scholarly journals Quantifying Drought Resistance of Drylands in Northern China from 1982 to 2015: Regional Disparity in Drought Resistance

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 100
Author(s):  
Maohong Wei ◽  
Hailing Li ◽  
Muhammad Adnan Akram ◽  
Longwei Dong ◽  
Ying Sun ◽  
...  

Drylands are expected to be affected by greater global drought variability in the future; consequently, how dryland ecosystems respond to drought events needs urgent attention. In this study, the Normalized Vegetation Index (NDVI) and Standardized Precipitation and Evaporation Index (SPEI) were employed to quantify the resistance of ecosystem productivity to drought events in drylands of northern China between 1982 and 2015. The relationships and temporal trends of resistance and drought characteristics, which included length, severity, and interval, were examined. The temporal trends of resistance responded greatest to those of drought length, and drought length was the most sensitive and had the strongest negative effect with respect to resistance. Resistance decreased with increasing drought length and did not recover with decreasing drought length in hyper-arid regions after 2004, but did recover in arid and semi-arid regions from 2004 and in dry sub-humid regions from 1997. We reason that the regional differences in resistance may result from the seed bank and compensatory effects of plant species under drought events. In particular, this study implies that the ecosystem productivity of hyper-arid regions is the most vulnerable to drought events, and the drought–resistance and drought–recovery interactions are likely to respond abnormally or even shift under ongoing drought change.

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.


2010 ◽  
Vol 14 (2) ◽  
pp. 193-204 ◽  
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in the Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using the Variable Infiltration Capacity (VIC) model and measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with a correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.


2019 ◽  
Vol 11 (3) ◽  
pp. 225 ◽  
Author(s):  
Haibo Wang ◽  
Xin Li ◽  
Mingguo Ma ◽  
Liying Geng

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Esmaeel Parizi ◽  
Seiyed Mossa Hosseini ◽  
Behzad Ataie-Ashtiani ◽  
Craig T. Simmons

Abstract The estimation of long-term groundwater recharge rate ($${GW}_{r}$$ GW r ) is a pre-requisite for efficient management of groundwater resources, especially for arid and semi-arid regions. Precise estimation of $${GW}_{r}$$ GW r is probably the most difficult factor of all measurements in the evaluation of GW resources, particularly in semi-arid regions in which the recharge rate is typically small and/or regions with scarce hydrogeological data. The main objective of this study is to find and assess the predicting factors of $${GW}_{r}$$ GW r at an aquifer scale. For this purpose, 325 Iran’s phreatic aquifers (61% of Iran’s aquifers) were selected based on the data availability and the effect of eight predicting factors were assessed on $${GW}_{r}$$ GW r estimation. The predicting factors considered include Normalized Difference Vegetation Index (NDVI), mean annual temperature ($$T$$ T ), the ratio of precipitation to potential evapotranspiration ($${P/ET}_{P}$$ P / E T P ), drainage density ($${D}_{d}$$ D d ), mean annual specific discharge ($${Q}_{s}$$ Q s ), Mean Slope ($$S$$ S ), Soil Moisture ($${SM}_{90}$$ SM 90 ), and population density ($${Pop}_{d}$$ Pop d ). The local and global Moran’s I index, geographically weighted regression (GWR), and two-step cluster analysis served to support the spatial analysis of the results. The eight predicting factors considered are positively correlated to $${GW}_{r}$$ GW r and the NDVI has the greatest influence followed by the $$P/{ET}_{P}$$ P / ET P and $${SM}_{90}$$ SM 90 . In the regression model, NDVI solely explained 71% of the variation in $${GW}_{r}$$ GW r , while other drivers have only a minor modification (3.6%). The results of this study provide new insight into the complex interrelationship between $${GW}_{r}$$ GW r and vegetation density indicated by the NDVI. The findings of this study can help in better estimation of $${GW}_{r}$$ GW r especially for the phreatic aquifers that the hydrogeological ground-data requisite for establishing models are scarce.


2005 ◽  
Vol 36 (2) ◽  
pp. 175-192 ◽  
Author(s):  
Caihong Hu ◽  
Shenglian Guo ◽  
Lihua Xiong ◽  
Dingzhi Peng

The Xinanjiang model has been widely used in the humid regions in southern China as a basic tool for rainfall–runoff simulation, flood forecasting and water resources planning and management. However, its performance in the arid and semi-arid regions of northern China is usually not so good as in the humid regions. A modified Xinanjiang model, in which runoff generation in the watershed is based on both infiltration excess and saturation excess runoff mechanisms, is presented and discussed. Three different watersheds are selected for assessing and comparing the performance of the Xinanjiang model, the modified Xinanjiang model, the VIC model and the TOPMODEL in rainfall–runoff simulation. It is found that the modified Xinanjiang model performs better than the Xinanjiang model, and the models considering the Horton and Dunne runoff generation mechanisms are slightly better than those models considering the single runoff generation mechanism in semi-arid areas. It is suggested that the infiltration excess runoff mechanism should be included in rainfall–runoff models in arid and semi-arid regions.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1406
Author(s):  
Zhonglin Ji ◽  
Yaozhong Pan ◽  
Xiufang Zhu ◽  
Jinyun Wang ◽  
Qiannan Li

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.


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