Rainfall Type Research Articles

Crop Yield Prediction Using Machine Learning

Machine learning plays a key role in helping farmers and decision-makers predict crop yields, assisting them in choosing which crops to grow and how to manage them throughout the growing season. To better understand the effectiveness of different machine learning methods, we conducted a systematic literature review. From six electronic databases, we initially gathered 567 relevant studies and, after applying our selection criteria, narrowed it down to 50 studies for in-depth analysis. We focused on identifying the commonly used algorithms and features in these studies. The analysis revealed that temperature, rainfall, and soil type are the most frequently used features for crop prediction, while Artificial Neural Networks (ANN) are the most applied machine learning models. In addition, we expanded our investigation to include studies specifically using deep learning techniques. We reviewed 30 papers in this category and found that Convolutional Neural Networks (CNN) are the most popular choice. Other widely used deep learning algorithms include Long Short-Term Memory (LSTM) networks and Deep Neural Networks (DNN). Overall, our findings offer insights into the prevalent approaches and highlight opportunities for future research in crop yield prediction using both machine learning and deep learning techniques.

Characterization of dam failure flooding in an urban reservoir under different rainfall conditions.

In water conservation projects, reservoirs play a key role in controlling and regulating floods. In recent years, extreme rainfall events have occurred more frequently. Urban reservoirs in danger under extreme rainfall happen occasionally. The breaching of urban reservoirs seriously threatens the lives and property of the population downstream. A lack of clarity regarding the characterization of the flooding response remains owing to urban reservoir overtopping under different rainfall conditions. This study used observed data from the heavy rainfall event in Zhengzhou on July 20, 2021, with Jiangang Reservoir in Zhengzhou as the study site. The study designed a storm-flood process with different rainfall scenarios that combines the long-duration rainstorm formula and Chicago rain pattern method, establishing a response relationship between extreme rainfall and flooding processes. Flood evolution simulation analyses were carried out for different scenarios of rainfall and flooding processes under three working conditions: rainfall only, dam failure only and dam failure coupled with rainfall, to characterize the flood response due to dam failure under different rainfall conditions. The results showed that the peak of the rainfall occurs earlier in the total rainfall calendar time, the faster the dam failure rate of the reservoir. As the rain peak occurs at a time that is later in the total rainfall calendar, and the recurrence period and rainfall duration increase, the flood volume increases and urban inundation becomes more severe. The rainfall type had less of an effect on the difference in the extent of inundation between different working conditions. The extent of flood inundation for the dam failure-coupled rainfall scenario is much greater than that for the dam failure only scenario. The increase in inundation extent in the dam failure coupled rainfall scenario at the higher levels of flood severity is smaller. Study results provide hydraulic element support for flood hazard assessment.

Simulating the exploration of the optimal irrigation of spring wheat in drought areas based on SWAP model

AbstractWater scarcity and low irrigation efficiency are the main factors causing yield losses in spring wheat in arid areas. To propose optimised irrigation scheduling for spring wheat in the arid areas of Ningxia, China, we first calibrated the SWAP (soil–water–atmosphere–plant) model and then utilised the well‐tested SWAP model during three typical rainfall years to evaluate the 542 irrigation schemes. The results showed that the model performed well in simulating the plant height, leaf area index (LAI) and biomass of spring wheat under different water stress conditions. The R2 values of the above three crop growth indicators were all greater than 0.89, and the normalised root mean square error (NRMSE) was less than 18% in the validation period. Additionally, the model achieved a high degree of accuracy in simulating the soil water content, evapotranspiration and yield, with RMSEs of 2%, 31 mm, and 326 kg/ha, respectively. The total irrigation amounts of 290, 320 and 350 mm with five, six and six irrigation times in the wet, average, and dry years, respectively, could achieve relatively high yields as well as improved water use efficiency (WUE) and irrigation water use efficiency (IWUE). The results can provide useful information for the efficient utilisation of irrigation water resources in arid areas.

Effects of Rainfall Variability and Land Cover Type on Soil Organic Carbon Loss in a Hilly Red Soil Region of Southern China

Rainfall intensity (RI) and land cover type are two important factors that affect soil erosion and thus the transfer and loss of soil organic carbon (SOC). However, the in situ quantitative monitoring of SOC loss under natural rainfall and various land cover types restored on eroded lands has not been thoroughly examined. In order to further study the effects of rainfall changes and vegetation types on SOC loss in the red soil region of Southern China, the Jiangxi Eco-Science Park of Soil and Water Conservation in De’an County, Jiangxi Province, was taken as the research object. Considering natural rainfall and based on the long-term field in situ monitoring of rainfall and runoff and sediment data, we studied the effects of three land cover types (bare land, orchards, and grass cover) on surface runoff, sediment production, and SOC loss in relation to 1 hour of RI during natural rainfall in the red soil region of Southern China during rainy seasons of 2020 and 2021 (March to August). Compared with bare land plots, the orchard and grass cover plots had surface runoff reductions of 67% and 98%, respectively, and sediment reductions of 79% and 99% over the two rainy seasons, respectively. With an increasing RI over 1 hour, total SOC loss increased for each of the three land cover types. More SOC loss was associated with sediments, and the enrichment ratio of SOC in the sediments (ERoc) decreased significantly. The ERoc values decreased in the following order: bare land (1.23) > orchard (1.08) > grass cover (0.81). Bare land exhibited the highest proportion of SOC associated with sediment in the total SOC loss (Ps), at 68.69%, followed by the orchard plots, at 55.02%, and then the grass cover plots at 49.24%. With the transfer of land cover type from bare land to orchard and to grass cover (decreased soil loss intensity, SLI), more SOC was lost associated with runoff in the form of dissolved organic carbon (DOC); the values of ERoc and organic carbon loss intensity (CLI) also decreased significantly. These findings are crucial to improving our understanding of the regulatory mechanisms of rainfall changes and land cover types on SOC loss during soil erosion.

Flood Vulnerability Assessment in Paradip Coastal Area Using Weighted Overlay Index

Paradip, a prominent coastal city on India's eastern seaboard, presents a unique combination of environmental challenges and urban pressures. It has the largest tidal range among major regional tidal gauge stations, including Vishakhapatnam and Chennai. The city experiences an annual sea level rise of 2.33 mm, with projections indicating an increase of 0.183 feet by 2047 and 0.76 feet over the next century. The city's unique environmental and urban issues are exacerbated by its placement within the Mahanadi River's deltaic deposits, floodplains, and coastal marine deposits along the Bay of Bengal. The shoreline of Paradip is topographically uniform, with ground elevations ranging from 3.6 to 5.7 meters. According to India's Wind and Cyclone Hazard Map, Paradip is in the "Very High Damage Risk Zone," indicating the presence of significant wind speeds along the coast. The paper digs into these vulnerabilities and their possible influence on the coastal community, emphasizing the city's need to strike a balance between urban growth and ecological preservation. One of the main problems is flooding. Rainwater enters government buildings and impoverished areas because of encroachments on natural drainage systems. Dryland capable of residential, agricultural, and other economic activities is submerged by overflowing water which causes loss of life, property, and infrastructure. This study evaluates flood vulnerabilities in Paradip, a coastal city, using GIS and a weighted overlay index. By integrating satellite imagery, digital elevation models (DEM), and historical data on rainfall and soil types, we developed a detailed flood vulnerability map that categorises areas into high, moderate, and low risk. ArcGIS Pro 2.7.3 software and a weighted overlay index were utilized for the collection, processing, and display of spatial and attribute data. spatial analysis was the process used to produce the flood vulnerability map. The results provide essential insights for urban planning and flood mitigation strategies, with direct implications for improving disaster preparedness and climate resilience. The flood vulnerability map was then compared to the masterplan of Paradip which was proposed for 2030, to know the actual vulnerable area and to defy some major which were proposed by Paradip development authority. The study illustrates how remote sensing and GIS techniques can be effectively utilized to generate flood vulnerability data essential for informed decision-making.

Analysis and Validation of Sponge City Construction in Mountainous Cities based on SWMM - Taking Nanan District of Chongqing as an Example

Sponge city construction is one of the key topics in today’s research. China’s sponge city construction has gradually entered the stage of optimization and effect evaluation. However, there are still some gaps in issues such as the verification of the effect of sponge cities. Therefore, in this paper, Storm Water Management Model (SWMM) is carried out by collecting data on surface runoff, waterlogging, peak flow, control rate, etc. in Nanan District of Chongqing City. At the same time, reasonable Low Impact Developments (LID) facilities such as rain gardens, permeable paving, bioretention tanks, rain barrels, rooftop rainwater harvesting and rooftop gardens are incorporated into the sponge city design and verified by using a typical rainfall process with a return period of 5a and a duration of 3 hours. The results of the study showed that the surface runoff drainage time in one catchment was 11.1% faster before and after the LID facilities were installed in contrast, the peak flow reduction rate of the main pipe was 71.55%, and the average runoff control rate in each catchment was around 40.57%. In conclusion, the LID facilities are not effective enough to reduce the rainfall runoff above the medium rainfall, while they are more effective below the medium rainfall.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

Spatial Aggregations of the Grey Field Slug Deroceras reticulatum Are Unstable Under Abnormally High Soil Moisture Conditions.

Deroceras reticulatum in arable fields display spatio-temporally stable slug patches that have been well documented under typical soil moisture conditions. The effect of abnormally high soil moisture on slug patch stability, however, is unknown. In this study, stepped gradient choice tests comparing soil moisture levels of 50-125% soil capacity showed slug preferences for levels in a range near to 125%. Activity became erratic, however, when given a choice of high moisture levels (125-370%), potentially because slugs searched for preferred conditions. Slug spatial aggregation was investigated in 21 commercial fields in 2023/24, a season of extreme rainfall, and then compared to years exhibiting typical rainfall (2015-2018). Slug patches occurred in 27.2% of assessment visits to fields during 2023/24 compared to 96.4% in typical years, suggesting weather conditions leading to abnormally high soil moisture are significantly associated with the breakdown of slug spatial aggregation behaviour. Random forest models identified the weather predictors (precipitation, relative humidity, temperature) with the highest impact on slug distribution and relative abundance, with the assessment date and region also related to relative abundance. However, a complex of environmental parameters affects soil moisture content, and no statistically significant effects of individual weather predictors emerged. The results are discussed in relation to slug behaviour in the context of their impact on targeted slug treatments.

Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China

Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China

Applicability of a Modified Gash Model for Artificial Forests in the Transitional Zone between the Loess Hilly Region and the Mu Us Sandy Land, China

Afforestation in the transitional zone between the loess hilly area and the Mu Us Sandy Land of China has reshaped the landscape and greatly affected eco-hydrological processes. Plantations are crucial for regulating local net rainfall inputs, thus making it necessary to quantify the closure loss of plantation species in drought and semi-arid areas. To quantify and model the canopy interception of these plantations, we conducted rainfall redistribution measurement experiments. Based on this, we used the modified Gash model to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. Herein, Caragana korshinskii, Salix psammophila, and Pinus sylvestris plantations in the Kuye River mountain tract were chosen to measure the precipitation distribution from May to October (growing season). The applicability of a modified Gash model for different stands was then evaluated using the assessed data. The results showed that the canopy interception characteristics of each typical plantation were throughfall, interception, and stemflow. The relative error of canopy interception of C. korshinskii simulated by the modified Gash model was 8.79%. The relative error of simulated canopy interception of S. psammophila was 4.19%. The relative error of canopy interception simulation of P. sylvestris was 13.28%, and the modified Gash model had good applicability in the Kuye River Basin. The modified Gash model has the greatest sensitivity to rainfall intensity among the parameters of the C. korshinskii and S. psammophila forest. The sensitivity of P. sylvestris in the modified Gash model is that the canopy cover has the greatest influence, followed by the mean rainfall intensity. Our results provide a scientific basis for the rational use of water resources and vegetation restoration in the transitional zone between the loess hilly region and the Mu Us Sandy Land. This study is of import for the restoration and sustainability of fragile ecosystems in the region.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

How to effectively reduce sloping farmland nutrient loss and soil erosions in the Three Gorges Reservoir area

Fertilization and soil conservation measures play crucial roles in influencing nutrient loss and soil erosion on sloping farmlands. However, the long-term effects of these measures and the characterization of nutrient loss and sediment yield under different rainfall types and crop growth stages were not well studied. Therefore, we designed six treatments for sloping farmlands in the Three Gorges Reservoir Area with a field experiment. A field experiment included downslope cultivation with chemical fertilizer (DF), downslope cultivation with chemical fertilizer plus manure (DFM), cross-slope cultivation with chemical fertilizer plus manure (CFM), no-till straw cover with chemical fertilizer plus manure (NSFM), ridge plant hedges with chemical fertilizer plus manure (RFM), and biochar interception ditches with chemical fertilizer plus manure (BFM). The results indicated that soil and water conservation measures in association with manure substitution significantly reduced runoff depth (14.3–22.5 %), sediment yield (10.3–46.5 %), and total nitrogen (TN) loss (13.5–36.5 %) compared to DF. NSFM significantly reduced total phosphorus (TP) loss by 17.4 % and the TP loss from the other treatments did not show significant differences compared to DF. Rainfall intensity and runoff depth were identified as critical factors influencing nutrient loss and soil erosion. NSFM showed maximal nutrient reduction performance under different rainfall intensities, while DFM was not significantly effective. NO3--N and particulate P dominated the loss of TN and TP. The first 30 minutes of runoff generation and the seedling stage were identified as risk periods for N and P loss. The study suggests that the NSFM treatment was the appropriate method to prevent soil and water nutrient loss. This provides important insights for the precise control of nutrient loss and soil erosion on sloping farmlands.

Climate Profile Over Jeldu Woreda of West Shewa Zone, Oromia, Ethiopia

Identifying the climate profile of an area is essential for effective water resource management, agricultural planning and disaster preparedness. This study focuses on characterizing rainfall patterns and identifying the variability of Jeldu woreda in Oromia, Ethiopia. Previously and recently, little study tried to show the woreda profile, which did not bases on station data properly. Forty years (1981-2020) of stations’ rainfall and temperature data were utilized. The study area had natural forest, highland and mid land with varying topography. Based on the annual rainfall cycle, the woreda had bi-modal rainfall types, with the peak of the year being July. Statistical methods and python script were applied to identify the climate profile. The results show that main rainy seasonal rainfall starts on average from 12-14 June and ceases averagely around 10-15 October. During second rainy period, the rain starts from 25 February to 10 March. The mean annual rainfall of woreda is 1395.1 mm, and the average maximum temperature is 19.5°. Kiremt contributes the highest percent, which is more than 64 % of the annual total rainfall. The 1987 and 2015 were the driest years, and 1996 and 2010 is the wettest during kiremt, 1987 and 2010 the wettest, and 1999 and 2015 the driest during Belg. Seasonal rainfall had a regular to moderate precipitation concentration over woreda. Identifying the causes of unseasonal rainfall that happened during dry season over the study area can help to decrease weather hazard during the season.

Fat storage and drought tolerance in a seasonally‐adapted primate: Implications for modeling the effects of animal responses to global climate change

AbstractGlobal warming is changing habitats and affecting biodiversity, and is expected to exacerbate aridification in many regions. Animals and plants in seasonally dry tropical forests often exhibit adaptations to cope with seasonal resource limitation. However, whether these adaptations will facilitate drought tolerance or increase drought vulnerability is unclear. Here, we combine long‐term individual‐based data on phenology, morphometrics, and demographics to investigate how drought impacts the food resources, health, reproduction, and behavior of a population of Verreaux's sifaka (Propithecus verreauxi), a critically endangered lemur inhabiting dry deciduous forests in Madagascar. Between December 2010 and May 2023, the population experienced 3 years of severe drought (2016, 2017, 2022). During green periods in severe drought, the availability of high‐quality sifaka foods (young leaves, fruit) was significantly reduced and fruit tree mortality increased. This reduced availability of fruit persisted in the year after a drought, despite typical rainfall. Yet surprisingly, we found no negative effects on body condition or commonly‐used metrics for reproductive success during drought years or years following a drought. Instead, sifaka exhibited significantly higher levels of subcutaneous body fat during severe droughts. We observed little change in sifaka behavior between drought and non‐drought periods. However, they were more likely to lick dew during severe drought, and spent significantly less time feeding on young and mature leaves. They also significantly increased their time feeding on flowers and fruits, despite the reduced abundance of fruit in the habitat. Together, our results suggest that increased consumption of water‐rich fruit and flowers during severe droughts could facilitate physiological mechanisms that help sifaka cope with water scarcity, including fructose‐mediated fat storage, metabolic water production, and water conservation. These results provide new insights into how critically endangered animals may respond to climate change, suggesting that behavioral and physiological adaptations to seasonal resource limitation may buffer some mammals from the effects of severe drought or other extreme weather events.

Bacterial accumulation dynamics in runoff from extreme precipitation

Extreme precipitation can significantly influence the water quality of surface waters. However, the total amount of bacteria carried by rainfall runoff is poorly understood. Here, thirty rainfall scenarios were simulated by artificial rainfall simulators, with designed rainfall intensity ranging from 19.3 to 250 mm/h. The instantaneous concentration ranges of R2A, nutrient agar (NA) culturable bacteria, and viable bacteria in runoff depended on the types of underlying surfaces. The instantaneous bacterial concentrations in runoff generated by forest lands, grasslands and bare soil were: R2A culturable bacteria = 104.5–6.3, 104.5–6.1, 104.0–5.3 colony-forming units (CFU)/mL, NA culturable bacteria = 104.0–6.0, 103.9–5.8, 103.2–4.9 CFU/mL, and viable bacteria = 106.4–8.0, 107.0–8.9, 106.4–7.6 cells/mL. Based on the measured bacterial instantaneous concentration in runoff, cumulative dynamic models were established, and the maximum amount of culturable bacteria and viable bacteria entering water sources were estimated to be 109.38–11.31 CFU/m2 and 1011.84–13.25 cells/m2, respectively. The model fitting and the bacterial accumulation dynamics were influenced by the rainfall types (p < 0.01). Surface runoff from the underlying surface of forest lands and grasslands had a high microbial risk that persisted even during the “Drought-to-Deluge Transition”. Bacterial accumulation models provide valuable insight for predicting microbial risks in catchments during precipitation and can serve as theoretical support for further ensuring the safety of drinking water under the challenge of climate change.

Nocturnal Extreme Rainfall over the Central Yungui Plateau under Cold and Warm Upper-Level Anomaly Backgrounds during Warm Seasons in 1980–2020

The spatiotemporal and cloud features of the extreme rainfall under the warm and cold upper-level anomalies over the central Yungui Plateau (YGP) were investigated using the hourly rain gauge records, ERA5 reanalysis data, TRMM, and Fengyun satellite data, aiming to refine the understanding of different types of extreme rainfall. Extreme rainfall under an upper-level negative temperature anomaly (cold events) presents stronger convective cloud features when compared with the positive temperature anomaly (warm events). The maximum rainfall intensity and duration in cold events is much larger than that of warm events, while the brightness temperature of the cloud top is lower, and the ratio of convective rainfall is higher. In cold events, the middle-to-upper troposphere is dominated by a cold anomaly, and an unstable configuration with upper (lower) cold (warm) anomalies is observed around the central YGP. Although the upper-level temperature anomaly is positive, the anomalous divergence and convergence of southerly and northerly winds, as well as the strong moisture center and upward motions, are also found over the central YGP in warm events. The stronger atmospheric instability and higher convective energy under the upper-level cold anomalous circulation are closely associated with the rainfall features over the central YGP. The results indicate that the upper tropospheric temperature has significant influences on extreme rainfall, and thus more attention should be paid to the upper tropospheric temperature in future analyses.

Determination of Sustainable Factory Locations for the Lemon Agroindustry using AHP, Mapping and Water Management

This research was conducted in Suntenjaya Village, Lembang, and West Bandung Regency, focusing on lemon agro-industrial development. Research was conducted using the Analytical Hierarchy Process (AHP) approach, area mapping, and water management to determine a sustainable factory location. The main objective is the selection of factory sites by integrating lemon production, considering sustainable agriculture aspects, product aspects, and water conservation programs. The results of this study provide a strong foundation for sustainable agro-industrial development that will support sustainable agriculture, local economy, and environmental protection. The research also combined qualitative and quantitative elements with a mixed approach that included Focus Group Discussion (FGD) and AHP, and the results showed that integrated drainage management was the top priority, followed by sanitation, clean water, reforestation, and sustainable agriculture. Mapping of areas based on geographical characteristics, such as rainfall, slope, and soil type, provided a map of water infiltration rates, which became a key guide in planning water conservation programs. The ultimate location for the lemon agroindustry is half of Desa Suntenjaya, mostly from the center to the northern area, which needs to consider proximity to markets in Bandung Regency and City, easy access to sources of raw materials for lemons, water availability, adequate transportation infrastructure, access to energy sources, suitable climate for lemon growth, and the availability of adequate labor in the region.

High‐Frequency Isotope Compositions Reveal Different Cloud‐Top and Vertical Stratiform Rainfall Structures in the Inland Tropics of Brazil

AbstractUnderstanding the key drivers controlling rainfall stable isotope variations in inland tropical regions remains a global challenge. We present novel high‐frequency isotope data (5–30 min intervals) to disentangle the evolution of six stratiform rainfall events (N = 112) during the passage of convective systems in inland Brazil (September 2019–June 2020). These systems produced stratiform rainfall of variable cloud features. Depleted stratiform events (δ18Oinitial ≤ −4.2‰ and δ18Omean ≤ −6.1‰) were characterized by cooler cloud‐top temperatures (≤−38°C), larger areas (≥48 km2), higher liquid‐ice ratios (≥3.1), and higher melting layer heights (≥3.8 km), compared to enriched stratiform events (δ18Oinitial ≥ −3.8‰ and δ18Omean ≥ −5.1‰). Cloud vertical structure variability was reflected in a wide range of δ18O temporal patterns and abrupt shifts in d‐excess. Our findings provide a new perspective to the ongoing debate about isotopic variability and the partitioning of rainfall types across the tropics.

Response of soil moisture to rainfall following deep soil drying in China's hilly loess lands

Deep soil drying is a physical soil phenomenon that has become increasingly characteristic to artificial afforestation on China's Loess Plateau. Current research is largely short of conclusive reports on soil moisture recovery following deep soil drying in afforested lands. In this study, a 10-m deep underground column was constructed at Pengyang Experimental Station in Ningxia. The CS650-CR1000 automatic soil moisture monitoring system and BLJW-4 small meteorological observation stations were used to respectively monitor soil moisture and meteorological conditions in the study area for the period 2014–2019. The local rainfall was classified and the characteristics of soil infiltration analyzed at both monthly and annual scales. The results showed that: i) Deep soil moisture recovery in the semi-arid Loess Plateau region depended mainly on 25–49.9 mm and >50 mm types of rainfall; together accounting for 35.44 % of the precipitation. ii) Deep soil moisture replenishment occurred mainly for the period from April to October. While this accounted for 30.13 % of the precipitation, evaporation loss accounted for 69.87 % of it. With increasing monthly rainfall (Pm), the variation in monthly infiltration depth (Zm) was quadratic in shape — where Zm = −0.0094 Pm2 + 3.7702 Pm (R2 = 0.9577). iii) At the annual scale, deep soil moisture replenishment was mainly driven by year-on-year infiltration water accumulation. This is because a single year precipitation infiltration was not enough to replenish deep soil moisture. The cumulative infiltration depth for 2014–2019 was 180, 260, 400, 700, 1000 > 1000 cm. It suggested that soil water infiltration and deep dry soil recovery occurred at different times under rainfed conditions in the semi-arid loess hills in China. This is key for in-depth studies of the hydrological process in dry soil regions.

Effects of mixed land use on urban stormwater quality under different rainfall event types

The joint effect of mixed land uses and rainfall event types was studied using a two-year field monitoring program in four urban catchments in Calgary, Alberta, Canada. Event mean concentration (EMC) and event pollutant load (EPL) were employed to evaluate the total suspended sediment (TSS), nitrogen and phosphorus. The correlation analysis showed that most nitrogen and phosphorus components (except for NO2−/NO3− and TDP) predominantly exist in particulate form in the study areas. The correlation for EPL was notably stronger than EMC, which can be attributed to varying rainfall characteristics. The differences in EMCs and EPLs of TSS, nitrogen and phosphorus across catchments indicated that the complexity and spatial distribution of mixed land use can influence the generation and transportation of pollutants in urban runoff. The impacts of rainfall characteristics on stormwater quality are integrated rather than driven by a single rainfall characteristic. Brief but intense events tended to elevate TSS, nitrogen and phosphorus concentrations, especially in complex land-use catchments. Events with long antecedent dry days and short duration also resulted in increased pollutant concentrations, while events with long duration and low intensity could result in higher EPLs. The effect of mixed land use on water quality can vary depending on rainfall event types. Seasonal variations were found in EMC and EPL of TSS, nitrogen and phosphorus, with higher values in the spring and summer than the fall. Seasonal variations are mainly influenced by rainfall conditions, temperature and anthropogenic activities (e.g. lawn fertilization and de-icing with sands). MLR considering rainfall characteristics is an effective method for predicting stormwater quality within a single catchment. Considering complexity and spatial distribution of mixed land use can improve the accuracy of the harmonized MLR model. This research provided insights into understanding the complexities introduced by mixed land use and rainfall event types in urban stormwater quality.