validate the spatial distribution of flow velocity. The preliminary results indicate that the proposed empirical method can reasonably determine the spatial distribution of water-flow velocity. Further efforts are still required to support the space-time change of flow velocity under the control of microtopography and instantaneous water depth.

Digital geoscientific and geospatial datasets are rapidly growing in both number and size. These data present powerful new resources for understanding the evolution of the earth, but working with them requires computational skills are not part of typical geoscience curricula at universities. To leverage the power of these growing geoscientific and geospatial data, students need targeted educational resources that provide basic computational skills.The new textbook Introduction to Python for Geographic Data Analysis provides a framework for learning to work with (geospatial) datasets of varying size from loading the data to producing interactive visualizations of processed data. Part 1 of the book covers the basics of programming using the Python language, introducing both programming concepts and their Python syntax. It also covers the analysis of tabular data using the pandas Python library and the basics of data visualization. Part 2 introduces working with geospatial data, including fundamental geospatial concepts, working with vector and raster data, geospatial data visualization, and loading data from online sources. Part 3 includes several case studies that build on things presented in the first two parts to demonstrate what can be done with the readers’ new skills. Finally, the appendices provide information about best practices in programing, version control with git and GitHub, and other practical coding tips that promote open, reproducible science.The book materials are freely available online at https://pythongis.org, and we anticipate that hard copies of the book will be available later in 2026. We hope the book will appeal to a broad range of “geo” scientists, including teachers who provide courses on introductory programming or data analysis for geology and geography students, those interested in learning to interact with and batch process large datasets, and those interested in finding open-source alternatives to commercial GIS software packages.

BackgroundDespite policy prominence and frameworks focusing on health inequalities, healthcare leaders do not feel they have the skills and knowledge to reduce health inequalities. This comparative case study explored four areas in England to understand the challenges, opportunities, and drivers of local health systems addressing health inequalities and what ‘good’ practice might look like.MethodsInterviews were held with 46 people working in health care services across the NHS, local authority or voluntary, community, social enterprise sectors. Key documents (n = ~ 10) in each of the four areas relating to reducing health inequalities were analysed using documentary analysis methods. Interviews and documents were coded and analysed independently before being integrated to synthesise findings. Analysis was conducted using a two-stage approach - firstly, an inductive analysis of emergent themes; secondly, to build knowledge on each case studys’ system approach of reduction of health inequalities, principles of the Action Scales Model were used.ResultsNineteen themes were identified across the four case studies; some themes were not apparent in all the case studies, nor in either the documentary analysis or interviews. These themes allowed us to compare between cases to explore what might be contributing to good practice. Themes identified included: understanding the local context; facilitators of how to tackle health inequalities and improve health and wellbeing; and future concerns. The secondary analysis highlighted potential levers for action from each case study; these included optimising retention and recruitment of workforce and allowing time and resources for longer-term planning. Two case study areas which appeared to have system resilience, demonstrated having a shared vision, strong partnerships, understanding of the system, and putting people and communities at the heart of decision making.ConclusionThis comparative case study makes a crucial contribution in the understanding of health systems addressing health inequalities in their local areas. The combined interview and documentary analysis findings provide rich insights of local systems’ documented strategies, plans and what is happening ‘on the ground’.