Rainfall Monitoring Network Research Articles

Infilling of high‐dimensional rainfall networks through multiple imputation by chained equations

AbstractAccurate precipitation records are an essential component when monitoring the climate and studying its changes. However, analysis is typically limited by the large quantities of missing values present. This article proposes two new imputation techniques for incomplete monthly data collected from a rainfall monitoring network in the Republic of Ireland from 1981 to 2010. The data considered is high‐dimensional due to the large number of over 1100 rain gauge stations present, and the methods presented are designed to handle such cases. These are Elastic‐Net Chained Equations (ENCE) and Multiple Imputation by Chained Equations with Direct use of Regularized Regression by elastic‐net (MICE DURR). Both methods predict missing data by a series of regularized regression models, where MICE DURR differs from ENCE by also using multiple imputation. Through various evaluations across different levels of missingness, ENCE and MICE DURR consistently outperformed existing imputation methods in terms of RMSE and . Moreover, they have provided the best results both seasonally and for accurately predicting extreme values. An RMSE of 14.16 and 14.17 mm per month were reported for ENCE and MICE DURR, respectively, when stations that were at least 50% complete during the study period were included. For increasingly sparser data, the imputation accuracy achieved from MICE DURR surpasses ENCE, demonstrating the efficacy of multiple imputation when handling a substantial amount of missing data. Validation metrics indicate that these methods compare very favourably to existing methods in the literature, such as those that use random forests or multiple linear regression.

Citizen scientists’ engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals’ attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study’s outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

Saharan rainfall climatology and its relationship with surface cyclones

The Sahara is the largest and driest of the hot deserts on Earth, with regions where rainfall reaches the surface on average less than once a year. Water resources are scarce, and rainfall tends to occur sporadically in space and time. While rain is a precious resource in the Sahara, heavy precipitation events (HPEs) in the desert have the potential to trigger flash floods on the barren soil. Because of the sparse rainfall monitoring network and the relatively poor performance of global models in representing rainfall over the Sahara, the analysis of Saharan HPEs has primarily relied on case studies. Therefore, general rainfall characteristics of Saharan HPEs are unexplored, and the prevailing weather conditions enabling such rainfall are unknown. To tackle this problem, we utilised satellite-derived precipitation estimations (IMERG) spanning 21 years (2000–2021) to identify ∼42⋅103 small (>103km2) to large (<106km2) HPEs in the Sahara and to extract their rainfall properties, and atmospheric reanalyses (ERA5) to examine the corresponding meteorological conditions in which they develop. Three case studies illustrate the relevance of cyclones for exceptionally large HPEs, including one in the driest region of the Sahara. Saharan HPEs occur, on average, every second day. They are more common in summer than in the other seasons, occur most frequently in the southern Sahara, and exhibit a clear convectively-driven diurnal cycle. Winter events have, on average, larger spatial extent, longer duration, and are characterised by larger areas exhibiting more extreme rainfall in terms of return periods. Autumn HPEs are concentrated in the western Sahara, while events in the north of the desert and in its driest core in the northeast occur mainly in winter and spring. In these regions, north of the Tropic of Cancer, events are highly associated with surface cyclones. HPEs that were associated with cyclones are characterised by larger spatial extent and rainfall volume. Considering that weather and climate models often depict synoptic-scale weather systems more accurately than rainfall patterns, the association of Saharan HPEs with surface cyclones and other synoptic-scale systems can aid in comprehending the effects of climate change in the desert. Furthermore, it underscores the potential for higher predictability of these events.

Meteorological Drought Monitoring Based on Satellite CHIRPS Product over Gamo Zone, Southern Ethiopia

Drought is a frequent occurrence in semidesert areas of southern Ethiopia that significantly affect regional, social, economic, and environmental conditions. Lack of rainfall monitoring network, instrument measurement, and failure are major bottlenecks for agro-and hydroclimate research in developing countries. The objectives of this study were to evaluate the performance of CHIRPS rainfall product and to assess meteorological drought using SPI for the period 2000 to 2020 over Gamo Zone, southern Ethiopia. The performance of CHIRPS v2 was assessed and compared to station observations (2000–2020) in the study domain to derive SPI on a three-month timescale. The Pearson correlation coefficient (R), bias, probability of bias (PBias), mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Nash simulation efficiency (NSE) values across the zone for CHIRPS v2 were found to be 0.88, 1.02, 2.56, 0.25, 22.41, 33.14, and 0.77, respectively. The results indicate that CHIRPS performed good ability to analyze the drought characteristics in the Gamo Zone. The spatial and temporal distribution method of meteorological drought has been evaluated using the Climate Data Tool (CDT). The Standardized Precipitation Index (SPI) was computed using the gamma distribution method. The magnitude of (SPI-3) of monthly and seasonal (MAM) meteorological drought in the zone from 2000 to 2020. The result shows that the known historic drought years (2014, 2015, 2010, 2009, and 2008) were indicated very well. Furthermore, sever and extreme droughts were observed in 2008 and 2009 with drought duration of 6.7 and 6.3, respectively, in most areas of the zone. Hence, this study revealed that CHIRPS can be a useful supplement for measuring rainfall data to estimate rainfall and drought monitoring in this region.

Automating rainfall recording: Ensuring homogeneity when instruments change

Sub-daily rainfall is used in modelling of small catchments, design of urban drainage, calculating soil erosion, understanding the water balance of vegetated catchments, and assessing the impact of climate change on short duration storms. Hence it is critical to ensure that sub-daily rainfall records are homogeneous, but with a global shift to automated measurement of rainfall, there has been a change in the rainfall recording instrumentation. Although changes in instrumentation should be documented, in many cases this is not the case and station metadata are missing. As result, studies of rainfall patterns, particularly those investigating trends, often cannot report their treatment of possible inhomogeneities and must assume the data is suitable for further analysis.Here, we argue that standard quality assurance methods for checking inhomogeneities in rainfall may not identify a change in the rainfall record due to a change in instrumentation. Through testing (1) an aggregation of rainfall to the coarsest instrument resolution, (2) removal of rainfall below a minimum rainfall depth, (3) removal of rainfall below a minimum accumulation (event) depth, and (4) a combination of the above adjustments, we present recommendations for ensuring sub-daily rainfall is homogenous with minimum alteration to the rainfall record.We assess the proposed methods using the case study of Australia’s sub-daily rainfall monitoring network, where, in 1996 the pluviograph network was switched to tipping buckets, creating a large-sample test bed with a known systematic instrumentation change. Our results show that event-based statistics are considerably more sensitive to instrument change than annually aggregated rainfall statistics and hence standard methods for quality assurance may not identify possible inhomogeneities. We suggest an aggregation of the data to the coarsest instrument resolution, with the removal of small rainfall accumulations, can alleviate inhomogeneities for event-based statistics, whereas, for annual statistics, aggregation to the coarsest instrument resolution is sufficient. In the absence of a known instrumentation change, removing rainfall at or below the instrument resolution is also a viable technique for improving rainfall record homogeneity for event-based statistics. These findings will aid future sub-daily rainfall studies by justifying the use of a minimum rainfall or event depth for subsequent analysis.

Addressing climate services in SouthAmerican Chaco region through a knowledge coproduction process

When engaging in an interdisciplinary and intersectoral knowledge coproduction process, what premises should (academic and non-academic) participants consider to prevent power dynamics and divergent interests from becoming epistemological obstacles (Bachelard, 1974)? What methodological devices should be adopted to enable a productive dialogue between heterogeneous actors and knowledge? Despite the plethora of literature on the relevance of participatory approaches and the promotion of open science to produce socially meaningful knowledge, most works neglect central challenges present in any coproduction experience. Namely, how to deal with power dynamics, how to challenge the identity anchors of the participants and how to ensure the epistemological conditions for initiating long-term collaboration. Hence, based on these concerns, this paper puts forth the theoretical-methodological basis of a knowledge coproduction approach for the development of climate services for family farming. In addition, we present two major results achieved in the framework of a coproduction process involving academic and non-academic participants: the development of a community-based rainfall monitoring network in the eastern region of the South American Gran Chaco and the codesign of a smartphone application.

Nested Augmentation of Rainfall Monitoring Network: Proposing a Hybrid Implementation of Block Kriging and Entropy Theory

Nested Augmentation of Rainfall Monitoring Network: Proposing a Hybrid Implementation of Block Kriging and Entropy Theory

Assessment of Spatiotemporal Variability of Meteorological Droughts in Northern Iraq Using Satellite Rainfall Data

The absence of a dense rainfall monitoring network and longer period data are the major hindrances of hydroclimatic study in arid and semi-arid regions. An attempt has been made for the evaluation of spatiotemporal changes in droughts at the northern semi-arid region of Iraq for the period 1981–2018 using high-resolution (0.05°) precipitation data of Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). The performance of CHIRPS in replicating rainfall and Standard Precipitation Index (SPI) for different timescales at eleven locations for the available period of observation data (2000–2014) was evaluated. The SPI was also used to estimate drought frequency and evaluate drought trends at all the CHIRPS grid points. A modified version of the non-parametric Mann-Kendall (MK) test was employed for a robust evaluation of the spatial distribution of temporal trends in droughts. The results showed a good ability of CHIRPS in reconstructing observed SPI with a correlation coefficient ranged from 0.64 to 0.87, BIAS between 1.05 and 1.81, Nash-Sutcliff efficiency from 0.39 to 0.55, and Willmott Index between 0.67 and 0.79. The CHIRPS also able to reconstruct the time series and probability distribution of observed SPI reasonably. Spatial distribution of droughts revealed a higher frequency of droughts of all categories and timescales in the east and north of Northern Iraq, mainly due to high rainfall variance. The MK test revealed a reduction in 6- and 12-month droughts in the northwest and an intensification at a few northeastern grids. It indicates droughts became more recurrent in the already drought-prone region and lessened in a less drought-prone region.

Communication protocols evaluation for a wireless rainfall monitoring network in an urban area

Rainfall monitoring networks are key elements for the development of alerts and prediction models for communities at risk of flooding during high intensity rainfall events. Currently, most of these networks send the precipitation measurement to a data center in real-time using wireless communication protocols, avoiding travel to the measurement site. An Early Warning System (EWS) for pluvial flash floods developed in Barranquilla (Colombia), used the GPRS protocol to send rain gauge data in real-time to a web server for further processing; however, this protocol has a high consumption of energy and also high maintenance costs. This article carried out an evaluation in terms of link budget, link profile, energy consumption and devices costs of three low-power wireless communication protocols, Zigbee, LoRaWAN and Sigfox, to determine which one is the most suitable for the EWS of the city of Barranquilla. To perform the evaluation, a wireless sensor network was designed and characterized for Zigbee and LoRaWAN with Radio Mobile tool taking into account the measurement points implemented with GPRS network. The evaluation included the power consumption of Zigbee, LoRaWAN and Sigfox. From the results of simulations, LoRaWAN and Zigbee network has similar radio signal received and the LoRaWAN network obtains the least losses per path. As for power consumption, the LoRaWAN devices has the lowest energy consumption, as well as, the LoRaWAN network sensor nodes are cheaper. Finally, the protocol with the best general performance was LoRAWAN, since complies with the communication, consumption and cost requirements.

An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data

Abstract Accurate and precise rainfall records are crucial for hydrological applications and water resources management. The accuracy and continuity of ground-based time series rely on the density and distribution of rain gauges over territories. In the context of a decline of rain gauge distribution, how to optimize and design optimal networks is still an unsolved issue. In this work, we present a method to optimize a ground-based rainfall network using satellite-based observations, maximizing the information content of the network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with an existing rain gauge network in the Rio das Velhas catchment, in Brazil. We use a greedy ranking algorithm to rank the potential locations to place new sensors, based on their contribution to the joint entropy of the network. Results show that the most informative locations in the catchment correspond to those areas with the highest rainfall variability and that satellite observations can be successfully employed to optimize rainfall monitoring networks.

Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks.

Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network.

Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows.

Application of information fusion techniques and satellite products in the optimal redesign of rain gauge networks

Rain gauge networks are usually redesigned to improve the accuracy of spatial and temporal estimates and reduce the monitoring costs. In addition to the rain-gauge data, satellite-based data can be used for the optimal redesign of rainfall monitoring networks. Despite high spatial and temporal resolutions of products of meteorological satellites, they must be calibrated using ground-based data. In this paper, a new methodology is presented for an optimal redesign of rain-gauge networks. In this methodology, after the bias correction of some satellite-based rainfall products (i.e., PERSIANN-CCS, TRMM3B42RTV7, and CMORPH), they are combined using a Bayesian information fusion technique. The spatiotemporal rainfall maps, which are developed using the fusion model, are used in an optimization model. The optimization model provides the optimal locations of the rain-gauge stations, considering the objectives of minimizing both the monitoring cost and the average standard deviation of estimation errors. To illustrate the applicability and efficiency of the proposed methodology, it is applied to the Khorasan Razavi Province in Iran. The results show that by using the satellite data and the Bayesian information fusion technique, the summation of standard deviations of estimation errors is significantly reduced from 18,787.9 to 778.28 mm.

Quiahua, the First Citizen Science Rainfall Monitoring Network in Mexico: Filling Critical Gaps in Rainfall Data for Evaluating a Payment for Hydrologic Services Program

Citizen science data can fundamentally advance the natural sciences, but concerns remain about its accuracy, reliability, and overall value. While some studies have evaluated accuracy of citizen science data, few have also assessed its potential contribution to conservation policy. This study focuses on rainfall data collection, with four goals: (1) to examine motivations of, and barriers for, volunteer participation in citizen science; (2) to evaluate accuracy of citizen science rainfall data in comparison to automatic rain gauge data; (3) to incorporate citizen science rainfall datasets into hydrological models; and (4) to apply the hydrologic model to gauge the contribution of citizen science data to the efficient design of payment for hydrological services (PHS) programs. Twelve citizen science volunteers were trained and collected rainfall data between June 2017 and February 2019 across two watersheds in Veracruz, Mexico. We found that these volunteers were highly motivated by conservation values and learning, while only a few volunteers faced barriers related to time availability for making daily measurements. The mean error in daily rainfall, computed by comparing the manual and automated gauge measurements, was less than 1 mm, or 12% of the average daily rainfall. Approximately one-third (29%) and two-thirds (71%) of the errors were attributed to missing data and misread data, respectively. Spatial patterns of rainfall distribution across the watersheds were similar between citizen science and automatic gauge data, revealing a large fraction of rainfall in middle elevations. Furthermore, the results show that if PHS areas are determined using the existing national rainfall network alone, without citizen science data, critical areas that contribute to dry-season flows would be missed. To our knowledge, this is the first citizen science network for collecting rainfall data in Mexico that has produced results that are relevant to conservation policy design.

On the sensitivity of modelled groundwater recharge estimates to rain gauge network scale

Rainfall is often the largest component of the water budget and even a small uncertainty percentage may lead to challenges for accurately estimating groundwater recharge as a calculated residual within a water budget approach. Watersheds are a common scale for water budget assessment, and rainfall monitoring networks typically have widely spaced gauges that are frequently outside the watershed of interest. The effects of rainfall spatial variability and uncertainty on groundwater recharge estimates have received little attention and may influence water budget-derived recharge estimations. In the present study, the influence of spatial density in rainfall measurement on the numerical estimation of groundwater recharge was investigated through a series of modelling scenarios utilizing field data obtained from progressively denser rain gauge networks associated with a typical watershed in southern Ontario. The uncertainty of the recharge component of the water budget was used as a metric to aid interpretation of results. The scenarios employed networks composed of: 1) one nearby national weather station (within 3 km), 2) a regional network of six stations (within 30 km), and 3) a local network of six stations, five of which were within the selected watershed. A coupled and fully distributed hydrologic model (MIKE SHE) was used in the scenario analysis and applied to the Alder Creek watershed on the Waterloo Moraine near Kitchener-Waterloo, Ontario. Rainfall showed poor spatial correlation, even at the daily time scale. Average annual results over a three-year period showed that recharge rates varied up to 140 mm per year (~40% of previously estimated annual recharge) among scenarios, with differences between scenarios greater than the water budget uncertainty during one of the years. These findings suggest that the availability of local rainfall measurements has the potential to influence the calibration of transient watershed hydrogeological models.

Developing a dual entropy-transinformation criterion for hydrometric network optimization based on information theory and copulas

Hydrometric information collected by monitoring networks is fundamental for effective management of water resources. In recent years, entropy-based multi-objective criterions have been developed for the evaluation and optimization of hydrometric networks, and copula functions have been frequently used in hydrological frequency analysis to model multivariate dependence structures. This study developed a dual entropy-transinformation criterion (DETC) to identify and prioritize significant stations and generate candidate network optimization solutions. The criterion integrated an entropy index computed with mathematical floor function and a transinformation index computed with copula entropy through a tradeoff weight. The best fitted copula models were selected from three Archimedean copula families, i.e., Gumbel, Frank and Clayton. DETC was applied to a streamflow monitoring network in the Fenhe River basin and two rainfall monitoring networks in the Beijing Municipality and the Taihu Lake basin, which covers different network classification, network scale, and climate type. DETC was assessed by the commonly used dual entropy-multiobjective optimization (DEMO) criterion and was compared with a minimum transinformation (MinT) based criterion for network optimization. Results showed that DETC could effectively prioritize stations according to their significance and incorporate decision preference on information content and information redundancy. Comparison of the isohyet maps of two rainstorm events between DETC and MinT showed that DETC had advantage of restoring the spatial distribution of precipitation.

Rain Rate Retrieval Test From 25-GHz, 28-GHz, and 38-GHz Millimeter-Wave Link Measurement in Beijing

Accurate rainfall monitoring is important, and previously it has been shown that microwave backhaul link between adjacent base station towers can be used for rainfall estimation. However, with the deployment of 5G using millimeter technology, there is opportunity for dense rainfall monitoring network using both backhaul and data transmission links. This paper presents our millimeter-wave measurement results during rainy days in Beijing, China. Our measurement design is an exemplary line-of-sight transmission link. We show that it is possible to retrieve the path-averaged rain rate from the measurement and to use both millimeter-wave transmission link and backhaul link to assist rainfall monitoring. Compared to the local rain measurement from rain gauge and disdrometer, we show that the correlation value of millimeter-wave link retrieved average rain rate varies between 0.6 and 0.9 for different rainfall events. There is room for improvement, and we find that if we can monitor the received signal for a sufficiently long period of time, we can quantify the bias due to fading and work out a better estimate of the rain retrieval model's reference level.

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Rain gauge networks are crucial for enhancing the spatio‐temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16‐year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation data set showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi‐objective monitoring networks.

Influence of the rain gauge network on the performance of a hydrological lumped model applied at different basin scales

ABSTRACT In this study was evaluated the influence of the rainfall monitoring network density and distribution on the result of rainfall-runoff daily simulations of a lumped model (IPH II) considering basins with different drainage scales: Turvo River (1,540 km2), Ijuí River (9,462 km2), Jacuí River (38,700 km2) and Upper Uruguay (61,900 km2). For this purpose, four rain gauge coverage scenarios were developed: (I) 100%; (II) 75%; (III) 50% and (IV) 25% of the rain gauges of the basin. Additionally, a scenario considering the absence of monitoring was evaluated, in which the rainfall used in the modeling was estimated based on the TRMM satellite. Was verified that, in some situations, the modeling produced better results for scenarios with a lower rain gauges density if the available gauges presented better spatial distribution. Comparatively to the simulations performed with the rainfall estimated by the TRMM, the results obtained using rain gauges’ data were better, even in scenarios with low rain gauges density. However, when the poor spatial distribution of the rain gauges was associated with low density, the satellite’s estimation provided better results. Thus, was conclude that spatial distribution of the rain gauge network is important in the rainfall representation and that estimates obtained by the TRMM can be presented as alternatives for basins with a deficient monitoring network.

Revisited rainfall network design: evaluation of heuristic versus entropy theory methods

Rain gauges are installed to measure pointwise precipitation and provide a comprehensive perspective of its spatiotemporal variations. Selection of an efficient and reliable rainfall monitoring network is a key role to reduce its maintenance and handling cost. The main purpose of the current paper is to compare efficiencies of various network design methods. The applied methods are entropy theory (as probabilistic multi-criteria decision-making) and genetic algorithm (as one of the heuristic methods) with three objective functions. Also, two classical (ordinary kriging; OK) and modern (Bayesian maximum entropy; BME) spatial simulation methods were undertaken to provide a comprehensive spatial simulation of precipitation. The proposed assessment was applied on spatial mean annual precipitation variability in the Namak Lake watershed located in the central part of Iran. The final efficiency of developed network design methods is evaluated in terms of three criteria known as mass estimation error, total error, and spatial bias of estimated rainfall. Based on the results, different network distributions have been proposed by the methods. Despite the reliability of the heuristic approach in nonlinear optimization due to its mathematical principle, the results indicated that the network design based on entropy theory can be used to estimate long-term mean annual precipitation more reliably and accurately. Results of the mass estimation error have shown 78 and 83% superiority of the entropy theory approach from the worst approach obtained from the OK and BME methods, respectively.