Average Precipitation Research Articles

Projected changes of thermal, rainfall and drought indices in Morocco from high resolution climate models

Abstract Over the past decade, global changes in temperature, average and extreme precipitation have been observed many times. The increase in temperature or even the change in precipitation systems is directly affect extreme weather events, for example, heat waves, heavy rainfall, storms, and droughts are becoming more frequent and more powerful worldwide due to climate change caused by several factors including human activity. This is why there is a necessity to monitor future changes in climate extremes. The objective of this paper is to evaluate the future changes in thermal and rainfall extremes projected by the four climate models over time horizon 2041-2060 relative to the 1981-2010 reference period, based on the 5 climate indices, including Tmm, Tx90p, WSDI, PRCPTOT and SPI. The projections are given under the RCP 4.5 medium scenario. The analysis in thermal aspect shows that Morocco could experience by 2041-2060, a significant warming, has manifested by an increase in average temperature. Future changes in rainfall indices show that the spatial distribution of rainfall in Morocco could change significantly by 2041-2060 compared to the reference period 1971-2010. The four climate models agree on a decrease in annual precipitation that could affect most of the Kingdom. In the same way to dryness, the future changes of the SPI index that characterizes the drought, indicates a move to a dry climate.

An application of the remote sensing derived indices for drought monitoring in a dry zone district, in tropical island

Recent research has shown that droughts have intensified in South Asia over the past two decades. As a natural disaster, this has severely impacted people’s livelihoods, especially in the dry zones of Sri Lanka. Thus, to ensure human well-being, security of water resources, and ecosystem health, it is essential to minimize the impacts of droughts using reliable information. Remote sensing (RS) data and techniques help bridge the gap by enabling the analysis of drought phenomena through a diverse array of indices developed in the fields. However, until now, there has been a lack of systematic monitoring and reliable data for accurately characterizing droughts in the study area. As the first comprehensive analysis, we tried to evaluate the spatial–temporal dynamics of drought conditions in two Divisional Secretariat Divisions of the Anuradhapura district, Sri Lanka using eight standardized remote sensing-derived indices over a decade (2013–2023) including the Standardized Precipitation Index (SPI). SPI values indicated that the region has experienced notably below-average precipitation. According to SPI results, a significant portion of the Medawchchiya area experienced arid conditions in 2023. All other indices proved that 2018 was the driest year and 2013 was the wettest year among the three time points, as reflected by their low and high index values. However, according to NVSMI and LST, the wettest year is 2023, with only 1.78 % of areas experiencing severe drought and a maximum LST of 31.4 °C. LULC change detection revealed that 14.3 % of agricultural lands and 5.1 % of forest areas were converted into barren lands over a decade. Overall, this conversion may be another leading factor contributing to increased LST and dryness in the area during the concerned period while increasing the mean LST of barren land by around 4.7 °C per decade. Land surface-related and vegetation-related indices, such as NDWI, NDMI, LST, and NDVI, exhibited a more pronounced impact on short-term drought occurrences. The findings revealed that average precipitation coincides with short-term drought episodes in the area, with 2018 standing out as having the least rainfall and the driest year. The study’s findings may provide additional insights for planning authorities, supporting environmental protection and enhancing agricultural production by mitigating droughts’ impacts through short- and long-term strategies. Although, the study focused on a small area, a similar approach could be extended to other areas by incorporating advanced machine learning techniques and additional drought indices in the future.

Analyzing the heavy rainfall event of July 2011 in Niigata using ground, satellite and radar rainfalls

In recent years the application of radar and satellite precipitation observations has become increasingly useful in poorly gauged areas. However, understanding the relationships between these data sources and ground observations is vital to correct the datasets and improve their application in hydrological studies. In this study we analyzed the spatial and temporal relationships between Global Satellite Mapping of Precipitation-Near Real Time (GSMaP_NRT) data set with radar and ground observations at downstream of Shinano River, Japan. GSMap_NRT observation showed better relationship with ground observations for longer-duration observations (eg. 3, 6, 12 and 18 hours) than hourly observations. The GSMap_NRT showed excellent relationship with ground rainfall at satellite observation time compared to non-observation time. Comparison of radar observations at various time scales and spatial resolutions showed that radar estimates at smaller-time-interval ratio and lower-spatial-scale ratio are better than longer-time-interval ratio and higher-spatial-scale ratio. We also observed that radar precipitation estimate well-represent the areal averaged precipitation of ground observations. Results from this study showed that radar precipitation estimates could serve as very important input to improve merging of ground and satellite precipitation as well satellite rainfall improvement systems.

ПРИМЕНЕНИЕ КОЛИЧЕСТВЕННОГО МЕТОДА В ИССЛЕДОВАНИИ ОПОЛЗНЕВОЙ ВОСПРИИМЧИВОСТИ СЕЛЕОПАСНОГО БАССЕЙНА РЕКИ АГСУЧАЙ

The article uses a quantitative method to analyze dangerous landslide processes occurring in the mudflow-prone Agsuchay River basin, taking into account the active development of tourism and recreational activities in the Shamakhi-Ismayilli region. In order to identify landslide susceptibility and the potential manifestation of landslides, the “weight” of 9 factors associated with landslides was determined, including hypsometry, slope angles (slope steepness), slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion networks, distance to roads and land use. By summing up all the landslide formation factors without exception and multiplying them by their “weight”, a map of landslide susceptibility of the mudflow-prone Agsuchay River basin was compiled. The reliability of the obtained models was assessed using the AUC ROC (area under the error curve) analysis, which showed a fairly high efficiency (up to 72 %) of the applied method.

Influence of Weather Parameters on Abundance of Litchi Fruit Borer (Conopomorpha sinensis, Bradely, 1986)

Litchi (Litchi chinensis Sonn) is a subtropical fruit belonging to family Sapindaceae. India is the second largest global producer of Litchi, next only to China. Litchi fruit borer (Conopomorpha sinensis Bradely,1986 (Lepidoptera: Gracillaridae), attacks Litchi plant during different seasons. Their seasonal abundance causes damage to different plant parts including the fruits and leaves throughout the year. Their abundance and therefore damaging potentialities are affected by different environmental factors such as temperature, relative humidity, seasonal variations etc. An attempt has been made through this research work to precisely ascertain the impact of some of these factors on the proliferation of this pest. The result obtained will help in devising their control measures. The correlation analysis revealed significant negative correlation between proliferation of pest and temperature, relative humidity, rainfall, wind velocity. Among various weather parameters average temperature and precipitation shows positive and significant correlation with fruit damage.

Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

Assessing glacier mass balance variations and climate drivers of Shisper and Muchuhar glaciers in the Karakoram region, Pakistan: a remote sensing and GIS approach

ABSTRACT Glaciers in the Karakoram region of Pakistan are crucial water sources for downstream communities, supporting irrigation and power generation. Global warming has led to significant glacier mass loss and size reduction, posing risks to ecology, climate, and water resources. Understanding Glacier Mass Balance (GMB) is essential for climate change insights and predicting future trends, mainly due to limited long-term GMB and Equilibrium Line Altitude (ELA) data from the Karakoram region. We employed hypsometrically controlled methods, Accumulation Area Ratio (AAR) and Accumulation Area Balance Ratio (AABR) to fill this gap. AAR values ranged from 0.4 to 0.8 at 0.5 intervals, and AABR from 0.9 to 2.2 at 0.01 intervals. Findings indicate that Shisper Glacier had a negative mass balance of –1.0 m/y w.e., with the area below ELAs expanding by 1.72 km². Muchuhar Glacier showed a negative mass balance of -0.05 m/y w.e., with the area below ELAs decreasing to 0.96 km². Surface temperature trends indicate 6.44°C in 2023, highlighting a warming trend. Rising average air temperature (Tavg) is the significant factor influencing ELAs (26% and 72%, respectively), followed by average precipitation (Pavg) at 20% and 26%. These findings highlight the critical role of temperature and precipitation in glacier mass balance.

Responses of Climatic Drought to Vegetation Cover Dynamics: A Case Study in Yunnan, China

Vegetation cover can regulate regional climate and associated dry–wet variations. However, the effects of the quantitative structure and landscape pattern of vegetation cover on climatic drought remain unclear. Yunnan Province in China, with its abundant vegetation resources, provides a good setting for addressing this research gap. Our objective is to provide guiding recommendations for climate-warming mitigation through the study of the topic. This study adopted four periods of vegetation cover data, from 1992 to 2020, and explored their dynamics. Monthly average precipitation and temperature data from 125 meteorological stations in Yunnan were used to calculate standardized precipitation–evapotranspiration index (SPEI) for 1992–2020 to understand the responses of climatic drought to vegetation cover dynamics. The correlations between quantitative structure, landscape pattern, and climatic drought were investigated by Pearson’s correlation coefficient in 10 km, 20 km, 30 km, and 40 km grid cells, respectively. The results indicate that changes in the quantitative structure of vegetation could influence regional climates, with the contributions to climatic drought mitigation ranked in the following order: broad-leaved forest > shrubland > needle-leaved forest > cropland > grassland. Landscape patterns significantly affected local climates, where broad-leaved and needle-leaved forests had the strongest and most stable correlations with climatic drought, whereas shrubland and grassland showed weaker correlations. The correlations between landscape patterns and climatic drought were stronger during the dry season than the rainy season. Factors such as the landscape dominance index, fragmentation index, and aggregation index had a significant impact on climatic drought. The dominant and aggregated-distribution broad-leaved forests were conducive to climatic drought mitigation, while needle-leaved forests, croplands, and grasslands might exacerbate climatic drought.

Constraining Regional Hydrological Sensitivity Over Tropical Oceans

AbstractRegional hydrological sensitivity (i.e., precipitation change per degree local surface warming) contributes substantially to the uncertainty in future precipitation projections over tropical oceans. Here, we investigate the sensitivity of relative precipitation (P*, precipitation divided by the basin average precipitation) to local sea surface temperature (SST) change by dissecting it into three components, namely the sensitivity of P* to relative SST (SSTrel, SST minus the tropical mean SST) changes, the sensitivity of P* to surface convergence changes, and the sensitivity of surface convergence to SST gradient changes. We show that the relationships between P* and SSTrel, and between P*, surface convergence, and SST gradients are largely constant during climate change. This allows us to constrain regional hydrological sensitivity based on present‐day SST‐precipitation relationships. The sensitivity of surface convergence to SST gradient changes is a main source of uncertainty in regional hydrological sensitivity and is likely underestimated in GCMs.

Developing a water budget for the Amman-Zarqa basin using water accounting plus and the pixel-based soil water balance model

AbstractWater resources assessments are essential for effective planning in water-scarce regions such as Jordan. Such assessments require sufficient data in space and time. The WaPOR-based Water Accounting Plus (WA +) framework is relevant as it integrates remote sensing data and the Pixel-Based Soil Water Balance model to simulate a basin’s water balance. However, since it relies on remote sensing, this framework only tracks water consumption in irrigated agriculture and does not consider non-irrigation water use and its return flow. This paper modifies the WaPOR-based WA + framework to include non-irrigation manmade consumption and its return flows. The modified framework provides a more comprehensive water budget for the Amman-Zarqa (AZ) basin, presented in a modified WA + resource base sheet for 2018 through 2021. The results show that water availability in the AZ basin is highly responsive to precipitation changes. Average precipitation was approximately 926 Mm3/year between 2018 and 2020, corresponding to an average available water of 485 Mm3/year. However, a reduction in average precipitation by 28% in 2021 corresponded to a reduction in available water to 243 Mm3/year. Nevertheless, substantial groundwater outflows to neighbouring basins may indicate that available water is being overestimated. Manmade consumption increased by 18% from 2018 to 2021, and the total demand exceeded the available supply by 150%. This underscores the pressing need to investigate supply augmentation and conservation methods. Future studies could focus on improving the representation of groundwater dynamics in the modified framework by improving groundwater dynamics in PixSWAB and testing the modified framework with other remote sensing datasets.

Spatiotemporal climate variability and food security implications in the Central Ethiopia Region

Studies focusing on the spatiotemporal distribution of climatic parameters and meteorological drought are of paramount significance for countries like Ethiopia, where climate change and variability cause major losses to rain-dependent agriculture. In this study, the National Meteorology Institute of Ethiopia provided an Enhanced National Climate Services (ENACTS) dataset at a spatial resolution of approximately 4 km by 4 km over 38 years (1981–2018) was used to study climate trends, spatiotemporal variability, and meteorological drought in the Central Ethiopia Region. Coefficient of variation (CV), Standardized Rainfall Anomaly (SRA), Standardized Precipitation Index (SPI), Mann-Kendall trend test, and Sen's slope were used for the analysis. The findings suggest that Belg rainfall (also known as "small-rain") varied greatly in space and time over the study area, with area-averaged CV of 29 % and pixel-level CVs ranging from 63 to 93 %. The average precipitation during Belg experienced a 15 % decrease from 2000 to 2019 compared to the preceding two decades, from 1981 to 1999. The maximum temperature has increased significantly during the Annual, Belg, and Bega seasons. The SPI and SRA showed that there have been multiple drought episodes with rising negative rainfall anomalies, with a drought occurring every 2.9 years during the Kiremt (also called "big rain", spanning from June to September) and Belg seasons. The growing negative rainfall anomaly, high CV, and highly significant increase in mean maximum temperature during the Belg season is concerning for food security and poverty eradication. The notable rise in rainfall during the June (sowing period) and November (harvesting period) also hurts crop production during the main cropping season. Thus, developing appropriate adaptation strategies and policies oriented toward climate-resilient agriculture is crucial to meet the global sustainable development goals (SDGs) and Africa Union's Agenda 2063.

Model-based analysis of the impact of climate change on hydrology in the Guayas River basin (Ecuador)

ABSTRACT Worldwide climate change will most likely lead to drastic changes in hydrology and food production. In this study, the impact of climate change on the hydrological regime and the fate of pesticides in the Guayas River basin is investigated using the Soil and Water Assessment Tool. Four general circulation models and three representative concentration pathways (RCP 4.5, RCP 6.0 and RCP 8.5) for three future periods were used to assess impact of climate change. Future projections showed a maximum increase in the average monthly precipitation of 40% in June, as well as an increase in an average minimum temperature of 3.85°C for July and an average maximum temperature of 4.5°C for August in 2080s. The model simulations based on RCP 8.5 scenario predict an increase in potential evapotranspiration by 11%, surface runoff of 39% and water yield of 33% in 2080s. The pesticide simulation showed the highest water concentrations during the wet season. Projections of trends in pesticide concentration indicate a similar trend to the current situation given the application rate remains the same. The results can be beneficial for the management and planning of the basin to mitigate flood and water quality-related impacts of food production and climate change.

Long Term Historical Precipitation and Temperature Trend Analysis for Selected Stations in Bale and East Bale Zones of Southeastern Ethiopia

Trend analysis for the long-term average temperature and precipitation of Bale and East Bale Zones during 1901 to 2016 has been performed for selected 5 stations to understand the pattern of these important meteorological features under climate change. The precipitation characterizations viz. standardized Precipitation Index has been considered for Sinana, Goro, Ginnir, Dallo menna and Agarfa stations. This study was aimed to analysis ‘’long term historical climate analysis selected stations in Bale and East Bale zones’’. The study considered station nearby kebeles was selected purposively from highland, midland and lowland districts. Standardized Precipitation Index drought index method was used to classify drought condition of the area. The findings showed that, the average temperature of the district showed an increased in the past three decades whereas the annual precipitation of the districts showed a decreasing trend with some variation from the mean in the period 1901- 2016 which was validated with 1984 – 2016 observed data. In line with this, representative respondents were considered from lowland, highland and midland agro-ecologies. Long term annual mean temperature showed increase trends by 0.17°C per decade at Sinana, Goro, Ginnir and Agarfa stations. Additionally, it was revealed more increment by 0.19°C per decade at Dallo menna station. From long term climate data analysis, it was indicated that extremely wet recorded in 1903, 1914, 1917, 1926, 1977 and 2013, while extremely dry conditions were recorded in 1919, 1938 and 1943 annual precipitation recorded for the whole period has been calculated and interpreted.

Long-term potato response to different irrigation scheduling methods using saline water in an arid environment

Crops’ water requirement is generally higher than the annual average precipitation in arid environments characterized by scarce freshwater resources. While using saline water for irrigation can help sustain agriculture in water-stressed regions, several challenges arises concerning productivity and soil salinization. However, adoption of efficient irrigation techniques such as drip irrigation, irrigation scheduling, and deficit irrigation can help optimize water productivity and mitigate salinity problems in irrigated agriculture. In southern Tunisia, potato is considered among the main cultivated horticultural crops due to its high economic value while it is considered as a crop sensitive to salinity. This crop (cv. Spunta) was the subject of long-term studies (2002–2020) conducted during the fall period in the arid region of Médenine. The crop response to full and deficit irrigation with saline water was assessed for several seasons under contrasting climatic conditions. Scheduling using the soil water balance (SWB) method consisted of the total and/or partial replacement of accumulated crop evapotranspiration (ETc), as derived from climatic data and crop coefficients. The impact of decreasing amounts of irrigation waters on crop yield and soil salinity with waters having a salinity ranging between 3 and 7 dS m−1 was evaluated. Results showed improvements in yield (30% to 37%) obtained with the SWB strategy under actual farming conditions, supporting the use of this strategy for irrigation. Appropriate scheduling also seems to be a key element in saving water (15%–22%) and in reducing risks of soil salinization. In the dry environment of southern Tunisia, optimum supply seems to correspond to a replacement of 100% to approximately 70%–80% of ETc. Applying such irrigation levels resulted in a lower salinity buildup in the root zone and higher crop water productivity. Natural salt leaching seems to be more effective under a more humid soil profile. Yield decreases and soil salinity increases almost linearly (r2 = 0.60) with decreasing irrigation water amounts. Future work should focus on the integration of management practices when using saline water. Investigating the relationship and interaction between irrigation amounts, cultivar, fertilizer supply, and salt leaching will help in resolving productivity and environmental issues.

Study on the Factors Affecting the Humus Horizon Thickness in the Black Soil Region of Liaoning Province, China

Understanding the spatial variability and driving mechanisms of humus horizon thickness (HHT) degradation is crucial for effective soil degradation prevention in black soil regions. The study compared ordinary kriging interpolation (OK), inverse distance weighted interpolation (IDW), and regression kriging interpolation (RK) using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and relative RMSE to select the most accurate model. Environmental variables were then integrated to predict HHT characteristics. Results indicate that: (1) RK was superior to OK and IDW in characterizing HHT with the smallest ME (11.45), RMSE (14.98), MAE (11.45), and RRMSE (0.44). (2) The average annual temperature (0.29), precipitation (0.27), and digital elevation model (DEM) (0.21) were the primary factors influencing the spatial variability of HHT. (3) The HHT exhibited notable variability, with an increasing trend from the southeast towards the central and northern directions, being the thinnest in the southeast. It was thicker in the northeast and southwest regions, thicker but less dense along the southern Bohai coast, thicker yet sporadically distributed in the northwest (especially Chaoyang and Fuxin), and thick with aggregated distribution over a smaller area in the northeastern direction (e.g., Tieling). These findings provide a scientific basis for accurate soil management in Liaoning Province.

СТРУКТУРЫ ИСПОЛЬЗОВАНИЯ ПАХОТНЫХ ЗЕМЕЛЬ В ЛЕСОСТЕПНОЙ ЗОНЕ БАШКИРСКОГО ЗАУРАЛЬЯ СО ВТОРОЙ ПОЛОВИНЫ 20 ВЕКА

We analyzed modern use of arable lands of 1985 in forest-steppe zone of Post-Ural of the Republic of Bashkortostan. Climate is temperate continental. An average annual temperature is 1.6℃ and an average annual precipitation of 427.9 mm. The hottest month is July with an average temperature of 17.5 and the coldest month is January with an average temperature of -14.8℃. Modern agricultural lands usually are used as a hay (43%). Use can be caused with local cattle breeding as traditional horse breeding. Overgrowth captures by 25% of Pinus silvestris. Scot’s pine is growing better on dry lands because it captures more area of overgrowth.

Quantifying recharge mechanisms in low-hilly areas of a loess region: Implications for the quantity and quality of groundwater

Groundwater plays a crucial role in sustaining water supply and irrigation in arid regions with thick vadose zones; however, the recharge mechanisms and their impact on water quality remain contentious. For flood-irrigated areas, irrigation water amount is not well known to evaluate the effect of irrigation on the recharge. In this study, we employed multiple tracers to investigate recharge mechanisms in a low-hilly region with some flood-irrigated areas, where irrigation water recharge was estimated by the chloride mass balance method combined with the tritium peak method. Deep soil samples from irrigated lands in vadose zone and groundwater from unconfined aquifers were collected to determine the contents of the water isotopes (2H, 18O, and 3H), chlorides (Cl-), and nitrates (NO3−). The findings revealed unique tritium peaks preserved in the loess vadose zone, where the recharge rate under the non-irrigated site was estimated to be only 8.6 mm yr−1, 2.4 % of the average annual precipitation. According to the estimated irrigated recharge rates, irrigation water dominates the potential diffuse recharge in vadose zone. For the aquifer recharge, we then quantified diffuse recharge (62.2 ± 4.7 %, piston flow) and focused recharge (37.8 ± 4.7 %, preferential flow) via the line-conditioned excess (lc-excess) balance method. Focused recharge occurred in low depressions, where surface runoff carrying sediments with high solutes converged in large rainfall events during wet season. Subsequently, focused mode rapidly recharges groundwater with NO3− and Cl- elevated, deteriorating water quality of phreatic aquifer. Finally, we concluded focused recharge does not dominate aquifer recharge, but significantly affects groundwater quality. These findings underscore the implications for the sustainability of agricultural water and soil resources management, emphasizing the potential impacts of soil conservation on groundwater quality in low-hilly regions.

Changes and driving factors of soil water in mixed-species plantations and different precipitation scenarios on the Loess Plateau of China

The implementation of the “Grain-for-Green” program in China led to the extensive introduction of artificial vegetation, thereby intensifying evapotranspiration and the overuse of water resources. Thus, selecting suitable ecological vegetation and planting patterns to save water is a major challenge in the region. Three typical plants were selected in the Loess Plateau of China. The plants had been growing for 13 years. The soil water dynamics were studied based on measurements and Hydrus-1D modeling simulations under different precipitation scenarios (wet, normal, and dry years) and planting patterns (Stipa monoculture (S), a mixture of Stipa and Medicago (SM), and a mixture of Stipa, Medicago, and Caragana (SMC)). The results indicated that the soil water contents with S and SM were 25 % and 8 % higher than the stable field water capacity (SFC), respectively, whereas the SMC was 33 % lower than the SFC in the 0–100 cm depth in relatively normal precipitation years. The evapotranspiration was lowest under S (296–524 mm), followed by SM (351–628 mm) and SMC (368–669 mm). The soil water storage were 19–91 % and 12–58 % higher under S and SM than SMC. The soil desiccation intensity was more severe under SMC (0.3–0.5) than SM (0.5–1.1) and S (0.6–1.4). The soil water storage increased significantly with the annual precipitation (P<0.05), and increased by 54–85 % in relatively abundant water scenarios compared with scarce water scenarios according to Hydrus-1D model simulations. Partial least squares path modeling demonstrated that the soil saturated hydraulic conductivity (path coefficient = 0.478) and soil porosity (–0.703) had significant effects on soil water storage (P<0.01). The results further suggested that using shallow-rooted grass as the dominant plant would reduce water usage in areas with water scarcity, and mixed-species plantations (i.e., a mixture of shallow-rooted and deep-rooted grass) could reduce the loss of sediment in regions.

Interplay of Environmental Shifts and Anthropogenic Factors with Vegetation Dynamics in the Ulan Buh Desert over the Past Three Decades

In arid and semiarid regions, vegetation provides essential ecosystem services, especially retarding the desertification process. Vegetation assessment through remote sensing data is crucial in understanding ecosystem responses to climatic factors and large-scale human activities. This study analyzed vegetation cover changes in the Ulan Buh Desert from 1989 to 2019, focusing on the impacts of human activities and key meteorological factors. The results showed that both climatic and human activities contributed to an increasing trend in vegetation cover (normalized difference vegetation index (NDVI)) over the 30-year period. Temperature and precipitation significantly affected the NDVI in the desert, with temperature having a more substantial influence. The combined impact of average temperature and precipitation on the NDVI was notable. Human activities and meteorological factors caused the vegetation restoration area in the desert to be approximately 35% from 1989 to 2019. Human activities were the primary influencers, responsible for about 60% of vegetation restoration across the study area. Especially from 2004 to 2019, the conversion to farmland driven by human activities dominated the region’s NDVI increase. The research underscores the importance of considering both climatic and human factors in understanding and managing ecosystem dynamics in arid areas like the Ulan Buh Desert. By integrating these factors, policymakers and land managers can develop more effective strategies for sustainable ecosystem management and combating desertification.