Multi-year Monthly Research Articles

Development of a modular distributed hydro-thermal coupled hydrological model for cold regions

Accurately quantifying the impact of permafrost degradation and soil freeze–thaw cycles on hydrological processes while minimizing the reliance on observational data are challenging issues in hydrological modeling in cold regions. In this study, we developed a modular distributed hydro-thermal coupled hydrological model for cold regions (DHTC) that features a flexible structure. The DHTC model couples heat-water transport processes by employing the conduction-advection heat transport equation and Richard equation considering ice-water phase change. Additionally, the DHTC model integrates the influence of organic matter into the hydrothermal parameterization scheme and includes a subpermafrost module based on the flow duration curve analysis to estimate cold-season streamflow sustained by subpermafrost groundwater. Moreover, we incorporated energy consumption due to ice phase changes to the available energy, enhancing the accuracy of evaporation estimation in cold regions. A comprehensive evaluation of the DHTC model was conducted. At the point scale, the DHTC model accurately replicates daily soil temperature and moisture dynamics at various depths, achieving average R2 of 0.98 and 0.87, and average RMSE of 0.61℃ and 0.03 m3·m−3, respectively. At the basin scale, DHTC outperformed (Daily: R2 = 0.66, RMSE = 0.75 mm; Monthly: R2 = 0.90, RMSE = 15.7 mm) the GLDAS/FLDAS Noah, GLDAS/VIC, and PML-V2 models in evapotranspiration simulation. The DHTC model also demonstrated reasonable performance in simulating daily (NSE = 0.70, KGE = 0.84), monthly (NSE = 0.86, KGE = 0.90), and multi-year monthly (NSE = 0.97, KGE = 0.93) streamflow in the Source Regions of Yangtze River. DHTC also successfully reproduced the snow depth in basin-averaged time series and spatial distributions (RMSE = 0.86 cm). The DHTC model provides a robust tool for exploring the interactions between permafrost and hydrological processes, and their responses to climate change.

Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

Can Turbidity Data from Remote Sensing Explain Modelled Spatial and Temporal Sediment Loading Patterns? An Application in the Lake Tana Basin

Understanding the spatial and temporal patterns of sediment loading in water bodies is crucial for effective water quality management. Remote sensing (RS) has emerged as a valuable and reliable tool for monitoring turbidity, which can provide insights into sediment dynamics in water bodies. In this study, we investigate the potential of turbidity data derived from RS to explain simulated spatial and temporal sediment loading patterns in the Lake Tana basin, Ethiopia. Utilizing existing RS lake turbidity data from Copernicus Global Land Service (CGLS) and simulated seasonal and multiyear trends of river sediment loadings into Lake Tana from the Soil and Water Assessment Tool (SWAT + model), we estimate correlations at different river inlets into Lake Tana. The results reveal a strong positive correlation (R2 > 0.66) between the multiyear monthly average sediment load from inflow rivers and RS lake turbidity at most river inlets. This indicates that the simulated river sediment loads and lake turbidity at river inlets exhibit similar seasonal patterns. Notably, higher turbidity levels are observed at the river inlet with the highest sediment load export. These findings highlight the potential of RS turbidity products in characterizing temporal and spatial patterns of sediment loadings, particularly in data-scarce regions, contributing to a better understanding of water quality dynamics in such areas.

Variability, cycles, and trends of mean air temperature north of Colombia

Climate variability is of global interest due to its socioeconomic and environmental effects on the world’s population. In Colombia, temperature changes affect food security, especially for the most vulnerable people in the Caribbean region. We analyzed monthly air temperature in northeastern Colombia (Cesar, La Guajira, and Magdalena departments). We reconstructed time series with missing data using nonlinear principal component analysis. Subsequently, temporal variability, associations with events of climatic variability, and temporal trends were evaluated. Periodicity analyses indicate the dominance of annual variability, although statistically significant associations with periods between 3 and 7 years show the influence of El Niño-Southern Oscillation (ENSO) events. The Spearman correlation coefficient with N = 360 and 95% significance shows a better association with the Multivariate ENSO Index (rsp mean = 0.38) and the Southern Oscillation Index (rsp mean = –0.32). The multi-year monthly analysis shows positive trends, with maximum values between March (1.04 ºC month–1), and June (1.07 ºC month–1) in the valley of the Cesar department, and a minimum in March, at the northernmost La Guajira (0.2 ºC month–1).

Mapping the soil organic matter content in a typical black-soil area using optical data, radar data and environmental covariates

Soil organic matter (SOM) plays an extremely important role in soil formation, soil fertility, environmental protection and the sustainable development of agriculture and forestry, especially in areas with fertile black soil. Remote sensing technology has unique advantages in SOM mapping and can quickly and accurately obtain SOM spatial distribution maps at a low cost. However, the current methods available for fusing radar data, optical data and environmental covariates for use in SOM mapping are far from sufficient. The study area is Youyi Farm, a typical black-soil area in Northeast China. Multiyear monthly composites of the bare-soil periods (April and May) from 2019 to 2022 in the study area were created in the Google Earth Engine (GEE), including VH-polarized and VV-polarized Sentinel-1 radar data, 10 spectral bands of Sentinel-2 data, elevation and slope terms representing terrain covariates, and multiyear average precipitation and temperature data representing climate covariates. SOM was mapped in the study area based on 188 cultivated-layer (0–20 cm) sampling points combined with a random forest regression algorithm, and the SOM mapping accuracies of different strategies were evaluated. The results show that 1) the SOM mapping accuracy obtained using Sentinel-1 data combined with Sentinel-2 data was improved slightly compared to that obtained using Sentinel-2 data only, with improvements observed mainly in the prediction accuracies of areas with SOM contents below 5 %; 2) the highest accuracy was obtained by using the May composite image, while the April and May composite images reduced the accuracy; 3) adding environmental covariates greatly improved the SOM mapping accuracy, with the highest R2 being 0.696 and the lowest RMSE being 0.712 %; and 4) the importance of climate covariates was found to be higher than that of terrain covariates. This study expands the existing SOM mapping research using radar and optical data and evaluates the effects of different environmental covariates on the SOM mapping results.

Vegetation Dynamics and Its Trends Associated with Extreme Climate Events in the Yellow River Basin, China

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China’s national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants’ growth and extreme climate, together with the reaction of plants’ growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants’ growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin.

Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring

Monitoring spatial and temporal trends of water use is of utmost importance to ensure water and food security in river basins that are challenged by water scarcity and climate change induced abnormal weather patterns. To quantify water consumption by the agriculture sector, continuous monitoring is required over different spatial scales ranging from field (< 1 ha) to basin. The demand driven requirement of covering large areas yet providing spatially distributed information makes the use of in-situ measurement devices unfeasible. Earth observation satellites and remote sensing techniques offer an effective alternative in estimating the consumptive use of water (Actual EvapoTranspiration (ETa) fluxes) by using periodic observations from the visible and infrared spectral region. Optical satellite data, however, is often hindered by noises due to cloud cover, cloud shadow, aerosols and other satellite related issues such as Scan Line Corrector (SLC) failure in Landsat 7 breaking the continuity of temporal observations. These gaps have to be statistically filled in order to compute aggregated seasonal and annual estimates of ETa. In this paper, we introduce an approach to develop a gap-filled multi-year monthly ETa maps at medium spatial resolution of 30 m. The method includes two major steps: (i) estimation of ETa using the python based implementation of surface energy balance model called PySEBAL and (ii) temporal interpolation using Locally Weighted Regression (LWR) model followed by spline based spatial interpolation to fill the gaps over time and space. The approach is applied to a large endorheic Lake Urmia Basin (LUB) basin with a surface area of ~ 52,970 km2 in Iran for the years 2013–2015 using Landsat 7 and 8 satellite data. The results show that the implemented gap filling approach could reconstruct the monthly ETa dynamics over different agriculture land use types, while retaining the high spatial variability. A comparison with a similar dataset from FAO WaPOR reported a very high correlation with R2 of 0.93. The study demonstrates the applicability of this approach to a larger basin which is extendible and reproducible to other geographical areas.

Vehicle-based in situ observations of the water vapor isotopic composition across China: spatial and seasonal distributions and controls

Abstract. Stable water isotopes are natural tracers in the hydrological cycle and have been applied in hydrology, atmospheric science, ecology, and paleoclimatology. However, the factors controlling the isotopic distribution, both at spatial and temporal scales, are debated in low and middle latitude regions, due to the significant influence of large-scale atmospheric circulation and complex sources of water vapor. For the first time, we made in situ observations of near-surface vapor isotopes over a large region (over 10 000 km) across China in both pre-monsoon and monsoon seasons, using a newly designed vehicle-based vapor isotope monitoring system. Combined with daily and multiyear monthly mean outputs from the isotope-incorporated global spectral model (Iso-GSM) and infrared atmospheric sounding interferometer (IASI) satellite to calculate the relative contribution, we found that the observed spatial variations in both periods represent mainly seasonal mean spatial variations, but are influenced by more significant synoptic-scale variations during the monsoon period. The spatial variations of vapor δ18O are mainly controlled by Rayleigh distillation along air mass trajectories during the pre-monsoon period, but are significantly influenced by different moisture sources, continental recycling processes, and convection during moisture transport in the monsoon period. Thus, the North–South gradient observed during the pre-monsoon period is counteracted during the monsoon period. The seasonal variation of vapor δ18O reflects the influence of the summer monsoon convective precipitation in southern China and a dependence on temperature in the North. The spatial and seasonal variations in d-excess reflect the different moisture sources and the influence of continental recycling. Iso-GSM successfully captures the spatial distribution of vapor δ18O during the pre-monsoon period, but the performance is weaker during the monsoon period, maybe due to the underestimation of local or short-term high-frequency synoptic variations. These results provide an overview of the spatial distribution and seasonal variability of water isotopic composition in East Asia and their controlling factors, and they emphasize the need to interpret proxy records in the context of the regional system.

Spatio-temporal Variations of Sea Surface Wind in Coral Reef Regions over the South China Sea from 1988 to 2017

The seasonal and interannual variabilities of sea surface wind (SSW) in the South China Sea (SCS), especially in coral reef regions such as Nansha Islands, Xisha Islands, Zhongsha Islands and Dongsha Islands were investigated in detail using the Blended Sea Winds dataset (1988–2017). Annual and monthly variations of SSW and sea surface temperature (SST) in the four zones were investigated. Empirical Orthogonal Function (EOF) analysis of wind field was performed to aid in better understanding the different spatial patterns. The results indicate that, as observed in the spatial distribution of the first mode of monthly mean wind speed anomaly, the magnitudes in the four island zones are all negative and are similar to each other, showing that the variations of SSW in the four island zones are consistent. In the second mode, the magnitudes in Nansha Islands are opposite to those in the other three zones. The spatial distribution of the third mode reflects regional differences. The maximum annual SSW appears in Dongsha Islands, and the minimum appears in Nansha Islands. The interannual variations of SSW in all island zones are basically concurrent. The island zones with high SSW mostly have low SST, and vice versa. There may be an inverse relationship between SSW and SST in coral reef regions in the SCS. The multi-year monthly variations of SSW in the island zones present a ‘W’-shaped structural variation. Each island undergoes two months of minimum SSW every year, one during March–May (MAM) and the other during September–November (SON). Both months are in monsoon transition periods. During the months with low SSW, high SST appears. The SST peaks almost correspond to the SSW troughs. This further indicates that SSW and SST may have opposite changes in coral reef regions. Coral bleaching events often correspond to years of high SST and low SSW.

Seasonal and Annual Probabilistic Forecasting of Water Levels in Large Lakes (Case Study of the Ladoga Lake)

The production functions of water-dependent sectors of the economy can include the water level in the lake as a natural resource. This characteristic must be able to reliably predict for the effective functioning of sectors of the economy. In the article the main attention is paid to the methods of forecasting based on the extrapolation of natural variations of the large lakes water level. As an example, is considered. In this paper, it is assumed that the level varies accordingly to a stochastic multi-cycle process with principal energy-containing zones in frequency bands associated with seasonal and multi-annual variations. Hence, the multi-year monthly and yearly averaged time series are represented by the ARIMA (auto-regression integrated moving average) processes. Forecasts are generated by using of the seasonal ARIMA-models, which take into account not only the seasonal but also the evolution non-stationarity. To compare the forecasts and the actual values, the relative errors are computed. It is shown that implementation of the models mainly allows receiving good and excellent forecasts. Subject Classification Numbers: UDC 556.555.2.06(4)

Seasonal and Annual Probabilistic Forecasting of Water Levels in Large Lakes (Case Study of the Ladoga Lake)

The production functions of water-dependent sectors of the economy can include the water level in the lake as a natural resource. This characteristic must be able to reliably predict for the effective functioning of sectors of the economy. In the article the main attention is paid to the methods of forecasting based on the extrapolation of natural variations of the large lakes water level. As an example, is considered. In this paper, it is assumed that the level varies accordingly to a stochastic multi-cycle process with principal energy-containing zones in frequency bands associated with seasonal and multi-annual variations. Hence, the multi-year monthly and yearly averaged time series are represented by the ARIMA (auto-regression integrated moving average) processes. Forecasts are generated by using of the seasonal ARIMA-models, which take into account not only the seasonal but also the evolution non-stationarity. To compare the forecasts and the actual values, the relative errors are computed. It is shown that implementation of the models mainly allows receiving good and excellent forecasts. Subject Classification Numbers: UDC 556.555.2.06(4)

Open-Source Satellite-Derived Solar Resource Databases Comparison and Validation for Indonesia

Solar resource data derived from satellite imagery are widely available nowadays, either as an open-source or paid database. This article is intended to assess open-source databases, which cover the region of Indonesia. Here, four known solar resource databases, which spatially cover the Indonesian archipelago, have been used, namely, Prediction of Worldwide Energy Resource (POWER), Surface Solar Radiation–Heliosat-East (SARAH-E), CM SAF Cloud, Albedo, Radiation edition 2 (CLARA-A2), and SolarGIS. In addition, a minor portion of the Meteonorm database by Meteotest, around five sample points across Indonesia, has been assessed in terms of coherency to the four mentioned databases. Correlation coefficient and relative bias of the multiyear monthly mean annual cycle global horizontal irradiation (GHI) between pairs of databases are inspected. Three out of four databases are then validated through the available irradiation ground measurement data provided by the World Radiation Data Centre (WRDC). The correlation between each pair varies mostly between 0.7 and 1, which shows that the four databases to a certain extent agree on how the intermonthly variation would behave throughout the year. On the other hand, the validation result reveals that the three databases, i.e., POWER, CLARA-A2, and SARAH-E, are suffering from positive bias error ranging from 3% to 7%. Despite that fact, the correlation between measured and estimated values is still acceptable with SARAH-E showing the best performance among the three. Careful selections and adjustment enable the possibility of these databases to be utilized as a tool for depicting interannual and intermonthly variations of solar irradiation throughout the Indonesian archipelago.

Effects of human activities on hydrological drought patterns in the Yangtze River Basin, China

As an extremely important region for the socioeconomic development of China, the Yangtze River Basin (YRB) is vulnerable to climate change and natural disasters. In recent decades, hydrological droughts have jeopardized regional water supply, local ecosystem services, and economic development in this region. This study simulates the characteristics of hydrological droughts in the YRB using the PCR-GLOBWB v2.0 model and the variable threshold method. High-spatial-resolution (about 10 km) PCR-GLOBWB v2.0 simulations considering the effects of human activities closely match the observed hydrological data for the YRB. Thus, the results indicate that human activities directly influence the tempo-spatial characteristics of hydrological droughts in the YRB. Reservoir operation decreases the multi-year monthly discharge but increases low flow in the severe drought years in the middle and lower subbasins of the YRB. These findings highlight the uneven effects of human activities on hydrological droughts in the YRB. In conclusion, anthropogenic activities must be considered when developing future mitigation and adaptation strategies for the YRB.

Supply and Demand Analysis of Water Resources. Case Study: Irrigation Water Demand in a Semi-Arid Zone in Mexico

To sustainably use water resources, it is important to quantify water availability in a certain region. Due to climate change, population increase, and economic development, water demand increases continuously. Consequently, the difference between supply and demand of water becomes a significant issue, especially in arid and semi-arid regions. In this research, the Soil and Water Assessment Tool (SWAT) model has been applied to the Guadalupe river basin, to assess supply and demand analysis of water resources in this area, specifically for the irrigation of agricultural crops and municipal uses. From the land use, soil type, and terrain slope maps, 763 Hydrostatic Release Units (HRU) were estimated, distributed in the diverse relief types making up the basin, featured by mountains, hills, plateaus, plains, and valleys. For the crop area, 159 HRU were found with the three slope classification types, where 57 HRU represent 91% of the cultivated area on slopes, from 0 to 15%, located in the Ojos Negros and Guadalupe Valleys. The Soil Conservation Service method (SCS) was used to estimate the average monthly runoff and soil moisture content. As a result, water resource parameters related to the supply were determined with this, e.g., runoff, aquifer recharge, flow, infiltration, and others. Crop coefficient values (Kc) were used to determine crop evapotranspiration (ETc), to estimate the water demand of these for each month, using the multi-year monthly average reference evapotranspiration (ETo) calculated with the SWAT model. Overall good performance was obtained considering average monthly discharges data from the Agua Caliente gauging station. The model was calibrated, modifying the parameters chosen according to sensitivity analysis: SCS curve number, base-flow factor, ground-flow delay, and the threshold for return-flow occurrence. The Soil and Water Assessment Tool–Calibration and Uncertainty Programs SWAT-CUP has different goodness-of-fit indicators for the model e.g., determination coefficient (R2), standard deviation of the measured data (RSR), Nash–Sutcliffe coefficient of efficiency (NSE), and others. Multiple iterations were performed, resulting in a ratio between the root mean square error and the standard deviation of the measured data (RSR) of 0.61, a coefficient of determination (R2) of 0.70, and a Nash–Sutcliffe efficiency coefficient (NSE) of 0.63. A supply–demand analysis of the volume generated by the runoff from the basin was performed using the method of estimating useful volume for a reservoir. It is observed in these results that only positive deviations were obtained, implying that runoff in this basin is not enough to meet monthly demand. Finally, the need to establish actions to ensure water management efficiency is highlighted, both for irrigation of agricultural crops and for supply to the region population.

Assessing the Impacts of Two Averaging Methods on AIRS Level 3 Monthly Products and Multiyear Monthly Means

AbstractThe Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua satellite provides more than 16 years of data. Its monthly gridded (Level 3) product has been widely used for climate research and applications. Since counts of successful soundings in a grid cell are used to derive monthly averages, this averaged by observations (ABO) approach effectively gives equal importance to all participating soundings within a month. It is conceivable then that days with more observations due to day-to-day orbit shift and regimes with better retrieval skills will contribute disproportionately to the monthly average within a cell. Alternatively, the AIRS Level 3 monthly product can be produced through an averaged by days (ABD) approach, where the monthly mean in a grid cell is a simple average of the daily means. The effects of these averaging methods on the AIRS version 6 monthly product are assessed quantitatively using temperature and water vapor at the surface and 500 hPa. The ABO method results in a warmer (slightly colder) global mean temperature at the surface (500 hPa) and a drier global mean water vapor than ABD method. The AIRS multiyear monthly mean temperature and water vapor from both methods are also compared with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product and evaluated with a simulation experiment, indicating the ABD method has lower error and is more closely correlated with MERRA-2. In summary, the ABD method is recommended for future versions of the AIRS Level 3 monthly product and more data services supporting Level 3 aggregation are needed.

An Active–Passive Microwave Land Surface Database From GPM

A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM’s dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10–166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25° gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.

Analysis of responses in the dynamics of sea ice area in separate regions of the Arctic to change of insolation

The subject of this research is the correlation analysis of changes in the area of sea ice in separate regions of the Arctic, and levels of internal regional correlations between multiyear monthly changes in the area of sea ice of different seas and the entire Arctic Ocean. The author also examines peculiarities in the annual amplitude course of interannual variability of monthly indices of the area of sea ice for separate districts of the Arctic, interregional links in the annual course of this amplitude of interannual variability, and determination of correlation between the annual indices of the area of sea ice with annual insolation contrast for various Arctic regions. The research method is the correlation data analysis on the area of distribution of sea ice in different districts of the Arctic and insolation contrast. The author builds an algorithm of the value forecast in the changes of sea ice area. Based on the analysis of internal correlations between multiyear and annual changes in the sea ice area in the Arctic regions, and connection with the insolation and insolation contrast, an algorithm is proposed for the value forecast of changes in the sea ice area in separate districts of the Arctic and Northern Hemisphere overall. For long-term forecast of annual values of the changes in sea ice area, the promising districts are Baffin Bay, Kara Sea, Barents Sea, Greenland Sea and Northern Hemisphere as a whole.

River discharge contribution to sea-level rise in the Yangtze River Estuary, China

Sea level changes in the Yangtze River Estuary (YRE) as a result of river discharge are investigated based on the monthly averaged river discharge from 1950 to 2011 at the Datong station. Quantification of the sea level contribution is made by model computed results and the sea level rates reported by the China Sea Level Bulletin (CSLB). The coastal modeling tool, MIKE21, is used to establish a depth-averaged hydrodynamic model covering the YRE and Hangzhou Bay. The model is validated with the measured data. Multi-year monthly river discharges are statistically calculated based on the monthly river discharges at Datong station from 1950 to 2011. The four characteristic discharges (frequency of 75%, 50% and 25%, and multi-year monthly) and month-averaged river discharge from 1950 to 2011 are used to study the seasonal and long-term changes of sea level. The computed sea level at the Dajishan and Lvsi stations are used to study the multi-time scale structure of periodic variation in different time scale of river discharge series. The results reveal that (1) the sea level rises as the river discharge increases, and its amplification decreases from upstream to the offshore. (2) The sea level amplification on the south coast is greater than that on the north coast. When river discharge increases by 20,000m3/s, the sea level will increase by 0.005–0.010m in most of Hangzhou Bay. (3) The sea level at the Dajishan station, influenced by river discharge, increased 0.178mm/y from 1980 to 2011. Correspondingly, the sea level rose at a rate of 2.6–3.0mm/y during the same period. These values were provided by the CSLB. The increase in sea level (1980–2011) at the Dajishan station caused by river discharge is 6.8–8.9% of the total increase in sea level. (4) The 19–20 year dominant nodal cycle of sea level at the Dajishan and Lvsi stations is in accord with 18.6 year nodal cycle of main tidal constituents on Chinese coasts. It implies that the sea-level change period on the coast of the Yangtze River is mainly controlled by tides.

3D Structure of Saharan Dust Transport Towards Europe as Seen by CALIPSO

We present a 3D multi-year monthly mean climatology of Saharan dust advection over Europe using an area-optimized pure dust CALIPSO product. The product has been developed by applying EARLINET-measured dust lidar ratios and depolarization-based dust discrimination methods and it is shown to have a very good agreement in terms of AOD when compared to AERONET over Europe/North Africa and MODIS over Mediterranean. The processing of such purely observational data reveals the certain seasonal patterns of dust transportation towards Europe and the Atlantic Ocean. The physical and optical properties of the dust layer are identified for several areas near the Saharan sources, over the Mediterranean and over continental Europe.

Tendencia anual de los grados día cafeto y los grados día broca en la región andina ecuatorial de Colombia

Se prevé que el cambio del clima modifique el actual patrón espacial del cultivo del café en las laderas montañosas de Colombia. No obstante, las aproximaciones utilizadas para llegar a estos resultados se basan en el uso de modelos climáticos (globales y regionales) así como modelos de distribución de especies que, en gran medida, se soportan en el concepto de nicho. Con el fin de evaluar la certidumbre de los resultados obtenidos con dichos modelos, se plantea una metodología en la que se utilizan datos de estaciones meteorológicas y un índice de tipo agroclimático (tiempo térmico) cuya unidad son los grados día. Se determinó la mejor aproximación para el cálculo del tiempo térmico y se generaron los acumulados mensuales multianuales de grados día cafeto y grados día broca (utilizando el método del triángulo sencillo) usando los datos diarios de temperaturas extremas de estaciones meteorológicas con información histórica superior a 20 años. Se empleó el análisis de tendencias no paramétrico (prueba de Mann-Kendall) y el análisis de correlación para determinar efectos de la variabilidad climática, asociada a la ocurrencia de los fenómenos de El Niño y La Niña, sobre el tiempo térmico de las dos especies mencionadas. Se encontraron tendencias positivas significativas de los grados día broca y los grados día cafeto en las alturas sobre el nivel del mar óptimas, reportadas por diferentes autores para estas especies; es decir, los óptimos actuales se hacen cada vez más aptos para las dos especies analizadas.