Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands

SPATIOTEMPORAL VARIATIONS AND PROJECTION OF CLIMATE CHANGE-INDUCED RAINFALL EROSIVITY OVER CENTRAL ASIA

气候变化引起的降水变异是导致世界大多数国家土壤过度流失的降雨侵蚀的主要原因。研究利用全球气候模型(GCMs) 对中亚地区降雨侵蚀度的时空预测进行了表征，并对降雨侵蚀度变化的影响进行了评价。该博士论文的研究内容为整个中亚地区的降雨侵蚀力，主要包含以下几点：（1）哈萨克斯坦年降雨侵蚀力时空变化及预测研究；（2）吉尔吉斯斯坦土壤侵蚀风险评估；（3）干旱-半干旱气候区域典型代表区域。研究结果表明：(1) 在代表性浓度路径 (RCPs) 2.6 和 8.5 下，采用 delta 方法对 GCMs(BCCCSM1-1、 IPSLCM5BLR、 MIROC5 和 MPIESMLR) 在“近”和“远” 未来 (2030 年代和 2070 年代) 两个时间段进行了统计学上的缩尺。这些 GCMs 数据被用于估计中亚地区的降雨侵蚀率及其预测变化。使用 WorldClim 数据作为研究区域的当前基线降水情景。利用修正的普遍土壤流失方程(RUSLE) 的降雨侵蚀率(R) 因子来确定降雨侵蚀率。结果表明，与基线相比，未来的年降雨量侵蚀率呈上升趋势。在所有 GCMs 中，与 402 MJ mm ha-1 h-1 year-1 基线相比，2030 年代的降雨侵蚀率平均变化为 5.6% (424.49 MJ mm ha-1 h-1 year-1) ， 2070 年代为 9.6% (440.57 MJ mm ha-1 h-1 year-1) 。变化的幅度随 GCMs 而变化，最大的变化是 26.6% (508.85 MJ mm ha-1 h-1 year-1)，发生在 2070 年代的 MIROC-5RCP8.5 场景中。尽管年降雨量侵蚀率呈稳定上升趋势，但 IPSLCM5ALR (RCP和周期)平均侵蚀率呈下降趋势。降雨量的增加是导致时空降雨侵蚀度增加的主要原因。(2) 研究了 1970-2017 年哈萨克斯坦降雨侵蚀度的时空变化。结果表明，过去 48 年哈萨克斯坦年平均降雨侵蚀度为 464 MJ mm ha-1 h-1 year-1。年降雨量侵蚀率没有明显的时间变化趋势。本文提出的一些结果与进一步研究哈萨克斯坦潜在的土壤侵蚀有关。东哈萨克斯坦、北哈萨克斯坦、阿拉木图地区受降雨侵蚀的威胁比其他地区更大。由于日降雨量总是有限的，了解干旱和半干旱地区过去和未来降雨侵蚀度的差异及其后果具有重要意义。采用 Delta 方法对 GCM场景 (GISSE2H、 HadGEM2-ES 和 NorESM1M) 进行三个周期的统计缩小。本研究利用过去和未来的气候数据估计了哈萨克斯坦年降雨量侵蚀率的长期变化。根据基线气候，在本世纪 30 年代、 50 年代和 70 年代，降雨侵蚀率的平均变化百分比分别为 26.9%、 26.4%和 35.2%。与基准气候相比，所有情景下所有气候模型的年降水量和侵蚀活动总量均呈现稳定增长。(3) 通过遥感和地理信息系统手段，利用 RUSLE 对吉尔吉斯斯坦的土壤流失进行了评估。结果表明，降雨侵蚀率(R)、土壤可蚀性(K)、坡长陡度(LS)、覆盖管理因子(C)、养护实践因子(P)的均值分别为 144.2–4509 MJ mm ha-1 h-1 year-1,0.014–0.042 t h MJ-1 mm-1, 0.0–44.6, 0.001–1.218 and 0.5–1.0。结果表明，平均年土壤侵蚀为200 t ha-1 year-1，平均年土壤侵蚀为 94.6 t ha-1 year-1，标准差为 206.3 t ha-1 year-1。吉尔吉斯斯坦的年土壤损失量为 17410 千吨。这些结果使我们能够确定易受侵蚀的区域。研究结果将将有助于出现组织情景，并为决策者提供有效管理土壤侵蚀风险的选择，以便对不同领域进行排序，进而充分执行政策。查明加剧侵蚀性增长的其他基本因素; 特别是未来土地利用和土地覆盖 (LULC) 的变化，仍有待于进一步的调查

Hydrological Forecasting under Climate Variability Using Modeling and Earth Observations in the Naryn River Basin, Kyrgyzstan

The availability of water resources in Central Asia depends greatly on snow accumulation in the mountains of Tien-Shan and Pamir. It is important to precisely forecast water availability as it is shared by several countries and has a transboundary context. The impact of climate change in this region requires improving the quality of hydrological forecasts in the Naryn river basin. This is especially true for the growing season due to the unpredictable climate behavior. A real-time monitoring and forecasting system based on hydrological watershed models is widely used for forecast monitoring. The study’s main objective is to simulate hydrological forecasts for three different hydrological stations (Uch-Terek, Naryn, and Big-Naryn) located in the Naryn river basin, the main water formation area of the Syrdarya River. We used the MODSNOW model to generate statistical forecast models. The model simulates the hydrological cycle using standard meteorological data, discharge data, and remote sensing data based on the MODIS snow cover area. As for the forecast at the monthly scale, the model considers the snow cover conditions at separate elevation zones. The operation of a watershed model includes the effects of climate change on river dynamics, especially snowfall and its melting processes in different altitude zones of the Naryn river basin. The linear regression models were produced for monthly and yearly hydrological forecasts. The linear regression shows R2 values of 0.81, 0.75, and 0.77 (Uch-Terek, Naryn, and Big-Naryn, respectively). The correlation between discharge and snow cover at various elevation zones was used to examine the relationship between snow cover and the elevation of the study. The best correlation was in May, June, and July for the elevation ranging from 1000–1500 m in station Uch-Terek, and 1500–3500 m in stations Naryn and Big-Naryn. The best correlation was in June: 0.87; 0.76; 0.84, and May for the elevation ranging from 1000–3500 m in station Uch-Terek, and 2000–3000 m in stations Naryn and Big-Naryn. Hydrological forecast modeling in this study aims to provide helpful information to improve our under-standing that the snow cover is the central aspect of water accumulation

Projected Rainfall Erosivity Over Central Asia Based on CMIP5 Climate Models

Climate change-induced precipitation variability is the leading cause of rainfall erosivity that leads to excessive soil losses in most countries of the world. In this paper, four global climate models (GCMs) were used to characterize the spatiotemporal prediction of rainfall erosivity and assess the effect of variations of rainfall erosivity in Central Asia. The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR) were statistically downscaled using the delta method under Representative Concentration Pathways (RCPs) 2.6 and 8.5 for two time periods: &ldquo;Near&rdquo; and &ldquo;Far&rdquo; future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity and its projected changes over Central Asia. WorldClim data was used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine rainfall erosivity. The results show an increase in the future periods of the annual rainfall erosivity compared to the baseline. For all GCMs, with an average change in rainfall erosivity of about 5.6% (424.49 MJ mm ha&minus;1 h&minus;1 year&minus;1) in 2030s and 9.6% (440.57 MJ mm ha&minus;1 h&minus;1 year&minus;1) in 2070s as compared to the baseline of 402 MJ mm ha&minus;1 h&minus;1 year&minus;1. The magnitude of the change varies with the GCMs, with the largest change being 26.6% (508.85 MJ mm ha&minus;1 h&minus;1 year&minus;1), occurring in the MIROC-5 RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity