Semi-arid Mountain Regions Research Articles

Remote sensing and geospatial approach: Optimizing groundwater exploration in semi-arid region, Nepal

Groundwater is the fundamental component of the hydrological system that acts as a major factor in comprehensions of the physical processes in both the land surface and the atmosphere. Determining groundwater, which directly affects the agricultural productivity of semi-arid mountainous regions, is crucial. Mountain ecosystems, once abundant with flowing water, now face immense pressure from a changing climate, evident in the drying of springs and the diminishing flow of groundwater. Ensuring a steady flow of water, fair access to it, and responsible use are the cornerstones of a secure future for mountain communities. This study aims to assess the groundwater potential zones using remote sensing and a geospatial approach in the Mustang Valley's rural municipalities (Thasang and Gharapjhong). Nine factors were assigned to assess the groundwater potential map: slope, drainage density, lineament density, geology, soil, land use/land cover, rainfall, aspect, and soil moisture. The Bayesian weights of evidence model was used to delineate the groundwater potential zone. The study categorized groundwater potential across the region, creating five zones: very high, high, moderate, low, and very low. These zones covered 0.6 %, 12.4 %, 51.2 %, 35.5 %, and 0.3 % of the study area. The accuracy of the groundwater potential map was assessed by comparing its predictions with the actual locations of springs, using the area under the curve metric. The receiver operating characteristics curve analysis yielded an area under the curve of 0.7226, indicating a 72.26 % accuracy in predicting the presence of groundwater. The findings of this paper contribute to a better understanding of groundwater potential zones, which can support policymakers and planners for hydrological, meteorological, and crop planning applications in this climatically vulnerable region.

The increases in extreme climatic events over the northeastern Tibetan Plateau and their association with atmospheric circulation changes

In the context of global warming, increasing frequency of climate extremes poses a great challenge to natural systems and humankind. However, the spatial and temporal characteristics, as well as the mechanisms of extreme climate change, remain insufficiently understood, particularly in arid and semi-arid mountainous regions with high climate sensitivity and ecological fragility. In this study, we analyzed the spatial and temporal characteristics of 27 extreme climate indices (including intensity, duration, and frequency) in the Qilian Mountains, northeastern Tibetan Plateau, from 1961 to 2016 using the CN05.1 meteorological grid dataset, explored the relationship between the extreme climate indices and internal variability of the climate system (e.g., the North Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Arctic Oscillation (AO)), and further probed into the mechanisms affecting climate extremes using ERA5 reanalysis dataset. The results showed that extreme temperatures in the Qilian Mountains were significantly enhanced in terms of intensity, frequency, and duration during 1961–2016. The decrease rates of frost days (FD0) and ice days (ID0) were − 4.4 d per decade and − 4.7 d per decade, respectively, and the increase rate of growing season length (GSL) was 2.9 d per decade. Extreme precipitation intensity increased significantly, with the intensity indices R95P and PRCPTOT showing the most significant change trends of 4 mm per decade and 14.9 mm per decade, respectively (p < 0.05). The number of consecutive dry days (CDD) displayed a significant downward trend, with a rate of −6 d per decade, the number of moderate precipitation days (R10) showed an increasing trend of 0.12 d per decade. Correlation analyses revealed significant associations between the increases in extreme climates and the AMO and PDO. Additionally, the enhancement of the AO contributed to the increased extreme precipitation. Enhanced anticyclonic circulation in Eurasia and geopotential height, as well as increased cloud cover in winter, were the main reasons for the increase in extreme temperatures in the Qilian Mountains, whereas the significant increase in water vapor transport entering the east boundary of the Qilian Mountains in summer mainly contributed to the increase in extreme precipitation. This study highlights internal oscillations in the climate system that regulate the extreme climate variability in the Qilian Mountains at various spatial-temporal scales.

Hydrogeochemical characterization of groundwater in mountainous catchment and its suitability for drinking purposes in Irob, Tigray, Northern Ethiopia

Abstract The primary source of drinking water in Ethiopia's semi-arid mountainous regions is groundwater. The present study aims to assess the hydrogeochemical characteristics of low-grade basement rocks dominated by mountainous catchments. Moreover, it examines the suitability of the groundwater quality for drinking purposes in Irob, Tigray, northern Ethiopia. However, relatively little is known about the water chemistry and groundwater quality of the resources in the area. Fifteen samples of groundwater were collected and examined for ions according to standard procedures. The outcomes were assessed against the World Health Organization (WHO) drinking water quality criteria. To identify the source of dissolved ions and the process involved, graphic interpretations were applied. The results show that Ca–Mg–SO4–HCO3 and Ca–SO4–HCO3 were the dominant water types. Gibbs plots and ionic ratios reveal that silicate weathering, carbonate dissolution and ion exchange control water chemistry. Furthermore, the findings reveal that 60, 80, 46.67, 46.67, 60, 6.67, 60 and 53.33% of samples are above the safe limits of the WHO for hardness, alkalinity, total dissolved solids, electrical conductivity, calcium (Ca2+), potassium (K+), bicarbonate (HCO3-) and sulfate (SO42-), respectively. Consequently, the groundwater quality assessment demonstrates that the water sources in lower parts of the catchment are unsuitable for drinking.

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims to understand the possibilities of S-1 SAR imagery to capture snowmelt dynamics and related changes in streamflow response in semi-arid mountains. The results proved that S-1 SAR imagery was able not only to capture the final spring melting but also all melting cycles that commonly appear throughout the year in these types of environments. The general change detection approach to identify wet snow was adapted for these regions using as reference the average S-1 SAR image from the previous summer, and a threshold of −3.00 dB, which has been assessed using Landsat images as reference dataset obtaining a general accuracy of 0.79. In addition, four different types of melting-runoff onsets depending on physical snow condition were identified. When translating that at the catchment scale, distributed melting-runoff onset maps were defined to better understand the spatiotemporal evolution of melting dynamics. Finally, a linear connection between melting dynamics and streamflow was found for long-lasting melting cycles, with a determination coefficient (R2) ranging from 0.62 to 0.83 and an average delay between the melting onset and streamflow peak of about 21 days.

Recent shift from dominant nitrogen to CO2 fertilization control on the growth of mature Qinghai spruce in China's Qilian Mountains

Terrestrial vegetation growth is stimulated by rising atmospheric CO2 concentration, a warmer climate, and increased soil nutrient availability. However, as plants age, progressive nutrient limitation is known to occur, especially in mature forests where soil nitrogen is deficient. Yet the long-term growth response of mature trees to rising CO2 accompanied by changing climate and nitrogen availability in semi-arid mountain regions is unclear. Here we used tree-ring widths and stable carbon (δ13C) and nitrogen (δ15N) isotopes to investigate the drivers of radial growth of mature Qinghai spruce (Picea crassifolia) in the central Qilian Mountains, northwest China, from 1840 through 2019. Tree growth benefited from improved nitrogen availability, chiefly via changes in bioavailable nitrogen pools modified by a favorable climate during 1930–1964. Enhanced intrinsic water-use efficiency (iWUE), driven by reduced stomatal conductance (gs) related to water deficit, lead to radial growth declines in 1985–2002. Recent acceleration of tree growth was largely attributed to a CO2 fertilization effect through enhanced iWUE during 2003–2019. Nitrogen availability was positively related to tree growth from the 1920s onward until greater CO2 fertilization ensued from 2000 onward. Hence, the negative effects of low nitrogen availability on growth could be mitigated or reversed by a high atmospheric CO2 concentration and warmer climate conditions. Our results suggest that mature spruce forests still harbor potential to increase ecosystem-level carbon sequestration and thereby partially mitigate climate warming. Such a nature-based solution in drought-prone forests would be achieved under warmer-wetter climate conditions in northwest China.

Impact of Elevational Gradients and Chemical Parameters on Changes in Soil Bacterial Diversity Under Semiarid Mountain Region.

Elevation gradients, often regarded as "natural experiments or laboratories", can be used to study changes in the distribution of microbial diversity related to changes in environmental conditions that typically occur over small geographical scales. We obtained bacterial sequences using MiSeq sequencing and clustered them into operational taxonomic units (OTUs). The total number of reads obtained by the bacterial 16S rRNA sequencing analysis was 1,090,555, with an average of approximately 45,439 reads per sample collected from various elevations. The current study observed inconsistent bacterial diversity patterns in samples from the lowest to highest elevations. 983 OTUs were found common among all the elevations. The most unique OTUs were found in the soil sample from elevation_2, followed by elevation_1. Soil sample collected at elevation_6 had the least unique OTUs. Actinobacteria, Protobacteria, Chloroflexi were found most abundant bacterial phyla in current study. Ammonium nitrogen (NH4+-N), and total phosphate (TP) are the main factors influencing bacterial diversity at elevations_1. pH was the main factor influencing the bacterial diversity at elevations_2, elevation_3 and elevation_4. Our results provide new visions on forming and maintaining soil microbial diversity along an elevational gradient and have implications for microbial responses to environmental change in semiarid mountain ecosystems.

Spatial distribution and controls of snowmelt runoff in a sublimation-dominated environment in the semiarid Andes of Chile

Abstract. Sublimation is the main ablation component of snow in the upper areas of the semiarid Andes (∼ 26 to ∼ 32∘ S and ∼ 69 to ∼ 71∘ W). This region has elevations up to 6000 m, is characterized by scarce precipitation, high solar radiation receipt, and low air humidity, and has been affected by a severe drought since 2010. In this study, we suggest that most of the snowmelt runoff originates from specific areas with topographic and meteorological features that allow large snow accumulation and limited mass removal. To test this hypothesis, we quantify the spatial distribution of snowmelt runoff and sublimation in a catchment of the semiarid Andes using a process-based snow model that is forced with field data. Model simulations over a 2-year period reproduce point-scale records of snow depth (SD) and snow water equivalent (SWE) and are also in good agreement with an independent SWE reconstruction product as well as satellite snow cover area and indices of winter snow absence and summer snow persistence. We estimate that 50 % of snowmelt runoff is produced by 21 %–29 % of the catchment area, which we define as “snowmelt hotspots”. Snowmelt hotspots are located at mid-to-lower elevations of the catchment on wind-sheltered, low-angle slopes. Our findings show that sublimation is not only the main ablation component: it also plays an important role shaping the spatial variability in total annual snowmelt. Snowmelt hotspots might be connected with other hydrological features of arid and semiarid mountain regions, such as areas of groundwater recharge, rock glaciers, and mountain peatlands. We recommend more detailed snow and hydrological monitoring of these sites, especially in the current and projected scenarios of scarce precipitation.

Assessing the Impact of Climate and Human Activities on Ecosystem Services in the Loess Plateau Ecological Screen, China

The ecosystem services (ES) can be influenced by various environmental factors. In order to efficiently allocate resources and manage ecosystems, it is important to understand the mechanisms by which these environmental effects impact the interactions and trade-offs among different ES. While previous studies have primarily examined the impact of individual environmental factors on ES, the intricate mechanisms underlying the effects of multiple environmental factors have been largely overlooked. In this study, we adopted a path analysis approach that considered interactions among explanatory variables. We analyzed multiple geospatial datasets from various sources, including remote sensing and climate data, to examine the main drivers—precipitation, temperature, FVC (fractional vegetation cover), NPP (net primary productivity), human activities, and altitude—affecting five ecosystem services: carbon sequestration service (C), habitat provision service (HP), soil conservation service (SCS), sand-stabilization service (SSS), and water conservation service (WCS) in arid and semi-arid mountainous regions. Our investigation found that all five ES have shown an upward trajectory over the past two decades. The most significant growth was observed in C, which increased by 39.4%. Among the environmental factors examined, precipitation has been identified as the predominant factor influencing the ES and the synergies and trade-offs among ES. The influence of precipitation on SCS reached a coefficient of 0.726. Human activity factors had the greatest influence on HP of the five ES with a path coefficient of 0.262. Conversely, temperature exhibited a suppressive influence on ES. The impact of factors such as NPP and altitude on ES was comparatively modest. Notably, human activities assumed a substantial contributory role in shaping the relationship encompassing WCS. It is worth noting that individual factors exerted differential effects on ES along distinct environmental gradients, including anthropogenic gradients. In this context, the combination of high altitude and substantial FVC demonstrated a notable contribution to WCS. Our study can provide valuable insights for the management of ES which can be utilized to optimize the regulation of the Loess Plateau Ecological Screen (LPES) ecological construction and promote regional sustainable development.

Vegetation Cover Dynamics in the High Atlas Mountains of Morocco

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as &gt;80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method.

A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region

Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product (94 % producer’s accuracy (PA) vs 84 %) and comparable user’s accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments.

Plant community assembly is jointly shaped by environmental and dispersal filtering along elevation gradients in a semiarid area, China.

Environmental filtering (EF) and dispersal filtering (DF) are widely known to shape plant community assembly. Particularly in arid and semi-arid mountainous regions, however, it remains unclear whether EF or DF dominate in the community assembly of different life forms or how they interact along elevational gradients. This research aims to reveal how different ecological processes influence herbaceous and woody community assembly and how they respond to various environmental drivers and elevational gradients. Here we integrated taxonomic diversity (TD), phylogenetic diversity (PD), and ecological drivers across an elevational gradient of 1,420 m in the Helan Mountain Nature Reserve, in typical arid and semi-arid areas of China. This study showed that the TD and PD of herbaceous communities significantly increase linearly with changing elevation gradients, while woody 'TD' showed a unimodal pattern, and there was little relationship between woody 'PD' and elevation. Herbaceous species exhibited significant phylogenetic clustering at low elevations, where they were influenced by climate, aspect, and tree cover. However, woody species exhibited random patterns across elevations. Herbaceous and woody species' taxonomic and phylogenetic beta diversity is governed primarily by spatial turnover rather than nestedness. Spatial turnover is caused primarily by EF and DF's combined influence, but their relative importance differs between herbaceous and woody communities. Therefore, we conclude that the responses of herbaceous and woody plants along elevation gradients in the Helan Mountains are decoupled due to their different adaptation strategies to climate factors in the drylands. These findings are important for understanding the assembly mechanisms driving plant communities in dryland under the context of dramatic increases in drought driven by climate warming.

Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling

Torrents play an essential role in water resources through rainfall in arid to semi-arid mountainous regions, serving large populations worldwide, and are also crucial in maintaining the downstream environment. The natural flows (floods, ephemeral flows) in arid hill regions result in potential hydrological fluctuations caused by climate change. However, the feasibility of eventual storage in remote hilly catchments would force a more sudden change. The current study was conducted in the lower part of the Khirthar National Range in the Sindh province of Pakistan, with the aim to explore spatial runoff storage sites for sustainable development to mitigate the impacts of climate change in arid areas. In total, 83 years of precipitation data were used to estimate water availability, along with satellite imagery for LULC pre- and post-monsoon conditions, delineation of watersheds, and identification of potential runoff storage locations and return periods, using Remote Sensing (RS)/Geographical Information System (GIS) 10.5.1, HEC-HMS 3.1, and Origin Pro 9.0 for statistical approaches. The model delineated two potential watersheds: Goth Sumar, covering an area of 61.0456 km2, wherein ten cascading reservoirs were identified, and Goth Baro, covering an area of 14,236 km2, wherein two cascading reservoirs were identified. Different storage capacities were determined for the cascade-type reservoirs. The maximum live volumetric potential storage of the reservoirs varies from 0.25 to 1.32 million cubic meters (MCM) in the villages of Baro and Sumar. The return periods have been estimated at 5, 10, 20, 25, 50, and 75 years, corresponding to 12.35, 16.47, 21.43, 21.72, 25.21, and 40.53 MCM for Goth Sumar, while Goth Baro’s storage capacity has been estimated at 2.88, 3.84, 5.00, 5.06, 5.88, and 9.45 MCM, respectively. All results obtained were authenticated using accuracy assessment, validation, and sensitivity analysis. The proposed potential storage sites were recommended for a planning period of five years. The live storage capacity of the identified cascade reservoirs can be improved by raising the marginal banks and developing the spillways to control inlet and outlet flow in order to maintain internal pressure on the reservoir banks. The stored water can be used for climate-friendly agricultural activities to increase crop production and productivity. The proposed study area has extensive experience with flood irrigation systems and rainwater harvesting to sustain agriculture due to rainfall being the only water resource (WR) in the region. However, the study area has enormous potential for surface runoff WRs, especially during the rainy season (monsoon); the current 2022 monsoon is showing flooding. The modeling approaches of Remote Sensing, GIS, and HEC-HMS play an important role in delineating watershed areas, developing hydrographs, and simulating water availability for different return periods by minimizing cost and time.

Application of Analytical Hierarchy Process and Geophysical Method for Groundwater Potential Mapping in the Tata Basin, Morocco

Ensuring water availability for agriculture and drinking water supply in semi-arid mountainous regions requires control of factors influencing groundwater availability. In most cases, the population draws its water needs from the alluvial aquifers close to villages that are already limited and influenced by current climatic change. In addition, the establishment of deep wells in the hard rock aquifers depletes the aquifer. Hence, understanding the factors influencing water availability is an urgent requirement. The use of geographic information system (GIS), and remote sensing (RS), together with decision-making methods like analytical hierarchy process (AHP) will be of good aid in this regard. In the Tata basin, located in SE Morocco, ten factors were used to explain the groundwater potentiality map (GWPM). Five categories of potential zones were determined: very low (8.67%), low (17.74%), moderate (46.77%), high (19.95%), and very high (6.87%). The efficiency of the AHP model is validated using the ROC curve (receiver operating characteristics) which revealed a good correlation between the high potential groundwater zones and the spatial distribution of high flow wells. Geophysical prospecting, using electrical resistivity profiles, has made it possible to propose new well sites. It corresponds to conductive resistivity zones that coincide with the intersection of hydrogeological lineaments.

Development of the Karun macroinvertebrate tolerance index (KMTI) for semi-arid mountainous streams in Iran.

The most robust approach to ecological monitoring and assessment is the use of regionally calibrated indicators. These should be calculated based on collocated biological (response) and physicochemical (stressor) variables and an objective rating and scoring system. In developing countries, a frequent lack of financial and technical resources for monitoring has led to many environmental problems being overlooked, such as the degradation of streams, rivers, and watersheds. In this paper, we propose the Karun Macroinvertebrate Tolerance Index (KMTI) for application to rivers in the Karun River basin, which is the largest watershed in Iran, draining semi-arid mountainous regions. The KMTI is the first biological index specifically developed and calibrated for Iranian water resources. Benthic macroinvertebrates, physical habitat, hydromorphic, and water quality data were collected and measured at 54 sites across four seasons in 2018 and 2019. A total of 101 families of benthic macroinvertebrates belonging to eight classes and 21 orders were identified, and tolerance values were determined for 95 families. The KMTI was found to be most efficient in identifying ecological degradation when data were used from winter samples with a discrimination efficiency (DE) 90% and a four-season mean of 84.3%. Also, the best DE of the water quality classification table based on the KMTI index was equal to 86.9%.

Detecting abrupt change in land cover in the eastern Hindu Kush region using Landsat time series (1988–2020)

Land cover change in the semi-arid environment of the eastern Hindu Kush region is driven by anthropogenic activities and environmental change impacts. Natural hazards, such as floods presumably influenced by climatic change, cause abrupt change of land cover. So far, little research has been conducted to investigate the spatiotemporal aspects of this abrupt change in the valleys. In order to explore the abrupt change in land cover and floods as its possible drivers in the eastern Hindu Kush, a semi-arid mountain region characterized by complex terrain, vegetation variation, and precipitation seasonality, we analyzed long-term Landsat image time series from 1988 to 2020 using Breaks For Additive Seasonal and Trend (BFAST). Overall, BFAST effectively detected abrupt change by using Landsat-derived Modified Soil Adjusted Vegetation Index (MSAVI). The results of our study indicate that approximately 95% of the study area experienced at least one abrupt change during 1988–2020. The years 1991, 1995, 1998, 2007, and 2016 were detected as the peak years, with the peaks occurring in different seasons. The annual trend of abrupt change is decreasing for the study area. The seasonality of abrupt change at the catchment level shows an increasing trend in the spring season for the southern catchments of Panjkora and Swat. The spatial distribution patterns show that abrupt change is primarily concentrated in the floodplains indicating that flooding is the primary driver of the land cover change in the region. We also demonstrated the accurate detection of past flood events (2015) based on the two case examples of Ayun, Rumbur, and Kalash valleys. The detection of the flood events was verified by fieldwork and historical high-resolution Google Earth imagery. Finally, our study provides an example of applying Landsat time series in a dry mountain region to detect abrupt changes in land cover and analyze impact of natural hazards such as floods.

Cloud water path, precipitation amount, and precipitation efficiency derived from multiple datasets on the Qilian Mountains, Northeastern Tibetan Plateau

Orographic clouds and precipitation are critical to the water cycle and ecology of arid and semiarid mountainous regions. This study intended to investigate the spatial and seasonal distribution of the cloud water path (CWP), precipitation amount, and precipitation efficiency (PE) on the Qilian Mountains (QM) of the northeastern Tibetan Plateau, as well as the related mechanisms linked with topographic distribution and water vapor transport. To solve the limitations of rain gauges and radar observations at high elevations of the QM, we used the latest multiple datasets, including the Global Precipitation Measurement (GPM), Advanced Very High Resolution Radiometer, and ECMWF Reanalysis v5 data from 2016 to 2019. The amount of precipitation retrieved from the two GPM products was evaluated using rain gauge data. The results show that the GPM data generally overestimate the precipitation amount on the QM and have a high correlation with rain gauge data in warm seasons and a low correlation in winter seasons, primarily due to the significant influences of snow and ice coverage. The terrain height and water vapor supply below 600 hPa are two critical factors that determine the spatial and seasonal distributions of clouds and precipitation in the QM. The precipitation generally increased with an increase in terrain elevation below 3000 m and decreased above 3000 m on the QM. CWP, precipitation amount, and PE in the mountainous areas are much higher than those in the surroundings and decrease from the southeast to the northwest because of the decrease in low-level water vapor supply. CWP, precipitation amount, and PE were the highest in summer because of the strong moisture transport from the southeast and the lowest in winter because of the weakest water vapor transport. Water vapor fluxes from the eastern, southern, and northern boundaries contributed to the wetness of the eastern and northeastern slopes of the QM. The lack of low-level water vapor in the westerly winds was the main reason for drought in the western QM.

Factors influencing chronic semi-arid headwater stream impairments: a southern California case study

&lt;abstract&gt; &lt;p&gt;Sources of stream impairments are well known; however, less attention has centered on characterizing the extent to which human-environmental factors influence headwater stream quality within semi-arid watersheds. This study quantified the extent to which seasonal weather patterns and landscape attributes contribute to the physicochemical characteristics of two perennial headwater tributaries and their confluence within the semi-arid mountainous region of the Santa Ana River Basin, California. &lt;italic&gt;In situ&lt;/italic&gt; sampling of stream temperature (℃), stream flow rate (m/s), nitrate (NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt;), ammonium (NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;), turbidity (NTU), dissolved oxygen (DO), conductivity, pH and lab assessments for. &lt;italic&gt;E. coli&lt;/italic&gt;, total coliform (TC) and total dissolved solids (TDS) occurred during dry and wet season conditions. Across sampling locations, multiple parameters (i.e. NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt;, NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, TDS, TC) consistently exceeded regulatory standards simultaneously during both the dry and wet seasons, however, the level of concentrations varied between a tributary catchment landscape with high percentage of impervious surfaces (i.e. roads, buildings) and wastewater infrastructure (i.e septic, sewer) versus one characterized by agricultural activities (i.e. crop, livestock) and barren land. Findings illustrate the need for hydrologically comprehensive strategies (i.e. stream headwaters to river mouth) that are community to agency-driven and that support the expansion of monitoring and shared knowledge to mitigate impairments within headwater streams and downstream. Potential avenues for community collaborations that support sustainable water management strategies are highlighted.&lt;/p&gt; &lt;/abstract&gt;

Haverkamp Modelin 4 Terimli Uzantısını Kullanılarak Teras Zeminin Doygun Hidrolik İletkenlik ve Sorptivite Tayini

Uzunçayır water retention Dam is located in the Tunceli province. This semi-arid mountainous region in eastern Turkey is prone to erosion. To combat this phenomenon, terraces along the edge of the Munzur University campus were built on sloping ground with pine plantations. The objective is twofold, on the one hand to mitigate soil erosion and protect the dam from sediments deposits, and on the other hand to promote the infiltration of rainwater and the vegetation of the sloping soil. However, the so-called ecofunctions of the terraces over the time are not proven. Monitoring the infiltration capacities of the terraces over time and space will allow a better understanding of their contribution to the hydrology of the watershed and their effect on soil erosion. The Beerkan infiltration technique offers simple and inexpensive experimental protocols. In addition, treatment methods are constantly improving. More recently, the four-term expansion of the implicit Haverkamp cumulative infiltration equation has been proposed. This complicated but robust formulation was used in this study to estimate the soil sorptivity S and saturated hydraulic conductivity Ks of terrace soil at Tunceli. The average Ks value of studied site was estimated to be 0.0852 mm/s with a standard deviation of 0.0112 mm/s. The soil sorptivity values were more extensive with mean value of 2.1227 mm/s0.5 and a standard deviation of 0.5855 mm/s0.5. The fit of the model used was perfect, showing that fast and robust estimation of S and Ks are possible using the new model.

Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.

Relationship between niche breadth and range shifts of Rhinopoma muscatellum (Chiroptera: Rhinopomatidae) in climate change scenarios in arid and semiarid mountainous region of Iran

Relationship between niche breadth and range shifts of Rhinopoma muscatellum (Chiroptera: Rhinopomatidae) in climate change scenarios in arid and semiarid mountainous region of Iran