Monthly Precipitation Estimates Research Articles

Prakiraan Jumlah Curah Hujan Berdasarkan Data Parameter Cuaca Tahun 2011-2021 di Kota Pangkalpinang

Tropical weather prevails in the Pangkalpinang city region, complemented with a changeable rainy season that experiences monthly variations. This phenomena results in a considerable amount of precipitation in the city of Pangkalpinang, making the area known for its extreme rainfall. Using multiple linear regression approaches to anticipate monthly precipitation is the aim of this work. Rainfall (RR), air temperature (T), air humidity (RH), and air pressure are all part of the dataset that was collected over an 11-year period (2011–2021). The information was obtained from the meteorological station in Depati Amir Pangkalpinang. This study uses the multiple linear regression method. The rainfall forecast results are compared to the observed actual rainfall amounts. Based on air temperature and air humidity as predictors, the results show that the estimation of cumulative monthly precipitation in the Pangkalpinang city region for the year 2021 produced an average root mean square error (RSME) value of 112.6 mm/month. Furthermore, the T-test analysis produced a 0.9 result. With air temperature, humidity, and pressure as predictors, the total monthly rainfall for 2021 was predicted with an average root mean square error (RMSE) value of 107.9 mm/month. A T-test was also performed, and the result was 0.4. The results of this study show that the monthly rainfall forecast model for the city of Pangkalpinang that includes factors related to temperature, humidity, and pressure produces better results than the model that only includes these variables. November and December are the months with the greatest variations.

Extreme precipitation detection ability of four high-resolution precipitation product datasets in hilly area: a case study in Nepal

Given Nepal's vulnerability to extreme precipitation (EP), it is imperative to conduct a comprehensive analysis to comprehend the historical trends of such events. However, acquiring precise precipitation data for EP remains challenging in mountainous countries like Nepal owing to the scarcity of densely gauged networks. This limitation impedes the dissemination of knowledge pertaining to EP variability events in Nepal. The current research on this topic is deficient for two main reasons: 1) there is a lack of studies leveraging recently released high-resolution precipitation products to identify their EP detection capabilities, which further hinders the usability of those products in data-scarce regions like Nepal, and 2) most studies have focused on the characterisation of EP events in Nepal rather than their spatial and temporal variability. To address these issues, this study evaluated the EP detection capabilities of four high-resolution precipitation product datasets (PPDs) across Nepal from 1985 to 2020. These datasets include the ERA5 Land reanalysis data, satellite-based precipitation data (PERSIANN_CCS_CDR and CHIRPS_V2.0) and a merged dataset (TPHiPr). We used various statistical and categorical indices to assess their ability to capture the spatial and temporal variability of EP events. The annual EP events were characterised by 11 indices divided into frequency and intensity categories. The TPHiPr merged dataset offered a robust depiction of monthly precipitation estimates, achieving the highest critical success index, accuracy, probability of detection and a low false alarm ratio for daily precipitation detection of 0.1 mm in Nepal. Conversely, the PERSIANN_CCS_CDR dataset exhibited poor performance. Most PPDs showed increasing trends in EP indices. However, the TPHiPr dataset showcased those trends with fewer errors and stronger correlations for many frequency (R10mm, R20mm and R25mm) and intensity (RX1day, RX5day, PRCPTOT and R99p) indices. The results indicate that TPHiPr outperformed other PPDs in accurately representing the spatial distribution of EP trends in Nepal from 1985 to 2020, particularly noting an exacerbation of EP events mostly in the eastern region of Nepal throughout the study period. While TPHiPr demonstrated superior performance in detecting various EP indices across Nepal, individual products like the ERA5 Land reanalysis dataset showed enhanced performance in the western region of Nepal. Conversely, PERSIANN_CCS_CDR and CHIRPS_V2.0 performed well in the eastern region compared to other PPDs.

Performance of CHIRPS for estimating precipitation extremes in the Wala Basin, Jordan

Abstract The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.

A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM

Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the RMSE decreased by 31% while the CC increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the MAE decreased by 27.8% while the KGE increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions.

Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?

Abstract Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.

Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.

Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Satellite remote sensing precipitation products with high temporal–spatial resolution and large area coverage have great potential in hydrometeorological research. This paper analyzes the performance of four satellite products from 2000 to 2008 in the Yarlung Zangbo River Basin, namely the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Climate Prediction Center morphing method (CMORPH). The four products are evaluated from three aspects: spatial distribution, temporal characteristics, and hydrological simulation. The results show that: (1) the four products exhibit similar annual and daily precipitation patterns, with the highest daily precipitation accuracy concentrated in the center, followed by the east and west; (2) TRMM, CHIRPS, and CMORPH exhibit the largest positive bias for monthly precipitation estimation in December, while PERSIANN shows the largest positive bias in July. All products overestimate the precipitation of 0.1–5 mm/d, and underestimate the precipitation above 5 mm/d, especially for PERSIANN; (3) certain Products tend to perform better than others at elevations of 3000–4000 m and in relatively humid zones. TRMM shows relatively stable performance for various elevation and climate zones; (4) for hydrological model validation, TRMM has the best performance during the calibration period, although it is inferior to CHIRPS during the validation period. Overall, TRMM has the highest applicability in the Yarlung Zangbo River Basin; however, its impact on the uncertainty of hydrological modeling needs to be further studied.

Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation

This paper describes the initial results of precipitation estimates from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter-wave Sounder (TMS) using the Precipitation Retrieval and Profiling Scheme (PRPS). The TROPICS mission consists of a Pathfinder CubeSat and a constellation of six CubeSats, providing a low-cost solution to the frequent sampling of precipitation systems across the Tropics. The TMS instrument is a 12-channel cross-track scanning radiometer operating at frequencies of 91.655 to 204.8 GHz, providing similar resolutions to current passive microwave sounding instruments. These retrievals showcase the potential of the TMS instrument for precipitation retrievals. The PRPS has been modified for use with the TMS using a database based upon observations from current sounding sensors. The results shown here represent the initial postlaunch version of the retrieval scheme, as analyzed for the Pathfinder CubeSat launched on 30 June 2021. In terms of monthly precipitation estimates, the results fall within the mission specifications and are similar in performance to retrievals from other sounding instruments. At the instantaneous scale, the results are very promising.

Evaluation of Satellite Precipitation Estimates over the South West Pacific Region

Rainfall estimation over the Pacific region is difficult due to the large distances between rain gauges and the high convection nature of many rainfall events. This study evaluates space-based rainfall observations over the South West Pacific Region from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP), the USA National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH), the Climate Hazards group Infrared Precipitation with Stations (CHIRPS), and the National Aeronautics and Space Administration’s (NASA) Integrated Multi-Satellite Retrievals for GPM (IMERG). The technique of collocation analysis (CA) is used to compare the performance of monthly satellite precipitation estimates (SPEs). Multi-Source Weighted-Ensemble Precipitation (MSWEP) was used as a reference dataset to compare with each SPE. European Centre for Medium-range Weather Forecasts’ (ECMWF) ERA5 reanalysis was also combined with Soil Moisture-2-Rain–ASCAT (SM2RAIN–ASCAT) to perform triple CA for the six sub-regions of Fiji, New Caledonia, Papua New Guinea (PNG), the Solomon Islands, Timor, and Vanuatu. It was found that GSMaP performed best over low rain gauge density areas, including mountainous areas of PNG (the cross-correlation, CC = 0.64), and the Solomon Islands (CC = 0.74). CHIRPS had the most consistent performance (high correlations and low errors) across all six sub-regions in the study area. Based on the results, recommendations are made for the use of SPEs over the South West Pacific Region.

Evaluation of seventeen satellite-, reanalysis-, and gauge-based precipitation products for drought monitoring across mainland China

In this study, a comprehensive evaluation of the reliability of seventeen satellite-, reanalysis-, and gauge-based precipitation products (i.e., IMERG-F, GSMaP-G, TMPA 3B42, CMORPH-CRT, PERSIANN-CDR, CHIRPS, IMERG-E, IMERG-L, GSMaP-RT, TMPA-RT, PERSIANN-RT, ERA5, ERA-Interim, MERRA2, GPCC, CPC, and CRU) was conducted for drought monitoring from June 2000 to December 2019 over mainland China. The girded China monthly Precipitation Analysis Product (CPAP) with a dense station network was used as the benchmark observed precipitation data. The Standardized Precipitation Index (SPI) was used as a drought monitoring tool. The comprehensive evaluation procedures were implemented by a monthly precipitation evaluation, various timescales SPI verification, and typical drought event analysis. The results indicated that the performance of per precipitation product is in accordance with the generally higher monthly accuracy in eastern China than that in western China. The post-process satellite precipitation products, particularly bias-adjusted GPM-based products, i.e., the IMERG-F and GSMaP-G, are significantly better at detecting the monthly precipitation than the reanalysis (ERA5 and ERA-Interim), and near real-time satellite precipitation datasets. The best performances are observed for MERRA2 from reanalysis-based precipitation datasets, and GPCC from gauge-based precipitation products, for both precipitation estimation and drought monitoring. Regarding drought monitoring, the performance ranking of most precipitation products is basically consistent with monthly precipitation estimation. The SPI behaviors based on the bias-adjusted GPM-based products have inferior performance than those in MERRA2 and GPCC at the continental scale. Nevertheless, some discrepancies exist in the reliability level of drought detection in sub-regions and multi-timescales. Integrally, the IMERG-F is evaluated to be the most ideal precipitation data among the seventeen precipitation products for drought monitoring in eastern and southwest China. However, in comparison to the GPCC performance, the accuracy of satellite precipitation products should be further improved in the Xinjiang and Tibet Plateau regions. The outcomes of this study will provide valuable insights into the drought monitoring by using multi-sources open access precipitation products across mainland China.

A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates

Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.

Evaluating the effect of the statistical downscaling method on monthly precipitation estimates of global climate models

<p>Researches to foresee the possible effects of climate change on the environment and living beings for taking necessary precautions on time have increased in recent years. In the improvement of these studies, especially the reduction of estimation errors by downscaling the outputs of global climate models played an important role. In this study, a model that can predict monthly precipitation amounts in the future by using downscaling methods in different global climate models were applied in Antakya district of Hatay province, and the model results were evaluated. The predictive parameters for global climate models were determined using downscaling methods by applying correlation analysis for the study area. As a result of this analysis, it was seen that the air temperature and specific humidity values at the pressure level of 925 hPa and the geopotential height value at the 300 hPa pressure level had the best correlation for the years 1970-2005. The usability of three different global climate models (CanESM2, GISS-E2H, and CSIRO Mk 3-6-0) for the forecast of future rainfall in the Antakya district of Hatay province was investigated using multiple linear regression analysis, one of the downscaling methods. As a result of the statistical analysis, it was seen that the use of the downscaling method increased the accuracy of all prediction models.</p>

PENENTUAN MUSIM TANAM PADI SAWAH MENGGUNAKAN DATA TIME SERIES CURAH HUJAN DI DAS LIMBOTO BONE BOLANGO

Paddy is a staple food for most Indonesian people. A number of efforts were made to maintain the availability of paddy in the country. One of these efforts is to determine the beginning of the paddy planting season, especially lowland paddy in order to get maximum results and minimize crop failure. This study aims to determine the beginning of the growing season in the Limboto Bone Bolango watershed area. The beginning of the rainy season is the best time to be the beginning of the paddy planting season which requires sufficient irrigation in the vegetative phase. The data used in this study is rainfall time series data which is processed from TRMM 3B43 data: Monthly Precipitation Estimates (TRMM/3B43V7) and ERA5-Land monthly averaged - ECMWF climate reanalysis (ECMWF/ERA5_LAND/MONTHLY). Based on the time series data, the beginning of the rainy season is in February (DOY > 60) and October (DOY > 273). Based on this, it is expected that farmers in the Limboto Bone Bolango watershed can start the planting season in February and October.

Modeling and Evaluating Systematic and Random Errors in Multiscale GPM IMERG Summer Precipitation Estimates Over the Sichuan Basin

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) is widely used in hydrological and meteorological studies, owing to its high spatial resolution and accuracy. The quantification of systematic and random errors for the IMERG estimates at different temporal resolutions is beneficial to the calibration of observation instruments and correction of estimates, especially for regions such as the Sichuan Basin where frequent geological disasters occur in the summer. At present, there are two most commonly used models, namely the additive and multiplicative models, for modeling the systematic and random errors of precipitation. However, it is unknown which model is more suitable for the IMERG summer hourly, daily, and monthly precipitation estimates. Therefore, in this study, two models’ separative capability of the systematic and random errors and predictive capability in the IMERG estimates are investigated, upon the evaluation of the models’ applicability. Results show that for the hourly and daily precipitation estimates, the multiplicative error model has better separative and predictive capabilities than the additive error model and is recommended to quantify the systematic and random errors. Conversely, as for the monthly precipitation estimates, the additive error model is a relatively better choice by comparing the overall performance of both models. However, it still has some weaknesses for heavy monthly precipitation, such as nonconstant fluctuations and reduced predictive capability. So, the additive model should be used with caution in analyzing the systematic and random errors of the heavy monthly precipitation.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Estimation of monthly and seasonal precipitation: A comparative study using data-driven methods versus hybrid approach

The purpose of this study is to estimate the monthly and seasonal precipitation using historical measured data. Firstly two data-driven methods such as Support Vector Regression (SVR), Gaussian Process Regression (GPR) along a distance-based k-nearest neighbor method (KNN) were used. Secondly, the hybrid Support Vector Regression with the Fruit fly optimization Algorithm (SVR-FOA) was used for comparing the estimated results. The historical measured meteorological data were entered into the model after preprocessing and determination of suitable input combinations based on the feature selection algorithms and sensitivity analysis. The results showed that the hybrid SVR-FOA approach performs better than the other methods, especially during seasonal precipitation estimation. In conclusion, the SVR-FOA model with r of 0.75, NS of 0.56, RMSE of 13.58 mm, and MAE of 10.01 mm during monthly precipitation estimation and r of 0.90, NS of 0.80, RMSE of 6.37 mm and MAE of 5.04 mm during seasonal precipitation is able to accurately estimate precipitation in the study area.

Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data

Accurate estimation of the spatial distribution of precipitation is crucial for hydrologic modeling. To achieve the realistic estimation of precipitation, developing a ground-based observatory system is a costly and time-consuming strategy compared with other solutions such as using a combination of satellite- and ground-based observations. In this paper, to improve the estimation accuracy of spatial precipitation variation, various linear regression methods were used that combine digital elevation model (DEM) data, rain gauge observations, and Tropical Rainfall Measuring Mission (TRMM) products. Specifically, fuzzy cluster-based linear regression (FCLR), local multiple linear regression using historical similarity (LMLR-HS), model tree (MT), and moving least squares (MLS) were used in the proposed methodology based on local data behavior. The results were compared with those obtained from multiple linear regression (MLR) methods including simple multiple linear regression (SMLR), robust multiple linear regression (RMLR), and generalized linear model (GLM) for monthly precipitation estimation. The study area was Namak Lake watershed, one of the largest watersheds in Iran. The results, estimated for wet and dry years (years 1999 and 2003, respectively), show superiority of local linear regression methods over the other linear methods. Based on the statistical metrics used for assessing the quality the results, FCLR and MLS outperformed other tested methods.

Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets

AbstractPrecipitation estimation at a global scale is essential for global water cycle simulation and water resources management. The precipitation estimation from gauge‐based, satellite retrieval, and reanalysis data sets has heterogeneous uncertainties for different areas at global land. Here, the 13 monthly precipitation data sets and the 11 daily precipitation data sets are analyzed to examine the relative uncertainty of individual data based on the developed generalized three‐cornered hat (TCH) method. The generalized TCH method can be used to evaluate the uncertainty of multiple (&gt;3) precipitation products in an iterative optimization process. A weighting scheme is designed to merge the individual precipitation data sets to generate a new weighted precipitation using the inverse error variance‐covariance matrix of TCH estimated uncertainty. The weighted precipitation is then validated using gauged data with the minimal uncertainty among all the individual products. The merged results indicate the superiority of the weighted precipitation with substantially reduced random errors over individual data sets and a state‐of‐the‐art multisatellite merged product, namely, the Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement at validated areas. The weighted data set can largely reproduce the interannual and seasonal variations of regional precipitation. The TCH‐based merging results outperform two other mean‐based merging methods at both monthly and daily scales. Overall, the merging scheme based on the generalized TCH method is effective to produce a new precipitation data set integrating information from multiple products for hydrometeorological applications.

Characterisation of Hydrological Drought and Implications for Sustainable Water Resources Management in the Sokoto-Rima River Basin (SRRB), Nigeria

Hydrological drought is usually characterised by water loss over time from both underground and surface supplies. Thus for this study, the assessment of hydrological drought was carried out by employing Cumulative Rainfall/Streamflow Anomaly as preliminary tools for the presence of drought signatures while detailed characterisation was via Streamflow Drought Index (SDI). The results revealed that hydrological drought was observed in all the stations; however, though in general, the stations could be classified as experiencing near normal drought conditions with mild drought signatures. The findings also revealed that the average streamflow deficit volume and durations of the hydrological drought severity were 1.780 Mm3 and 192 months, 1.444 Mm3 and 252 months, 3.148 Mm3 and 252 months, and 0.159 Mm3 and 372 months for Bakolori, Goronyo (pre dam construction era), Goronyo (post dam construction era) and Zobe stations, respectively. The results also revealed the relevance of flow duration curve and analysis of frequency of drought state transition for the development of scenario-based basin water resources management protocol. The coefficient of determination (R2) statistic of the developed regression models indicate that 73.3% and 86.5% variation in streamflow dynamics across the Basin can be explained by climate change variables. However, for sustainable management of water resources in the Basin, it is imperative that characterisation of hydrological drought and monitoring should employ robust indices which use improved monthly precipitation estimates under global warming scenario in addition to ensuring that there is a shift from reactive to proactive approach in order to combat hydrological risk. Hence, a robust framework that finds application both for planning mitigation actions which embody strategic, tactical and emergency components should be designed; to this end, analysis of persistence and recurrence of drought in time and determination of possible recurrent patterns are necessary.

Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China

This study evaluated the performance and utility of the Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset for monitoring drought over Yunnan Province, China. First, several statistical metrics were used to evaluate the CHIRPS data against 122 gauge observations acquired during 1981–2015. Then, the variation of drought was investigated further based on the Standardized Precipitation Index (SPI) over multiple timescales. The results showed the following: (1) the CHIRPS data performed well in terms of monthly precipitation estimation (CC = 0.89, RMSE = 44.26, and MAE = 27.89), albeit with slight overestimation of total precipitation (Bias = 2.9%). Moreover, the CHIRPS data were found adequate in capturing the spatial distribution of precipitation. (2) The CHIRPS-based SPI was shown to be able to capture the occurrence and characteristics of drought events, suggesting that the CHIRPS dataset could be used as an alternative precipitation source for monitoring drought. (3) On the annual scale, it was found that the regions affected most frequently by drought are areas in central, western, and southern parts of Yunnan Province. High frequency of drought occurrence was observed in both spring and winter. Moreover, the results revealed a widespread drying trend over Yunnan Province during 1981–2015, especially in autumn and winter.