Remotely-sensed Precipitation Research Articles

Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets

Reliable runoff modeling is essential for water resource allocation and management. However, a key uncertainty source is that the true precipitation field is difficult to measure, making reliable runoff modeling still challenging. To account for this uncertainty, this study developed a two-step approach combining ensemble average and cumulative distribution correction (i.e., EC) to incorporate information from the GR4J (modèle du Génie Rural à 4 paramètres Journalier) hydrological model and multiple remotely sensed precipitation datasets. In the EC approach, firstly, the ensemble average is applied to construct transitional fluxes using the reproduced runoff information, which is yielded by applying various remotely sensed precipitation datasets to drive the GR4J model. Subsequently, the cumulative distribution correction is applied to enhance the transitional fluxes to model runoff. In our experiments, the effectiveness of the EC approach was investigated by runoff modeling to incorporate information from the GR4J model and six precipitation datasets in the Pingtang Watershed (PW; Southwest China), and the single precipitation dataset-based approaches and the ensemble average were used as benchmarks. The results show that the EC method performed better than the benchmarks and had a satisfactory performance with Nash–Sutcliffe values of 0.68 during calibration and validation. Meanwhile, the EC method exhibited a more stable performance than the ensemble averaging method under different incorporation scenarios. However, the single precipitation dataset-based approaches tended to underestimate runoff (regression coefficients < 1), and there were similar errors between the calibration and validation stages. To further illustrate the effectiveness of the EC model, five watersheds (including the PW) of different hydrometeorological features were used to test the EC model and its benchmarks. The results show that both the EC model and the ensemble averaging had good transferability, but the EC model had better performance across all the test watersheds. Conversely, the single precipitation dataset-based approaches exhibited significant regional variations and, therefore, had low transferability. The current study concludes that the EC approach can be a robust alternative to model runoff and highlights the value of the incorporation of multiple precipitation datasets in runoff modeling.

Evaluation of typical remote-sensing precipitation products in hydrological simulation

Evaluation of typical remote-sensing precipitation products in hydrological simulation

Evaluation of Three Long-Term Remotely Sensed Precipitation Estimates for Meteorological Drought Monitoring over China

Remotely sensed precipitation estimates (RSPEs) play an essential role in monitoring drought, especially in ungauged or sparsely gauged areas. In this study, we evaluated the ability of three popular long-term RSPEs (PERSIANN, CHIRPS, and MSWEP) in capturing the meteorological drought variations over the 10 first-level water resource basins of China, based on the standardized precipitation index (SPI). Drought events were identified by run theory, and the drought characteristics (i.e., duration, severity, and intensity) were also evaluated and compared with a gridded in situ observational precipitation dataset (CMA). The results showed that the three RSPEs could generally capture the spatial patterns and trends of the CMA and showed better performance in the wetter basins. MSWEP had the best performance for the categorical skill of POD, followed by CHIRPS and PERSIANN for the four timescales. SPI6 was the optimal timescale for identifying meteorological drought events. There were large skill divergences in the 10 first-level basins for capturing the drought characteristics. CHIRPS can efficiently reproduce the spatial distribution of drought characteristics, with similar metrics of MDS, MDI, and MDP, followed by MSWEP and PERSIANN. Overall, no single product always outperformed the other products in capturing drought characteristics, underscoring the necessity of multiproduct ensemble applications. Our study’s findings may provide useful information for drought monitoring in areas with complex terrain and sparse rain-gauge networks.

DeepPrecip: a deep neural network for precipitation retrievals

Abstract. Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval algorithm from a deep convolutional neural network (DeepPrecip) to estimate 20 min average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays a high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 160 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5–2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.

Managed aquifer recharge in karstic systems:Site suitability mapping by coupling multi-criteria decision analysis with remote sensing and hydrologic modeling

Managed aquifer recharge (MAR) offers a promising strategic management alternative for water storage and subsequent recovery to alleviate water shortage and to protect coastal aquifers from saltwater intrusion. Selecting potentially suitable recharge sites remains challenging, particularly in heterogeneous karst systems. In this study, MAR site suitability in a karst coastal aquifer is examined using a new geospatial approach that accounts for aquifer rechargeability properties, water availability, and economic-environmental attractiveness. For this purpose, multi-criteria decision analysis (MCDA), supported by pairwise comparisons, with an intrinsic karst aquifer rechargeability index is coupled with a raster-based hydrologic model that was forced by remotely-sensed precipitation, temperature, and land use data. The approach was successfully used to define exclusionary zones and to identify sites with high MAR potential that were independently collocated with hydrogeological indicators ascertaining its potential for site suitability mapping in systems with prevailing karstic aquifers.

Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau

Remote sensing precipitation products provide rich data for ungauged basins. Evaluating the accuracy and detection capability of remote sensing precipitation products is crucial before application. In this study, an index system in terms of quantitative differences, capturing capacity and precipitation distribution was constructed to evaluate three precipitation products, TRMM 3B42 V7, GPM IMERGE Final and CMORPH V1.0, at various temporal and spatial scales on the Tibetan Plateau from 2001 to 2016. The results show that the correlations among the three products were larger at the monthly scale than at the annual scale. The lowest correlations between the products and observation data were found in December. GPM performed the best at the monthly and annual scales. Particularly, the GPM product presented the best capability of detection of both precipitation and non-precipitation events among the three products. All three precipitation products overestimated 0.1~1 mm/day precipitation, which occurred most frequently. An underestimation of precipitation at 10~20 mm/day was observed, and this intensity accounted for the majority of the precipitation. All three precipitation products showed an underestimation in terms of the annual maximum daily precipitation. The accuracy of the same product varied in different regions of the Tibetan Plateau, such as the south, the southeast, eastern–central region and the northeast, and there was a certain clustering of the accuracies of neighboring stations. GPM was superior to TRMM and CMORPH in the southern Tibetan Plateau, making it recommended for applications.

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

Improved Monitoring and Assessment of Meteorological Drought Based on Multi-Source Fused Precipitation Data.

Meteorological drought, one of the most frequent climate-related disasters, causes great danger for human health and socioeconomic development. With an aim to improve the accuracy of meteorological drought monitoring, this study collected multi-source remotely-sensed precipitation products, i.e., the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement Mission (GPM), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and compared their performance over Hubei Province, China. The geographic difference analysis was used to blend the best-fitted product with gauged precipitation data. Based on the fused dataset with verification, the spatio-temporal characteristics of drought were investigated. Results showed that GPM performed the best in precipitation numerical evaluation and event detection with a 5 mm/d threshold. The fused data accurately captured 80% of historical drought events and indicated that extreme annual droughts mainly occurred in the northern and northwestern regions, while slight, moderate, and severe droughts mainly occurred in the central and eastern parts. The short-term drought exhibited the highest frequency of 33% in summer and the lowest frequency of 27% in spring, while the medium-term drought showed a higher frequency in autumn and winter. This could be a preliminary assessment of drought based on multi-source fused precipitation data for precise drought outlook and risk management.

Downscaling and Correction of Remotely Sensed Precipitation Data Based on Random Forest

Downscaling and Correction of Remotely Sensed Precipitation Data Based on Random Forest

A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone.

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

Detecting and predicting extreme events are of major importance for socioeconomic, healthcare and ecological purposes. This study proposes an alternative model to detect extreme events based on analyses of probability distribution functionffnss (f(X)), called Optimum Probability Distribution Function Searcher Model (Opt.PDF-model). The Opt.PDF-model involves the optimization of a fitness function between an histogram and a set of theoretical f(X), and the subsequent evaluation of the Probability Point Function (PPF) of the fittest theoretical (f(X)) to assess threshold values for the classification of extreme events. Any occurrence in the dataset with a PPF value equal to or greater than 90% was considered an extreme event candidate. A satellite-derived precipitation time-series (Climate Hazards Group InfraRed Precipitation with Station data) was used to calibrate and validate the proposed model, with data on accumulated precipitation from more than 30 years (Jan.1981 to Dec.2018) of the Brazilian Amazon region. The proposed method was pairwise cross-validated with two other extreme event models based on Gamma and Gaussian distributions, as applied by the European Drought Observatory of the European Environment Agency. Aditionally, all three extreme event classification models were cross-validated relative to the El Niño Southern Oscillation (ENSO). By means of the Opt.PDF-model, it was possible to evidence two positive temporal trends for the area of study: one for more intense precipitation events, and another for less intense events. The pairwise cross-validation analysis returned specific Kappa’s similarity indices, and the highest similarity was observed between the Gamma and the Opt.PDF models (48% for PPF(97.7%)). This analysis indicated that extreme event detection models are highly sensitive to distribution family priors and to threshold definitions. The proposed approach and the results obtained here are potentially useful for climate change warnings, and can be extended to other scientific areas that involve time-series analyses.

Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan

Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.

Spatial Downscaling of Remote Sensing Precipitation Data in the Beijing-Tianjin-Hebei Region

Precipitation is an important part of the global hydrological cycle. The large- scale, high-precision continuous precipitation data obtained by satellite remote sensing detection technology has become an important source of spatial precipitation data. However, because the spatial resolution of remote sensing precipitation data is still low, it is difficult to meet the needs of hydrological research, which restricts their application in drought and flood analysis, hydrological simulation, etc. In response to this problem, this paper takes the Beijing-Tianjin-Hebei region as the research area, downscaling the TRMM data and the GPM data space of the continuation plan, and increasing the spatial resolution of the data to 1 km. Compared with the original data, spatial downscaling data not only greatly improves the spatial resolution, but also increases the accuracy of the data, which has better applicability.

Drought Detection over Papua New Guinea Using Satellite-Derived Products

This study evaluates the World Meteorological Organization’s (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project precipitation products over Papua New Guinea (PNG). The products evaluated were based on remotely-sensed precipitation, vegetation health, soil moisture, and outgoing longwave radiation (OLR) data. The satellite precipitation estimates of the Climate Prediction Center/National Oceanic and Atmospheric Administration’s (CPC/NOAA) morphing technique (CMORPH) and Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) were assessed on a monthly timescale over an 18-year period from 2001 to 2018. Station data along with the ERA5 reanalysis were used as the reference datasets for assessment purposes. In addition, a case study was performed to investigate how well the SWCEM precipitation products characterised drought in PNG associated with the 2015–2016 El Niño. Overall statistics from the validation study suggest that although there remains significant variability between satellite and ERA5 rainfall data in remote areas, this difference is much less at locations where rain gauges exist. The case study illustrated that the Vegetation Health Index (VHI), OLR anomaly and the Standardized Precipitation Index (SPI) were able to reliably capture the spatial and temporal aspects of the severe 2015–2016 El Niño-induced drought in PNG. Of the three, VHI appeared to be the most effective, in part due to its reduced incidence of false alarms. This study is novel as modern-day satellite-derived products have not been evaluated over PNG before. A focus on their value in monitoring drought can bring great value in mitigating the impact of future droughts. It is concluded that these satellite-derived precipitation products could be recommended for operational use for drought detection and monitoring in PNG, and that even a modest increase in ground-based observations will increase the accuracy of satellite-derived observations remotely.

Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan

Understanding the performance of remotely-sensed precipitation with rain gauge as reference is essential before their application in estimating multiple processes. In current study, the Global Precipitation Measurements from Integrated Multi-satellite Retrievals (GPM-IMERG) and Tropical Rainfall Measuring Mission (TRMM-3B42) precipitation products were evaluated, taking the rain gauge data as a reference for the period of 2004 to 2018 over Pakistan. The data at daily, monthly, annual and seasonal timescales were evaluated using multiple statistical (three continuous and four categorical) error metrics. The Satellite Precipitation Products (SPPs) showed consistent precipitation climatology with some regional differences. All SPPs overall performed well in the central and southern regions compared to the northern region of study area. Evaluation revealed that the performance of SPPs was influenced by topography, while the error characteristics of SPPs were dependent on the precipitation intensity and climatic condition of the region. The SPPs were able to capture the extreme precipitation events (95th percentile) across different climatic regions. The highest values for probability of detection (POD), critical success index (CSI), and accuracy (ACC) at the rate of 0.7, 0.4, and 0.9 were observed respectively, while the lowest value for false alarm ratio (FAR) is 0.4. The IMERG final run harmonizes rain gauges very closely in plain and medium elevation regions, while root mean square difference greater than 20 mm/month were observed for high altitude. The spatial distribution of correlation coefficients with the values greater than 0.7 suggest that selected SPPs well captured the seasonality and mutual comparison shows that IMERG final run performed well, followed by TRMM-3B42, whereas in winter and pre-monsoon, the IMERG early run and IMERG late run captured higher precipitation values in the northeast of the study area. The results can help the researchers to select reliable SPPs as an alternate source of rain gauge in the country and other adjoining regions and the data evaluation information can help algorithm developers to rectify the errors and improve the selected SPPs.

Bayesian estimation of Lassa virus epidemiological parameters: Implications for spillover prevention using wildlife vaccination.

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averaged R0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.

Remote-sensing precipitation and temperature evaluation using soil and water assessment tool with multiobjective calibration in the Shiyang River Basin, Northwest China

Precipitation and temperature are crucial drivers for hydrological models. At present, numerous remote-sensing or reanalysis precipitation and temperature datasets are available on a global scale. This study aims to appraise the performance of three open-access precipitation datasets (gauge-adjusted research-grade Global Satellite Mapping of Precipitation (GSMaP_Gauge), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and Climate Forecast System Reanalysis (CFSR)) and CFSR temperature dataset for the monthly simulation of streamflow and evapotranspiration (ET) using the Soil and Water Assessment Tool (SWAT) model with multiobjective calibration in the upper Shiyang River Basin of Northwest China. Comprehensive comparisons of four scenarios using the remote-sensing products to driving the SWAT model revealed the following: (i) although the precipitation products had similar spatial patterns, CFSR differed markedly by exhibiting an overestimation; (ii) the CFSR temperature product could generate good performance in simulation of monthly streamflow and ET; (iii) among the three precipitation products, CHIRPS displayed the best performance; and (iv) the CHIRPS precipitation and CFSR temperature combination yielded satisfactory performance in streamflow and ET simulation, making it a promising alternative data source for hydrological simulation in some gauge-scarce areas.

Evaluation of remotely sensed precipitation sources for drought assessment in Semi-Arid Iraq

Despite high impacts of recurrent droughts, studies related to drought characterization and risk assessment is very limited in Iraq due to unavailability of sufficient and reliable climate data. This paper attempts to assess the possibility of using Remotely Sensed Precipitation Data (RSPD) for monitoring droughts in Iraq. Two monthly RSPD namely, TRMM for the period 1998–2017 and PERSIANN for the period 1983–2016 were used for the estimation of Standardized Precipitation Index (SPI) of different climatic zones of the country for three time-scales (SPI-3, SPI-6, and SPI-12). Obtained results were validated using sparsely distributed meteorological Ground Stations (GS) data. The results showed the potential of RSPD in monitoring droughts in Iraq. The RSPD was found capable of capturing the spatial distribution of precipitation estimated using GS data. The correlation coefficients of observed and TRMM SPI-3, SPI-6 and SPI-12 time series were found 0.8, 0.75, and 0.745, respectively, while those for PERSIANN were found 0.74, 0.69, and 0.7, respectively. Moreover, good consistency in SPI time series estimated using RSPD and GS data was observed. The TRMM was also found to estimate the dry periods and the Temporal Coincidence (TC) of droughts more accurately. The results indicated the suitability TRMM precipitation in monitoring droughts in different climatic zones of Iraq.

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Watershed runoff is essential for water management. However, runoff materials are lacking in poorly gauged catchments and not always accessible. Microwave remote sensing offers emerging capabilities for hydrological simulation. In this study based on multi-satellite retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) products, and World Meteorological Organization (WMO) interpolated precipitation data, we simulated runoff using a variable infiltration capacity (VIC) model and studied the differences among the results. Then, we analyzed the impacts of the runoff on a moderate-resolution imaging spectroradiometer vegetation leaf area index (LAI) during dry seasons. The results showed that (1) IMERG V5 and TRMM products are capable of monitoring the night-day rainfall diurnal cycle and have higher correlations than the WMO daily observation interpolations. However, the WMO shows less overestimation of total precipitation than remote-sensing precipitation; (2) in the downstream, the TRMM shows better runoff simulation accuracy in the tributaries, and the WMO shows better results in the mainstreams. Therefore, at basin outlets in mainstreams, the Nash–Sutcliffe efficiency coefficients of monthly runoff by the WMO are higher than the simulations by the TRMM; (3) for the whole basin during dry seasons, the LAI variation is correlated with the outlet runoff, which is similar to the correlation with three- to six-month accumulated precipitation. TRMM products can be used to depict both precipitation deficit and runoff deficit, which cause vegetation variations. Our research suggests the potential of microwave precipitation products for detailed watershed runoff simulations and water management.

Soda Bottle Science—Citizen Science Monsoon Precipitation Monitoring in Nepal

Citizen science, as a compliment to ground-based and remotely-sensed precipitation measurements, is a promising approach for improving precipitation observations. During the 2018 monsoon (May to September), SmartPhones4Water (S4W) Nepal - a young researcher-led water monitoring network - partnered with 154 citizen scientists to generate 6,656 precipitation measurements in Nepal with low-cost (< 1 USD) S4W gauges constructed from repurposed soda bottles, concrete, and rulers. Measurements were recorded with Android-based smartphones using Open Data Kit Collect and included GPS-generated coordinates, observation date and time, photographs, and observer-reported readings. A year-long S4W gauge intercomparison revealed a -2.9 % error compared to the standard 203 mm (8-inch) gauge used by the Department of Hydrology and Meteorology (DHM), Nepal. We analyzed three sources of S4W gauge errors: evaporation, concrete soaking, and condensation, which were 0.5 mm day-1 (n = 33), 0.8 mm (n = 99), and 0.3 mm (n = 49), respectively. We recruited citizen scientists by leveraging personal relationships, outreach programs at schools/colleges, social media, and random site visits. We motivated ongoing participation with personal follow-ups via SMS, phone, and site visit; bulk SMS; educational workshops; opportunities to use data; lucky draws; certificates of involvement; and in certain cases, payment. The average citizen scientist took 42 measurements (min = 1, max = 148, stdev = 39). Paid citizen scientists (n = 37) took significantly more measurements per week (i.e. 54) than volunteers (i.e. 39; alpha level = 0.01). By comparing actual values (determined by photographs) with citizen science observations, we identified three categories of observational errors (n = 592; 9 % of total measurements): unit (n = 50; 8 % of errors; readings in centimeters instead of millimeters); meniscus (n = 346; 58 % of errors; readings of capillary rise), and unknown (n = 196; 33 % of errors). A cost per observation analysis revealed that measurements could be performed for as little as 0.07 and 0.30 USD for volunteers and paid citizen scientists, respectively. Our results confirm that citizen science precipitation monitoring with low-cost gauges can help fill precipitation data gaps in Nepal and other data scarce regions.