Global Precipitation Research Articles

The magnitude and frequency of detected precipitation determine the accuracy performance of precipitation data sets in the high mountains of Asia

The grid-based precipitation dataset is an important source for studying precipitation change in the high mountains of Asia due to where precipitation stations are sparse. It is essential to evaluate the accuracy of grid-based precipitation datasets in the high mountains of Asia before selecting an appropriate grid-based dataset. Therefore, this study comprehensively evaluated the precipitation errors of four commonly utilized precipitation datasets (multi-source weighted-ensemble precipitation (MSWEP), global precipitation climatology centre (GPCC), global precipitation measurement (GPM), and soil moisture to rain-advanced scatterometer (SM2RAIN-ASCAT)) in the high mountains of Asia from temporal and spatial perspectives. It then decomposed the precipitation errors to reveal their sources. The results showed that MSWEP, GPCC, GPM, and SM2RAIN-ASCAT overestimated precipitation amount and probability compared with station observations. Meanwhile, all precipitation data sets except MSWEP data underestimated precipitation in the dry season. In terms of the average values of the error metrics, GPCC performed the best. There was an evident annual periodicity in the error assessment metrics for the four precipitation data sets. Multiple linear regression analysis revealed that four precipitation-related factors (false alarm precipitation, missed amount of precipitation, precipitation detected presented, and precipitation detected event) explained the root mean square error values for four precipitation data sets, with precipitation detected presented having the largest weight. The root mean square error of each product exhibits periodic fluctuations with changes in precipitation quantity, attributed to the occurrences of precipitation detected presented and precipitation detected events. These findings provide useful reference information for correcting biases in precipitation data sets for high mountains of Asia.

Quantile mapping technique for enhancing satellite-derived precipitation data in hydrological modelling: a case study of the Lam River Basin, Vietnam

ABSTRACT Accurate precipitation is crucial for hydrological modelling in sparse gauge regions like the Lam River Basin (LRB) in Vietnam. Gridded precipitation data from satellite and numerical models offer significant advantages in such areas. However, satellite precipitation estimates (SPEs) are subject to uncertainties, especially in high variable of topography and precipitation. This study focuses on enhancing the accuracy of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH) using the Quantile Mapping (QM) technique, aligning the cumulative distribution functions of the observed precipitation data with those of the SPEs, and assessing the impact on hydrological predictions. The study highlights that the post-correction IMERG precipitation using QM performs better than other data sets, enhancing the hydrological model's performance for the LRB at different temporal scales. Nash–Sutcliffe efficiency values increased from 0.60 to 0.77, surpassing the original IMERG's 0.52 to 0.74, and correlation coefficients improved from 0.79 to 0.89 (compared with the previous 0.75–0.86) for hydrological modelling. Additionally, Percent Bias (PBIAS) decreased from approximately −1.66 to −2.21% (contrasting with the initial −20.22 and 4.6%) with corrected SPEs. These findings have implications for water resource management and disaster risk reduction initiatives in Vietnam and other countries.

A novel error decomposition and fusion framework for daily precipitation estimation based on near-real-time satellite precipitation product and gauge observations

Near-real-time satellite precipitation products (SPPs) possess inherent application prospects in the hydro-meteorological field due to their convenient acquisition and accessibility. Integrating gauge-based measurements with near-real-time SPPs is an effective approach for achieving precise spatial precipitation estimates at daily scale. This study proposed a newly developed error decomposition and fusion framework, named EDGWR, which integrates error decomposition and geographically weighted regression (GWR). The final merged product, denoted as IMERG-EDGWR, was obtained from the near-real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-E) product. IMERG-EDGWR was compared with the raw IMERG-E, near-real-time IMERG-L, post-real-time IMERG-F, global multi-source merged precipitation data (MSWEP), interpolated results (IDW and OK), and direct application of GWR outcomes (IMERG-GWR), utilizing the daily ground measurements from 12 meteorological stations located in the Yellow River source region (YRSR). The evaluation results from 2014 to 2018 revealed that IMERG-EDGWR exhibited significant enhancements over the raw IMERG-E, surpassed the research-level IMERG-F, and generally outperformed IDW, OK, MSWEP, and IMERG-GWR. Notably, IMERG-EDGWR enhances the detection of heavy precipitation events, refining estimates of both magnitude and frequency for precipitation over 25 mm. During the winter season, IMERG-EDGWR produced the most accurate precipitation estimates with notable improvement of precipitation detection capabilities. An experiment reducing input data by excluding gauge observations from the GPCC dataset tested the robustness of the EDGWR algorithm, confirming its superiority even with diminished input data. The merging framework proposed in this study constitutes an efficacious and implementable solution to enhance the accuracy of near-real-time SPPs and is expected to be implemented in different regions in future research.

Tree-Ring δ13C and Intrinsic Water-Use Efficiency Reveal Physiological Responses to Climate Change in Semi-Arid Areas of North China

Climate change has had a widespread and profound impact on global temperature and precipitation patterns, especially in semi-arid areas. Plant δ13C and iWUE indicate the trade-off between carbon uptake and water loss, which is pivotal for understanding plant responses to climate change. Information about the long-term responses of the physiological and ecological processes of different tree species to climate change is also required. To investigate the impact of different forest stand structures and site conditions on long-term growth and physiological processes of coniferous and broad-leaved trees in the mountainous area of Beijing, we analyzed the tree-ring δ13C variation of four tree species (Platycladus orientalis, Pinus tabuliformis, Quercus variabilis, Robinia pseudoacacia) sampled from 64 plots with varying site and stand conditions. We found that the tree-ring δ13C of the four tree species varied from each other and was mainly affected by density and slope aspect, followed by slope and age. Both tree-ring δ13C and iWUE of the four tree species showed increasing trends over time, mechanistically linked to long-term changes in global CO2 concentration. This indicates the four native tree species have adapted well to climate change, and the risk of decline is relatively low. The increased iWUE translated into different growth patterns which varied with tree species, site, and stand condition. Different tree species have varying sensitivities to environmental factors. The iWUE of coniferous tree species is more sensitive to climate change than that of broad-leaved tree species, especially to temperature (T), the Standardized Precipitation Evapotranspiration Index (SPEI), and vapor pressure deficit (VPD).

Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

Deep learning-based bias correction of ISMR simulated by GCM

Simulated data of atmospheric variables like precipitation is very important for climate science research, especially for understanding future scenarios. Such data is generated by global or regional climate models (GCM/RCM), for both past and future periods under different initial conditions. Unfortunately, these models frequently display biases in simulated precipitation data compared to ground observations, due to their inability to incorporate the entire physics of the process accurately. It is imperative to mitigate such biases using contemporary techniques to make the simulated data a valuable end product. Our aim in this research is to correct seasonal (from June to September) Indian Summer Monsoon Rainfall (ISMR) data as simulated by the Climate Forecast System (CFS) over the Indian subcontinent, with the Global Precipitation Climatology Project (GPCP) data serving as the ground truth reference. This period is important as more than one-third of India's annual rainfall occurs during these months. This study presents a novel Deep Learning (DL) based architecture known as the Convolutional Neural Network for Bias Correction (CNNBC) that aims to calibrate the model-simulated data with past observations, to address these biases while preserving the statistical properties and spatial correlations relationships among grid points and enhancing the spatial mean of precipitation estimates. To evaluate the effectiveness of our proposed method, we compare its performance to that of three other statistical and DL techniques: quantile mapping (QM), quantile delta mapping (QDM), and the SRDRN model. The comparative analysis demonstrates that the CNNBC model outperforms the other in terms of our task.

Pemodelan Debit Banjir Rencana Berbasis Data Hujan Satelit pada Daerah Tangkapan Air Bendungan Meninting

Rainfall stations around the Meninting Dam catchment area are very limited. Global Precipitation Measurement (GPM) satellite rainfall data can be utilized to fulfill the lack of rainfall data. This research aims to improve the quality of flood discharge in the Meninting Dam catchment area through rainfall data correction. GPM rainfall data was corrected using the probability curve method, while flood discharge analysis used HEC-HMS modeling. The results showed that the correlation coefficient of GPM rainfall data with Sesaot Rainfall Station was 0,73, the average HHMT error decreased by 2.99% after correction, and the correction factor level was 0-1,2 for each rainfall value range. The flood discharge (Q) of HEC-HMS modeling for each return period was obtained Q2th = 117,1 m3/sec, Q5th = 142,5 m3/sec, Q25th = 242,3 m3/sec, Q100th = 335,6 m3/sec, Q1000th = 529,3 m3/sec, and QPMF = 930,9 m3/sec.

Sensitivity of the global hydrological cycle to the altitude of stratospheric sulphate aerosol layer

Abstract Stratospheric aerosol geoengineering (SAG) has been proposed as one of the potential options to offset the impacts of anthropogenically induced climate change. Previous modelling studies have shown that the efficacy of the cooling via SAG increases with altitude of the aerosol layer. It has been also shown that the stratospheric heating associated with SAG could stabilize the tropical atmosphere and weaken the tropical hydrological cycle. Using a global climate model, we perform a systematic study by prescribing volcanic sulphate aerosols at three different altitudes (22 km, 18 km and 16 km) and assess the sensitivity of the global and tropical mean precipitation to the altitude. We find that even though the efficacy of cooling increases with altitude of the aerosol layer, the global and tropical mean precipitation changes are less sensitive to the height of the aerosol layer. This is because the magnitude of both the global and tropical mean precipitation reduction increases with aerosol altitude in response to increasing efficacy of aerosols, but this sensitivity related to the slow response is nearly offset by the sensitivity of fast precipitation adjustments to aerosol altitude. A perspective and analysis based on atmospheric energy budget is presented to explain the lack of sensitivity of the hydrological cycle to the altitude of the stratospheric sulphate aerosol layer.

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method

High-spatiotemporal-resolution rainfall data are vital for investigating local terrestrial water cycles. Although remote-sensing satellite retrieval of precipitation products effectively reproduces spatial patterns of rainfall, it suffers from low spatial resolution. To overcome such limitations, a two-step downscaling approach is proposed here. First, 80 % of the meteorological-station data is utilized to calibrate the original Global Precipitation Measurement (GPM) data, enhancing the correlation between GPM and station data. Subsequently, utilizing elevation, slope, aspect, the normalized difference vegetation index (NDVI), wind direction, water vapor, and land surface temperature, as well as slope and aspect correction factors, as independent variables, multiscale geographically weighted regression (MGWR) and temporal lag MGWR (TL-MGWR) models were constructed. Through the aforementioned steps, downscaled monthly and daily precipitation data for the geographic region under investigation in 2022 at a spatial resolution of 0.01° were obtained.Our findings indicate that selectively employing suitable MGWR or TL-MGWR models on a monthly basis can effectively downscale monthly GPM rainfall data. The downscaled (original) monthly precipitation data exhibited a correlation of 0.94 (0.768), with a mean absolute error (MAE) of 16.233 mm/month, root-mean-square error (RMSE) of 27.106 mm/month, and bias of −0.043. Similar enhancement was likewise noted in daily precipitation, displaying a correlation coefficient of 0.863 (0.318) for downscaled (original) data, and a RMSE of 3.209 mm/day, MAE of 1.082 mm/day, and bias of −0.06. The downscaled results show a correlation increase of 0.172 monthly and 0.545 daily, with MAE reductions of 18.43 mm/month and 1.658 mm/day, RMSE reductions of 26.172 mm/month and 4.183 mm/day, and bias reductions of 82.7 % and 56.8 %. In summary, the data after downscaling, both for monthly and daily datasets, was markedly improved in accuracy. The proposed downscaling method is applicable for reconstructing high-resolution grid data in the complex terrain of the southwest China highland canyon area.

How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon?

AbstractThis study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses.

IMERG BraMaL: An improved gridded monthly rainfall product for Brazil based on satellite‐based IMERG estimates and machine learning techniques

AbstractPrecipitation is one of the main components of the hydrological cycle and its precise quantification is fundamental to providing information for the understanding and prediction of physical processes. Precipitation observations based on ground‐based devices (manual and automatic rain gauges) are highly accurate but have limited spatial coverage. On the other hand, remote sensing products cover large areas but with lower accuracy. In this context, this study aims to provide a more accurate monthly precipitation estimating product, with lower latency than other products but without directly relying on field data. The methodology consists of applying a machine learning method (k‐nearest neighbours algorithm) to satellite‐based remote sensing data (IMERG Early Run product) and re‐analysis‐based (MERRA‐2) variables with a particular connection to precipitation. The method was applied over the Brazilian territory, which features a large range of precipitation regimes. This methodology resulted in the development of an adjusted IMERG product (IMERG BraMaL). Compared with the original IMERG products (Early Run and Final Run), IMERG BraMaL has improved the evaluated metrics between ground‐based and satellite data in almost all analyses. For instance, KGE (Kling‐Gupta efficiency) went from lower values (0.70 and 0.82 for Early and Late Run, respectively) to values above 0.86 in the IMERG BraMaL. The adjusted product also presented superior performance statistics compared with other global precipitation products (CHIRPS, PERSIANN‐CDR, and MSWEP). The main advantages of IMERG BraMaL compared with IMERG Final Run are (i) much faster availability to the end‐users; (ii) non‐dependency on any field data, allowing its application in areas where rain gauge data is unavailable or of low quality; (iii) the non‐relationship of errors to local features; and (iv) the much‐improved estimations in regions in Brazil where, historically, satellite‐based products usually underestimate the observed data.

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

AbstractPassive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr).

Analysis of hydrological responses using semi-distributed conceptual models in a mountainous catchment in the Hindu Kush Himalayan region

ABSTRACT The Hindu Kush Karakoram Himalaya region, an important global water unit, is the origin of 10 major river basins. The Upper Indus basin, a part of this region, is a primary water source for the downstream population and is primarily fed by snow and glacier melt. Four semi-distributed conceptual hydrological models were used to understand this basin’s hydrological responses. As the observed data was unavailable, global gridded precipitation and discharge data from the Global Flood Awareness System (GloFAS) were used to set up the model at the catchment outlet. Degree-day factor (DDF)-based snow and glacier modules are incorporated into the model structure to measure melt contribution. The results showed very good performance of all four models with Nash-Sutcliffe efficiency > 0.75, with the GR4J model exhibiting superior performance. The dominance of snow and glacier melt during summer monsoon affects the peak discharge and offers insights for investigations at the sub-basin scale.

Pre-processing satellite rainfall products improves hydrological simulations with machine learning

ABSTRACT A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data.

Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)

Coastal lowlands and river deltas worldwide are increasingly exposed to coastal, pluvial and fluvial flooding as well as relative sea-level rise (RSLR). However, information about both single and multiple flood-type hazards, their potential impact and the characteristics of areas, population and assets at risk is often still limited as high-quality data either does not exist or is not accessible. This often constitutes a main barrier for generating sound assessments, especially for scientific and public communities in the so-called Global South. We provide a standardised, integrative approach for the first-order assessment of these single and multiple flood-type hazards and show how this can be conducted for data-sparse, hardly accessible and inaccessible coastal lowlands such as the Ayeyarwady Delta in Myanmar by using only open accessible and freely available datasets of satellite imagery, global precipitation estimates, satellite-based river discharge measurements, elevation, land use, and population data. More than 70% of the delta, mainly used for agriculture, and about 40% of its present population are prone to flooding due to either monsoon precipitation and runoff, storm surge, and RSLR, or their combination, jeopardising food security and economic development in the region. The approach allows for the integration and combination of various datasets, combined in a highly flexible workflow that performs at low computational capacities, supporting the evaluation of flood-prone areas on regional and local scale for data-sparse coastal lowlands worldwide. It thereby allows to attribute different types of flood hazards, complements concepts of vulnerability and risk, and supports risk-informed decision making and development of effective multi-flooding adaptation strategies.

Satellite gravimetry observations on the state of groundwater level variability in the Arabian Peninsula Region and the associated socio-economic sustainability challenges

Groundwater is an important resource for the Arabian Peninsula Region. The population increase, rise in agricultural activities, and Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) inclination towards economic diversification and tourism promotion have heightened the freshwater demand. As a result of climate change and varying weather patterns, the situation has become more complicated. Due to arid conditions, recharge is mostly less than withdrawal which consequently results in underground water level decline over time. In the research, we have used Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) MASCON solutions, Global Land Data Assimilation System (GLDAS) soil moisture, and the Integrated Multi-satellite Retrievals for Global Precipitation Mission (IMERG) rainfall data to observe the Equivalent Water Thickness (EWT), and rainfall patterns in this region for the past two decades (2002–2023). The results indicate that in Saudi Arabia the water level is declining nearly at a linear rate and the linear regression model fits well with the data (R2 value, the coefficient of determination, for different cities of Saudi Arabia is ≥ 0.94). In the Al Jouf Area, the water decline is the highest at −1.69 cm/year which is 43% greater than the previous calculations. The lowest decline rate is in Sanaa (Yemen) which is −0.13 cm/year. Furthermore, all the other studied locations show a groundwater declining trend. In Saudi Arabia's Makkah, Madina, Riyadh, and Damam the reduction rate is −0.36, −0.48, −0.72, and −0.48 (cm/year) respectively. Kuwait, UAE's Dubai, and Al Ain show a similar groundwater reduction rate of −0.19 cm/year. In Oman's Masqat, the groundwater decline rate is −0.22 cm/year. Also, in the recent data, one can see the higher seasonal amplitudes that are indicative of greater fluctuations in EWT data in recent times. If water mining continues at the same pace, this important resource can become a rare commodity. Limited water supply can likely become a limiting factor for further social, agricultural, and industrial development. That's why major reviews and shifts are necessary in the current policies related to water resource management and conservation.

Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products

Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.

Performance of CMIP5 and CMIP6 models in reproducing the Interdecadal Pacific Oscillation and its global impacts

AbstractThis study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

Carbon Cycle Instability for High-CO2 Exoplanets: Implications for Habitability

Implicit in the definition of the classical circumstellar habitable zone (HZ) is the hypothesis that the carbonate-silicate cycle can maintain clement climates on exoplanets with land and surface water across a range of instellations by adjusting atmospheric CO2 partial pressure (pCO2). This hypothesis is made by analogy to the Earth system, but it is an open question whether silicate weathering can stabilize climate on planets in the outer reaches of the HZ, where instellations are lower than those received by even the Archean Earth and CO2 is thought likely to dominate atmospheres. Since weathering products are carried from land to ocean by the action of water, silicate weathering is intimately coupled to the hydrologic cycle, which intensifies with hotter temperatures under Earth-like conditions. Here, we use global climate model simulations to demonstrate that the hydrologic cycle responds counterintuitively to changes in climate on planets with CO2-H2O atmospheres at low instellations and high pCO2, with global evaporation and precipitation decreasing as pCO2 and temperatures increase at a given instellation. Within the Maher & Chamberlain (or MAC) weathering formulation, weathering then decreases with increasing pCO2 for a range of instellations and pCO2 typical of the outer reaches of the HZ, resulting in an unstable carbon cycle that may lead to either runaway CO2 accumulation or depletion of CO2 to colder (possibly snowball) conditions. While the behavior of the system has not been completely mapped out, the results suggest that silicate weathering could fail to maintain habitable conditions in the outer reaches of the nominal HZ.