Annual Precipitation Patterns Research Articles

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.

PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China

Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.

Using quantile mapping and random forest for bias‐correction of high‐resolution reanalysis precipitation data and CMIP6 climate projections over Iran

AbstractClimate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias‐correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). First, bias‐correction was performed on ERA5‐Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5‐Land reanalysis data was considered as measured data. Based on the corrected ERA5‐Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias‐corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5‐Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM‐RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC‐ESM2 revealed the highest RMSE in both SSP2‐4.5 and SSP3‐7.0 amounting to 331.74 and 201.84 mm·year−1, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5‐8.5, notably in the case of ACCESS‐ESM1‐5 (RMSE = 431.39 mm·year−1), while the RMSE reduced after using QM‐RF method (197.75 mm·year−1). Obviously, a significant enhancement in results was observed upon implementing the QM‐RF combination method in CMCC‐ESM2 under both SSP2‐4.5 (RMSE = 139.30 mm·year−1) and SSP3‐7.0 (RMSE = 151.43 mm·year−1) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year−1, respectively. Although each bias‐corrected model output was evaluated individually, multi‐model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM‐RF method revealed the lower errors in correcting model outputs, we used the QM‐RF technique to create the MME. Based on SSP2‐4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3‐7.0. Moreover, MME projected dramatic declines under SSP5‐8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5.

A Climatology of Convective Precipitation over Europe

Abstract The annual, seasonal, and diurnal spatiotemporal heavy convective precipitation patterns over a pan-European domain are analyzed in this study using a combination of datasets, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) precipitation rate product, E-OBS ground-based precipitation gauge data, European climatological gauge-adjusted radar precipitation dataset (EURADCLIM), Operational Programme for the Exchange of Weather Radar Information (OPERA) ground-based radar-derived precipitation rates, and fifth major global reanalysis produced by ECMWF (ERA5) total and convective precipitation products. Arrival Time Difference Network (ATDnet) lightning data are used in conjunction with IMERG and EURADCLIM precipitation rates, with an imposed threshold of 10 mm h−1 to classify precipitation as convective. Annually, the largest convective precipitation accumulations are over the European seas and coastlines. In summer, convective precipitation is more common over the European continent, though relatively large accumulations exist over the northern coastal waters and the southern seas, with a seasonal localized maximum over the northern Adriatic Sea. Activity shifts southward to the Mediterranean and its coastlines in autumn and winter, with maxima over the Ionian Sea, the eastern Adriatic Sea, and the adjacent coastline. Over the continent, 1%–10% of the total precipitation accumulated is classified as convective, increasing to 10%–40% over the surrounding seas. In contrast, 30%–50% of ERA5 precipitation accumulations over land is produced by the convective parameterization scheme and 40%–60% over the seas; however, only 1% of ERA5 convective precipitation accumulations are from rain rates exceeding 10 mm h−1. Regional analyses indicate that convective precipitation rates over the inland mountains follow diurnal heating, though little to no diurnal pattern exists in convective precipitation rates over the seas and coastal mountains.

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

AbstractSub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Characterizing variability of spatial patterns of annual and seasonal precipitation of Turkey and identifying the probable driving factors including teleconnection patterns

Abstract This study aimed to identify primary modes of annual and seasonal precipitation in Turkey using a rotated Empirical Orthogonal Function (EOF) method, and the resulting patterns were described concerning the prevalent atmospheric circulations, orography, and continentality. The varimax rotation of the EOF determines modes that are more localized in space than the conventional EOF modes The first three EOFs accounted for approximately 67% and 62% of the total variance in the annual and wet season precipitation series, respectively, whereas only 50% of the variance was captured by the first three EOFs for dry season precipitation. Spatially different atmospheric circulation mechanisms are major drivers of variability in Turkish precipitation on annual and seasonal timescales. The spatial coherence of the highest negative and positive EOF1 loadings of the annual data was observed in the western and southern regions where westerly and northwesterly circulations prevailed during the wet season. A lower spatial coherency was observed with the dry season precipitation. The contribution of atmospheric moisture advection to precipitation variability diminishes in summer, whereas that of local land surface processes increases. Some regional teleconnection patterns, such as the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO), also contributed to the annual variability in precipitation.

Capability of satellite data to estimate observed precipitation in southeastern South America

AbstractPrecipitation is a fundamental component of the water cycle. Satellite‐derived precipitation estimates with high spatial resolution and daily to subdaily temporal resolution become very important in regions with a limited ground‐based measurement network, such as southeastern South America (SESA). This study evaluates the performance of four state‐of‐the‐art satellite products, including IMERG V.06 Final Run, PERSIANN, PERSIANN CCS‐CDR and PDIR‐NOW in representing observed precipitation over SESA during the 2001–2020 period. The ability of each product to represent observed annual and seasonal precipitation patterns was assessed. Statistical and categorical evaluation metrics were used to evaluate the performance of satellite precipitation estimates at monthly and daily timescales. Our results report that IMERG and CCS‐CDR achieve the best performance in estimating observed precipitation patterns at annual and seasonal timescales. While all satellite products effectively identify autumn and spring precipitation patterns, they struggle to represent winter and summer patterns. Notably, all satellite precipitation products have a better agreement with observed precipitation in wetter regions compared to drier regions, as indicated by the spatial distribution of continuous validation metrics. IMERG stands out as the most accurate product, reaching the highest correlation coefficients (0.75 < CC < 0.95) and Kling–Gupta efficiencies (0.65 < KGE < 0.85, rate as good to very good performance). Regarding categorical statistical metrics, IMERG correctly estimates the fraction of observed rainy days (POD > 0.7, CSI > 0.6) and shows the lowest fraction of estimated precipitation events that did not occur. PERSIANN, CCS‐CDR and PDIR‐NOW exhibit lower performances, mainly in drier areas. Moreover, PERSIANN and PDIR‐NOW tend to overestimate observed precipitation in almost the entire SESA region. We expect this validation study will provide greater reliability to satellite precipitation estimates, in order to provide an alternative that complement the scarce observed information available for decision‐making in water management and agricultural planning.

Zero discharge: An optimized strategy to improve the hydric deficit in the Mediterranean area by pumped hydro storage. Case study of Alicante, Spain

Alterations in annual temperature and precipitation patterns are imposing heightened strain on both urban and agricultural systems. The insufficiency of available water resources to satisfy the escalating demand necessitates a concerted effort to optimize alternative sources. In the context of agriculture, the utilization of treated water is currently executed on a sporadic basis. The elevated energy expenditures and the absence of regulatory frameworks in these systems contribute to a substantial portion of these resources being discharged into natural water bodies, or in the case of coastal municipalities, near the sea. This research endeavors to proffer a comprehensive strategy aimed at eliminating the discharge of treated water into the sea. It proposes the annual allocation of 16.7 hm3 of treated water to irrigation communities grappling with acute water deficits. The strategy, when applied to a tangible case study, ensures the reliable provision and distribution of the entire designated volume. This approach takes into account the fluctuating agricultural demands through the implementation of hybrid systems boasting an installed capacity of 15 MW. Additionally, it establishes the deployment of pumped hydro-storage reservoirs to regulate and secure the requisite energy for distribution and the osmosis process involved in treating a portion of the reclaimed water, thereby presenting a high-quality solution amenable to agricultural consumption.

Performance of Regional Climate Model Precipitation Simulations Over the Terrain‐Complex Andes‐Amazon Transition Region

AbstractRegional climate models (RCMs) are widely used to assess future impacts associated with climate change at regional and local scales. RCMs must represent relevant climate variables in the present‐day climate to be considered fit‐for‐purpose for impact assessment. This condition is particularly difficult to meet over complex regions such as the Andes‐Amazon transition region, where the Andean topography and abundance of tropical rainfall regimes remain a challenge for numerical climate models. In this study, we evaluate the ability of 30 regional climate simulations (6 RCMs driven by 10 global climate models) to reproduce historical (1981–2005) rainfall climatology and temporal variability over the Andes‐Amazon transition region. We assess spatio‐temporal features such as spatial distribution of rainfall, focusing on the orographic effects over the Andes‐Amazon “rainfall hotspots” region, and seasonal and interannual precipitation variability. The Eta RCM exhibits the highest spatial correlation (up to 0.6) and accurately reproduces mean annual precipitation and orographic precipitation patterns across the region, while some other RCMs have good performances at specific locations. Most RCMs simulate a wet bias over the highlands, particularly at the eastern Andean summits, as evidenced by the 100%–2,500% overestimations of precipitation in these regions. Annual cycles are well represented by most RCMs, but peak seasons are exaggerated, especially at equatorial locations. No RCM is particularly skillful in reproducing the interannual variability patterns. Results highlight skills and weaknesses of the different regional climate simulations, and can assist in the selection of regional climate simulations for impact studies in the Andes‐Amazon transition zone.

The Water Dynamics of Norway Spruce Stands Growing in Two Alpine Catchments in Austria

Forests are highly relevant for the water dynamics of mountain areas. This study assesses the water balance of two mountainous watersheds in Austria (Rindbach and Schmittental) with similar average annual precipitation patterns but different parent material, i.e., limestone in Rindbach versus greywacke in Schmittental. The biogeochemical mechanistic ecosystem model Biome-BGC with parameter settings developed for the central European tree species was obtained to assess the energy, nutrient, and water cycle as relevant for tree growth (=carbon cycle). The seasonal precipitation pattern, the snow accumulation, the evapotranspiration, the transpiration, the water-use efficiency, and the outflow are investigated. For the period 1960 to 2022, no precipitation trends are detectable, but a temperature increase of 1.9 °C in Rindbach and 1.6 °C in Schmittental is evident, leading to a declining snow accumulation. The model simulations suggest that transpiration and evapotranspiration rates increase with increasing LAI, indicating higher rates in Rindbach compared to Schmittental. The water use efficiency increases up to an LAI = 3 m2 m−2 and declines afterwards. The water balance variables follow the same pattern, i.e., with increasing LAI, the water outflow at the Rindbach catchment declines from 78% to 29% and from 72% to 31% in Schmittental. This confirms that forest cover is important to reduce water outflow and thus enhances the protection function of mountain forests.

Evaluating the impact of dam construction on extreme shrinkage of Urmia Lake using spatial data.

In this study, the extreme shrinkage of Urmia Lake is investigated, aiming to assess the impact of anthropogenic factors, particularly the over-construction of dams and natural anomalies associated with climate change. Historically available multispectral spatial data obtained from Landsat missions 4-5 TM and Landsat 8 OLI were utilized which totally covers a period of 36 years (1967-2020). Additionally, this data was employed to identify the locations of constructed water reservoirs and determine their construction timelines by analyzing the normalized difference vegetation index (NDVI). To examine the temporal patterns of annual precipitation in the lake basin, we obtained time series data from historical precipitation records, which were then converted into rasterized format. Our findings indicate that approximately 22% of the lake basin has been designated for feeding dam reservoirs. The impact of precipitation anomalies on the lake's water level was found to be relatively less significant when compared to the increased storage capacity of the dams. Furthermore, the construction of dams prior to 2000 contributed to enhancing the lake's stability during periods of drought. However, the substantial increase in the total storage capacity of dams after 2000 has significantly accelerated the shrinkage process. As a result, it was concluded that any effective rescue plan should prioritize ignoring a considerable portion of the reservoirs' storage capacity by releasing stored water, thereby allowing the lake to attain a stable condition.

Predicting Exotic Annual Grass Abundance in Rangelands of the Western United States Using Various Precipitation Scenarios

Expansion of exotic annual grass (EAG), such as cheatgrass (Bromus tectorum L.) and medusahead (Taeniatherum caput-medusae [L.] Nevski), could cause irreversible changes to arid and semiarid rangeland ecosystems in the western United States. The distribution and abundance of EAG species are highly affected by weather variables such as temperature and precipitation. The study's goal is to understand how different precipitation scenarios affect EAG abundance estimates and dynamics, and we develop a machine learning modeling approach to predict how changes in annual and immediate past precipitation patterns could affect the abundance of EAG. The machine learning predictive model used seed source from previous years, weather variables, and soil profiles to drive its predictions. We achieved excellent training accuracy (r = 0.95 and median absolute error [MdAE] = 2.36% cover) and strong test accuracy (r = 0.79 and MdAE = 4.54% cover). We developed five versions of EAG abundance maps for 2022 with different precipitation scenarios: 9 yr of average precipitation, half of the average, three-fourths of the average, one and one-half times the average, and two times the average. The approach presented can be replicated to new study domains and easily modified for use with other precipitation scenarios. Developing multiple versions of a year's EAG spatially explicit abundance dataset predictions from multiple weather-based scenarios can provide important information to land managers as they prepare for variable EAG dynamics each year. Informed annual predictions based on weather scenario−driven models have the potential to improve fire preparation decisions.

Phenological development of subterranean clover (Trifolium subterraneum L.) in New Zealand in response to different climate change scenarios

Climate predictions for New Zealand for the coming decades suggest rising temperatures (+0.7–3 °C), increased diurnal temperature and variable precipitation patterns that differ around the country and with seasons. The most common pattern of annual precipitation indicates the largest increases in the west of the South Island and the largest decreases in the east of the North Island and coastal Marlborough. The phenophases of subterranean clover were estimated and quantified using a thermal time-based model under different climate scenarios based on greenhouse gases and aerosol pathways over the 21st century, known as the Representative Concentration Pathways (RCPs), at two periods (mid and end of the century). The estimated flowering (R3) and post- flowering (R6-R11) date change showed a consistent trend across all districts. The largest date advances (≥ 5 days) were predicted for the three current latest flowering districts (Mackenzie, Queenstown and Central Otago). A reduction of the plant’s life cycle, for both ‘Early’ and ‘Late’ maturity cultivars may have undesired consequences for forage yield so genetic material with later flowering dates may be useful. Adaptation strategies to mitigate the future warming effects include using ‘Late’ flowering cultivars and greater use of supplementary feed through summer dry conditions.

Influence of Localized Rainfall Patterns on Landslide Occurrence—A Case Study of Southern Hiroshima with eXtended Radar Information Network Data during the July 2018 Heavy Rain Disasters

In this study, we use GIS and other analytical platforms to analyze the landslide distribution pattern in the July 2018 heavy rain disasters in the southern part of Hiroshima Prefecture in Japan in conjunction with chronological XRAIN (eXtended Radar Information Network) radar-acquired localized rainfall data in order to better understand the relationship between rainfall characteristics and landslide probability. An analysis of event rainfall from the July 2018 disasters determines that landslide-inducing rainfall started from 8:30 AM on 5 July and continued until 7:30 AM on 7 July, accumulating to up to 368 mm in total precipitation, and that there were two intensity peaks, one around 7:30 PM on 6 July, and another one around 4:30 AM on 7 July. These two events are associated with particularly high landslide activity, which indicates that landslide activation is related to peak-intensity rainfall combined with accumulated continuous precipitation. The XRAIN data were also used together with landslide reports to calculate the intensity–duration (i.e., I-D) rainfall threshold for the area. The mean annual precipitation in the whole study area ranged between 2025 mm and 3030 mm, with an average value of about 2300 mm. The spatial distribution of rainfall throughout the sampled years indicates that rainfall is remarkably localized, with higher values concentrated on elevated areas. However, it was also observed that the maximum precipitation volumes are not so closely related to landslide occurrence, and the highest landslide activity was found in intermediate precipitation class zones instead. Correlating the localization patterns of event precipitation and mean annual precipitation using Pearson’s correlation coefficient, we found an r value of 0.55, which is considered a moderate correlation between the two datasets (i.e., event precipitation and mean annual precipitation).

From Past to Present: Decoding Precipitation Patterns in a Complex Mediterranean River Basin

Enhancing spatial data attributes is crucial for effective basin-scale environmental modelling and improving our understanding and management of precipitation patterns. In this study, we focused on reconstructing homogeneous areal precipitation data in the complex terrain of the Calore River Basin (CRB) in Southern Italy. Until 1869, weather observations in the region were inconsistent, unstandardised, and lacked coordination, but the establishment of meteorological observatories brought a more unified approach to weather monitoring. We relied on the rainfall data obtained from two of these historical observatories: Benevento (1869–present) and Montevergine (1884–present). We utilised a statistical regression framework that considered rainfall measurements and temporal properties from specific locations to reconstruct and visually analyse the evolution patterns of annual mean areal precipitation (MAP) in the CRB from 1869 to 2020. The analysis revealed that mean MAP decreased from 1153 mm yr−1 (1869–1951) to 998 mm yr−1 (1952–2020). This decrease was accompanied by a reduction in interannual variability (from 168 mm yr−1 to 147 mm yr−1 standard deviation), and the difference between the means was significant (p < 0.0001), suggesting a sudden shift in the time-series. These findings provide a basis for CRB water resource management and insights for modelling other complex Mediterranean basins.

Drought diminishes aboveground biomass accumulation rate during secondary succession in a tropical forest on Hainan Island, China

With rising temperatures and altered annual precipitation patterns, climatic factors are likely to play an increasingly important role in controlling the biomass accumulation rate of tropical secondary forests. However, the relative importance of biotic and abiotic factors and demographic progresses (growth, mortality, and recruitment) driving biomass dynamics in these ecosystems remain largely unclear, especially in tropical Asia. We explored aboveground biomass (AGB) dynamics in old-growth and secondary tropical forests on Hainan Island, China, across two consecutive five-year intervals (2010–2020). Drought frequency was higher in the second vegetation census interval. We studied the relationships of AGB and its three demographic components with drought frequency, tree diversity, soil nutrient and microorganismal abundance, and successional stage. We found that AGB accumulation rate decreased as AGB increased gradually during secondary succession. The different drought frequency between the two intervals was the dominant factor predicting the net change of AGB. With high-frequency droughts, the loss of AGB intensified and the increment rate of AGB decreased, especially in old-growth forests during second interval (2015–2020). Considering the impact of the three demographic progresses on net change in AGB, the relative influence of mortality was the largest (61.1%), followed by growth (38.3%) and recruitment (0.6%). Successional stage was another major factor affecting AGB and its dynamics, showing a positive relationship with AGB stock and a negative relationship with net AGB change. Species richness and fungal richness had a significant positive association with AGB, while soil available nutrient had a significant negative relationship. Overall, our results show that drought frequency had a strong impact on AGB dynamics, and that the resilience of AGB accumulation to drought decreased in later successional stages. Thus, with the intensification of global climate change, the effects of high-frequency drought events should be taken into account when studying the factors affecting AGB recovery in tropical secondary forests.

High-resolution climate projection over the Tibetan Plateau using WRF forced by bias-corrected CESM

The Tibetan Plateau (TP) has undergone significant climate warming with a stronger amplitude than that experienced elsewhere in the Northern Hemisphere during the past years, but it is still challenging for most regional climate models to realistically simulate the present-day climate and promisingly project the future climate over the TP. In this study, high-resolution simulation using the Weather Research and Forecasting model (WRF) driven by bias-corrected CESM is conducted from 1979 to 2100, with the period from 2006 to 2100 under RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios. The simulated present-day climate is evaluated firstly and then the future climate is studied secondly. The results show that compared with station observation, WRF successfully captures the spatial pattern of annual mean surface air temperature (T2m) and precipitation over the TP, with the spatial correlation coefficients larger than 0.95 for T2m and larger than 0.70 for precipitation. However, great underestimation of T2m over the southeastern TP is found in the cold season which is related to the underestimation of snow there, and the snow-temperature positive feedback develops. WRF shows limited ability in reducing the dry bias in summer, which is related to the simulated weaker water vapor transport over the southern and eastern TP. For the future changes, substantial warming, general increase in precipitation and decrease in snow are projected under RCP8.5. The warming magnitude is greater over the western TP where the more significant decrease of snow will occur by the end of 21st century. Projected precipitation tends to consistently decrease over the western TP and along the south flank of TP which is related to the low-level circulation change. The occurring frequency of light precipitation will decrease while that of non-precipitation and extreme precipitation will increase especially in the far future, with the more obvious change occurring under RCP8.5. Overall, WRF shows high ability in simulating the present-day climate and thus reliable performance in future climate projection.

Spatiotemporal Trends and Variation of Precipitation over China’s Loess Plateau across 1957–2018

Better understanding of the spatiotemporal characteristics of precipitation is essential in developing the best management practices for ecological restoration and soil erosion control on China’s Loess Plateau, an arid and semiarid region with severe soil erosion. This study aimed to explore the spatiotemporal trends of precipitation using long-term precipitation data from 1957 to 2018 from the 100 national standard meteorological stations across the Loess Plateau. The nonparametric Mann–Kendall statistical test and geospatial interpolation were used to detect the trends and to analyze spatial patterns of annual and seasonal precipitation. Wavelet analysis was applied to examine periodical variation across the plateau. The results reveal that regional annual precipitation over the Loess Plateau decreased during the 62 years of the study at a mean rate of −1.05 mm/10 years (p > 0.1). The changes in annual precipitation showed a periodical fluctuation with an increasing trend from 1957 to 1969, a decreasing trend from 1970 to 1999, and an increasing trend again in the first 18 years of the 21st century. The annual precipitation decreased in all eight sub-annual periods except for winter. Spatially, a decreasing trend occurred in the southern and eastern parts of the Loess Plateau, whereas a slight increase existed in the northwest in all periods. The decrease in six stations was statistically significant (p < 0.05), and a significant increase occurred in four stations (p < 0.1). These changes can be explained by an evident southward shift of the precipitation isohyets especially for 350 mm, 450 mm and 550 mm from the 1960s to the 1990s, and a clearly northward shift after the 1990s. Findings from this study facilitate an understanding of the spatial temporal trends of precipitation so appropriate countermeasures can be developed for effective vegetation restoration and soil erosion control across the Loess Plateau.

To what extent horizontal resolution improves the simulation of precipitation in CMIP6 HighResMIP models over Southwest China?

Southwest China (SWC) is located in the eastern part of Tibetan Plateau (TP) with large elevation differences and complex topography, which has always been a challenge to the simulation of precipitation in climate modeling community. In this study, the differences in the simulation of precipitation over the SWC are evaluated using the lower and higher resolution models (LR and HR) from the High–Resolution Model Intercomparison Project (HighResMIP) protocol in Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results indicate that the spatial patterns of annual precipitation over the SWC for the period 1985–2014 are well reproduced in most of the HR and LR models, with an increasing tendency from the northwest to southeast. Compared with LR models, the wet biases over the eastern TP and the dry biases over the Sichuan Basin are significantly reduced in HR models. The bias for annual precipitation of the multi–model ensemble mean (MME) has been reduced from 0.97 mm/day (LR) to 0.72 mm/day (HR). In addition, the simulation of extreme precipitation is significantly improved in the finer horizontal resolution models, showing effectively reduced simulation biases in the Sichuan Basin compared with the LR models. The frequency and intensity of extremes are represented by heavy precipitation days (R10 mm) and maximum consecutive 5 days precipitation (Rx5day), which the relative changes have been decreased from 66% (LR) to 47% (HR) in R10 mm and decreased from 23% (LR) to 19% (HR) in Rx5day. We further examine the possible reasons for the difference between LR and HR models in precipitation simulation, showing that the HR models could generate “additional” cyclonic circulation and promote more upward motion with the water vapor convergence, thus correcting the dry biases of precipitation simulation over the Sichuan Basin. This indicates that atmospheric circulation and moisture conditions could be simulated more realistically in climate model with a finer resolution, further improving precipitation simulation performance.