The objective of this work was to perform a quantitative analysis of the correlation between the forest burning index and abnormal decrease in river discharge under conditions of cryolithozone of Siberia. We analyzed the long-term and seasonal variation of rivers discharge in Central Siberia (Nizhnyaya Tunguska and Podkamennaya Tunguska rivers) and in Eastern Siberia (Aldan, Vilyui rivers) together with the forest burning dynamics within the river basins. The data on rivers discharge was obtained from the archive of The Global Runoff Data Centre for 1939–2015. Relative burned area (RBA) index was calculated from wildfires database collected using satellite technique for 1996–2017. RBA was evaluated as ratio of annual burned area within the river basins to the total area of the river basin. RBA values of 2.5–6.1% per year were considered as extremely high. The analysis of available chronologies of extreme fire events in Central and Eastern Siberia showed high correlation (r > –0.55) with long-term data on the runoff minima. Abnormally low level of discharge was 68–78% of the averaged annual rate. The most significant response of river discharge to the wildfire effect was shown for summer-autumn period of season after extreme burning in mid-summer.

Soil moisture takes an important part in involving climate, vegetation and drought. This paper explains that how to calculate the soil moisture index and the role of soil moisture. The objective of this study is to assess the amount of moisture content in soil and soil moisture mapping by using remote sensing data, in the selected study area. We applied the remote sensing technique with the purpose of relies on the use of soil moisture index (SMI) which in its algorithm uses the data obtained from satellite sensors. The relation between land surface temperature (LST) and normalized difference vegetation index (NDVI) are based on experimental parameterization for Soil moisture index. Multispectral satellite data (visible, NIR and TIRS) were utilized for assessment of Land Surface Temperature (LST) and make vegetation indices map. GIS and image processing software utilized to determine the LST & NDVI. NDVI and LST are considered as essential data to obtain SMI calculation. The statistical regression analysis of NDVI and LST were shown in standardized regression coefficient. NDVI values are within range -1 to 1 where negative values present loss of vegetation or contaminated vegetation, whereas positive values explain that healthy and dense vegetation. LST values are the surface temperature in °C. SMI is categorized into classes from no drought to extreme drought to quantitatively assess drought. The final result is obtainable with the values range from 0 to 1, where values near 1 are the regions with a low amount of vegetation and surface temperature and present a higher level of soil moisture. The values near 0 are the areas with a high amount of vegetation and surface temperature and present the low level of soil moisture. The results indicate that this method can efficiently applied to estimation of soil moisture from multi-temporal Landsat images, which is valuable for monitoring agricultural drought and flood disasters assessment.

The Fisheating Creek watershed located in Florida, USA is the focus of intense efforts to reduce nutrient transport into Lake Okeechobee which is located downstream. Public agencies and private land owners have proposed constructing large nutrient removal basins in the watershed to reduce the overall nutrient load into Lake Okeechobee. This is challenging given the nature of the watershed with its low water availability and sensitivity to drought. This study evaluates the feasibility of implementing nutrient removal systems in such a watershed including the overall risk and uncertainty of system performance. The study uses statistical evaluations of available water resources data and model simulations using HEC-HMS to evaluate watershed flow conditions. Then, the study outlines alternatives for nutrient removal system implementation. The study revealed that considerable nutrient reduction is feasible but not optimal due to low overall water availability. The primary conclusion is that while nutrient removal projects as large as 294 hectares can be constructed, the overall system operation will have to be very flexible to account for widely ranging inflows including very low flows during drought situations.

In the present study, using static land system parameters such as geomorphology, land cover and relief, we calculated water yield potential (RP) of all the watersheds of the Jhelum basin (Kashmir Valley) using analytical hierarchy process (AHP) based watershed evaluation model (AHP-WEM). The results revealed that among the 24 watersheds of the Jhelum basin, Vishav watershed with the highest RP is the fastest water yielding catchment of the Jhelum basin followed by Bringi, Lidder, Kuthar, Sind, Madhumati, Rembiara, Sukhnag, Dal, Wular-II, Romshi, Sandran, Ferozpur, Viji-Dhakil, Ningal, Lower Jhelum, Pohru, Arin, Doodganga, Arapal, Anchar, Wular-I, Gundar, and Garzan in case of same intensity storm event. The results were validated with the mean annual peak discharge values of the watersheds and a strong positive correlation of 0.71 was found. Further, for forecasting the floods in the watersheds having small lag time, such as in case of Vishaw, Bringi and Lidder, we evaluated the performance of HEC-GeoHMS hydrological model to simulate stream discharge during storm events. It was observed that the model performs well for august-september period with strong positive correlation (0.94) between the observed and simulated discharge and hence could be used as a flood forecasting model for this period in the region.

So-called “Type IX” chute spillways with impact baffle blocks have been used successfully around the globe for over 50 years. A key advantage of the chute spillway is the elimination of a costly stilling basin allowing for a more simplistic outlet works design. The current design process is based upon physical models developed in the 1950s and observation of completed projects over the last 50 years. The design procedure is empirical and provides the designer with a range of workable layouts, baffle heights, and baffle spacing. Unfortunately, this approach may not be optimal. This first study of a longer research effort focus uses Monte-Carlo simulations and computational fluid dynamics (CFD) to examine the design methodology and physical model basis for the current design procedure. Initially, the study examined the design procedure with a Monte-Carlo simulation to explore the range of acceptable designs that can be realized. Then, using CFD, full-scale prototype (located in Gila, Arizona USA) physical model result that were a key basis for the current design procedure were recreated. The study revealed that a wide range of acceptable chute designs can result from following the current design procedure but that some of these may be better than others. The study also outlines future research efforts needed to revise the current design methodology.

Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar–based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a sub-tropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash-Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.

As the alteration of the precipitation regime due to climate change, extreme precipitation events causing floods with the negative impacts on urban water infrastructure are observed today and expected to be observed in the future. This study examines the potential impacts of climate change and investigates the impact of these changes into urban stormwater network design. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for the (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest and used for future period. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for the observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend. The magnitude of future period extreme rainfall decreases with respect to observations. Return periods of the extreme rainfall increase in the future period therefore, not considering these trends may lead to overdesign of the stormwater network.

Existing urban infrastructure design criteria and assumptions may underestimate the loads such as peak flow, precipitation height, etc. under changing climate conditions. Extreme precipitation patterns that are used as design criteria for urban drainage networks is expected to change due to climate. Therefore, a strong need has emerged to study extreme events to reveal potential frequency and intensity alterations under changing climate conditions. In this study historical and future extreme precipitation and land use/cover change analyses results of Ankara province are applied for a newly built stormwater network of a pilot study area in Etimesgut, Ankara. Performance of the system investigated under current and changing conditions and different approaches such as stationary and nonstationary extreme value assumption. The system operated in a satisfactory state and it can be said that according to climate change projections for the extreme rainfall, the maximum volume that the system face will not exceed baseline design criteria throughout the projection period. Combination of changing climatic and land use/cover conditions also reveal a satisfactory performance for the baseline design which used 15 minutes storm duration and 2 years return period rainfall intensity and a runoff coefficient of 0,8 as design input. On the other hand the system may fail under the loads derived separately or together with longer storm duration (such as 30 minutes or more) or higher return periods (such as 5 years and more) that is computed from stationary and nonstationary observed data analysis which is a preferred design input for such a critical facility and area. Cost analyses of various options also conducted for the pilot study stormwater network according to the changing design inputs for the climate change and land use scenarios.

Annual maximum daily rainfalls will change in the future because of climate change, according to climate projections provided by EURO-CORDEX. This study aims at understanding how the expected changes in precipitation extremes will affect the flood behaviour in the future. The expected changes in precipitation extremes cannot be transformed directly into changes in runoff, as for a given rainfall event, the flood magnitude depends on the initial moisture content in the catchment, which in turn also depends on precipitation and temperature in the days before its occurrence. Therefore, hydrological modelling is required to characterise the rainfall-runoff process adequately in a changing climate to estimate flood changes.

Precipitation and temperature projections given by climate models in the control period usually do not fit exactly the observations in the same period from a statistical point of view. To correct such errors, bias correction methods are used. This paper aims at finding the most adequate bias correction method for both temperature and precipitation projections, minimising the errors between observed and simulated precipitation and flood frequency curves.

Four catchments located in central western Spain have been selected as case studies. The HBV hydrological model has been calibrated, using the observed precipitation, temperature and streamflow data available at a daily scale. Daily rainfall and temperature projections for RCP 4.5 and 8.5 provided by EURO-CORDEX have been used.

The results have shown that the correct calibration of some parameters of the HBV model is essential to obtain coherent results, mainly those related to surface runoff generation. In addition, soil moisture content at the beginning of flood events affects flood magnitudes. Consequently, expected changes in precipitation extremes are usually smoothed by the reduction of soil moisture content due to expected increases in temperatures and decreases in mean annual precipitation. Because of this the rainfall is the most signifcant imput to the model and the best bias correction is quantille mapping polynomial.

A significant spatio-temporal alteration of traditional patterns for hydrological components´ behavior is currently being observed. In this sense, a higher variability, more frequent and unpredictable extreme events (rainfall, flood and drought) occur in many areas. This is primarily due to global warming, which is producing a growing variability and uncertainty of water systems and specially, rivers´ runoff. The understanding of these modifications and, consequently, this adaptive rivers´ behavior is not trivial and requires new approaches incorporating dynamic and stochastic approaches. Causal Reasoning, supported by Bayesian modelling is a powerful stochastic approach to extract the time dependent logical structure that inherently underlies hydrological series. The river basin memory is dynamically and stochastically characterized in terms of the runoff dependence strength over the time. In this study, by means of causality, the Temporally Conditioned/Non-conditioned runoff (TCR/TNCR) fractions are identified and quantified. This research has important implications and applications, such as to the knowledge of the historical adaptive rivers´ behavior, or to reservoirs´ dimensioning optimization. This approach has been successfully applied to an unregulated river basin in Spain with a very high dependence temporal runoff behavior, within Júcar river basin (Mijares). Having a tool that could dynamically adjust the reservoir and/or channel capacity may help for reaching the optimal design and dimensioning of hydraulic infrastructures, which involves a lot of economic savings. Further work will largely comprise the introduction of the spatial dimension so the tool can integrated a full spatio-temporal analysis. Furthermore, the analyzed runoff behavior trends will be further used for building predictive models.