Spatial Rainfall Research Articles

A novel method for ranking CMIP6 global climate models over the southeast Asian region

AbstractThe performance of 28 CMIP6 models in simulating the Southeast Asian (SEA) climate is investigated. An evaluation methodology is developed to evaluate spatiotemporal patterns of precipitation, near‐surface temperature, and 850‐hPa wind. Mean annual cycles of temperature and rainfall are assessed separately over Indochina, the Maritime Continent, and the Philippines; and the summer and winter seasons are selected for studying spatial patterns. Within these distinctions, statistics comparing the models to reference data are calculated. The patterns' shape and phase are notably studied, and wind direction differences are assessed specifically over the most major wind flows. We present a scoring procedure allowing us to give each model a unique score reporting on the diverse evaluation aspects, hence establishing a ranking. The process rests on a scoring function growing exponentially with the performance: it favours the models combining good performances according to the largest number of criteria. The assessment highlights EC‐Earth3 and EC‐Earth3‐Veg, which conduct excellent simulations of the regional patterns of rainfall and wind. Their temperature outputs are also reliable, but CNRM‐CM6 ranks first for this variable, while finishing third globally. Local differences are discussed: we emphasize, among other things, the diverse impact of topography on temperature and rainfall, or the frequent deviation of winter winds circulating from the South China Sea to the Java Sea. In addition, a similarity study reveals major difficulties for simulating near‐equatorial annual cycles of temperature and precipitation whereas the summer monsoon spatial rainfall patterns bring out the largest discrepancies. In any event, the top models pointed out by our ranking constitute a high‐performance subset that further climate modelling experiments in SEA can rely on.

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Evaluation of WRF microphysics schemes in the simulation of a squall line over IRAN using radar and reanalysis data

A squall line was recorded in Dayyer port over southwest of Iran, on 19 Mar 2017. In the present paper, we have simulated the characteristic features associated with the squall line by Weather Research and Forecasting (WRF) model using five different microphysics (MP) schemes. For validating the simulated characteristics of the squall line, the latitude-height and longitude-height cross section reflectivity and precipitation value derived from observed reflectivity gathered by Doppler Weather Radar at Bushehr, synoptic weather station data at Dayyer port along with NCEP-NCAR and ERA-INTERIM reanalyzes data were used. To verify the simulated precipitation, the Fractions Skill Score (FSS) curve was calculated. Examining the simulation results for geopotential and sea level pressure show that the model simulations using different MP schemes, agree well with the verifying reanalyzes. Also, the spatial rainfall distribution of simulations and verifying observations did not show big differences. However, there are significant differences in the details of simulations such as the maximum reflectivity of the convective cells, vertical extent of the storm cells, speed and direction of the wind, rainfall values and FSS curves. Though, all of the simulations have shown convective cells over Dayyer port at the time of occurrence of the squall line, but, only the model simulation using Lin MP scheme is consistent with the corresponding radar reflectivity and vertical extent. The FSS chart showed that the skill changes with spatial scale. Results using Lin microphysics scheme crossed the FSSuniform line at lower scales when compared to other MP schemes.

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

Speleothem growth phases in the central Eastern Desert of Egypt reveal enhanced humidity throughout MIS 5

Speleothem deposits in nowadays arid environments are important climate archives, as they indicate phases of enhanced precipitation and can precisely be dated by uranium-series dating. So far only very few of such archives have been found in the today hyper-arid Saharo-Arabian Desert (SAD). Therefore, the study at hand fills a gap that exists for speleothem climate archives in Northeast Africa. A new record from Saqia Cave (Central Eastern Desert, Egypt) documents for the first-time speleothem growth in Egypt for all sub-stages of MIS 5 and singular phases during MIS 6. Most important growth phases occur during periods of strong increase and maximum orbitally-forced northern hemisphere insolation, but also during phases of low insolation, which are in general attributed to aridity in North Africa. Here, at least semi-arid climate conditions are proposed for periods of low insolation during stadials of MIS 5. This suggests an impact of different possible sources of precipitation, apart from large scale shifts of the African monsoon, such as Red Sea Troughs, tropical plums and a larger spatial extent of the Mediterranean winter-rainfall zone.Concerning the spatial rainfall pattern in Egypt, we propose a considerable east to west rainfall gradient in Egypt for the Last Interglacial and suggest a stronger impact of variable moisture sources in the Eastern Desert near the Mediterranean and Red Sea in comparison to the more continental Western Desert of Egypt. This reveals more favorable pre-conditions for an enhanced land use potential in the past. Therefore, the more sustaining wetter climate in the Eastern Desert point to a recurring feasible dispersal corridor for Homo sapiens from the tropical climate into the temperate climate regime throughout MIS 5. Such a more humid climate provided an ideal basis for long-term, favorable environmental conditions east of the Nile Valley, creating a kind of contiguous landscape corridor that may have been attractive to humans and wildlife alike, linking the East African tropical climate regime with that of the temperate zone in Northeast Africa and Eurasia.

Simulating the evolution of the topography–climate coupled system

Abstract. Landscape evolution models simulate the long-term variation of topography under given rainfall scenarios. In reality, local rainfall is largely affected by topography, implying that surface topography and local climate evolve together. Herein, we develop a numerical simulation model for the evolution of the topography–climate coupled system. We investigate how simulated topography and rain field vary between “no-feedback” and “co-evolution” simulations. Co-evolution simulations produced results significantly different from those of no-feedback simulations, as illustrated by transects and time evolution in rainfall excess among others. We show that the evolving system keeps climatic and geomorphic footprints in asymmetric transects and local relief. We investigate the roles of the wind speed and the time lags between hydrometeor formation and rainfall (called the delay time) in the co-evolution. While their combined effects were thought to be represented by the non-dimensional delay time, we demonstrate that the evolution of the coupled system can be more complicated than previously thought. The channel concavity on the windward side becomes lower as the imposed wind speed or the delay time grows. This tendency is explained with the effect of generated spatial rainfall distribution on the area–runoff relationship.

Evaluation of short-term streamflow prediction methods in Urban river basins

Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management.

Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment

Abstract. Spatial rainfall patterns exert a key control on the catchment-scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge, particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps, where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict the hydrological response. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations and a regression analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights on dominant predictors are then used to investigate the optimal rain gauge network density for predicting the streamflow response metrics, including an extensive test of the effect of down-sampled rain gauge networks and an event-based rainfall–runoff model to evaluate the resulting optimal rain gauge network configuration. The analysis unravels that, besides rainfall amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions and the parameterization of along-stream processes in rainfall–runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field (with at least three rain gauges) is of prime importance for capturing the key characteristics of the hydrologic response in terms of generated runoff volumes and delay for the studied catchment (0.22 rain gauges per square kilometer). The potential of the developed rainfall monitoring and analysis framework for rainfall–runoff analysis in small catchments remains to be fully unraveled in future studies, potentially also including urban catchments.

Updated analysis of gauge-based rainfall patterns over the western tropical Pacific Ocean

This work considers observed changes in tropical Pacific Ocean rainfall amounts and the influence of climate variability cycles upon them. Observations were taken from the Comprehensive Pacific Rainfall Database (PACRAIN), using strict data selection criteria of >99% data completeness from eight locations for the period 1971–2017. These data were used to analyze temporal and spatial rainfall patterns based on several indicators that considered rainfall amount and frequency, 95th percentile extreme rainfall events, and length of consecutive rain/drought events. These indicators were also computed using satellite-derived observations from the Global Precipitation Climatology Project (GPCP), as a means to compare gauge-based values with nearby estimates from the GPCP product. Results show a temporal pattern that tended towards a reduction in rainfall amounts and frequency across the tropical Pacific Ocean. The impact of phase changes of the Interdecadal Pacific Oscillation (IPO) was also examined. There was some evidence of the impact of the IPO, as well as of the El-Niño Southern Oscillation (ENSO), when seasonal and monthly trends in these indicators were analyzed. Comparison of the temporal patterns observed from the rain gauges with the trends computed using the GPCP estimates showed inconsistencies that varied considerably when comparing trends calculated at different island locations. Future work should consider further comparison of GPCP and gauge-based rainfall trends, as well as the attribution of climate change and other climate variability cycles to these trends.

Evaluating reanalysis and satellite-based precipitation at regional scale: A case study in southern Mexico

Accurate precipitation data is essential for any hydrometeorological study, particularly for calibration and simulation of hydrological models. In this paper, we evaluate the precipitation of two different reanalysis products (the ERA5 and GLDAS), and two satellite-based precipitation products (TRMM 3B42 and CHIRPS) over the La Sierra river basins in Southern Mexico, on regional and daily time scales, from 2008 to 2010. We compare the collocated gridded precipitation data against in-situ precipitation measurements in each gauge station, as well as the mean areal precipitation (MAP) over the catchments in the study area for the different products. The Pearson correlation coefficient, the root mean square error, and the multiplicative bias metrics suggest that CHIRPS and ERA5 are the highest quality precipitation products over the study area. CHIRPS performs better on the grid to point comparison, estimating better precipitation events from 10-50 mm, above 100 mm, and for all the values without threshold. ERA5 does better for precipitation from 0-10 and 50-100 mm. These two datasets also have better performance on representing the spatial rainfall variability according to the mean annual precipitation and MAP analysis, showing statistical values close to each other.

Rainfall shocks and children’s school attendance: evidence from Uganda

ABSTRACT The increasing frequency of negative rainfall shocks presents households with the challenging choice of whether to send their children to school or to withdraw them in order for them to provide support in the household. We use high-resolution spatial rainfall data matched with the georeferenced Uganda National Panel Survey data to estimate the effect of negative rainfall shocks on children’s school attendance. We find that exposure to negative rainfall shocks significantly reduces children’s school attendance by almost 10%. These results have important policy implications for improving children’s schooling, particularly in geographical areas that receive particularly erratic rainfalls, in Uganda.

Spatial\u2013temporal characterization of rainfall in Pakistan during the past half-century (1961\u20132020)

Spatial–temporal rainfall assessments are integral to climate/hydrological modeling, agricultural studies, and water resource planning and management. Herein, we evaluate spatial–temporal rainfall trends and patterns in Pakistan for 1961–2020 using nationwide data from 82 rainfall stations. To assess optimal spatial distribution and rainfall characterization, twenty-seven interpolation techniques from geo-statistical and deterministic categories were systematically compared, revealing that the empirical Bayesian kriging regression prediction (EBKRP) technique was best. Hence, EBKRP was used to produce and utilize the first normal annual rainfall map of Pakistan for evaluating spatial rainfall patterns. While the largest under-prediction was estimated for Hunza (− 51%), the highest and lowest rainfalls were estimated for Malam Jaba in Khyber Pakhtunkhwa province (~ 1700 mm), and Nok-kundi in Balochistan province (~ 50 mm), respectively. A gradual south-to-north increase in rainfall was spatially evident with an areal average of 455 mm, while long-term temporal rainfall evaluation showed a statistically significant (p = 0.05) downward trend for Sindh province. Additionally, downward inter-decadal regime shifts were detected for the Punjab and Sindh provinces (90% confidence). These results are expected to help inform environmental planning in Pakistan; moreover, the rainfall data produced using the optimal method has further implications in several aforementioned fields.

Novel statistical downscaling emulator for precipitation projections using deep Convolutional Autoencoder over Northern Africa

This study employed Machine Learning (ML) technique known as Convolutional Autoencoder to build Statistical Downscaling Model (SDM) emulator. Eight General Circulation Models (GCMs) rainfall datasets were selected under the Representative Concentration Pathway (RCP4.5) emission scenario over Northern Africa. Historical rainfall simulation for the period 1951–2005 from 8 GCMs were applied to train/validate the SDM. To evaluate the SDM performance emulating latest Rossby Centre (RCA4) RCM, SDM results were investigated against RCM projection products (2006–2100). Continuous statistics were employed to examine the SDM performance. The SDM has exhibited positive correlation of 0.75 < R < 0.95 and low RMSE values ranging between 6.9 and 15.8 mm/month. Similarly, the bias ratio scored a low value ranging from −8.94 < bias <8.25. The SDM showed good performance in reproducing the temporal rainfall projections, whereas unsatisfactory simulation was recorded regarding the spatial rainfall projections. In conclusion, the SDM showed better performance reproducing the projections of the mean ensemble rather than the individual RCMs. For future work, the SDM could be employed to downscale the mean ensemble projections of different climate variables.

Crop Choice, Drought and Gender: New Insights from Smallholders’ Response to Weather Shocks in Rural Uganda

AbstractWe analyse gender differences in the response of smallholder farmers to droughts, taking the duration and severity of the event into account. Using a novel weather shock measure that combines spatial rainfall data with detailed cropping calendars, survey data from Uganda and standard econometric techniques, we find that adverse weather events provide an opportunity for women to enter the commercial crop market by allocating land from subsistence to income generating crops. This counterintuitive pattern is, in part, explained by the greater propensity of men to allocate time to non‐agricultural activities in the event of weather shocks.

Evaluation of Spatial Rainfall Products in Sparsely Gauged Region Using Copula Uncertainty Modeling with Triple Collocation

AbstractThis study presents a topography-dependent error adjustment technique for observed spatial rainfall estimate (OSRE) with the application of a multivariate t-copula model in a sparsely gauge...

Effects of El Nino southern oscillation events on rainfall variability over northeast Ethiopia

This paper analyzed the effects of various stages of El Nino southern oscillation (ENSO) events on the variability of temporal and spatial summer rainfall over northeast Ethiopia, where multiple hazards occurred. For this paper, precipitation and ENSO index data were used from 1983 to 2015. To investigate the relationship between summer rainfall and ENSO stages over the region, correlation and composite analysis was used. The composite analysis aimed to determine the link between sea surface temperature (SST) and rainfall in the study area. The findings show that there is a strong and positive connection between southern India, the Atlantic, and most of the western Pacific Ocean and rainfall in northeastern Ethiopia. The results also showed that La Nina’s development stage favors increased rainfall with extended spatial distribution over the region and that El Nino’s development stage favors less rainfall with the restricted spatial distribution. Future progress in the ENSO forecast is therefore important for future increases in summer rainfall in northeast Ethiopia.

Quantifying rainfall seasonality across South Africa on the basis of the relationship between rainfall and temperature

South Africa spans the subtropics at the interface between tropical, subtropical, and temperate weather systems, and consequently experiences distinct summer-, winter- and year-round rainfall zones (SRZ, WRZ and YRZ). Spatio-temporal characteristics of the various weather systems are broadly understood, however, the rainfall seasonality classification at the transition between these rainfall zones remains disputed. This surrounds the complexity of rainfall regimes, however, metrics with dissimilar rainfall seasonality definitions have been applied, hindering comparability. To address this dispute, meteorological data spanning 1987–2016 from 46 weather stations is used to assess the utility of a metric posited to quantify rainfall seasonality through a seasonality score derived from a ratio of monthly rainfall: temperature. This score statistically discriminates SRZ, WRZ and YRZ conditions, fulfilling an important requirement for a metric applied to South Africa. Nelspruit (NEL; score = 1.59) represents the strongest SRZ conditions across 30 eastern and central locations with scores > 0.30. Cape Town Wo (CTW; score = − 1.04) represents the strongest WRZ conditions across seven southwestern Cape and west coast locations with scores < − 0.30. Characterising the SRZ-to-WRZ transition region with scores from − 0.30 to 0.30, nine YRZ locations were classified. With the weakest score, Oudtshoorn (OUD; score = − 0.05), within the Cape Fold mountains, most represents YRZ conditions. Applicability across all weather stations, compatibility with known rainfall drivers, and agreement with known spatial rainfall seasonality characteristics demonstrates the ratio’s utility. Strong correspondence of scores between station and gridded data applications demonstrates additional confidence in the ratio, establishing its value for further application.

Spatial Heterogeneity Analysis of Short-Duration Extreme Rainfall Events in Megacities in China

Given the fact that researchers require more specific spatial rainfall information for storm flood calculation, hydrological risk assessment, and water budget estimates, there is a growing need to analyze the spatial heterogeneity of rainfall accurately. This paper provides insight into rainfall spatial heterogeneity in urban areas based on statistical analysis methods. An ensemble of short-duration (3-h) extreme rainfall events for four megacities in China are extracted from a high-resolution gridded rainfall dataset (resolution of 30 min in time, 0.1° × 0.1° in space). Under the heterogeneity framework using Moran’s I, LISA (Local Indicators of Spatial Association), and semi-variance, the multi-scale spatial variability of extreme rainfall is identified and assessed in Shanghai (SH), Beijing (BJ), Guangzhou (GZ), and Shenzhen (SZ). The results show that there is a pronounced spatial heterogeneity of short-duration extreme rainfall in the four cities. Heterogeneous characteristics of rainfall within location, range, and directions are closely linked to the different urban growth in four cities. The results also suggest that the spatial distribution of rainfall cannot be neglected in the design storm in urban areas. This paper constitutes a useful contribution to quantifying the degree of spatial heterogeneity and supports an improved understanding of rainfall/flood frequency analysis in megacities.

Monitoring agricultural drought in Iran using time series of vegetation health index

In this study, in order to examine the severity of drought risk in Iran during 18-year period (2001-2018), we used rain meteorological data of 176 synoptic meteorological stations, ground surface temperature product (MOD11C3) and vegetation product (MOD13C2) of Moderate Resolution Imaging Spectro radiometer (MODIS). The study results show that the highest drought severity occurred in 2001 and 2003, in 2001 in spring 85.8% and in the summer of 2003, 93.8% of the country was affected by different degrees of drought. During the 18-year period, the highest percentage of drought was related to mean (45.3%), mild (25%), and severe (8.9%) classes, and no drought (20.79%), respectively. The results indicate that in the whole country, there is no severe drought region in the studied period. According to the map of the rainy regions, Iran's rainfall is highly dependent on the roughness, latitude and frequency of the arrival of synoptic systems in the region. The spatial rainfall map in Iran is well suited to VHI drought maps. The regions that receive less than 100 mm of rainfall represent the real deserts of the country. In these regions, severe drought conditions occur. The rainy regions between 100 and 200 mm, in terms of drought conditions, are in the mean class of drought. The rainy regions with a range of 200 to 300 mm are in the mild class of drought and rainy regions with more than 300 mm are usually not affected by drought stress.

Variability of precipitation areal reduction factors in the conterminous United States

Many hydrologic and hydraulic (H&H) engineering applications require spatial rainfall distribution over a watershed, but point precipitation frequency estimates, such as those provided by NOAA Atlas 14, are only applicable for relatively small areas. For larger areas, areal reduction factors (ARFs) are commonly used to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. The most common source of ARFs for the United States is Technical Paper 29 (TP-29), published in 1958, although there have been significant increases in record length and types of available data and several new methods for computing ARFs have been proposed over the last several decades. This study applied up-to-date precipitation data products and analysis methods with a watershed-based approach to investigate factors that affect ARF variabilities, and to compare ARFs across multiple US hydrologic regions. Our overall findings are in line with other recent studies showing that ARFs decrease with increasing area, increase with increasing duration, and decrease with increasing return period. In particular, we found a strong geographical variability across different US hydrologic regions, suggesting that ARF are specific to regional climate patterns and geographical characteristics and should not be applied arbitrarily to other locations. The results also reveal the importance of record length, especially for long return period ARFs. The study demonstrates the need to improve ARFs with new data and methods to support more reliable areal precipitation frequency estimates for H&H applications.