Regional Rainfall Research Articles

Palaeoenvironmental sequences surrounding Border Cave, South Africa, and review of conditions during middle and later stone age occupation

As a result of selective anthropogenic accumulation of plant and faunal remains in the sedimentary record at Border Cave, palaeoclimatological records at the site can only be broadly interpreted and cannot be reconstructed with any precision. To aid environmental reconstructions spanning the sedimentary record, we review published climate change proxy records from both marine and terrestrial archives within 500 km in the surrounding the summer rainfall region of the site to derive the history of environmental change. These published records, which show supporting evidence between the marine and terrestrial sequences, of which the former is more continuous, suggest that frequent environmental changes on a millennial scale involved fluctuations in temperature, seasonality, and moisture conditions to which the human occupants of the cave must have had to adapt continuously, e.g., cooler, windy climates, and more open vegetation during Marine Isotope Stage 2. Pollen previously extracted from the Border Cave deposits showed limited potential for climate reconstruction, but further research and comprehensive sampling of the sedimentary succession may potentially provide additional information on local environments to complement the existing palaeobotanical data.

Australia’s Future Extratropical Cyclones

Abstract Extratropical cyclones are responsible for the majority of total and extreme rainfall in many regions of the extratropics, including in southern Australia. Using an ensemble of projections from 12 regional climate models, we show that both the number of cyclone days and total cyclone-related rainfall are projected to decline across southern Australia during the twenty-first century. This is a robust signal in projections across models and explains more than 80% of projected declines in total cool-season rainfall in the region. Using cyclone-centered composites, we show that cyclone intensity based on wind speed is projected to decrease but cyclone maximum rainfall is projected to increase by close to 7% K−1 in the southeast. This results in a shift in the distribution of cyclone rainfall, with a decrease in moderate rainfall but little change or an increase in extreme rainfall. Significance Statement Extratropical cyclones are very important for southern Australian rainfall, and they are expected to become less frequent in a warming climate. Our research shows this explains at least 80% of the projected decline in cool-season rainfall in southern Australia. However, the frequency of extratropical cyclones with very heavy rainfall may increase, particularly in Tasmania and the southeast coast, with increases in rainfall intensity in line with the Clausius–Clapeyron relationship. This contributes to increases in the frequency of very heavy rainfall in the future.

Prediction Skill of GEFSv12 in Depicting Monthly Rainfall and Associated Extreme Events over Taiwan during the Summer Monsoon

Abstract The skillful prediction of monthly scale rainfall in small regions like Taiwan is one of the challenges of the meteorological scientific community. Taiwan is one of the subtropical islands in Asia. It experiences rainfall extremes regularly, leading to landslides and flash floods in/near the mountains and flooding over low-lying plains, particularly during the summer monsoon season [June–September (JJAS)]. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12), to support stakeholders for subseasonal forecasts and hydrological applications. In the present study, the performance evaluation of GEFSv12 for monthly rainfall and associated extreme rainfall (ER) events over Taiwan during JJAS against CMORPH has been done. There is a marginal improvement of GEFSv12 in depicting the East Asian summer monsoon index (EASMI) as compared to GEFS-SubX. The GEFSv12 rainfall raw products have been calibrated with a quantile–quantile (QQ) mapping technique for further prediction skill improvement. The results reveal that the spatial patterns of climatological features (mean, interannual variability, and coefficient of variation) of summer monsoon monthly rainfall over Taiwan from QQ-GEFSv12 are very similar to CMORPH than Raw-GEFSv12. Raw-GEFSv12 has an enormous wet bias and overforecast wet days, while QQ-GEFSv12 is close to reality. The prediction skill (correlation coefficient and index of agreement) of GEFSv12 in depicting the summer monsoon monthly rainfall over Taiwan is significantly high (>0.5) in most parts of Taiwan and particularly more during peak monsoon months, September, and August, followed by June and July. The calibration method significantly reduces the overestimation (underestimation) of wet (ER) events from the ensemble mean and probabilistic ensemble forecasts. The predictability of extreme rainfall events (>50 mm day−1) has also improved significantly.

Utilization of GPM Satellite and PERSIANN Satellite Data for Estimated Monthly Rainfall in South Sumatera

Rainfall data are widely used to predict regional rainfall. Limited rainfall data is a problem that has an impact on decreasing accuracy, one of which is in the area of South Sumatra. This can be overcome by using satellites. However, to utilize satellitebased rainfall data, it is necessary to carry out an analysis to determine the accuracy of rainfall data. This research aims to evaluate rainfall data from the GPM satellite and PERSIANN satellite with validation and calibration analysis so that the value of rainfall data from the Satellite is close to the measurement result and can be used to estimate monthly rainfall. In this study, the data used were measured monthly rainfall in the field, GPM, and PERSIANN obtained from 9 South Sumatra districts for 2019 until 2021. The research method was validated using correlation coefficient, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Calibration is done using a combination method, a solver algorithm in Microsoft Excel, and manually. The estimated monthly rainfall analysis is carried out using the isohyet method with the IDW interpolation method. The research results were obtained based on the validation and calibration of monthly rainfall data showing that data from the GPM showing it is closer to the results of field rainfall measurements than the data obtained from PERSIANN satellite. Based on the results of research on satellite data that has been calibrated, it can be used to estimate monthly rainfall in the South Sumatra Region

Prospective of the utilization of rainfall in coastal regions in the context of climatic changes: case study of Egypt

Changes in climate drivers are projected to have a considerable impact on coastal freshwater supply and demand systems in future decades. The utilization of rainfall in coastal regions to secure sufficient freshwater to satisfy the rising demands of socioeconomic development has become a critical duty for decision-makers. This article assesses the present state of rainwater utilization in the water resources system for the coastal regions in Egypt. Volumes of annual rainfall that precipitate in 11 coastal Egyptian cities are estimated using ArcGIS maps and the run off rational equation. A future vision of using the estimated rainwater harvesting (RWH) from urban coastal cities in the water resources system is presented. Several scenarios are suggested and analyzed for using these volumes of water in the total water resources system in Egypt and also in the water resources system corresponding to each selected coastal governorate. The results indicated that over the areas under consideration, the annual maximum total amount of precipitation is limited to 1.8 km3. However, it is anticipated that 1 km3 of the average annual quantity of rainfall water is efficiently used for agriculture. These findings are intended to help managers, planners, and decision-makers to choose locations, invest in water resources, and employ RWH as a substitute for traditional water sources.

Innovative polygon trend analyses with star graph for rainfall and temperature data in agricultural regions of Turkey

Innovative polygon trend analyses with star graph for rainfall and temperature data in agricultural regions of Turkey

Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks

Traditional weather forecasting approaches use various numerical simulations and empirical models to produce a gridded estimate of rainfall, often spanning multiple regions but struggling to capture extreme events. The approach presented here combines modern meteorological forecasts from the ECMWF SEAS5 seasonal forecasts with convolutional neural networks (CNNs) to improve the forecasting of total monthly regional rainfall across Great Britain. The CNN is trained using mean sea-level pressure and 2-m air temperature forecasts from the ECMWF C3S service using three lead-times: 1 month, 3 months and 6 months. The training is supervised using the equivalent benchmark rainfall data provided by the CEH-GEAR (Centre for Ecology and Hydrology, gridded estimates of areal rainfall). Comparing the CNN to the ECMWF predictions shows the CNN out-performs the ECMWF across all three lead times. This is done using an unseen validation dataset and based on the root mean square error (RMSE) between the predicted rainfall values for each region and benchmark values from the CEH-GEAR dataset. The largest improvement is at a 1-month lead time where the CNN model scores a RMSE 6.89 mm lower than the ECMWF. However, these differences are exacerbated at the extremes with the CNN producing, at a 1-month lead time, RMSEs which are 28.19 mm lower than the corresponding predictions from the ECMWF. Following this, a sensitivity analysis shows the CNN model predicts increased rainfall values in the presence of a low sea-level pressure anomaly around Iceland, followed by a high sea-level pressure anomaly south of Greenland.

Flood occurrence analysis in small urban catchments in the context of regional variability.

An original method for analyzing the influence of the meteorological, as well as physical-geographical conditions on the flooding of stormwater in small urban catchment areas is proposed. A logistical regression model is employed for the identification of the flooding events. The elaborated model enables to simulate the stormwater flooding in a single rainfall event, on the basis of the rainfall depth, duration, imperviousness of the catchment and its spatial distribution within the analyzed area, as well as the density of the stormwater network. The rainfall events are predicted considering the regional convective rainfall model for 32 rain gauges located in Poland, based on 44 years of rainfall data. In the study, empirical models are obtained to calculate the rainfall duration conditioning the flooding of stormwater in a small urban catchment area depending on the characteristics of the examined urban basins. The empirical models enabling to control the urbanization process of catchment areas, accounting for the local rainfall and meteorological characteristics are provided. The paper proposes a methodology for the identification of the areas especially sensitive to stormwater flooding in small urban catchment areas depending to the country scale. By employing the presented methodology, the regions with most sensitive urban catchments are identified. On this basis, a ranking of towns and cities is determined from the most sensitive to flooding in small urban catchment areas to the regions where the risk of flooding is lower. Using the method developed in the paper, maximum impervious catchment area are determined for the selected regions of the country, the exceedance of which determines the occurrence of stormwater flooding.

Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India

AbstractHere, we investigated the impact of assimilating the satellite‐based product of Global Positioning System (GPS) radio occultation (RO) refractivity profiles data on the simulation of selected extreme rainfall events in three states of southern India: Tamil Nadu, Telangana, and Kerala. We assimilated the GPS RO data into the weather research and forecasting model using a 3DVar assimilation technique and evaluated the results against unassimilated (control) simulations. Various observations (e.g., rainfall measurements from AWS/rain‐gauge) and observation‐based gridded rainfall were used. The assimilation of the data yielded improved prediction of the spatial distributions of extreme rainfall regions and the amounts of rainfall. The analysis of the simulated dynamical and thermodynamic processes indicated that the assimilation of the data enabled the model to simulate significantly deep convection, high instability, and strong vertical motions. A vorticity budget analysis confirmed the marginally strengthened low‐level convergence. The vertical motions because of assimilation facilitated an increased vertical advection of vorticity, which enhanced the extreme conditions in storms. Moreover, the assimilation of the data resulted in enhanced water vapor condensation and high levels of ice, cloud, and rain water in clouds, all of which contributed to extreme rainfall.

Effects of Design and Climate on Bioretention Effectiveness for Watershed-Scale Hydrologic Benefits

Bioretention areas are a common form of green stormwater infrastructure (GSI). There is significant research on the performance of individual bioretention cells, but the watershed-scale benefits of GSI are still unclear. Furthermore, differences in bioretention design and rainfall patterns make it difficult to compare results between studies. We used the Storm Water Management Model (SWMM) to assess the effects of bioretention size, soil infiltration rate, storm size, and climate on the watershed-scale performance of GSI. We first divided the contiguous US into 10 rainfall regions based on similarities in precipitation amount, intensity, and other storm characteristics. We then modeled the effects of bioretention areas in a single watershed under these different rainfall regimes. Bioretention areas did provide watershed-scale benefits, although performance declined as (1) bioretention areas became smaller, (2) soil infiltration rates decreased, and (3) precipitation depth increased. High-intensity rainfall was the primary cause of outflow from bioretention areas, although back-to-back storm events also caused outflow in some climates. There were some clear discrepancies between subbasin-scale and watershed-scale GSI performance. Generally, runoff volume reduction was greater when measured at the subbasin scale. Peak flow reduction, however, was greater at the watershed-scale, likely because bioretention areas changed the shape of subbasin runoff hydrographs, leading to watershed-scale peak flow reduction that was greater than the sum of the parts. We provide recommendations for design, management, and future research to help advance effective application of GSI for achieving watershed-scale hydrologic benefits.

Drought tolerant maize hybrids have higher yields and lower water use under drought conditions at a regional scale

Drought is an enduring abiotic constraint to stable and consistent maize productivity under climate change, especially for low rainfall regions with limited irrigation. One adaptation for severe drought is using drought-tolerant (DT) hybrids. Here, we characterize differences between conventional and DT hybrids in terms of yield and water-use efficiency under drought conditions at a regional scale of the Texas High Plains (THP). Using a validated version of APSIM-Maize, we simulated yields of conventional and DT hybrids across 11 water regimes and 25 counties in THP from 1984 to 2018. When irrigation amounts were constrained to 90%, 80%, 70%, 60% and 50% of total irrigation used for the baseline scenario (BS; a simulated scenario of conventional hybrid under full irrigation), DT hybrids showed lower yield penalties under drought stress relative to conventional hybrids. This improved total production by 19%, 24%, 26%, 26%, and 21% for each of the above irrigation levels. When the yield-target was set as 90%, 80%, 70%, and 60% of BS, total regional irrigation applied to DT hybrid could be saved more than that to the conventional hybrid, and therefore reduced more 17%, 16%, 15%, and 15% of BS irrigation, respectively. We showed that DT hybrids had greater yield gain and water savings through improved water productivity under deficit irrigation, highlighting the potential of deficit irrigation for increasing yield for the adoption of DT hybrid. Our quantitative evaluation of the yield advantage and water saving potential associated with DT hybrids also highlighted the regional benefits associated with adoption of drought adaptive hybrids.

Localizing the analysis of drought impacts on KwaZulu-Natal's commercial forests

Southern African countries are susceptible to recurrent droughts which enhance forest degradation and destructive pest outbreaks, which then significantly hinder the productivity of these forests. It is understood that drought impacts could be experienced within high rainfall regions and at a plantation level. However, very few studies have analyzed droughts within a plantation level in humid subtropical regions. Hence, there is a demand for efficient methods of analyzing droughts and the impacts they impose on forest vegetation within catchments that receive high rainfall. This study utilized a Kernel-support vector machine (SVM), random forests (RF), rotation forests (RTF) and extreme gradient boosting (XGBoost) to classify drought-damaged trees. The 2013 to 2017 study period is inclusive of the intense and prolonged 2015–2016 drought experienced in Southern Africa. The algorithms were employed on the vegetation moisture condition index (VMCI), vegetation condition index (VCI), normalized vegetation index (NDVI) and normalized difference water index (NDWI) derived from Landsat 4 and 5, 7 and 8 imageries. Results showed that all four algorithms were capable of effectively detecting trees that exhibit drought damage, especially RF and RTF and when classifying using conditional drought indices. This was based on the accuracy achieved by these algorithms where RTF and RF both achieved an overall accuracy (OA) of 96.30%, which was better than Kernel-SVM's 94.44% and the XGBoost's 90.74%. Overall, the study demonstrated that machine learning algorithms (MLAs), conditional drought indices and Landsat data could be used to classify drought damage on commercial forest vegetation within a high rainfall catchment.

Middle to Late Miocene tectonic forcing of intensified humidity in the Xunhua Basin, NE Tibetan Plateau

The mechanisms and controls of the Middle-Late Miocene paleoenvironmental evolution of the northeastern Tibetan Plateau remain elusive. Here, we present an integrated multiproxy study of total organic carbon isotopes, lipid biomarkers, petrographic components and micropaleontological records in the well-dated Xigou section of the Xunhua Basin. Our data reveal a large decrease (−1.1 ‰) in bulk organic carbon isotopes (δ13Corg) at ∼12.7 Ma, a broad minimum at ∼12.7–8.0 Ma, and a large increase (+1.3 ‰) at ∼8.0 Ma. The decline in δ13Corg at 12.7 Ma was accompanied by pronounced changes in n-alkane indices, clay minerals, palynology, and ostracods, recording a major rise of lake level of paleo-Lake Xunhua due to increased precipitation in the regional catchment. Our inference of sustained elevated humidity in the Xunhua Basin at 12.7–8.0 Ma is contrary to previously reported cooling and drying trends on the NE Tibetan Plateau for the Middle-Late Miocene (∼14–8 Ma), thus precluding the role of global climate change as the dominant driver. Rather, we attribute the paleoenvironmental evolution of the Xunhua Basin to changes in regional rainfall patterns related to orogenic uplift. The Jishi Shan mountain range reached a critical threshold elevation at ∼12.7 Ma, becoming an orographic barrier to Westerlies-transported moisture and thus enhancing intensified precipitation within the Xunhua Basin. By ∼8 Ma, further uplift caused this orogen to become a barrier to the East Asian monsoon, resulting in aridification of the Xunhua Basin.

Effects of intercropping sunn hemp into maize at different times and densities on productivity under rainwater harvesting technique

Maize is a major food crop in Sub-Saharan Africa (SSA), and its productivity is affected by climate change-adaptive sustainable management practices. A 2-year field study (2019/20 and 2020/21 growing seasons) was carried out to evaluate the effect of sunn hemp (Crotalaria junceaL.) living mulch management on maize (Zea maysL.) production. Three sunn hemp planting periods were simultaneous with maize planting (P1), V15 maize growth stage (P2), and R1 maize growth stage (P3) and three densities 16.1 plants m−2(D1-low), 32.1 plants m−2(D2-medium), and 48.1 plants m−2(D3-high). The intercrop components were planted in a split-plot treatment arrangement as an additive series with three replications and laid out in a randomized complete block design under the in-field rainwater harvesting (IRWH) technique. The growing season conditions revealed significant differences in rainfall distribution. Therefore, the planting period had highly significant (p< 0.001) effects on sunn hemp biomass, with an LSD value of 610.2 kg ha−1, showing that the highest was obtained in P2 (2636.7 kg ha−1) compared to P3 (811.3 kg ha−1). However, the P3 treatment resulted in maize grain yield penalty, with yields as follows: P3 (2775.2 kg ha−1), sole maize (3263.8 kg ha−1), P2 (3281.9 kg ha−1), and P1 (3287.8 kg ha−1). P2 yielded a significantly (p< 0.05) high-income equivalent ratio of 2.09, indicating a 109% advantage for increasing farmers' income by integrating sunn hemp under the no-till area of IRWH. P1 and P2 sunn hemp planting periods are viable options for smallholder farmers in summer rainfall regions to improve economic benefits.

Abrupt diatom responses to recent climate and land use changes in the Cantabrian Mountains (NW Spain)

The multi-proxy study of sediment cores from Lake Isoba (43° 02′ N, 5° 18′ W; 1400 m a.s.l.) allows a detailed assessment of the past hydrological and environmental dynamics in north-western Iberia resulting from the interplay between climate variability and anthropogenic impact. The combination of diatom stratigraphy, sedimentology and high-resolution elemental geochemistry along with a robust chronological framework (established by 210Pb, 137Cs and 14C dating) provides a detailed environmental reconstruction for the past ~ 500 years. Abrupt changes in the fossil diatom assemblages indicate a high sensitivity of this small lake to past environmental change and allow identifying four major stages related to the main climate fluctuations of the Little Ice Age (LIA) and recent warming. High lake levels, enhanced runoff and higher productivity characterised the middle phase of the LIA (~ 1550 to 1630 CE), indicating an overall wet climate. Conversely, shallow lake levels, decreased runoff and relatively low productivity prevailed during the last phase of the LIA and the onset of the Industrial Era (~ 1630 to 1925 CE), likely due to colder and drier conditions. High lake levels and higher carbonate input occurred after ~ 1925 CE until the 1980s CE, when our data show an abrupt drop in lake levels probably caused by a regional negative rainfall anomaly related to climate warming during the past decades. Finally, since ~ 1997 CE a remarkable and abrupt increase in the lake nutrient load and turbidity is detected, probably associated with the replacement of transhumant sheep flocks with staying cattle. The main environmental changes reconstructed at Lake Isoba mostly agree with other palaeoclimatic records from northern Spain. However, the hydrological patterns reconstructed are opposed to those observed on the northern slopes of the Cantabrian Mountains. The recent and strong impact of land-use changes on the lake, causing more ecological disruptions than previous climate changes, is noteworthy and demonstrates the high sensitivity of mountain lakes to human activities in a global change context.

Inflow Analysis into Bili-Bili Reservoir

Bili-bili Reservoir was built with the main objective of controlling flooding in Makassar City. With a flood control capacity of 41 million m3 with an initial capacity of 347.82 million cubic to 239.76 million cubic due to increased sediment in the reservoir so that if there is high rainfall it has the potential to cause flooding. The research entitled “Inflow Analysis to Bili-Bili Reservoir”, has a problem formulation of how the relationship between rainfall and inflow (discharge) entering the reservoir and how the comparison between inflow based on rainfall data and AWLR (Automatic Water Level Recorder). The purpose of this study was to analyze the relationship between rainfall and inflow discharge into the reservoir and to compare the inflow discharge based on the results of rainfall analysis with the inflow based on AWLR. This study uses secondary data from the Pompengan Jeneberang River Basin Center (BBWS). The regional average rainfall analysis used the Thiessen method, while the Synthetic Unit Hydrograph (HSS) Gama I method was used for flood discharge analysis. For flood discharge analysis, the maximum annual rainfall was selected according to the maximum inflow recorded at the Bilibili Dam AWLR. From the calculation of the HSS Gama 1 parameter, the rise time is 2.72 hours and the peak ordinate is 10.290 m3/second. When compared with the inflow (discharge) data on the Bili-Bili AWLR, the results of the calculation of the discharge with HSS Gama I are smaller in value with a difference of 20.50% to 31.46%.

Comparisons of soil organic carbon enrichment and loss in sediments among red soil, black soil, and loess in China

Water erosion could cause wide and serious soil organic carbon (SOC) loss, but differences in SOC loss and enrichment in sediments among red soil, black soil, and loess in China have received less attention. This study investigates the transport of sediments and generation regulation of runoffs during the erosion process by collecting data from indoor or outdoor artificial simulated rainfall experiments and selecting typical regional rainfall intensity and slope gradient for bare cultivate soil slopes as well as 5–8 m length and 1.5–2 m width runoff plots or soil pans. Then, the change in SOC loss for the three widely distributed and seriously eroded soils, from south to north in China, is clarified. Results show that the stable value and growth rate of soil and SOC loss rates followed the following order: black soil < red soil < loess. The SOC loss rate of loess was more sensitive to rainfall intensity and slope gradient than those of the two other soils. The SOC enrichment ratio (ERocs) of the sediments of the red soil and loess soil is higher than that of the black soil, and this difference increases as the soil loss rate decreases. ERocs generally has a negative exponential relationship with soil loss, but it has a negative logarithmic relationship with soil loss for the loess soil with high aggregate and clay contents. SOC and clay content determine the SOC enrichment in sediments for different soils. In addition, this study provides recommendations for improving SOC dynamic models for soil under water erosion.

Microphysical characteristics of seasonal rainfall observed by a Parsivel disdrometer in the Tianshan Mountains, China

Raindrop size distribution (DSD) is critical for understanding rainfall microphysical processes. Correlated DSD relationships, including the radar reflectivity factor-rainfall rate (Z–R), shape factor-slope parameter (μ–∧), and the rainfall kinetic energy-R (mass-weighted average diameter) (KE–R (Dm)) are significant in many fields such as quantitative precipitation estimation, parameterization schemes in weather forecast models, and assessing rainfall kinetic energy; however, the collective scientific understanding of DSD in arid regions remains insufficient. Here, 2020–2021 DSD data from the Tianshan Mountains, China, were collected to assess various corresponding characteristics for a typical arid region in China across different seasons, rainfall types, and rainfall rate classes. Rainfall in the fall (summer) had the most (least) small raindrops (diameter < 1 mm), the least (most) mid-size raindrops (1 ≤ diameter ≤ 3 mm), and large raindrops (diameter > 3 mm). Mean Dm was the largest (smallest) in the summer (fall) at 1.059 mm (0.876 mm), while the mean generalized intercept parameter was the largest (smallest) in the fall (winter) at 3.738 (3.505). For stratiform rainfall, fall (summer) had the most (least) small raindrops, as well as the least (most) mid-size and large raindrops, whereas, for convective rainfall, summer displayed fewer small and mid-size raindrops compared to spring. Regarding the rainfall rate classes, the concentration of mid- and large-size raindrops in spring changed from a minimum at C1 (0.1 ≤ R < 0.5 mm·h−1; notably comparable to fall rates) to C4 (2 ≤ R < 5 mm·h−1; comparable to summer), before peaking beyond summer rates at C5 (R ≥ 5 mm·h−1). The localized Z-R, μ–∧, KEtime–R, and KEmm–Dm relationships also exhibited marked seasonal characteristics, which ultimately impact estimates of regional radar quantitative precipitation and rainfall kinetic energy, as well as efforts aimed at improving model parameterization schemes. Additionally, the more abundant water vapor, the more frequent cold rain process, and the stronger warm–dry atmospheric vertical environment in summer appeared as the most important influencing factors leading to the fewest small-, and greatest mid- and large-size raindrops.

“Cold and wet” and “warm and dry” climate transitions at the East Asian summer monsoon boundary during the last deglaciation

Studies on millennial‒centennial abrupt climate events during the last deglaciation have significant relevance to modern-day abrupt and extreme climate changes that are occurring more frequently in the context of global warming. The northwestern margin of the modern East Asian summer monsoon (EASM) region is particularly sensitive to such climatic changes. In this study, we employed the continuous sedimentary core sequence of Dali Lake, located on the north-western edge of the EASM region. Carbonate clumped isotope (Δ47) thermometry was applied to both fine-grained endogenic carbonates and ostracod (Limnocythere inopinata [Baird]) shells in the lake core sediments to quantitatively reconstruct lake water temperature and oxygen isotope (δ18OW [VSMOW]) changes during the period from approximately 15.6–10.5 cal kyr BP (calibrated years before the present; abbreviated to “kyr”). Fine-grained endogenic carbonate Δ47 temperatures suggest that the lake surface water temperature changes may have been mainly controlled by the northern high latitudes, reflected in the corresponding millennial‒centennial abrupt climate events (such as a 5–6 °C decrease of summer surface water temperature during approximately 12.9–11.8 kyr, indicative of Younger Dryas [YD] cooling). Ostracod shell Δ47 temperatures complement the endogenic carbonates but appear to be affected by changes in calcification season, likely linked to climatic changes. These data suggest a climatic shift at approximately 15.2 kyr, before the onset of the Bølling‒Allerød (BA) interstadial, which is interpreted as general warming.δ18OW from the endogenic carbonates is interpreted as changes in precipitation and conforms well with 18O isotope changes in Greenland ice cores, suggesting that the precipitation changes may have been influenced by the northern high latitudes. Regional rainfall increased during the Heinrich event 1 (H1) and YD stadials, whereas it appears to have decreased in the BA and during the early Holocene (EH). This study indicates that the primary climate pattern at the northwestern boundary of the EASM region during the last deglaciation was characterized by alterations of “cold and wet” and “warm and dry” climate, rather than the more typical “cold and dry” versus “warm and wet” pattern.

Evaluation of traditional rain-fed agricultural terraces for soil erosion control through UAV observation in the middle mountain of Nepal

The livelihood of most people in the mountainous region of Nepal depends on traditional farming practices. Farmers modify their lands by constructing narrow outward sloping terraces to control soil erosion in the mountain and hill of Nepal. However, preliminary observation reveals that farmers adopt different strategies to manage outward sloping terraces according to the amount of rainfall so as to avoid the impact from soil erosion due to excessive rainfall. Thus, this study aims to evaluate the effectiveness of the traditional practice of topographic management of outward sloping rain-fed terraces to control soil erosion. The study makes a comparative analysis of topographic values of terraces and the estimated rates of soil erosion in different rainfall regions. Topographic values were derived from high spatial resolution (20 cm) Digital Elevation Model (DEM) data acquired by Unmanned Aerial Vehicle (UAV). Revised Universal Soil Loss Equation (RUSLE) was used to estimate the rate of soil erosion based on this high-resolution DEM together with other required variables. The research revealed that the traditional practice of topographic modification is highly effective to control soil erosion, as no significant differences in the rates of soil erosion were found even though the amount of rainfall considerably varies at different sites.