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Zhu, Ye, Yi Liu, Xieyao Ma, Liliang Ren, and Vijay Singh. "Drought Analysis in the Yellow River Basin Based on a Short-Scalar Palmer Drought Severity Index." Water 10, no. 11 (October 26, 2018): 1526. http://dx.doi.org/10.3390/w10111526.
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Focusing on the shortages of moisture estimation and time scale in the self-calibrating Palmer drought severity index (scPDSI), this study proposed a new Palmer variant by introducing the Variable Infiltration Capacity (VIC) model in hydrologic accounting module, and modifying the standardization process to make the index capable for monitoring droughts at short time scales. The performance of the newly generated index was evaluated over the Yellow River Basin (YRB) during 1961–2012. For time scale verification, the standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) at a 3-month time scale were employed. Results show that the new Palmer variant is highly correlated with SPI and SPEI, combined with a more stable behavior in drought frequency than original scPDSI. For drought trend detection, this new index is more inclined to reflect comprehensive moisture conditions and reveals a different spatial pattern from SPI and SPEI in winter. Besides, two remote sensing products of soil moisture and vegetation were also employed for comparison. Given their general consistent behaviors in monitoring the spatiotemporal evolution of the 2000 drought, it is suggested that the new Palmer variant is a good indicator for monitoring soil moisture variation and the dynamics of vegetation growth.
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Hopp, L., C. Harman, S. Desilets, C. Graham, J. McDonnell, and P. Troch. "Hillslope hydrology under glass: confronting fundamental questions of soil-water-biota co-evolution at Biosphere 2." Hydrology and Earth System Sciences Discussions 6, no. 3 (June 18, 2009): 4411–48. http://dx.doi.org/10.5194/hessd-6-4411-2009.
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Abstract. Recent studies have called for a new unifying hydrological theory at the hillslope and watershed scale, emphasizing the importance of coupled process understanding of the interactions between hydrology, ecology, pedology, geochemistry and geomorphology. The Biosphere 2 Hillslope Experiment aims at exploring how climate, soil and vegetation interact and drive the evolution of the hydrologic hillslope behavior using a set of three large-scale hillslopes (18 m by 33 m each) that will be built in the climate-controlled experimental biome of the Biosphere 2 facility near Tucson, Arizona, USA. By minimizing the initial physical complexity of these hillslopes, the spontaneous formation of flow pathways, soil spatial heterogeneity, surface morphology and vegetation patterns can be observed over time. This paper documents the hydrologic design process for the Biosphere 2 Hillslope Experiment, which was based on design principles agreed upon among the Biosphere 2 science community. Main design principles were that the hillslopes should promote spatiotemporal variability of hydrological states and fluxes, facilitate transient lateral subsurface flow without inducing overland flow and be capable of supporting vegetation. Hydrologic modeling was used to identify a hillslope configuration (geometry, soil texture, soil depth) that meets the design objectives. The recommended design for the hillslopes consists of a zero-order basin shape with a 10 degree overall slope, a uniform soil depth of 1 m and a loamy sand soil texture. The sensitivity of the hydrologic response of this design to different semi-arid climate scenarios was subsequently tested. Modeling results show that the timing of rainfall in relation to the timing of radiation input controls the spatiotemporal variability of moisture within the hillslope and the generation of lateral subsurface flow. The Biosphere 2 Hillslope Experiment will provide an excellent opportunity to test hypotheses, observe emergent patterns and advance the understanding of interactions.
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Hopp, L., C. Harman, S. L. E. Desilets, C. B. Graham, J. J. McDonnell, and P. A. Troch. "Hillslope hydrology under glass: confronting fundamental questions of soil-water-biota co-evolution at Biosphere 2." Hydrology and Earth System Sciences 13, no. 11 (November 6, 2009): 2105–18. http://dx.doi.org/10.5194/hess-13-2105-2009.
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Abstract. Recent studies have called for a new unifying hydrological theory at the hillslope and watershed scale, emphasizing the importance of coupled process understanding of the interactions between hydrology, ecology, pedology, geochemistry and geomorphology. The Biosphere 2 Hillslope Experiment is aimed at tackling this challenge and exploring how climate, soil and vegetation interact and drive the evolution of the hydrologic hillslope behavior. A set of three large-scale hillslopes (18 m by 33 m each) will be built in the climate-controlled experimental biome of the Biosphere 2 facility near Tucson, Arizona, USA. By minimizing the initial physical complexity of these hillslopes, the spontaneous formation of flow pathways, soil spatial heterogeneity, surface morphology and vegetation patterns can be observed over time. This paper documents the hydrologic design process for the Biosphere 2 Hillslope Experiment, which was based on design principles agreed upon among the Biosphere 2 science community. Main design principles were that the hillslopes should promote spatiotemporal variability of hydrological states and fluxes, facilitate transient lateral subsurface flow without inducing overland flow and be capable of supporting vegetation. Hydrologic modeling was used to identify a hillslope configuration (geometry, soil texture, soil depth) that meets the design objectives. The recommended design for the hillslopes consists of a zero-order basin shape with a 10 degree overall slope, a uniform soil depth of 1 m and a loamy sand soil texture. The sensitivity of the hydrologic response of this design to different semi-arid climate scenarios was subsequently tested. Our modeling showed that the timing of rainfall in relation to the timing of radiation input controls the spatiotemporal variability of moisture within the hillslope and the generation of lateral subsurface flow. The Biosphere 2 Hillslope Experiment will provide an excellent opportunity to test hypotheses, observe emergent patterns and advance the understanding of interactions.
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Khan, Muhammad Imran, Xingye Zhu, Muhammad Arshad, Muhammad Zaman, Yasir Niaz, Ikram Ullah, Muhammad Naveed Anjum, and Muhammad Uzair. "Assessment of spatiotemporal characteristics of agro-meteorological drought events based on comparing Standardized Soil Moisture Index, Standardized Precipitation Index and Multivariate Standardized Drought Index." Journal of Water and Climate Change 11, S1 (October 15, 2020): 1–17. http://dx.doi.org/10.2166/wcc.2020.280.
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Abstract Drought indices that compute drought events by their statistical properties are essential stratagems for the estimation of the impact of drought events on a region. This research presents a quantitative investigation of drought events by analyzing drought characteristics, considering agro-meteorological aspects in the Heilongjiang Province of China during 1980 to 2015. To examine these aspects, the Standardized Soil Moisture Index (SSI), Standardized Precipitation Index (SPI), and Multivariate Standardized Drought Index (MSDI) were used to evaluate the drought characteristics. The results showed that almost half of the extreme and exceptional drought events occurred during 1990–92 and 2004–05. The spatiotemporal analysis of drought characteristics assisted in the estimation of the annual drought frequency (ADF, 1.20–2.70), long-term mean drought duration (MDD, 5–11 months), mean drought severity (MDS, −0.9 to −2.9), and mild conditions of mean drought intensity (MDI, −0.2 to −0.80) over the study area. The results obtained by MSDI reveal the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. The results of this study provide valuable information and can prove to be a reference framework to guide agricultural production in the region.
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Shrestha, Prabhakar. "Clouds and Vegetation Modulate Shallow Groundwater Table Depth." Journal of Hydrometeorology 22, no. 4 (April 2021): 753–63. http://dx.doi.org/10.1175/jhm-d-20-0171.1.
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AbstractA 10-yr simulation of shallow groundwater table (GWT) depth over a temperate region in northwestern Europe, using a physics-based integrated hydrological model at kilometer scale, exhibits a strong seasonal cycle. This is also well captured in terms of near-surface soil moisture anomalies, terrestrial water storage anomalies, and shallow GWT depth anomalies from observations over the region. The modeled monthly anomaly of GWT depth exhibits a statistically significant (p < 0.05) moderate positive/negative correlation with non-rain- and rain-affected monthly anomalies of incoming solar radiation. The vegetation cover also produces a strong local control on the variability of shallow GWT depth. Thus, much of the variability in the simulated seasonal cycle of shallow GWT depth could be linked to the distribution of clouds and vegetation. The spatiotemporal distribution of clouds, partly influenced by the Rhine Massif, modulates the seasonal variability of incoming solar radiation and precipitation over the region. Particularly, the southwestern and northern part of the Rhine Massif divided by the Rhine Valley exhibits a dipole behavior with relatively high (low) shallow GWT depth fluctuations, associated with positive (negative) anomaly of incoming solar radiation and negative (positive) anomaly of precipitation.
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Stoyanova, Julia S., Christo G. Georgiev, and Plamen N. Neytchev. "Drought Monitoring in Terms of Evapotranspiration Based on Satellite Data from Meteosat in Areas of Strong Land–Atmosphere Coupling." Land 12, no. 1 (January 12, 2023): 240. http://dx.doi.org/10.3390/land12010240.
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This study was focused on a key aspect of drought monitoring that has not been systematically studied in the literature: evaluation of the capacity of evapotranspiration data retrieved using geostationary meteorological satellites for use as a water stress precursor. The work was methodologically based on comparisons between constructed indexes of vegetation water stress (evapotranspiration drought index (ETDI) and evaporative stress ratio (ESR)) derived from the EUMETSAT LSASAF METREF and DMET satellite products and soil moisture availability (SMA) from a SVAT model. Long-term (2011–2021) data for regions with strong land–atmosphere coupling in Southeastern Europe (Bulgaria) were used. Stochastic graphical analysis and Q–Q (quantile–quantile) analyses were performed to compare water stress metrics and SMA. Analyses confirmed the consistency in the behavior of vegetation water-stress indexes and SMA in terms of their means, spatiotemporal variability at monthly and annual levels, and anomalous distributions. The biophysical aspects of the drought evaluation confirmed the complementary and parallel interaction of potential (METREF) and actual (DMET) evapotranspiration (in view of the Bouchet hypothesis) for the studied region. Anomalies in evapotranspiration stress indexes can provide useful early signals of agricultural/ecological drought, and the results confirm the validity of using their satellite-based versions to characterize SMA in the root zone and drought severity.
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Wang, Jingjing, Jun Cui, Zhen Teng, Wei Fan, Mengran Guan, Xiaoya Zhao, and Xiaoniu Xu. "Effects of simulated nitrogen deposition on soil microbial biomass and community function in subtropical evergreen broad-leaved forest." Forest Systems 28, no. 3 (November 12, 2019): e018. http://dx.doi.org/10.5424/fs/2019283-15404.
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Aim of the study: The aim of this study was to examine the effects of a 5-year simulated nitrogen (N) deposition on soil microbial biomass carbon (MBC), nitrogen (MBN), microbial community activity and diversity in subtropical old-growth forest ecosystems.Area of study: The study was conducted in forest located at subtropical forest in Anhui, east China.Material and methods: Three blocks with three fully randomized plots of 20 m × 20 m with similar forest community and soil conditions were established. The site applied ammonium nitrate (NH4NO3) to simulate N deposition (50 and 100 kg N ha−1 year −1). From three depths (0–10, 10–20 and 20–30 cm), were collected over four seasons (December, March, June and September), and then measured by community-level physiological profiles (CLPPs).Main results: N addition had no significant effect on MBC and MBN. The spatiotemporal variations in MBC and MBN were controlled by seasonality and soil depth. Soil microbial activities and diversity in the growing season (June and September) were apparently higher than the dormant season (March and December), there were significantly lower diversity indices found following N addition in September. However, N addition enhanced microbial activities and increased diversity indices in the dormant season. Redundancy analysis showed that pH, soil moisture, NO3--N and total phosphorus were the most important factors controlling the spatial pattern of microbial metabolic activity.Research highlights: These results suggest that soil microbial community function is more easily influenced than microbial biomass. The site has a trend of P-limited or near-N saturation, and will threaten the whole forest ecosystem with the increasing duration of N addition.Keywords: Nitrogen deposition; Seasonality; Soil microbial biomass; Microbial community; Subtropical old-growth forest.
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Hasan, Shaakeel, Peter A. Troch, J. Boll, and C. Kroner. "Modeling the Hydrological Effect on Local Gravity at Moxa, Germany." Journal of Hydrometeorology 7, no. 3 (June 1, 2006): 346–54. http://dx.doi.org/10.1175/jhm488.1.
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Abstract A superconducting gravimeter has observed with high accuracy (to within a few nm s−2) and high frequency (1 Hz) the temporal variations in the earth’s gravity field near Moxa, Germany, since 1999. Hourly gravity residuals are obtained by time averaging and correcting for earth tides, polar motion, barometric pressure variations, and instrumental drift. These gravity residuals are significantly affected by hydrological processes (interception, infiltration, surface runoff, and subsurface redistribution) in the vicinity of the observatory. In this study time series analysis and distributed hydrological modeling techniques are applied to understand the effect of these hydrological processes on observed gravity residuals. It is shown that the short-term response of gravity residuals to medium- to high-rainfall events can be efficiently modeled by means of a linear transfer function. This transfer function exhibits an oscillatory behavior that indicates fast redistribution of stored water in the upper layers (interception store, root zone) of the catchment surrounding the instrument. The relation between groundwater storage and gravity residuals is less clear and varies according to the season. High positive correlation between groundwater and gravity exists during winter months when the freezing of the upper soil layers immobilizes water stored in the unsaturated zone of the catchment. To further explore the spatiotemporal dynamics of the relevant hydrological processes and their relation to observed gravity residuals, a GIS-based distributed hydrological model is applied for the Silberleite catchment. Driven by observed atmospheric forcings (precipitation and potential evapotranspiration), the model allows the authors to compute the variation of water storage in three different layers: the interception store, the snow cover store, and the soil moisture store. These water storage dynamics are then converted to predicted gravity variation at the location of the superconducting gravimeter and compared to observed gravity residuals. During most of the investigated period (January 2000 to January 2004) predictions are in good agreement with the observed patterns of gravity dynamics. However, during some winter months the distributed hydrological model fails to explain the observations, which supports the authors’ conclusion that groundwater variability dominates the hydrological gravity signal in the winter. More hydrogeological research is needed to include groundwater dynamics in the hydrological model.
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Lemos Filho, Luis César De Aquino, Luís Henrique Bassoi, and Manoel Alves De Faria. "VARIABILIDADE ESPACIAL E ESTABILIDADE TEMPORAL DO ARMAZENAMENTO DE ÁGUA EM SOLO ARENOSO CULTIVADO COM VIDEIRAS IRRIGADAS." IRRIGA 1, no. 1 (June 18, 2018): 319. http://dx.doi.org/10.15809/irriga.2016v1n1p319-340.
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VARIABILIDADE ESPACIAL E ESTABILIDADE TEMPORAL DO ARMAZENAMENTO DE ÁGUA EM SOLO ARENOSO CULTIVADO COM VIDEIRAS IRRIGADAS LUIS CÉSAR DE AQUINO LEMOS FILHO1; LUÍS HENRIQUE BASSOI2 E MANOEL ALVES DE FARIA3 1 Professor Doutor, Universidade Federal Rural do Semi-Árido, Departamento de Ciências Ambientais e Tecnológicas - Campus Mossoró, Av. Francisco Mota, nº 572, Costa e Silva, 59.625-900, Mossoró, Rio Grande do Norte, Brasil. E-mail: lcalfilho@ufersa.edu.br2 Pesquisador Doutor, Embrapa Instrumentação - São Carlos, São Paulo. E-mail: luis.bassoi@embrapa.br3 Professor Doutor, Universidade Federal de Lavras, Departamento de Engenharia - Lavras, Minas Gerais. E-mail: mafaria@deg.ufla.br 1 RESUMO O comportamento espaço-temporal da água no solo é fundamental para um manejo preciso da irrigação. Assim, este trabalho analisa o comportamento espacial e a estabilidade temporal do armazenamento de água no solo, a fim de determinar pontos da área que possam representar o valor médio espacial da umidade. O trabalho foi realizado numa área com solo predominantemente arenoso (Neossolo Quartizarênico), cultivado com videira e irrigado por microaspersão, em Petrolina-PE. O monitoramento da água no solo foi realizado por tensiometria. As análises estatísticas e geoestatísticas foram realizadas pelos softwares Statistica Development Environment, GS+ e GeoR. As técnicas sugeridas por Vachaud et al. (1985) e Kachanoski e De Jong (1988) foram usadas para avaliar a estabilidade temporal da água no solo, ao longo do tempo. A geoestatística foi adequada para descrever a estrutura de dependência espacial do armazenamento de água no solo e, para algumas datas, estabilidade temporal foi boa, em todos os pontos (39) monitorados na área. Para a camada de 0-20 cm, os pontos 32 e 10 foram os mais indicados como representativos da média espacial do armazenamento de água no solo; já para a camada de 20-40 cm foi o ponto 37 que melhor representou a média espacial. Logo, esses pontos (10, 32 e 37) são os melhores representantes para realização de uma amostragem de água no solo; além disso, esses pontos, poderiam ser utilizados para o monitoramento, para fins de manejo de irrigação. A variabilidade espacial da água no solo mostra a importância do manejo diferenciado da irrigação, considerando-se as diferentes zonas do solo e não a área como sendo homogênea (como é feito na atualidade). Palavras-chave: semivariogramas, krigagem, teste de Spearman, teste de Pearson. LEMOS FILHO, L. C. de A.; BASSOI, L. H.; FARIA, M. A. deSPATIAL VARIABILITY AND TIME STABILITY OF WATER STORED IN A SANDY SOIL CULTIVATED WITH IRRIGATED VINES 2 ABSTRACT The water spatiotemporal behavior in soil is crucial for precise irrigation management. This work analyzes the spatial behavior and the temporal stability of water storage in the soil, in order to determine points of the area that can represent the spatial average value of moisture. The study was conducted in an area with predominantly sandy soil (Quartzarenic Neosol), cultivated with vine and micro sprinkler irrigation system in Petrolina. Monitoring soil water was carried out by tensiometry. Statistical analysis and geostatistical were performed by Statistica software development environment, GS + and GeoR. The techniques suggested by Vachaud et al. (1985) and Kachanoski and De Jong (1988) were used to evaluate the temporal stability of the water in the soil over time. Geostatistics was adequate to describe the spatial dependence structure of water storage in the soil and, for certain dates, temporal stability was good in all points (39) in the monitored area. For 0-20 cm, paragraphs 32 and 10 were the most indicated as representative of the spatial average water storage in the soil; already for the 20-40 cm layer was point 37 that best represented the spatial average. Therefore, these points (10, 32 and 37) are the best representatives for the realization of a water sampling in soil; In addition, these points could be used for monitoring for the irrigation purposes. The spatial variability of soil water shows the importance of different irrigation management, considering the different soil zones and not the area to be homogeneous (as is done today). Keywords: variogram, kriging, Spearman test, Pearson test.
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Khan, S. I., P. Adhikari, Y. Hong, H. Vergara, T. Grout, R. F. Adler, F. Policelli, D. Irwin, T. Korme, and L. Okello. "Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa." Hydrology and Earth System Sciences Discussions 7, no. 4 (July 22, 2010): 4785–816. http://dx.doi.org/10.5194/hessd-7-4785-2010.
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Abstract. Floods and droughts are common, recurring natural hazards in East African nations. Studies of hydro-climatology at daily, seasonal, and annual time scale is an important in understanding and ultimately minimizing the impacts of such hazards. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia Basin. The spatiotemporal variability of the water cycle components were quantified using a physically-based, distributed hydrologic model, with in-situ and multi-satellite remote sensing datasets. Moreover, the hydrologic capability of remote sensing data such as TRMM-3B42V6 was tested in terms of reconstruction of the water cycle components. The spatial distribution and time series of modeling results for precipitation (P), evapotranspiration (ET), and change in storage (dS/dt) showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The hydrologic model captured the spatial variability of the soil moisture storage. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. Relatively high flows were experienced near the basin outlet from previous rainfall, with a new flood peak responding to the rainfall in the upper part of the basin. The monthly peak runoff was observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. The model was found to be useful in poorly gauged catchments using satellite forcing data and showed the potential to be used not only for the investigation of the catchment scale water balance but also for addressing issues pertaining to sustainability of the resources within the catchment.
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Dey, Animesh, and P. S. Chaudhuri. "Species Richness, Community Organization, and Spatiotemporal Distribution of Earthworms in the Pineapple Agroecosystems of Tripura, India." International Journal of Ecology 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/3190182.
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The impact that plant communities may have on underground faunal diversity is unclear. Therefore, understanding the links between plants and organisms is of major interest. Earthworm population dynamics were studied in the pineapple agroecosystems of Tripura to evaluate the impact of monoculture plantation on earthworm communities. A total of thirteen earthworm species belonging to four families and five genera were collected from different sampling sites. Application of sample-based rarefaction curve and nonparametric richness estimators reveal 90–95% completeness of sampling. Earthworm community of pineapple agroecosystems was dominated by endogeic earthworms andDrawida assamensiswas the dominant species with respect to its density, biomass, and relative abundance. Vertical distribution of earthworms was greatly influenced by seasonal variations. Population density and biomass of earthworms peaked during monsoon and postmonsoon period, respectively. Overall density and biomass of earthworms were in increasing trend with an increase in plantation age and were highest in the 30–35-year-old plantation. Significant decrease in the Shannon diversity and evenness index and increase in Simpson’s dominance and spatial aggregation index with an increase in the age of pineapple plantation were recorded. Soil temperature and soil moisture were identified as the most potent regulators of earthworm distribution in the pineapple plantation.
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Sharma, Bharat, Jitendra Kumar, Auroop R. Ganguly, and Forrest M. Hoffman. "Carbon cycle extremes accelerate weakening of the land carbon sink in the late 21st century." Biogeosciences 20, no. 10 (May 22, 2023): 1829–41. http://dx.doi.org/10.5194/bg-20-1829-2023.
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Abstract. Increasing surface temperature could lead to enhanced evaporation, reduced soil moisture availability, and more frequent droughts and heat waves. The spatiotemporal co-occurrence of such effects further drives extreme anomalies in vegetation productivity and net land carbon storage. However, the impacts of climate change on extremes in net biospheric production (NBP) over longer time periods are unknown. Using the percentile threshold on the probability distribution curve of NBP anomalies, we computed negative and positive extremes in NBP. Here we show that due to climate warming, about 88 % of global regions will experience a larger magnitude of negative NBP extremes than positive NBP extremes toward the end of 2100, which accelerate the weakening of the land carbon sink. Our analysis indicates the frequency of negative extremes associated with declines in biospheric productivity was larger than positive extremes, especially in the tropics. While the overall impact of warming at high latitudes is expected to increase plant productivity and carbon uptake, high-temperature anomalies increasingly induce negative NBP extremes toward the end of the 21st century. Using regression analysis, we found soil moisture anomalies to be the most dominant individual driver of NBP extremes. The compound effect of hotness, dryness, and fire caused extremes at more than 50 % of the total grid cells. The larger proportion of negative NBP extremes raises a concern about whether the Earth is capable of increasing vegetation production with a growing human population and rising demand for plant material for food, fiber, fuel, and building materials. The increasing proportion of negative NBP extremes highlights the consequences not only of reduction in total carbon uptake capacity but also of conversion of land to a carbon source.
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Liu, Xinyu, Xixi Lu, Ruihong Yu, Heyang Sun, Hao Xue, Zhen Qi, Zhengxu Cao, Zhuangzhuang Zhang, and Tingxi Liu. "Greenhouse gases emissions from riparian wetlands: an example from the Inner Mongolia grassland region in China." Biogeosciences 18, no. 17 (September 1, 2021): 4855–72. http://dx.doi.org/10.5194/bg-18-4855-2021.
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Abstract. Gradual riparian wetland drying is increasingly sensitive to global warming and contributes to climate change. Riparian wetlands play a significant role in regulating carbon and nitrogen cycles. In this study, we analyzed the emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from riparian wetlands in the Xilin River basin to understand the role of these ecosystems in greenhouse gas (GHG) emissions. Moreover, the impact of the catchment hydrology and soil property variations on GHG emissions over time and space was evaluated. Our results demonstrate that riparian wetlands emit larger amounts of CO2 (335–2790 mgm-2h-1 in the wet season and 72–387 mgm-2h-1 in the dry season) than CH4 and N2O to the atmosphere due to high plant and soil respiration. The results also reveal clear seasonal variations and spatial patterns along the transects in the longitudinal direction. N2O emissions showed a spatiotemporal pattern similar to that of CO2 emissions. Near-stream sites were the only sources of CH4 emissions, while the other sites served as sinks for these emissions. Soil moisture content and soil temperature were the essential factors controlling GHG emissions, and abundant aboveground biomass promoted the CO2, CH4, and N2O emissions. Moreover, compared to different types of grasslands, riparian wetlands were the potential hotspots of GHG emissions in the Inner Mongolian region. Degradation of downstream wetlands has reduced the soil carbon pool by approximately 60 %, decreased CO2 emissions by approximately 35 %, and converted the wetland from a CH4 and N2O source to a sink. Our study showed that anthropogenic activities have extensively changed the hydrological characteristics of the riparian wetlands and might accelerate carbon loss, which could further affect GHG emissions.
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Eickenscheidt, T., J. Heinichen, J. Augustin, A. Freibauer, and M. Drösler. "Nitrogen mineralization and gaseous nitrogen losses from waterlogged and drained organic soils in a black alder (<i>Alnus glutinosa</i> (L.) Gaertn.) forest." Biogeosciences 11, no. 11 (June 5, 2014): 2961–76. http://dx.doi.org/10.5194/bg-11-2961-2014.
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Abstract. Black alder (Alnus glutinosa (L.) Gaertn.) forests on peat soils have been reported to be hotspots for high nitrous oxide (N2O) losses. High emissions may be attributed to alternating water tables of peatlands and to the incorporation of high amounts of easily decomposable nitrogen (N) into the ecosystem by symbiotic dinitrogen (N2)-fixation of alder trees. Our study addressed the question to what extent drainage enhances the emissions of N2O from black alder forests and how N turnover processes and physical factors influence the production of N2O and total denitrification. The study was conducted in a drained black alder forest with variable groundwater tables at a southern German fen peatland. Fluxes of N2O were measured using the closed chamber method at two drained sites (D-1 and D-2) and one undrained site (U). Inorganic N contents and net N mineralization rates (NNM) were determined. Additionally a laboratory incubation experiment was carried out to investigate greenhouse gas and N2 fluxes at different temperature and soil moisture conditions. Significantly different inorganic N contents and NNM rates were observed, which however did not result in significantly different N2O fluxes in the field but did in the laboratory experiment. N2O fluxes measured were low for all sites, with total annual emissions of 0.51 ± 0.07 (U), 0.97 ± 0.13 (D-1) and 0.93 ± 0.08 kg N2O–N ha−1 yr−1 (D-2). Only 37% of the spatiotemporal variation in field N2O fluxes could be explained by peat temperature and groundwater level, demonstrating the complex interlinking of the controlling factors for N2O emissions. However, temperature was one of the key variables of N2O fluxes in the incubation experiment conducted. Increasing soil moisture content was found to enhance total denitrification losses during the incubation experiment, whereas N2O fluxes remained constant. At the undrained site, permanently high groundwater level was found to prevent net nitrification, resulting in a limitation of available nitrate (NO3−) and negligible gaseous N losses. N2O flux rates that were up to four times higher were measured in the incubation experiment. They reveal the potential of high N2O losses under changing soil physical conditions at the drained alder sites. The high net nitrification rates observed and high NO3− contents bear the risk of considerable NO3− leaching at the drained sites.
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Tupek, B., K. Minkkinen, J. Pumpanen, T. Vesala, and E. Nikinmaa. "CH<sub>4</sub> and N<sub>2</sub>O dynamics in the boreal forest–mire ecotone." Biogeosciences 12, no. 2 (January 16, 2015): 281–97. http://dx.doi.org/10.5194/bg-12-281-2015.
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Abstract. In spite of advances in greenhouse gas research, the spatiotemporal CH4 and N2O dynamics of boreal landscapes remain challenging, e.g., we need clarification of whether forest–mire transitions are occasional hotspots of landscape CH4 and N2O emissions during exceptionally high and low ground water level events. In our study, we tested the differences and drivers of CH4 and N2O dynamics of forest/mire types in field conditions along the soil moisture gradient of the forest–mire ecotone. Soils changed from Podzols to Histosols and ground water rose downslope from a depth of 10 m in upland sites to 0.1 m in mires. Yearly meteorological conditions changed from being exceptionally wet to typical and exceptionally dry for the local climate. The median fluxes measured with a static chamber technique varied from −51 to 586 μg m−2 h−1 for CH4 and from 0 to 6 μg m−2 h−1 for N2O between forest and mire types throughout the entire wet–dry period. In spite of the highly dynamic soil water fluctuations in carbon rich soils in forest–mire transitions, there were no large peak emissions in CH4 and N2O fluxes and the flux rates changed minimally between years. Methane uptake was significantly lower in poorly drained transitions than in the well-drained uplands. Water-saturated mires showed large CH4 emissions, which were reduced entirely during the exceptional summer drought period. Near-zero N2O fluxes did not differ significantly between the forest and mire types probably due to their low nitrification potential. When upscaling boreal landscapes, pristine forest–mire transitions should be regarded as CH4 sinks and minor N2O sources instead of CH4 and N2O emission hotspots.
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Chen, X., T. J. Bohn, and D. P. Lettenmaier. "Model estimates of climate controls on pan-Arctic wetland methane emissions." Biogeosciences 12, no. 21 (November 2, 2015): 6259–77. http://dx.doi.org/10.5194/bg-12-6259-2015.
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Abstract. Climate factors including soil temperature and moisture, incident solar radiation, and atmospheric carbon dioxide concentration are important environmental controls on methane (CH4) emissions from northern wetlands. We investigated the spatiotemporal distributions of the influence of these factors on northern high-latitude wetland CH4 emissions using an enhanced version of the Variable Infiltration Capacity (VIC) land surface model. We simulated CH4 emissions from wetlands across the pan-Arctic domain over the period 1948–2006, yielding annual average emissions of 36.1 ± 6.7 Tg CH4 yr−1 for the period 1997–2006. We characterized historical sensitivities of CH4 emissions to air temperature, precipitation, incident long- and shortwave radiation, and atmospheric [CO2] as a function of average summer air temperature and precipitation. Emissions from relatively warm and dry wetlands in the southern (permafrost-free) portion of the domain were positively correlated with precipitation and negatively correlated with air temperature, while emissions from wetter and colder wetlands further north (permafrost) were positively correlated with air temperature. Over the entire period 1948–2006, our reconstructed CH4 emissions increased by 20 %, the majority of which can be attributed to an increasing trend in summer air temperature. We estimated future emissions in response to 21st century warming as predicted by CMIP5 (Coupled Model Intercomparison Project Phase 5) model projections to result in end-of-century CH4 emissions 38–53 % higher than our reconstructed 1997–2006 emissions, accompanied by the northward migration of warmer and drier than optimal conditions for CH4 emissions, implying a reduced role for temperature in driving future increases in emissions.
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Ma, Yueling, Carsten Montzka, Bagher Bayat, and Stefan Kollet. "Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe." Hydrology and Earth System Sciences 25, no. 6 (June 23, 2021): 3555–75. http://dx.doi.org/10.5194/hess-25-3555-2021.
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Abstract. Many European countries rely on groundwater for public and industrial water supply. Due to a scarcity of near-real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods for estimating wtd anomalies (wtda) using other hydrometeorological observations routinely available near real time. In this work, we explore the potential of Long Short-Term Memory (LSTM) networks for producing monthly wtda using monthly precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks that are useful for detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. In the proposed methodology, spatiotemporally continuous data were obtained from daily terrestrial simulations of the Terrestrial Systems Modeling Platform (TSMP) over Europe (hereafter termed the TSMP-G2A data set), with a spatial resolution of 0.11∘, ranging from the years 1996 to 2016. The data were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtda maps from LSTM networks agreed well with TSMP-G2A wtda maps on spatially distributed dry and wet events, with 2003 and 2015 constituting drought years over Europe. Moreover, we categorized the test performances of the networks based on intervals of yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), soil type (St), and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd < 3 m, ET > 200 mm, θ>0.15 m3 m−3, and Sw<10 mm, revealing a significant impact of the local factors on the ability of the networks to process information. Furthermore, results of the cross-wavelet transform (XWT) showed a change in the temporal pattern between TSMP-G2A pra and wtda at some selected pixels, which can be a reason for undesired network behavior. Our results demonstrate that LSTM networks are useful for producing high-quality wtda based on other hydrometeorological data measured and predicted at large scales, such as pra. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe that is relevant to water management.
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Nur Hidayati, Iswari, R. Suharyadi, and Projo Danoedoro. "Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery." KnE Social Sciences, August 5, 2019. http://dx.doi.org/10.18502/kss.v3i21.4981.
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The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.
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Sahraei, Amir, Alejandro Chamorro, Philipp Kraft, and Lutz Breuer. "Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow." Frontiers in Water 3 (June 3, 2021). http://dx.doi.org/10.3389/frwa.2021.652100.
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Estimating the maximum event water fraction, at which the event water contribution to streamflow reaches its peak value during a precipitation event, gives insight into runoff generation mechanisms and hydrological response characteristics of a catchment. Stable isotopes of water are ideal tracers for accurate estimation of maximum event water fractions using isotopic hydrograph separation techniques. However, sampling and measuring of stable isotopes of water is laborious, cost intensive, and often not conceivable under difficult spatiotemporal conditions. Therefore, there is a need for a proper predictive model to predict maximum event water fractions even at times when no direct sampling and measurements of stable isotopes of water are available. The behavior of maximum event water fraction at the event scale is highly dynamic and its relationships with the catchment drivers are complex and non-linear. In last two decades, machine learning algorithms have become increasingly popular in the various branches of hydrology due to their ability to represent complex and non-linear systems without any a priori assumption about the structure of the data and knowledge about the underlying physical processes. Despite advantages of machine learning, its potential in the field of isotope hydrology has rarely been investigated. Present study investigates the applicability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict maximum event water fractions in streamflow using precipitation, soil moisture, and air temperature as a set of explanatory input features that are more straightforward and less expensive to measure compared to stable isotopes of water, in the Schwingbach Environmental Observatory (SEO), Germany. The influence of hyperparameter configurations on the model performance and the comparison of prediction performance between optimized ANN and optimized SVM are further investigated in this study. The performances of the models are evaluated using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe Efficiency (NSE). For the ANN, the results showed that an appropriate number of hidden nodes and a proper activation function enhanced the model performance, whereas changes of the learning rate did not have a major impact on the model performance. For the SVM, Polynomial kernel achieved the best performance, whereas Linear yielded the weakest performance among the kernel functions. The result showed that maximum event water fraction could be successfully predicted using only precipitation, soil moisture, and air temperature. The optimized ANN showed a satisfactory prediction performance with MAE of 10.27%, RMSE of 12.91%, R2 of 0.70, and NSE of 0.63. The optimized SVM was superior to that of ANN with MAE of 7.89%, RMSE of 9.43%, R2 of 0.83, and NSE of 0.78. SVM could better capture the dynamics of maximum event water fractions across the events and the predictions were generally closer to the corresponding observed values. ANN tended to underestimate the events with high maximum event water fractions and to overestimate the events with low maximum event water fractions. Machine learning can prove to be a promising approach to predict variables that are not always possible to be estimated due to the lack of routine measurements.
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CHEN Weiliang, 陈维梁，王树学，齐统祥，焦磊，王聪，买尔当·克依木，李宗善，傅伯杰. "Spatiotemporal dynamics and temporal stability of soil moisture at black locust plantations with different restoration years on hilly region of the loess plateau, China." Acta Ecologica Sinica 41, no. 14 (2021). http://dx.doi.org/10.5846/stxb202008272223.
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LIAN Honglin, 廉泓林，李卫，冯金超，杨文斌，熊伟，于海蛟，敖铁胜. "Spatiotemporal characteristics of soil moisture and its responses to environmental factors in two typical sand-fixing plantations at the south edge of Horqin Sandy Land." Acta Ecologica Sinica 41, no. 20 (2021). http://dx.doi.org/10.5846/stxb202012023078.
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