Annual Precipitation Estimates Research Articles

Evaluation of Wintertime Precipitation Estimates and Forecasts in the Mountains of Colorado

Abstract Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

Evaluation of satellite rainfall estimates using PERSIANN-CDR and TRMM over three critical cells in Jordan

Abstract Effective management of water resources is heavily dependent on accurate knowledge of rainfall patterns. Satellite rainfall estimates (SREs) have become increasingly popular due to their ability to provide spatial rainfall data. However, the accuracy of SREs is limited by a variety of factors including a lack of observations, inadequate evaluation techniques, and the use of short evaluation durations. To improve our understanding of SREs, this study evaluated the long-term performance of Tropical Rainfall Measuring Mission (TRMM) and PERSIANN-CDR by analyzing their spatiotemporal patterns. Daily, monthly, seasonal, and annual precipitation estimates were evaluated using statistical measures and data from 71 rain gauges across three critical cells in Jordan from 2000 to 2013. The results showed that while both SREs had low accuracy on a daily scale, TRMM 3B43 performed better than PERSIANN-CDR at the monthly level. Additionally, TRMM 3B43 exhibited superior performance during the heavy rainy season, whereas PERSIANN-CDR showed better results during other seasons. In annual studies, TRMM 3B43 was found to be more accurate than PERSIANN-CDR for the north and south cells, while PERSIANN-CDR had a higher correlation coefficient for the middle cell. These findings can contribute to the development of more reliable and accurate SREs, thereby improving water resource management studies.

Estimation of Average Annual Precipitation Considering Spatio-Temporal Variability Through the Use of Multiple Linear Regression in the State of Paraíba

Objective: The objective of this work was to calibrate a multiple linear regression equation to relate average annual precipitations to spatial localization of 145 stations placed on three mesoregions of Paraíba State, Brazil, Borborema, Agreste, and Mata Zone. Theoretical framework: The variability of precipitations in space and time is directly related to the hydrological cycle, land use and occupation, and local economy. In this sense, regional models for rainfall may provide relevant information, supporting the decision-making process associated with water resources planning and management. Method: For the study case, stations with rainfall series with more than 10 years of daily data were considered. The posts that their few flaws in their series had the data filled in by the Inverse Distance Weighted (IDW) deterministic method, which assings weitghted weights to the sample data, Where the influence of one station over another decreases with the distance to the station to be estimated. Results and conclusion: The results were moderate, with a determination coefficient of 0.62 for the studied mesoregions. The average percentage errors of the estimate showed that most of the analyzed surface is classified with errors between 8.3% and 22,8%. Implications of the research: The multiple regression statistical tool was used for precipitation data with high variability, proving effective modeling. This type of study helps with decision-making in water resource management and climate monitoring studies. Originality/value: Using the multiple regression statistical tool, it was possible to obtain a linear equation to estimate the average annual precipitation in three mesoregions of the Paraíba State.

New palaeoclimatic constraints from paleosols on the Middle-Late Jurassic landscape, Western Colorado, U.S.A

ABSTRACT The Wanakah Formation and Tidwell Member of the Morrison Formation record intervals of paedogenesis in the Paradox Basin and Central Colorado trough, western Colorado during the Middle-Late Jurassic. Detailed field description of paleosols and paedogenic carbonates at different stratigraphic horizons from three localities document four main pedotypes: vertisols, gleysols, oxisols, and protosols. Generally, gleysols reflect reducing conditions, protosols and oxisols reflect oxidizing conditions, and vertisols reflect fluctuations in oxidizing/reducing conditions (seasonality). Major elemental geochemical ratios in samples from these paleosols suggest variable redox conditions and a sub-humid to humid paleoclimate with seasonal precipitations during paleosol development. Estimated mean annual temperature based on elemental analyses of paleosol B in paleosols from Ribbon Trail and Escalante Canyon in western Colorado suggest mesic – thermic paleoclimate. Mean annual precipitation indicates sub-humid to humid regional palaeoclimatic conditions marked by seasonal precipitation. Clay mineralogy of these paleosols, determined by X-ray diffraction, shows the abundance of illite most likely formed from smectite alteration due to episodic wetting and drying. δ13C and δ18O isotope analyses of carbonate veins in these paleosols suggest that they formed recently from meteoric water and therefore are not considered for palaeoclimatic reconstructions. Mean annual temperature and mean annual precipitation estimates in the Wanakah Formation and Tidwell Member of the Morrison Formation provide new information on the local and regional palaeoclimatic and paleoenvironmental conditions during the Middle Jurassic in western Colorado as well as data for comparison with the Upper Morrison paleoclimate from northern and southern localities in the western United States.

Representativeness of the Precipitation Observing Network for Monitoring Precipitation Change and Variability in Canada

ABSTRACT While there are thousands of precipitation stations in the Canadian climate archive, it has been challenging to estimate regional and national averages of precipitation for the purpose of monitoring climate change and variability, because of evolution of the monitoring network and generally sparse network density, in particular in the North. Changes in the observing network have resulted in segmentation and inhomogeneities in precipitation records. We used monthly precipitation from the Canadian Regional Climate Model (CanRCM4) large ensemble simulations as a proxy of observations with complete spatial and temporal coverage. By comparing results from the complete-coverage dataset with versions masked by observational coverage, we examined the representativeness of two long-term precipitation datasets for the estimation of annual mean precipitation at regional and national levels for the purpose of monitoring precipitation change and variability in Canada. We also analysed the implications of changes in the network and the possible added value of data processing, such as in-filling through spatially interpolating station records. We find that at the best coverage of approximately 450 precipitation stations in the Adjusted and/or Homogenized Canadian Climate Data dataset, station coverage is, in general, adequate for the purpose of monitoring long term precipitation trends. However, this capability is severely compromised if station density changes (reduces) with time or if there is a substantial number of missing values over time. The addition of station records in regions already better represented (i.e. regions with more population) does not provide significant improvement. In-filling over space through spatial interpolation does add value, provided that there is sufficient information in the station network. Our analysis demonstrates the importance of maintaining a consistent long-term network with sufficient station density for the purpose of monitoring climate change and variability.

Performance Evaluation of ERA5 Extreme Precipitation in the Yangtze River Delta, China

Accurate extreme precipitation information is crucial for disaster risk management, social and economic development security, and climate change research. Taking the Yangtze River Delta (YRD), China, a high-impact area of extreme precipitation, as an example, this study evaluates the spatiotemporal performance of extreme precipitation in the latest fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (i.e., ECMWF ERA5) for 1961–2018 based on surface observational precipitation data. The results showed that the 90th-percentile threshold of extreme precipitation extracted from ERA5 data with a daily precipitation amount >1 mm is closer to the actual observations. The ERA5 data can effectively capture the spatiotemporal patterns of the observed extreme precipitation in the YRD. The ERA5 data can successfully represent the seasonal cycle and interannual variability of daily, daytime, and nighttime extreme precipitation. However, the daytime (nighttime) extreme precipitation frequencies and amounts tend to be overestimated (underestimated) for the period 1961–2000, whereas they were significantly underestimated for the period 2000–2018. The trend estimation of seasonal and annual extreme precipitation in ERA5 needs to be improved. The ERA5 data revealed that the extreme precipitation in the YRD was dominated by large-scale precipitation, followed by convective precipitation, but their long-term trends were not clear. This study has conducted a detailed and reliable evaluation of the ERA5 extreme precipitation data. The findings serve as valuable guidance and provide accurate references to extreme climatic variables for data users and algorithm developers.

Understanding process controls on groundwater recharge variability across Africa through recharge landscapes

• Classifications can help consolidate our understanding of recharge controls. • Wetter Recharge Landscapes generate more recharge than dry Recharge Landscapes. • Mean annual recharge rates increase with increasing recharge ratios. • Global datasets limit our ability to explain the spatial variability of recharge. Groundwater is critical in supporting current and future reliable water supply throughout Africa. Although continental maps of groundwater storage and recharge have been developed, we currently lack a clear understanding on how the controls on groundwater recharge vary across the entire continent. Reviewing the existing literature, we synthesize information on reported groundwater recharge controls in Africa. We find that 15 out of 22 of these controls can be characterised using global datasets. We develop 11 descriptors of climatic, topographic, vegetation, soil and geologic properties using global datasets, to characterise groundwater recharge controls in Africa. These descriptors cluster Africa into 15 Recharge Landscape Units for which we expect recharge controls to be similar. Over 80% of the continents land area is organized by just nine of these units. We also find that aggregating the Units by similarity into four broader Recharge Landscapes (Desert, Dryland, Wet tropical and Wet tropical forest) provides a suitable level of landscape organisation to explain differences in ground-based long-term mean annual recharge and recharge ratio (annual recharge / annual precipitation) estimates. Furthermore, wetter Recharge Landscapes are more efficient in converting rainfall to recharge than drier Recharge Landscapes as well as having higher annual recharge rates. In Dryland Recharge Landscapes, we found that annual recharge rates largely varied according to mean annual precipitation, whereas recharge ratio estimates increase with increasing monthly variability in P-PET. However, we were unable to explain why ground-based estimates of recharge signatures vary across other Recharge Landscapes, in which there are fewer ground-based recharge estimates, using global datasets alone. Even in dryland regions, there is still considerable unexplained variability in the estimates of annual recharge and recharge ratio, stressing the limitations of global datasets for investigating ground-based information.

Analysis of the IMERG-GPM Precipitation Product Analysis in Brazilian Midwestern Basins Considering Different Time and Spatial Scales

Precipitation products derived from satellites have emerged as a promising approach for obtaining precipitation estimates, enabling accurate long-term observations and describing the water cycle dynamics from a global scale to a local scale. The quality of these products has improved significantly in the last decades, especially with the emergence of TRMM missions and its successor GPM. The objective of this study was to evaluate the daily, monthly and annual precipitation estimates provided by IMERG version 05 of the GPM, with the data observed by the rainfall stations of the Brazilian Agency of Water and Sanitation (ANA) in the basins of the Brazilian midwest. In order to compare the data, the spatialization of the data of the rainfall stations was performed by means of the ordinary kriging technique, interpolating the data for grids of 0.1° × 0.1° that correspond to the specialized grids of the GPM satellite. The data were evaluated quantitatively by means of statistical metrics. The GPM satellite precipitation product performed relatively well on a daily scale for regions with smooth topography, and was able to describe the rainfall regime on larger time scales, regardless of the terrain conditions. However, the satellite retrievals were unable to reproduce rainfall extremes in virtually all situations, which may limit their application in frequency analyses.

Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China

Remote sensing brings unprecedented opportunities to estimate precipitation at regional, continental, and even global scales. Nevertheless, the spatial resolutions of current mainstream satellite precipitation estimates are still too coarse to be directly used in the hydrological and meteorological applications over the medium and small scales. The spatio-temporal distribution of precipitation is often affected by various land surface factors, such as geographical location, vegetational cover, topographic characteristics, and ground temperature. Based on the relationship between precipitation and multi-geospatial factors, this study proposed a new spatial downscaling approach named as gradient boosting decision tree (GBDT) to downscale the annual satellite precipitation estimates of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) from 0.1° to 0.01° gridded resolution over Mainland China from 2015 to 2018. As for the preprocessing of downscaling algorithm, the entire study area of Mainland China was separated into four different climate regions due to the spatial discrepancy of the relationship between precipitation and geospatial factors. To validate the effectiveness of GBDT approach, we compared it with other two types of downscaling methods based on random forests (RF) and support vector machine (SVM), respectively. Our evaluation results show that the geographical location (including both latitude and longitude) seems to be relatively more important and stable for GBDT modelling than other land surface factors across the four climate regions in Mainland China. In terms of large spatial scales, the GBDT and RF algorithms generally are superior to SVM for downscaling the IMERG precipitation, as SVM reduced the considerable accuracy of the downscaled results. Last but not least, GBDT exhibits more robust features in downscaling the satellite precipitation retrievals than RF over complex terrains, where the amount of precipitation has strong spatial heterogeneity.

Pliocene to Middle Pleistocene climate history in the Guadix-Baza Basin, and the environmental conditions of early Homo dispersal in Europe

The Guadix-Baza Basin (GBB) in Andalucía, Spain, comprises palaeontological and archaeological sites dating from the Early Pliocene to the Middle Pleistocene, including some of the earliest sites with evidence for the presence of early humans (Homo sp.) in Europe. Thus, the history of climate and environments in this basin contributes significantly to our understanding of the conditions under which early humans spread into Europe during the Early Pleistocene. Here we present estimates of precipitation and primary productivity in the GBB from the Pliocene to the Middle Pleistocene based on dental ecometrics in fossil communities of large herbivorous mammals, and perform an ecometrics-based distribution modelling to analyse the environmental conditions of Early and Middle Pleistocene human sites in Europe. Our results show that Early Pleistocene humans generally occupied on average relatively diverse habitats with ecotones, such as woodlands and savannas, but avoided very open and harsh (cool or dry) environments. During the Middle Pleistocene in Europe, humans occupied a comparatively much broader range of environments than during the Early Pleistocene, but were on average more concentrated in environments where the dental ecometric of mammals indicate wooded palaeoenvironments. In the earliest human occupation sites of the GBB, Barranco León and Fuente Nueva 3, the mean annual precipitation and net primary production estimates indicate climatic conditions close to modern Mediterranean sclerophyllous woodland environments, but with slightly higher primary productivity, indicating some similarity with East African woodlands. On the other hand, the environments did not resemble African grassland savannas. The browse-dominated diets of ungulates from Barranco León and Fuente Nueva 3 further suggest palaeoenvironments where grasses were a minor component of the vegetation. In the slightly older site of Venta Micena that has no evidence for the presence of hominins, dental ecometric estimates indicate climate and environments similar to Mediterranean “forest steppe” environments existing in the surroundings of Baza today. Grasses were prevalent in the diet of some taxa, especially equids, in Venta Micena, but most of the species show browse-dominated diets even there.

Improving spatial estimation of hydrologic attributes via optimized moving search strategies

The anisotropy and directional variation of spatial attributes is one of the most important challenges in spatial estimation of environmental features. This problem received a suitable response in the realm of stochastic modeling using directional variography; however, there is not any specific solution in the field of deterministic methods. In this research, Moving Least Square (MLS) and Moving Inverse Distance Weighting (MIDW) have been used to assess a moving domain as the remedy; although, there has been no research on the spatial estimation with the MLS method. The methods are evaluated in three case studies, (I) using anisotropic piezometric head data from the Wolfcamp aquifer in Texas, (II) long-term annual average precipitation measurements in Namak Lake Watershed (NLW) located in the central part of Iran, and (III) estimation of annual precipitation with remote-sensing products, TRMM, in NLW as well. To achieve the best performance of the methods, parameters have been optimized using the modified version of shuffled complex evolutionary method. Assessment of statistical metrics, Taylor Diagram, and numerical results showed that spatial interpolation using MIDW and MLS revealed better results, with an improvement of 45.3% on average in terms of Root Mean Square Error (RMSE), in comparison with original IDW as the benchmark evaluation approach. This shows the power of moving data partitioning on the performance of deterministic estimators. Additionally, MLS was more effective than MIDW with an average of 0.55, 10.53, and 17.19% reduction in RMSE of case I, II, and III, respectively.

Spatio‐temporal evaluation of gridded precipitation products for the high‐altitude Indus basin

AbstractThe high‐altitude Indus basin is one of the most complex and inadequately explored mountain terrains in the World, where reliable observations of precipitation are highly lacking. Therefore, spatially distributed precipitation products developed at global/regional scale are often used in several scientific disciplines. However, large uncertainties in precipitation estimates of such precipitation data sets often lead to suboptimal outcomes. In this study, performance of 27 widely used gridded precipitation products belonging to three different categories of gauge‐based, reanalysis and merged products is evaluated with respect to high‐quality reference climatologies of mean monthly precipitation. Widely used statistical measures and quantitative analysis techniques are used to analyse the spatial patterns and quantitative distribution of mean monthly, seasonal and annual precipitation at sub‐regional scale. Mean annual precipitation estimates of the gridded data sets are cross validated with the corresponding adjusted streamflows using Turc‐Budyko non‐dimensional analysis. Results reveal poor to moderately good performance of the gridded data sets. Marked differences in spatiotemporal and quantitative distribution of precipitation are found among the data sets. All data sets are consistent in their patterns showing negative or dry bias in wet areas and positive or wet bias in dry areas, although considerable differences in the magnitudes of the biases are noticed at sub‐regional scale. None of the data sets is equally good for all sub‐regions due to very high spatiotemporal variability in their performance at sub‐regional scale. Gauge‐based and merged products performed better in dry regions and during monsoon season, while reanalysis products provided better estimates in wet areas and during winter months. GPCC V8, ERA5 and MSWEP2.2 are found better than their counter‐grouped data sets. Overall, ERA5 is found most acceptable for all sub‐regions, particularly at higher‐altitudes, in wet areas and during winter months.

Estimation and characterization of annual precipitation based on spatiotemporal kriging in the Huanghuaihai basin of China during 1956–2016

Understanding the spatiotemporal (ST) characterization of precipitation is one key component in evaluating the climatic environment, which provides executable and effective suggestions for the implementers of climatic strategy. In this paper, the ST ordinary kriging method was used to obtain the ST distribution of annual precipitation (AP) based on the data collected from the 961 meteorological monitoring stations of the Huanghuaihai basin in China during 1956–2016. Then, stochastic site indicators were applied to quantitatively assess the ST uncertainties, and the results indicated the probability that higher precipitation (beyond 1000 mm) occurred simultaneously at any two regions was extremely low. In terms of the AP intensity analysis, the annual rainfall in 50% of the regions exceeded 500 mm, and the largest fragmentation of AP was calculated from this threshold. Furthermore, the ST characterization and the trend of AP were analyzed in different climatic environments, indicating that (1) the AP increased from west to east, exhibiting a significant gradient distribution; (2) overall, the temperature of the study area increased by approximately 0.027 °C per year, while the AP decreased by 0.2 mm per year; (3) the temperature in three subregions [high-altitude region (HAR), mid-altitude region (MAR), low-altitude region (LAR)] exhibited an increasing trend, and the significant increasing trend of temperature occurred primarily in the LAR; (4) the precipitation in the MAR and LAR exhibited a nonsignificant decreasing trend, while the precipitation in the HAR showed a significant increasing trend.

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

The presence of moisture deficits in Miocene New Zealand

Abstract New Zealand climate during the early to middle Miocene was subtropical to warm-temperate, in contrast to the predominance of cool-temperate climates in New Zealand today. Both modern and Miocene environmental settings of New Zealand are strongly moderated by the surrounding ocean. Seasonal moisture deficits occur today in rain shadow regions in New Zealand, but the potential and nature of seasonal moisture deficits under globally warmer conditions is uncertain. Here, we reconstruct seasonal moisture balance (precipitation – potential evapotranspiration) using seasonal temperature and mean annual precipitation estimates derived from early to middle Miocene (23–11 Ma) fossil leaf assemblages, combined with a Penman-Monteith model for potential evapotranspiration. The model incorporates uncertainty in temperature, precipitation, vapor pressure deficit, radiation and wind speed. Our results suggest that three out of nine sites investigated have a very high potential for summer moisture deficits (lowest monthly moisture balance

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

AbstractIn this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

Risk assessment of drought in Yun-Gui-Guang of China jointly using the Standardized Precipitation Index and vulnerability curves

Drought is one of the most serious natural disasters in the world and causes great economic losses in China every year, especially in its southwest region. Yet, few studies have reported the quantitative comprehensive risk of drought in the Yunnan, Guizhou, and Guangxi provinces of China. Taking these three provinces as the study area, we obtained annual precipitation, disaster loss, and agricultural planting data during 1964–2013. Following an optimal estimation of annual precipitation by the Bayesian maximum entropy method, we mapped the annual Standardized Precipitation Index. Based on the theory of information diffusion and exceeding probability, the hazard of drought was evaluated. We also fit the vulnerability curves using the drought loss data. As a basis, we constructed a multiplicative formula to calculate the comprehensive risk of drought, which integrates the hazard and the vulnerability and produces drought loss rate (DLR) maps. We found that the DLR caused by mild drought was about 3%, moderate drought 10%, severe drought 25%, and extreme drought 50%. We also created a risk zoning map to provide practical information, such as a scientific basis for optimization of regional allocation of resources for drought preparedness and response.

Late Pleistocene chronology, palaeoecology and stratigraphy at a suite of sites along the Albany River, Hudson Bay Lowlands, Canada

Stratigraphic records from formerly glaciated regions are critical for detailed study of the timing, onset and dynamics of past ice sheets and the palaeoecology of previous ice-free intervals. We examined three stratigraphic sections from an 18-km stretch of the Albany River, Hudson Bay Lowlands, Canada, located at the geographic center for many Late Pleistocene ice sheets. Till characterization and correlation suggest that at least three glacial advances from shifting ice centers within the Labrador sector of the Laurentide Ice Sheet were preserved in these stratigraphic records. Non-glacial units (fluvial, organic-bearing sediments) were constrained via optically stimulated luminescence to two possible periods at ca. 73,000 to 68,000yr BP and ca. 60,000yr BP. Boreal and peatland taxa (Picea, Pinus, Poaceae, Betula, Cyperaceae, Sphagnum) dominated the pollen record at each site, whereas plant macrofossils analyzed at one site confirm the local presence of conifer trees (bark, needles, seed wings), bryophytes (largely Scorpidium spp), herbaceous plants (Caryophyllaceae, Carex, Poaceae), and an aquatic setting (e.g. Potamogeton, ephippia of Daphnia spp). Pollen-derived average summer temperature reconstructions suggested that local temperatures at the Albany sites were between 12 and 15°C, which is similar to present-day estimates for the region (14.2°C). Reconstructed annual precipitation estimates were 580–640mm, which is similar to slightly higher than present-day estimates (564mm). Non-glacial intervals at the Albany sites likely represent abandoned fluvial environments that supported water-logged peatland biota. Results from this research contribute toward ongoing efforts to constrain ice sheet dynamics over North America during the last glacial cycle (e.g. 71,000–14,000yr BP) and provide insight into the complex Late Pleistocene palaeoclimate record at the innermost area of the glaciated region.

Evidence of early C4 grasses, habitat heterogeneity, and faunal response during the Miocene Climatic Optimum in the Mojave Region

Major changes to landscapes, climate, and mammalian faunas occurred at the regional scale in western North America during the Miocene Climatic Optimum (MCO) between ~17 and 14Ma, but few studies have looked at how the MCO affected basin-scale environments. Paleoenvironmental reconstructions coupled with mammalian fossil assemblages from the Crowder and Cajon Valley formations in the Mojave Desert, California, contribute insights into local-scale (10s of kilometers) responses to warming during the Hemingfordian and Barstovian North American Land Mammal Ages. By integrating lithological descriptions, phytolith analyses, carbon isotope composition of preserved soil organic matter (δ13CSOM), and elemental geochemistry of paleosols, we provide reconstructions of depositional environments, vegetation, and precipitation through the MCO. Phytolith and δ13CSOM evidence suggest that paleoenvironments were predominately C3 grasslands, with the earliest potential presence of C4 grasses in the region found within the Crowder Formation at ~17Ma. Based on elemental geochemistry of paleosols, mean annual precipitation estimates are 807 (±182) mm yr−1 at 17Ma in the Crowder Formation and 740–800 (±182) mm yr−1 from 16 to 15Ma in the Cajon Valley Formation. Our multiproxy approach indicates that the Crowder and Cajon Valley basins were stable, large-scale braided stream and floodplain systems with intermittent paleosol development and heterogeneity in vegetation and moisture conditions across spatial scales. Overall, we find a signal of local paleoenvironmental stability during the MCO; however, a significant drying trend in the Crowder Formation does correspond with faunal turnover and reduction in the number of taxa present. High species diversity within faunal assemblages, especially among small mammals, and fewer shared species between the Crowder and Cajon Valley assemblages than expected based on bootstrap analysis indicate that peak mammal diversity was accommodated at both local and regional scales during sustained tectonic activity and the MCO in western North America.

Contemporary changes to herding systems in China and effects on pasture quality: a case study in Gansu Province, 2000-2012.

Post-2000 efforts to protect China's pastoral areas are distinct from earlier efforts in that funding for the most recent round of policies and programs is commensurate with the task. Even with appropriate funding, however, effective methods of mitigating pasture degradation are widely disputed. The most controversial of the current policies include herding family resettlement, pasture fallow programs (herding "bans"), and the promotion of confined animal feeding operations (CAFOs). Ideally, the policies are intended to protect grassland ecological systems while assuring acceptable revenues to affected families and regions. This article presents a case study of 49 townships in three counties in central Gansu investigating the interactions among changes in mean interpolated annual precipitation and livestock density and pasture quality. Pasture quality is assessed using mean township values of the enhanced vegetation index (EVI) from 2000 to 2012 obtained by the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor of the Terra satellite. The research joins remotely sensed environmental data, interpolated annual precipitation estimates, and livestock counts at the township scale for the years from 2000 to 2012 but is also informed by in-depth interviews with herding families and husbandry officials. Joining biophysical analyses of changes in pasture with archived data and in-depth interviews, we adopt a synthetic approach to determine changes to pasture quality under post-2000 policy interventions and possible reasons for these changes. Pasture quality has improved as CAFO livestock have increased; however, herders and local officials report that some of the new policies and programs may have important unanticipated negative impacts related to pasture ecology and water consumption.