scholarly journals Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

2017 ◽  
Author(s):  
Wenmin Zhang ◽  
Martin Brandt ◽  
Xiaoye Tong ◽  
Qingjiu Tian ◽  
Rasmus Fensholt
Keyword(s):  
Rainfall Intensity ◽  
Arid Zone ◽  
Growing Season ◽  
Seasonal Distribution ◽  
Seasonal Rainfall ◽  
Wet Season ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Semi Arid ◽  
Rainy Days

Abstract. Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy rainfall events, longer dry spells, and a shifted timing of the wet season. Yet, the aboveground net primary productivity (ANPP) in drylands is usually explained by annual rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tests the importance of seasonal rainfall metrics (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days and heavy rainfall events) on growing season ANPP. We focus on the Sahel and north-Sudanian region (100–800 mm year−1) and apply daily satellite based rainfall estimates (RFE-2.0) and growing season integrated NDVI (MODIS) as a proxy for ANNP over the study period 2001–2015. Growing season ANPP in the arid zone (100–300 mm year−1) was found to be rather insensitive to variations in the seasonal rainfall metrics, whereas vegetation in the semi-arid zone (300–700 mm year−1) was significantly impacted by most metrics, especially by the number of rainy days and timing (start and cessation) of the wet season. We analyzed critical breakpoints for all metrics, showing that growing season ANPP is particularly negatively impacted after > 10 consecutive dry days and that a rainfall intensity of 7 mm day−1 is detected for optimum growing season ANPP. We conclude that number of rainy days and the timing of the wet season are seasonal rainfall metrics being decisive for favorable vegetation growth in semi-arid Sahel which needs to be considered when modelling primary productivity from rainfall in the dryland's of Sahel and elsewhere.

scholarly journals Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

2018 ◽  
Vol 15 (1) ◽  
pp. 319-330 ◽  
Author(s):  
Wenmin Zhang ◽  
Martin Brandt ◽  
Xiaoye Tong ◽  
Qingjiu Tian ◽  
Rasmus Fensholt
Keyword(s):  
Primary Productivity ◽  
Rainfall Intensity ◽  
Arid Zone ◽  
Growing Season ◽  
Seasonal Distribution ◽  
Wet Season ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Semi Arid ◽  
Rainy Days

Abstract. Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy-rainfall events, longer dry spells, and a shifted timing of the wet season. Yet the aboveground net primary productivity (ANPP) in drylands is usually explained by annual-rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tested the importance of rainfall metrics in the wet season (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days, and heavy-rainfall events) for growing season ANPP. We focused on the Sahel and northern Sudanian region (100–800 mm yr−1) and applied daily satellite-based rainfall estimates (CHIRPS v2.0) and growing-season-integrated normalized difference vegetation index (NDVI; MODIS) as a proxy for ANPP over the study period: 2001–2015. Growing season ANPP in the arid zone (100–300 mm yr−1) was found to be rather insensitive to variations in the seasonal-rainfall metrics, whereas vegetation in the semi-arid zone (300–700 mm yr−1) was significantly impacted by most metrics, especially by the number of rainy days and timing (onset and cessation) of the wet season. We analysed critical breakpoints for all metrics to test if vegetation response to changes in a given rainfall metric surpasses a threshold beyond which vegetation functioning is significantly altered. It was shown that growing season ANPP was particularly negatively impacted after >14 consecutive dry days and that a rainfall intensity of ∼13 mm day−1 was detected for optimum growing season ANPP. We conclude that the number of rainy days and the timing of the wet season are seasonal-rainfall metrics that are decisive for favourable vegetation growth in the semi-arid Sahel and need to be considered when modelling primary productivity from rainfall in the drylands of the Sahel and elsewhere.

The Landscape Cycle under Semi-Arid Conditions

1939 ◽  
Vol 76 (9) ◽  
pp. 402-407
Author(s):  
C. A. Cotton
Keyword(s):  
Mediterranean Region ◽  
Arid Regions ◽  
Seasonal Distribution ◽  
Rainfall Regime ◽  
Vegetative Cover ◽  
Wet Season ◽  
Arid Conditions ◽  
The Mediterranean ◽  
Semi Arid

It is difficult to draw the line between aridity and semi-aridity, just as it-is difficult to differentiate between humid and semi-arid climates as they affect the development of landscape forms. No hard and fast divisions based on rainfall figures can be adopted. Evaporation, in part controlled by temperature, and the seasonal distribution of rainfall are also important factors. “The rainfall regime is divisible”, according to. Bryan, “into the episodic and the periodic. In the episodic type rain falls in storms that are highly variable in intensity and are scattered through the year; in the periodic type precipitation is concentrated in one season, either summer or winter. In areas having the periodic type vegetation is adjusted to the wet season, and a relatively greater vegetative cover is possible with low rainfall. The Mediterranean region and California have the periodic type of rainfall, with winter maximum and mild temperatures. Thus in many sub-areas the land forms under mean annual rainfalls of 15 to 20 inches are very similar to those of humid regions, although the soils … are quite like those of other arid regions. The episodic rainfall, because of its variability in time throughout the year, is less effective in promoting growth, and the vegetative cover may be so scant with rainfalls of 5 to 7 inches that geomorphologically the region is essentially a desert. Episodic rainfall as high as 15 to 20 inches may produce steppe conditions. … In general the warmer areas have a relatively scantier vegetation with the same rainfall regime. Including this relation all varieties of hot and cold deserts or semi-arid climates are possible (2).”

scholarly journals How does a rubber plantation affect the spatial variability and temporal stability of throughfall?

2018 ◽  
Vol 50 (1) ◽  
pp. 60-74 ◽  
Author(s):  
Jiaqing Liu ◽  
Wenjie Liu ◽  
Weixia Li ◽  
Huanhuan Zeng
Keyword(s):  
Spatial Variability ◽  
Large Scale ◽  
Rainfall Intensity ◽  
Southwest China ◽  
Temporal Stability ◽  
Rubber Plantation ◽  
Area Index ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Heavy Rainfall Events

Abstract In Xishuangbanna, southwest China, the large-scale monoculture rubber plantation replaced the primary tropical forest, which changed the regional hydrology processes and biogeochemical cycles. As throughfall was an important component of the forest ecosystem water input, we researched the spatial variability and temporal stability of throughfall in the rubber plantation. We recorded 30 rainfall events by using 90 rain gauges during 2015–2016. We found a highly significant linear relationship between rainfall and throughfall, and a strong power correlation between the peak 30 min rainfall intensity and throughfall. The coefficient of variation for throughfall was significant and negatively correlated with rainfall and rainfall intensity. We also observed that throughfall had a strong spatial autocorrelation that would decrease during heavy rainfall events. The results indicate that the leaf area index did not have a significant relationship with throughfall. However, the lateral translocation of the throughfall in the canopy significantly affected the spatial distribution of the throughfall. Generally, the lower throughfall positions were close to the nearest rubber trunk, and the higher throughfall positions were mostly below the slope. This study contributes to the knowledge of the spatiotemporal heterogeneity of throughfall and helps elucidate the interception processes in the rubber plantation.

Vegetation indices as a proxy for spatio-temporal variations in water availability in the semi-arid Rio Santa valley (Callejón de Huaylas, Peru)

2021 ◽  
Author(s):  
Lorenz Hänchen ◽  
Cornelia Klein ◽  
Fabien Maussion ◽  
Wolfgang Gurgiser ◽  
Georg Wohlfahrt
Keyword(s):  
Time Series ◽  
Plant Growth ◽  
Water Availability ◽  
Vegetation Indices ◽  
Growing Season ◽  
Rainfall Data ◽  
Season Length ◽  
Wet Season ◽  
Semi Arid ◽  
Over Time

<p>In the semi-arid Peruvian Andes, the agricultural growing season is mostly determined by the timing of the onset and cessation of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa valley (Callejón de Huaylas) bordered by the glaciated Coordillera Blanca to the east and the unglaciated Coordillera Negra to the west, reported increasing challenges in the predictability of the onset, more frequent dry spells and extreme precipitation events during the wet season. Previous studies based on time-series of local rain gauges however did not show any significant changes in either timing or intensity of the wet season. Both in-situ and satellite rainfall data for the region lack the necessary spatial resolution to capture the highly variable rainfall distribution typical for complex terrain, and are often of questionable quality and temporal consistency. As in other Andean valleys, there remains considerable uncertainty in the Rio Santa basin regarding hydrological changes over the last decades. These changes are of a great concern for the local society and the lacking knowledge about changes in water availability (i.e. rainfall) and water demand (i.e. land use practices) hinder the assessment of relevant factors for the development of adaption strategies.</p><p>The over-archiving goal of this study was to better understand variability and recent changes of plant growth and rainfall seasonality and the interactions between them in the Rio Santa basin. Specifically, we aimed to illustrate how satellite-derived information on vegetation greenness can be exploited to infer a robust and highly resolved picture of recent changes in rainfall and vegetation across the region: As the semi-arid climate causes water availability (i.e. precipitation) to be the key limiting factor for plant growth, patterns of precipitation occurrence and the seasonality of vegetation indices (VIs) are tightly coupled. Therefore, these indices can serve as an integrated proxy of rainfall. By combining a 20 year time series of MODIS Aqua and Terra VIs (from 2000 to today) and datasets of precipitation (both remote-sensing and observations) we explore recent spatial and temporal changes in vegetation and water availability by combining VIs timeseries and derived land surface phenology (LSP) with measures of wet season onset and cessation from rainfall data. Furthermore, we analyse the interaction of El Niño Southern Oscillation (ENSO) and the wet and growing season.</p><p>We find spatially variable but significant greening over the majority of the Rio Santa valley domain. This greening is particularly pronounced during the the dry season (Austral winter) and indicates an overall increase of plant available water over time. The start of the growing season (SOS) is temporally highly variable and dominates the variability of growing season length over time. Peak and end of season (POS, EOS) are significantly delayed in the 20 year analysis. By partitioning the results into periods of three stages of ENSO (neutral, Niño, Niña) we find an earlier onset of the rainy and growing season and an overall increased season length in years associated with El Niño.</p>

scholarly journals Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

2012 ◽  
Vol 13 (6) ◽  
pp. 1760-1783 ◽  
Author(s):  
Vera Thiemig ◽  
Rodrigo Rojas ◽  
Mauricio Zambrano-Bigiarini ◽  
Vincenzo Levizzani ◽  
Ad De Roo
Keyword(s):  
Heavy Rainfall ◽  
River Basins ◽  
Superior Performance ◽  
Rainfall Estimation ◽  
Monthly Basis ◽  
Rainfall Events ◽  
Mountainous Regions ◽  
Heavy Rainfall Events ◽  
Trmm 3B42 ◽  
Rainy Days

Abstract Six satellite-based rainfall estimates (SRFE)—namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]—were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba–Shabelle, and Baro–Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003–06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.

Ant Communities in the Gulf Region of Australia Semiarid Tropics - Species Composition, Patterns of Organization, and Biogeography

1993 ◽  
Vol 41 (4) ◽  
pp. 399 ◽  
Author(s):  
AN Andersen
Keyword(s):  
Species Composition ◽  
Arid Zone ◽  
Annual Rainfall ◽  
Pitfall Trapping ◽  
Wet Season ◽  
Gulf Region ◽  
Ant Communities ◽  
Number Of Species ◽  
Hot Climate ◽  
Semi Arid

The ant communities of nine sites near Lawn Hill (540 mm mean annual rainfall) in semi-arid north-western Queensland are documented, and compared with the known faunas of arid, semi-arid and seasonally arid sites elsewhere in Australia. The sites were surveyed primarily by pitfall trapping, during April (end of wet season) 1991, September (late dry season) 1991, and February (mid-wet season) 1992. A total of 111 ant species was recorded, with the most common being Iridomyrmex spp. and Rhytidoponera rufithorax. The richest genera were Melophorus (26 species), Monomorium (17), Iridomyrmex (16) and Camponotus (10) and Pheidole (10). The maj or functional groups were Dominant Dolichoderinae (Iridomyrmex spp.; 14% of the total number of species, 47% of the total number of ants in traps), Hot-climate specialists (mostly Melophorus spp.; 39%, 22%) and Generalised Myrmicinae (mostly Monomorium and Pheidole spp.; 20%, 11%). Multivariate analysis indicated that site differences in species composition were related primarily to landform, geology and soil type. Comparisons with other ant faunas show the Lawn Hill fauna to have closer affinities with those of the central arid zone than with those of high rainfall areas of the seasonal tropics. The arid-zone characteristics of the Lawn Hill fauna include a high proportion (38%) of Eyrean species, a high mean number of species per genus (6.5), and a very high combined representation of Iridomyrmex, Melophorus and Camponotus (45% of the total number of species, 69% of the total number of ants in traps).

scholarly journals Assessment of spatial uncertainty of heavy local rainfall using a dense gauge network

2018 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche
Keyword(s):  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Spatial Uncertainty ◽  
Wet Season ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Areal Rainfall ◽  
The Impact ◽  
Heavy Rainfall Events

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at sub-pixel scales and over short periods of time. Just a few studies have applied a similar approach, employing dense gauge networks, to local-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, we assess spatial uncertainty in observed heavy rainfall events. The network is located in southeastern Austria over an area of 20 km × 15 km with no significant orography. First, the spatial variability of rainfall in the region was characterised using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between wet and dry seasons are apparent and we selected heavy rainfall events, the upper 10 % of wettest days during the wet season, for further analyses because of their high potential for causing hazard risk. Secondly, we investigated uncertainty in estimating mean areal rainfall arising from a limited gauge density. The number of gauges required to obtain areal rainfall with > 20 % accuracy tends to increase roughly following a power law as time scale decreases. Lastly, the impact of spatial aggregation on extreme rainfall was examined using gridded rainfall data with horizontal grid spacings from 0.1° to 0.01°. The spatial scale dependence was clearly observed at high intensity thresholds and high temporal resolutions. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the rational use of the data in relevant applications. Our findings are generalisable to other plain regions in mid-latitudes, however the degree of uncertainty could be affected by regional variations, like rainfall type or topography.

scholarly journals Projected changes of rainfall event characteristics for the Czech Republic

2016 ◽  
Vol 64 (4) ◽  
pp. 415-425 ◽  
Author(s):  
Vojtěch Svoboda ◽  
Martin Hanel ◽  
Petr Máca ◽  
Jan Kyselý
Keyword(s):  
Czech Republic ◽  
Heavy Rainfall ◽  
Rainfall Intensity ◽  
Extreme Values ◽  
Rainfall Event ◽  
Rainfall Events ◽  
The Czech Republic ◽  
Precipitation Total ◽  
Projected Changes ◽  
Heavy Rainfall Events

Abstract Projected changes of warm season (May–September) rainfall events in an ensemble of 30 regional climate model (RCM) simulations are assessed for the Czech Republic. Individual rainfall events are identified using the concept of minimum inter-event time and only heavy events are considered. The changes of rainfall event characteristics are evaluated between the control (1981–2000) and two scenario (2020–2049 and 2070–2099) periods. Despite a consistent decrease in the number of heavy rainfall events, there is a large uncertainty in projected changes in seasonal precipitation total due to heavy events. Most considered characteristics (rainfall event depth, mean rainfall rate, maximum 60-min rainfall intensity and indicators of rainfall event erosivity) are projected to increase and larger increases appear for more extreme values. Only rainfall event duration slightly decreases in the more distant scenario period according to the RCM simulations. As a consequence, the number of less extreme heavy rainfall events as well as the number of long events decreases in majority of the RCM simulations. Changes in most event characteristics (and especially in characteristics related to the rainfall intensity) depend on changes in radiative forcing and temperature for the future periods. Only changes in the number of events and seasonal total due to heavy events depend significantly on altitude.

scholarly journals Multiscale Evaluation of Extreme Rainfall Event Predictions Using Severity Diagrams

2012 ◽  
Vol 27 (1) ◽  
pp. 174-188 ◽  
Author(s):  
Davide Ceresetti ◽  
Sandrine Anquetin ◽  
Gilles Molinié ◽  
Etienne Leblois ◽  
Jean-Dominique Creutin
Keyword(s):  
Time Scales ◽  
Rainfall Intensity ◽  
Mesoscale Model ◽  
Extreme Rainfall ◽  
Space Time ◽  
Extreme Rainfall Event ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Rainfall Depth ◽  
Heavy Rainfall Events

Abstract Observations and simulations of rainfall events are usually compared by analyzing (i) the total rainfall depth produced by the event and (ii) the location of the rainfall maximum. A different approach is proposed here that compares the mesoscale simulated rainfall fields with the ground rainfall observations within the multiscale framework of maximum intensity diagrams and severity diagrams. While the first simply displays the maximum rainfall intensity of an event at a number of scales, the second gives the frequency of occurrence of the maximum rainfall intensities as a function of the spatial and temporal aggregation scales, highlighting the space–time scales of the event severity. For use in a region featuring complex relief, severity diagrams have been generalized to incorporate the regional behavior of heavy rainfall events. To assess simulation outputs from a meteorological mesoscale model, three major storms that have occurred in the last decade over a mountainous Mediterranean region of southern France are analyzed. The severity diagrams detect the critical space–time scales of the rainfall events for comparison with those predicted by the simulation. This validation approach is adapted to evaluate the ability of the mesoscale model to predict various types of storms with different regional climatologies.

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