Various indices have been used to investigate recent changes in the annual frequencies of extreme temperature events in Mongolia. A high-quality daily temperature dataset including 53 station records was used to determine trends from 1961 to 2010. The Climdex1.0 software was used to calculate 11 extreme temperature indices for this study. The results showed significant changes in important temperature indices over the study period, especially in the Gobi Mongolia. The analysis showed that an apparent increase in summer days was observed, while an appreciable decrease in frost days was observed. The maximum values of daily maximum temperature (TXx) and daily minimum temperature (TNx) and the minimum values of Tmax (TXn) and Tmin (TNn) tended to increase. However, for TXx and TNx, this increasing trend was mainly observed in the Gobi Mongolia, while for TXn and TNn, this increasing trend was observed over the entire country. A significant reduction at a rate of −0.6 d decade−1 (−1.0 d decade−1) occurred for cool nights (days), and a significant increase at a rate of 2.8 d decade−1 (3.1 d decade−1) occurred for warm nights (days). The reduction of cool nights and cool days occurred over four seasons, while the increase of warm days and warm nights occurred mainly in summer.
Abstract The Budyko framework, widely regarded as a simple and convenient tool to synthesize catchment water balance, is often employed with the atmospheric evaporative potential (E p ) that responds to water availability over a land surface. In this study, we demonstrated how the responsiveness of E p to soil moisture deficiency affects outcomes from a conventional Budyko equation. We combined a two‐parameter Budyko equation with the state‐of‐the‐art complementary relationship (CR) of evaporation (E), and analytically showed that the two‐parameter Budyko equation corrects E p to the wet environment E (E w ) of the CR. Using the Budyko equation combined with the CR, we assessed runoff sensitivity to climatic and land surface changes. Results showed that the CR could become a constraint for calibrating the implicit parameter of the Budyko equation. When compared to the Turc‐Mezentsev equation with E p , the shape parameters of the two‐parameter Budyko equation increased to regenerate an ensemble of global E data sets. Correcting E p to E w via the Budyko equation with CR reduced runoff elasticities to land property changes, suggesting that climatic changes are more important to changes in runoff than a prior sensitivity assessment would suggest. This study also suggests that the two‐parameter Budyko equation isolates the effect of the E p adjustment from the shape parameter, allowing it to more properly account for surface energy availability.
[1] Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance closure, or with spatially distributed prognostic models that simultaneously balance both energy and water budgets over landscapes using predictive equations for land surface temperature and moisture states. Each modeling approach has complementary advantages and disadvantages, and in combination they can be used to obtain more accurate ET estimates over a variety of land and climate conditions, particularly for areas with limited ground truth data. In this study, energy and water flux estimates from diagnostic Atmosphere-Land Exchange (ALEXI) and prognostic Noah land surface models are compared over the Nile River basin between 2007 and 2011. A second remote sensing data set, generated with Penman-Monteith approach as implemented in the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 ET product, is also included as a comparative technique. In general, spatial and temporal distributions of flux estimates from ALEXI and Noah are similar in regions where the climate is temperate and local rainfall is the primary source of water available for ET. However, the diagnostic ALEXI model is better able to retrieve ET signals not directly coupled with the local precipitation rates, for example, over irrigated agricultural areas or regions influenced by shallow water tables. These hydrologic features are not well represented by either Noah or MOD16. Evaluation of consistency between diagnostic and prognostic model estimates can provide useful information about relative product skill, particularly over regions where ground data are limited or nonexistent as in the Nile basin.
Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds.
A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality.
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