Hydrologic Data Collection Research Articles

The application of satellite sensors, current state of utilization, and sources of remote sensing dataset in hydrology for water resource management

ABSTRACT Hydrologists rely heavily on satellite sensors because they provide useful information for tracking, evaluating, managing water resources, aiding provision of safe drinking water, help preventing waterborne diseases, and address the challenges posed by climate change. Water conservation and the collection of hydrologic data have made remote sensing (RS) an invaluable tool. As a result, there are fewer hydrologic stations globally in terms of space because of various topography landforms, human limitations, and financial limits. A thorough examination of the RS satellite products' hydrological applications is essential to finding a solution to this issue. By doing this, academicians, researchers, and conservationists in various professions can better understand the products and obtain the data needed for conservation. This paper primarily focuses on the following two objectives: 1). To synthesize the scientific information on satellite remote sensing application for hydrology, and 2). To explain the RS dataset sources for hydrologic parameters. Extensive literature search from reputable journal publishers. This review article synthesized vital sources of information for academicians, researchers, and government agencies involved in hydrology and water resources management. It is recommended that RS can be used as a data source for scarce, sparsely gauged, and inaccessible regions.

Influence of large wood dynamics on flow and channel morphology in a forest stream

The riparian zone is a dynamic ecosystem formed by the interaction of aquatic and terrestrial components of rivers and serves as a source of the large wood (LW). We aimed to understand the LW dynamics and the LW influence on river morphology, sediment distribution, and flow velocity within three reaches of a low-order stream. The study was performed in a segment (770 m in length) of the Perdizes stream, located in Aparados da Serra National Park, southern Brazil. We framed the study by focusing three issues: i) the processes explaining the LW dynamics in the Araucaria Forest biome; ii) the effects of LW on the flow velocity along the study reaches, and iii) the presence of LW affecting the sediment longitudinal connectivity. The methodology consisted of: (i) collection of hydrological data (rainfall, discharge, and streamflow velocity), (ii) Perdizes stream characterization by topo-bathymetric survey and river-flow simulation with HEC-RAS 5.0.7 1D. and (iii) detailed field surveys of LW and bed sediments. The LW descriptions were performed at four moments (July 10, 2019; November 19, 2019; July 24, 2020; and December 18, 2020). Abundant LW, mainly from araucaria (Araucaria angustifolia (Bertol.) Kuntze), was observed before meanders. A notable reduction in LW volume and an increased proportion of araucaria, compared to deciduous trees, were observed within the channel. Mass balance analysis indicated fluctuations in riparian vegetation biomass, emphasizing the connectivity between vegetation and the river. Streamflow velocity decreased in LW presence, from occurrence to the margin. Bed sediment diameters were consistently smaller in cross-sections with LW presence. The accumulation of smaller sediments with LW indicated partial downstream sediment disconnectivity. HEC-RAS simulations confirmed increased LW deposition in lower velocity zones. This understanding of fluvial dynamics contributes to effective natural environment management, especially in the Araucaria Forest context.

Characterizing streamflow regimes using a distributed model for sustainable resource management in the humid tropics

Estimating streamflow regimes on small tropical islands is challenging due to limited observational data, poor accuracy of remote sensing, and the limitations of coarse-scale climate models, restricting our understanding of hydrological processes and the management of water resources. The inadequacy of data has also affected the development of instream flow standards (i.e., environmental flows, e-flows) needed to protect ecological and cultural values central to island communities. Estimates of water availability for agriculture, hydropower, and drinking water supply are also critical for informed investments in water resource planning. We utilized high-resolution (250 m) gridded daily rainfall data to parameterize the Soil and Water Assessment Tool (SWAT) to characterize streamflow regimes at ungauged locations in a watershed on Kaua‘i Island. The resultant flow statistics were then used to describe the daily, seasonal, and annual availability of surface water to evaluate the implications of potential e-flow scenarios on water available for run-of-river hydropower. Compared to rainfall data derived from a single station within the watershed, the gridded daily rainfall product increased the accuracy of model output from not satisfactory to good. Model techniques using high resolution rainfall data may substitute for long-term hydrological data collection across complex tropical environments and provide data for making sustainable management decisions at ungauged locations.

Comprehensive performance analysis of training functions in flow prediction model using artificial neural network

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days’ river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions’ performance, and identification of the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, this model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.

Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers.

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.

Assessing Extreme Drought Events and Their Temporal Impact: Before and after the Operation of a Hydropower Plant

The probabilistic analysis of streamflow and drought event durations plays a crucial role in the efficient and sustainable management of existing water resources in the region. This approach involves the collection of historical hydrological data from river gauging stations, the use of statistical and probabilistic models, and the assessment of hydrological projections at different return periods to provide valuable information for society to understand the potential impacts of extreme events. The analysis is carried out on the Sinú River in Colombia, with consideration given to both the presence and absence of the Hydropower Plant Urrá I. The results reveal that, under natural conditions, a higher number of return periods correspond to less extreme drought flows and longer temporal durations. However, when the hydropower plant is operational, the occurrence and duration of drought are influenced by the regulations implemented during energy generation. The results of this analysis can guide water resource management policies, considering the operation of the hydroelectric plant, thereby enabling decisions that enhance the resilience and sustainability of the river’s hydrological conditions and communities that depend on it.

Flood Study From the Gr2m Model in the Mono Basin at the Outlet of Athieme (South-benin)

Around the world, in particular in Benin and more precisely in the town of Athiémé, floods, caused by high floods or by the overflow of the Mono River as was the case in September 2016, cause great social and economic damage. The main objective of this work is to study the floods in the Mono basin at the Athiémé outlet thanks to the GR2M model. The methodological approach used consisted in the collection and processing of rainfall and hydrological data over the period from 1965 to 2011. These different data are processed by descriptive statistical methods and applied in the GR2M model. The Nash values obtained during the application of GR2M on the Mono to Athiémé, vary from 73% to 88.6% in calibration, and from 67.9% to 93.7% in validation. This study therefore made it possible to note that hydrological fluctuations are sensitive to rainfall forcing, and that rainfall variability has a great impact both on the flow and on recharge and evaporation. In addition, the GR2M model is effective in simulating floods on the Mono in Athiémé.

A multi‐method approach for the identification and quantification of water balance components of two lentic small water bodies

AbstractThe protection of the globally widespread lentic small water bodies (LSWB) must be based on detailed knowledge about their hydrological connectivity and water balance. The study aimed to identify and quantify water balance components as well as surface‐groundwater interaction of two LSWB in a characteristic lowland region with a combination of different methods. This includes the collection of hydrological data and the use of bromide and water stable isotopes (δ2H and δ18O) as tracers. With their help, mixing models were established, and daily water balances were assessed. The results show a strong bidirectional interaction of both LSWB systems with shallow groundwater. Bromide and stable isotope tracers allowed for the identification of the most relevant in‐ and outflow sources and pathways. Thereby, isotope data revealed isotopic enrichment typical for open‐water bodies and only minor precipitation inputs mainly relevant at the end of the dry season. Water balance calculations suggested accentuated seasonal dynamics that were strongly influenced by shallow groundwater, which represented large inputs into both LSWB. By that, different phases could be identified, with high inflow rates in winter and spring and decreasing fluxes in summer. In one LSWB, a drainage system was found to have a major impact next to the shallow groundwater interaction. The findings of this research provide detailed insights into the influence and importance of shallow groundwater for LSWB in lowland regions. This impacts the diffuse input of agricultural pollutants into these ecologically important landscape features.

Assessment of Wastewater Management at the Etoudi Slaughterhouse in the Municipality of Yaoundé - Cameroon

The management of wastewater in developing countries is a crucial challenge. The overall objective of this study was to evaluate the impact of discharges from the slaughterhouse Etoudi on physico-chemical and bacteriological water quality of the Ako'o. This study was conducted in two phases. The first phase (preliminary) in April 2022, during which a prospecting along the rivers and its watershed was performed. The second phase extended from May to October 2022, was dedicated in the collection of hydrological data, samples of water samples and bacteriological at each station coupled to the data analysis. This wastewater were collected from different stations, stored in refrigerated cooler and brought back to the laboratory for monitoring physical, chemical and bacteriological parameters according to standard protocols. The results show the effluent discharges from the slaughterhouse Etoudi exhibit high levels of pollution physicochemical (CND, MES, NO₃, NH₄⁺, PO₄^3ˉ, COD and BOD 5) and bacteriological (CF and SF) in the receiving environment having a negative impact on the watercourse Ako'o. negative impacts were increased to 90% by the dunghill faeces oxen or invading the rights of way areas. Existing gutters are littered with faeces of any kind and are not priests. Which causes recurring floods during the rainy season, the proliferation of insects and rodents and the emanation of foul odors and the persistence of certain diseases such as malaria typhoid fever, dysentery etc. this wastewater join the River Ako'o. This constitutes a major source of environmental pollution in general and rivers of waters Ako'o in particular. The positive impacts threshold is the use of cattle feces for farming populations who find themselves in different fields. Effluent discharges from the slaughterhouse Etoudi present an environmental and health risk because of high concentrations of physicochemical and bacteriological pollutants. A treatment system is needed to ensure environmental and health safety.

Assessment of a Coastal Aquifer in the Framework of Conjunctive Use of Surface Water and Groundwater—The Case of the River Nestos Western Delta, NE Greece

This paper presents research regarding the assessment of the hydrogeological system of the River Nestos Western Delta, NE Greece, during the period of 2019. The procedure included the collection and analysis of relevant hydrological and hydrogeological data concerning the aquifer system of the study area. Specifically, groundwater level measurements and sampling were carried out in a monitoring well network in the shallow unconfined and the deep confined aquifers of the study area, respectively; and surface water sampling was conducted from the River Nestos at selected locations in each of the main drainage canals, as well as in lagoons of the study area; followed by analysis and processing of the relevant chemical analyses results. Finally, piezometric, hydrochemical maps and diagrams were constructed to augment the evaluation of results and the assessment of the system. The present study contributes to the development and management of water resources in the River Nestos Delta area by providing insight into the hydrodynamic and hydrochemical status of the system based on comprehensive contemporary data that can support and justify the compilation of realistic measurements. The conjunctive management of the surface and groundwater in the study area can improve the quantitative and qualitative characteristics of the water. The water level in piezometric maps varies from −4 m up to 16 m for both time periods (May 2019 and October 2019). Moreover, the maximum values of EC are 2700 μS/cm and 2390 μS/cm for the confined and unconfined aquifer systems, respectively. The maximum values of Cl− concentrations are 573.89 mg/L for the confined aquifer system and 514.73 mg/L for the unconfined aquifer system for both time periods (May 2019 and October 2019).

BLP3-SP: A Bayesian Log-Pearson Type III Model with Spatial Priors for Reducing Uncertainty in Flood Frequency Analyses

Gauge stations have uneven lengths of discharge records owing to the historical hydrologic data collection efforts. For watersheds with limited water data length, the flood frequency model, such as the Log-Pearson Type III, will have large uncertainties. To improve the flood frequency prediction for these watersheds, we propose a Bayesian Log-Pearson Type III model with spatial priors (BLP3-SP), which uses a spatial regression model to estimate the prior distribution of the parameters from nearby stations with longer data records and environmental factors. A Markov chain Monte Carlo (MCMC) algorithm is used to estimate the posterior distribution and associated flood quantiles. The method is validated using a case study watershed with 15 streamflow gauge stations located in the San Jacinto River Basin in Texas, US. The result shows that the BLP3-SP outperforms other choices of the priors for the Bayesian Log-Pearson Type III model by significantly reducing the uncertainty in the flood frequency estimation for the station with short data length. The results have confirmed that the spatial prior knowledge can improve the Bayesian inference of the Log-Pearson Type III flood frequency model for watersheds with short gauge period.

Testing the potential of streamflow data to predict spring migration of ungulate herds.

In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the 'spring pulse.' Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.

Freshwater management in Aotearoa-New Zealand: is trading a viable option for water quantity allocation?

ABSTRACT Water trading has been proposed as an effective mechanism for delivering economic benefits within environmental limits. It represents one possibility for applying a dynamic allocation framework within which environmental river flows can be monitored and improved to support ecological, cultural, aesthetical and recreational values. Based on international experience, requirements for the successful implementation of water trading are assessed in the Aotearoa-New Zealand context. It is concluded that, if the environment is recognised as a water user, water trading has potential to promote sustainable water use but is associated with significant set-up and ongoing costs. A co-governance policy framework for water trading has not been agreed by iwi/hapū and the Crown based on Te Tiriti o Waitangi (1840). The institutional capacity required for development and implementation of trading is currently lacking, and the roles that government agencies and iwi/hapū would play are unclear. Market operational requirements relating to collection, collation and communication of hydrological data are only partially in place. Finally, New Zealand’s flashy hydrology is not conducive to seasonal water trading and smaller catchments may contain insufficient users to constitute a functioning market. Water trading should therefore not be viewed as an easily implementable solution for improved freshwater management.

Approach to a new forecasting method of hydrological variables in tropical basins

In this paper, an approach to a new method for the spatio-temporal forecasting of hydrological variables in basins is proposed termed as CIHAM-UC-SPF-HV. This was demonstrated using as hydrological variables to the precipitation and evaporation measured in 174 and 82 monitoring stations respectively, during period 1960–2000 located in the UTM zone 19 North of Venezuela and the indirect variable estimated by a water balance. This comprises the stages: 1) Collection of hydrological data, 2) Estimation of hydrological variable's spatial prediction, 3) Temporal forecasting of parameters derived from statistical spatial prediction model for hydrological variables, 4) Spatio-Temporal forecasting of hydrological variables, and 5) Validation of the results in the forecasted hydrological variables. The main advantages are: 1) the combination of deterministic and spatially correlated random components, both of these supported by historical records associated to time series of hydrological variables, and 2) multiple mathematical structures contribute to predict the parameters of statistical spatial forecasting model of the hydrological variables, selecting that by which the seasonal pattern and trend to be the closest to the observed values. The method is suitable to reproduce the spatio-temporal pattern between observed and forecasted values below two standard deviation of the mean values.

Depths Inferred from Velocities Estimated by Remote Sensing: A Flow Resistance Equation-Based Approach to Mapping Multiple River Attributes at the Reach Scale

Remote sensing of flow conditions in stream channels could facilitate hydrologic data collection, particularly in large, inaccessible rivers. Previous research has demonstrated the potential to estimate flow velocities in sediment-laden rivers via particle image velocimetry (PIV). In this study, we introduce a new framework for also obtaining bathymetric information: Depths Inferred from Velocities Estimated by Remote Sensing (DIVERS). This approach is based on a flow resistance equation and involves several assumptions: steady, uniform, one-dimensional flow and a direct proportionality between the velocity estimated at a given location and the local water depth, with no lateral transfer of mass or momentum. As an initial case study, we performed PIV and inferred depths from videos acquired from a helicopter hovering at multiple waypoints along a large river in central Alaska. The accuracy of PIV-derived velocities was assessed via comparison to field measurements and the performance of an optimization-based approach to DIVERS was quantified by comparing calculated depths to those observed in the field. We also examined the ability of two variants of DIVERS to reproduce the discharge recorded at a gaging station. This analysis indicated that the accuracy of PIV-based velocity estimates varied considerably from hover to hover along the reach, with observed vs. predicted R2 values ranging from 0.22 to 0.97 and a median of 0.57. Calculated depths were also reasonably accurate, with median normalized biases from −4% to 9.9% for the two versions of DIVERS, but tended to be under-predicted in meander bends. Discharges were reproduced to within 1% and 4% when applying the optimization-based technique to individual hovers or reach-aggregated data, respectively. The results of this investigation suggest that, in addition to the velocity field derived via PIV, DIVERS could provide a plausible, first-order approximation to the reach-scale bathymetry. This framework could be refined by incorporating hydraulic processes that were not represented in the initial iteration of the approach described herein.

Calibration and error investigation of large tipping bucket flow meters

Inherent errors in tipping bucket flow meters may limit monitoring data reliability. In this work, we perform the static and dynamic calibration of four large tipping buckets, apply different regression curves and investigate the possible measurement error sources. The volumetric capacity (static calibration) of each piece of equipment was determined. They were tested (dynamic calibration) under ten flow intensities, ranging from low to high rainfall intensities (return period larger than 100 years). For each flow rate, the measurement was recorded during six time intervals (1, 2, 5, 10, 20 and 30 min) and four regression equations - linear, potential, T vs. 1/Q and quadratic - were tested. According to the static calibration, the equipment has a volumetric capacity of 11.63 mL (TB1), 64.16 mL (TB2), 139.86 mL (TB3) and 660.95 mL (TB4). When tested under different flow rates (dynamic calibration), underestimations were identified according to the size of the cavity: TB1 (3.31%), TB2 (5.75%), TB3 (9.33%) and TB4 (13.57%). Among the alternative curves, linear regression showed the best correlation (above 99%) with the monitored data. Using this method, the measurement errors were reduced to −1.35% (TB1), 0.04% (TB2), 3.18% (TB3) and 3.73% (TB4). We investigated how the different variables (tipping speed, cavity volumetric capacity and time interval of data collection) influenced the error. Errors follow a parabolic function of tipping velocity and a linear function of cavity volumetric capacity. The time interval of data collection interfered in the data sampled, however no statistical correlation was found. Among those variables, cavity size is the most important one. Given its low cost we aimed to minimize the inherent error in large tipping buckets flow meters and encourage its application, increasing in-situ collection of hydrological data.

Assessment of the intra-annual flow distribution of the Shu-Talas river basin over the past decade

Based on the collection and analysis of hydrological and meteorological data, the article estimates the distribution of intra-annual runoff within the Shu-Talas River basin over the past decades. To assess changes in the intra-annual distribution of runoff in the Shu-Talas water management basin, observation data for 10 active hydrological posts were collected from hydrological yearbooks. Limiting and non-limiting periods are established for the studied basin, including the period of predominance of meltwater runoff, the period of predominance of the rain source of food and the period of manifestation of ice phenomena. Empirical and analytical curves are constructed for each set period. According to these curves, three gradations of water content are defined: a high-water year (p<33.3%), an average year by water content (33.3%≤p≤66.7%), a low-water year (P>33.3%) and are classified by water content of years in three groups. The intra-annual distribution of runoff over the past decades was estimated, and the results of the studies were compared and analyzed with the results given in the" Surface Water Resources of the USSR " for the basin under consideration. The calculations carried out allow us to determine various water management parameters for the basin, to ensure the safety of settlements within the basin.

DEVELOPMENT OF A GEOGRAPHIC INFORMATION SYSTEM BASED ON MICROSERVICE ARCHITECTURE

The paper discusses the use of microservice architecture in the development of distributed geographic information systems (GIS) for collecting, processing and analyzing data. Microservice architecture is used to build applications in information systems related to solving business problems, and is not widespread in the development of geographic information systems in the scientific field. However, its application is now becoming increasingly important. Decomposition of the software implementation and GIS infrastructure associated with computations and data processing into components in the form of microservices has a number of advantages, such as: increased fault tolerance, increased flexibility, reduced maintenance effort, simplified scaling, and others. The first results of the application of the microservice approach in the development of a geoinformation system for the collection and processing of hydrological and hydrobiological data on the state of water bodies are shown. The architecture, main components, and features of building infrastructure are shown.

I2-RED: A Massive Update and Quality Control of the Italian Annual Extreme Rainfall Dataset

The collection and management of hydrological data in Italy has been dealt with at national level, initially, by the National Hydrological Service (SIMN), and at regional level in the last 40 years. This change has determined problems in the availability of complete and homogeneous data for the whole country. As of 2020, an updated and quality-controlled dataset of the historical annual maxima rainfall in Italy is still lacking. The Italian Rainfall Extreme Dataset (I-RED) has recently been created to allow studies to be performed with a homogeneous dataset at a national level. In this paper, the methodological approach adopted to build an improved and quality-controlled version of I-RED (in terms of both the rainfall depth values and the position of the rain gauges) is presented. The new database can be used as a more reliable research support for the frequency analysis of the rainfall extremes. This new I2-RED database contains rainfall annual maxima rainfall of 1, 3, 6, 12 and 24 h from 1916 until 2019, counts 5265 rain gauges and has been corroborated by a re-positioning and elevation-checking of 15% of the stations. A descriptive analysis of the maximum values of the stations, which provides an additional quality check and reveals different intriguing spatial features of Super-Extreme rainfall events, is also presented.